Hybrid Renewable Energy Systems and Microgrids 9780128217245, 0128217243

Table of contents :
Hybrid Renewable Energy Systems and Microgrids
Copyright
Contents
List of contributors
1 Introduction to power systems
1.1 Introduction
1.2 Fundamentals of electric power systems
1.2.1 Basics of power in ac systems
1.2.2 Kirchhoff’s laws
1.2.3 Instantaneous and complex power in ac systems
1.3 Balanced three-phase systems
1.3.1 Balanced Y connection
1.3.2 Balanced Δ connection
1.4 Per-unit system
1.5 Power generation and electric machines
1.5.1 The principles of electromechanical energy conversion
1.5.2 Generator operation of electric machines
References
2 Centralized power generation
2.1 Introduction
2.2 Hydropower power plant
2.2.1 Reservoir-based hydropower plants and dams
2.2.2 Pumped-storage hydropower
2.2.3 Hydraulic turbines
2.3 Thermal power plants
2.3.1 Coal-fired power plants
2.3.2 Gas-fired power plants
2.3.3 Gas-turbine principle
2.4 Nuclear power plant
2.4.1 Nuclear fission
2.4.2 Fusion
2.4.3 Nuclear fission reactors
2.4.3.1 Boiling water reactor
2.4.3.2 Pressurized water reactor
2.4.3.3 The pressurized heavy-water reactor (Canada Deuterium Uranium)
2.4.3.4 Gas-cooled reactors
References
3 Distributed generation and microgrids
3.1 Introduction
3.2 Microgrid
3.3 Distributed generation
3.3.1 Diesel generator
3.3.2 Microturbine
3.3.3 Fuel cell
3.3.4 Wind turbine
3.3.5 Photovoltaic panel
3.4 The load model of the microgrid
3.5 Optimization algorithm
3.5.1 Objective functions
3.5.1.1 Loss index
3.5.1.2 Voltage index
3.5.2 Constraints
3.5.2.1 Distributed generation constraint
3.5.2.2 Voltage of busses constraint
3.5.3 Intelligent algorithm
3.5.3.1 Multiobjective gray wolf optimization algorithm
3.5.3.2 Fuzzy method
3.6 Numerical results
3.7 Conclusion
References
4 Renewable energy systems
4.1 Chapter overview
4.2 Photovoltaic power generation
4.2.1 Principles of solar radiation
4.2.1.1 Measuring radiation
4.2.2 Photovoltaic cell fundamentals
4.2.2.1 Solar module
4.2.3 Photovoltaic systems
4.3 Wind power generation
4.3.1 Wind resource
4.3.1.1 Wind shear
4.3.1.2 Wind direction
4.3.1.3 Turbulence
4.3.1.4 Wind speed histograms
4.3.1.5 Duration curve
4.3.1.6 Wind speed distributions
4.3.1.7 Wind atlas
4.3.1.8 Wind measurement and instrumentation
4.3.1.8.1 Wind speed measuring instrumentation
4.3.1.8.2 Wind direction measuring instrumentation
4.3.1.8.3 Vegetation indicators
4.3.2 Wind potential assessment (siting)
4.3.2.1 Hybrid power systems
4.3.2.2 Offshore wind energy
4.3.2.2.1 The offshore wind resource
4.4 Hydroelectric power generation
4.4.1 Conventional hydroelectric power
4.4.1.1 Measuring precipitation
4.4.1.2 System components
4.4.1.2.1 Dam, weir, or barrage
4.4.1.2.2 Intake
4.4.1.2.3 Penstock
4.4.1.2.4 Turbines
4.4.1.2.5 Outlet
4.4.1.2.6 Overall system
4.4.1.2.7 Regulation
4.4.1.3 Classification of hydroelectric plants
4.4.1.3.1 Low-head plants
4.4.1.3.2 Medium-head plants
4.4.1.3.3 High-head plants
4.4.1.4 Operation behavior
4.4.2 Hydrokinetic energy
4.4.2.1 Runoff measuring
4.4.2.2 Hydrokinetic energy exploitation systems
4.4.2.2.1 River current
4.4.2.2.2 Marine current
4.4.3 Wave energy
4.4.3.1 Wave energy exploitation systems
4.4.3.1.1 Tapered channel wave energy conversion device system
4.4.3.1.2 Oscillating water column system
4.4.3.1.3 Pelamis Wave Power
4.4.4 Tidal energy
4.4.4.1 Tidal energy exploitation systems
4.4.4.1.1 Tidal power station
4.4.4.1.2 Tidal stream
4.5 Biomass power generation
4.5.1 Biomass fundamentals
4.5.1.1 Biomass photosynthesis
4.5.1.2 Biomass sources
4.5.1.3 Potential energy crop production opportunities and challenges in the growing demand for biomass
4.5.1.4 Forest biomass energy plantations
4.5.2 Biomass characteristics
4.5.2.1 Composition of plant biomass
4.5.2.2 The energy content of biomass
4.5.2.3 Physical characteristics
4.5.2.4 Chemical characteristics
4.5.2.4.1 Proximate analysis
4.5.2.4.2 Polymeric composition
4.5.2.5 Heat value
4.5.3 Biomass conversion into useful energy
4.5.3.1 Prime mover systems and fuels
4.5.3.2 Cofiring of biomass in coal-fired power plants
4.5.3.3 Cofiring technologies
4.5.3.4 Performance and costs
4.5.3.5 Sustainability, potential, and barriers
4.6 Conclusion
References
5 Hybrid renewable energy sources power systems
5.1 Introduction
5.2 Renewable energy-based hybrid power system
5.3 PV–diesel–battery system overview
5.3.1 Technical and nontechnical challenges
5.3.1.1 Voltage impact
5.3.1.2 Harmonics
5.3.1.3 Impacts from PV inverters
5.4 Holistic planning approach for PV–diesel–battery system
5.4.1 Addressing stakeholders’ opinion
5.4.2 Power system optimization and techno-economic analysis
5.4.3 Integrating software-based analysis
5.4.4 Power quality analysis
5.5 Integrating PV forecasting mechanism
5.5.1 PV forecasting technologies
5.5.2 Short-term PV forecasting using sky imagery mechanism
5.5.3 Developing a proprietary forecasting tool
5.6 Share of other renewable resources in the energy mix
5.7 Conclusion
Acknowledgment
References
6 Power electronics for hybrid energy systems
6.1 Introduction
6.2 Classification
6.3 AC bus connected HES
6.4 DC-bus connected HES
6.5 DC-side integration of HES
6.5.1 Cascaded DC-connection
6.5.2 Series DC connection
6.5.3 Parallel DC connection
6.5.4 DC-side integrated hybrid energy storage systems
6.6 Three-port converters
6.7 DC–DC converter based
6.8 High-frequency link
6.9 Neutral-point-clamped multilevel converters with multiple energy sources
6.10 Cascaded and modular multilevel converters
6.11 Solid-state transformers
6.12 Summary
Acknowledgment
References
7 Photovoltaic power plant planning and modeling
7.1 Introduction
7.2 Photovoltaic plant planning for hybrid microgrids
7.2.1 Load matching index
7.2.2 Photovoltaic utilization index
7.2.3 Solar irradiance variability index
7.3 Hybrid microgrid design and photovoltaic plant planning
7.3.1 Synchronous versus inverter-based grid forming
7.3.2 Centralized versus decentralized control
7.3.3 Centralized versus distributed generation
7.3.4 AC versus DC coupling
7.4 Special technical considerations for hybrid microgrids
7.4.1 Management of photovoltaic intermittency
7.4.2 Management of excess photovoltaic output
7.4.3 Frequency stability
7.4.4 System strength
7.5 Conclusion
References
Appendix: Standard photovoltaic plant planning considerations
Site selection
Photovoltaic plant layout
Electrical system design
Mounting system design
Photovoltaic module and inverter selection
Energy yield simulations
Grid integration modeling
Environmental and social impacts
8 Wind power plant planning and modeling
8.1 Chapter overview
8.2 Wind resource
8.2.1 Impact of the height
8.2.2 Temperature and altitude correction for air density
8.3 Types of wind turbines
8.3.1 Horizontal axis wind turbines
8.3.2 Vertical axis wind turbines
8.3.3 System elements
8.3.3.1 The rotor
8.3.3.2 The gearbox
8.3.3.3 The generator
8.3.3.4 The yaw mechanism (horizontal axis wind turbine)
8.3.3.5 The tower
8.3.3.6 The foundations
8.4 Wind energy production estimate
8.4.1 Power in the wind
8.4.2 Betz limit
8.4.3 Airfoil fundamental concepts
8.4.3.1 Lift coefficient
8.4.3.2 Drag coefficient
8.4.3.3 Drag versus lift wind turbines
8.4.4 Wind speed distribution (Weibull and Rayleigh)
8.4.5 Wind turbine energy production estimates
8.5 Wind turbine control and hybrid systems
8.5.1 Wind turbine control systems
8.5.1.1 Standard control configurations
8.5.1.2 Advanced control methods
8.5.1.3 Power control
8.5.2 Hybrid power systems
8.5.2.1 Sizing of photovoltaic/wind hybrid renewable energy system
8.5.2.2 Optimization of photovoltaic/wind hybrid renewable energy system
8.5.2.3 Reliability analysis
8.5.2.4 Performance assessment
8.5.3 Particular operating conditions for the wind power plant
8.5.3.1 Operation in extreme climatic conditions
8.5.3.2 Special purpose applications
8.5.3.2.1 Water pumping
8.5.3.2.2 Wind-powered desalination
8.6 Environmental impacts of wind energy projects
8.6.1 Visual impact of wind turbines
8.6.2 Wind turbine noise
8.6.3 Bird and bat interaction with wind turbines
8.6.4 Other impact considerations
8.7 Economic and financing aspects of wind energy projects
8.7.1 Revenues and financing of wind energy projects
8.7.1.1 Average wind energy cost
8.7.1.2 Offsetting energy use and costs
8.7.1.3 Wind energy financial incentives
8.7.1.4 Production tax credits and investment tax credits
8.7.1.5 Equity capital and tax equity
8.7.1.6 Equity sponsor
8.7.2 Economic evaluation of wind energy projects
8.7.2.1 Simple payback
8.7.2.2 Net present value
8.8 Conclusion
References
9 Fuel cell and hydrogen power plants
9.1 Chapter overview
9.2 Fuel cells
9.2.1 Principle of operation
9.2.2 Construction of fuel cell stack
9.2.2.1 Electrical configuration
9.2.2.2 Physical configuration
9.2.3 Classification of fuel cell
9.2.3.1 Solid oxide fuel cell
9.2.3.2 Molten carbonate fuel cell
9.2.3.3 Phosphoric acid fuel cell
9.2.3.4 Alkaline fuel cell
9.2.3.5 Proton exchange membrane fuel cell
9.2.3.6 Direct methanol fuel cell
9.3 Hydrogen-based power plants
9.3.1 Hydrogen generation processes
9.3.1.1 Hydrogen generation from fossil fuel
9.3.1.1.1 Hydrogen from natural gas methane-steam reforming
9.3.1.1.2 Hydrogen from hydrocarbon partial oxidation
9.3.1.1.3 Hydrogen from coal gasification
9.3.1.2 Hydrogen generation from water
9.3.1.2.1 Water electrolysis
9.3.1.3 Hydrogen generation from biomass
9.3.1.4 Hydrogen generation from biological process
9.3.1.4.1 Microbial hydrogen production
9.3.1.4.2 Photobiological hydrogen production
9.3.2 Large scale stationary power plants
9.3.3 Hybrid distributed generation systems
9.3.3.1 FC as main power source in DG
9.3.3.2 FC as auxiliary power source in DG
9.3.4 Combined heat and power systems
9.4 FC energy system modeling
9.4.1 Fuel cell
9.4.2 DC–DC converter
9.4.3 Controller design
9.4.4 Simulation results
9.4.4.1 Results with constant resistive load
9.4.4.2 Results with variable resistive load
9.5 Conclusion
9.6 Nomenclature
References
10 Hybrid energy storage systems
10.1 Chapter overview
10.2 Hybrid energy storage system configuration classification
10.2.1 Passive configuration
10.2.2 Semiactive configuration
10.2.3 Series-active configuration
10.2.4 Parallel-active configuration
10.3 Control strategies for hybrid energy storage system configurations
10.4 Control of microgrid configuration based on solar photovoltaic–wind turbine, and hybrid energy storage system
10.4.1 Control of wind turbine
10.4.2 Control of solar photovoltaic
10.4.3 Control of Ni–Cd batteries
10.4.4 Control of SCs
10.4.5 Control of the interfacing inverter
10.4.5.1 Control for standalone operation mode
10.4.5.2 Control for grid-connected mode
10.5 Results and discussion
10.5.1 Performance at the DC bus
10.5.2 Performance at the AC bus
10.6 Conclusion
References
11 Control systems for hybrid energy systems
11.1 Chapter overview
11.2 Configuration of HES-based MG
11.2.1 AC/DC hybrid-MG configurations based on two ESs
11.2.2 AC/DC hybrid-MG configurations based on three ESs
11.2.3 AC/DC hybrid-MG configuration based on four ESs
11.3 AC/DC hybrid-MG configuration under study
11.3.1 Operation modes of selected AC/DC hybrid-MG configuration
11.4 Control for AC/DC hybrid-MG configuration
11.4.1 Hierarchical control
11.4.1.1 Primary control
11.4.1.1.1 Control of the variable speed wind turbine
11.4.1.1.2 Control of the solar photovoltaic system
11.4.1.1.3 Control of the variable speed diesel generator
11.4.1.1.4 Control battery storage system
11.4.1.1.5 Control of the AC/DC interfacing inverter
11.4.1.2 Secondary control
11.4.1.3 Tertiary control
11.5 Results and discussion
11.6 Conclusion
References
12 Microgrids and their control
12.1 Introduction
12.2 Primary controllers of DDERs and BESs
12.2.1 DDER’s primary controller
12.2.2 Battery energy storage systems’ primary controller
12.3 Microgrid’s secondary controller
12.3.1 Dynamic power ratio adjustment
12.3.2 Droop curve adjustment
12.3.3 Selection of a suitable internal balancing inductance
12.3.4 Corrective controller
12.3.5 Preventive controller
12.4 Network’s tertiary controller
12.4.1 Self-healing capability
12.4.2 Coupling of two microgrids
12.4.3 Coupling of more than two microgrids
12.4.3.1 Decision-making approach
12.4.3.2 Optimization approach
References
13 Demand-side management
13.1 Chapter overview
13.2 Demand-side management
13.2.1 Demand-side management categories
13.2.1.1 Energy efficiency
13.2.1.2 Demand response
13.2.1.3 Virtual power plants
13.2.1.4 Spinning reserve
13.2.2 Demand-side management stakeholders
13.2.3 Demand-side management drivers and benefits
13.2.4 Demand-side management cost-effectiveness
13.3 Demand response
13.3.1 Price-based demand-response programs
13.3.2 Incentive-based demand-response programs
13.3.3 Potential benefits
13.3.4 Limitation and barriers
13.4 Advanced demand-side management technologies
13.4.1 Smart loads and smart grids
13.4.2 Internet of Things
13.4.3 Blockchain-based demand-side management programs
13.5 Conclusion
References
Index

Citation preview

Hybrid Renewable Energy Systems and Microgrids

Hybrid Renewable Energy Systems and Microgrids Edited by

Ersan Kabalci Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Nevsehir Haci Bektas Veli University, Nevsehir, Turkey

Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1650, San Diego, CA 92101, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom Copyright © 2021 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress ISBN: 978-0-12-821724-5 For Information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals

Acquisitions Editor: Lisa Reading Editorial Project Manager: Leticia M. Lima Production Project Manager: Nirmala Arumugam Cover Designer: Victoria Pearson Typeset by MPS Limited, Chennai, India

Contents

List of contributors 1

2

Introduction to power systems Ersan Kabalci and Yasin Kabalci 1.1 Introduction 1.2 Fundamentals of electric power systems 1.2.1 Basics of power in ac systems 1.2.2 Kirchhoff’s laws 1.2.3 Instantaneous and complex power in ac systems 1.3 Balanced three-phase systems 1.3.1 Balanced Y connection 1.3.2 Balanced Δ connection 1.4 Per-unit system 1.5 Power generation and electric machines 1.5.1 The principles of electromechanical energy conversion 1.5.2 Generator operation of electric machines References

Centralized power generation Ersan Kabalci, Aydın Boyar and Yasin Kabalci 2.1 Introduction 2.2 Hydropower power plant 2.2.1 Reservoir-based hydropower plants and dams 2.2.2 Pumped-storage hydropower 2.2.3 Hydraulic turbines 2.3 Thermal power plants 2.3.1 Coal-fired power plants 2.3.2 Gas-fired power plants 2.3.3 Gas-turbine principle 2.4 Nuclear power plant 2.4.1 Nuclear fission 2.4.2 Fusion 2.4.3 Nuclear fission reactors References

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Distributed generation and microgrids Hossein Shayeghi and Masoud Alilou 3.1 Introduction 3.2 Microgrid 3.3 Distributed generation 3.3.1 Diesel generator 3.3.2 Microturbine 3.3.3 Fuel cell 3.3.4 Wind turbine 3.3.5 Photovoltaic panel 3.4 The load model of the microgrid 3.5 Optimization algorithm 3.5.1 Objective functions 3.5.2 Constraints 3.5.3 Intelligent algorithm 3.6 Numerical results 3.7 Conclusion References

Renewable energy systems Leidy Tatiana Contreras Montoya, Santiago Lain, Mohamad Issa and Adrian Ilinca 4.1 Chapter overview 4.2 Photovoltaic power generation 4.2.1 Principles of solar radiation 4.2.2 Photovoltaic cell fundamentals 4.2.3 Photovoltaic systems 4.3 Wind power generation 4.3.1 Wind resource 4.3.2 Wind potential assessment (siting) 4.4 Hydroelectric power generation 4.4.1 Conventional hydroelectric power 4.4.2 Hydrokinetic energy 4.4.3 Wave energy 4.4.4 Tidal energy 4.5 Biomass power generation 4.5.1 Biomass fundamentals 4.5.2 Biomass characteristics 4.5.3 Biomass conversion into useful energy 4.6 Conclusion References

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Hybrid renewable energy sources power systems Taskin Jamal and Sayedus Salehin 5.1 Introduction 5.2 Renewable energy-based hybrid power system 5.3 PV diesel battery system overview 5.3.1 Technical and nontechnical challenges 5.4 Holistic planning approach for PV diesel battery system 5.4.1 Addressing stakeholders’ opinion 5.4.2 Power system optimization and techno-economic analysis 5.4.3 Integrating software-based analysis 5.4.4 Power quality analysis 5.5 Integrating PV forecasting mechanism 5.5.1 PV forecasting technologies 5.5.2 Short-term PV forecasting using sky imagery mechanism 5.5.3 Developing a proprietary forecasting tool 5.6 Share of other renewable resources in the energy mix 5.7 Conclusion Acknowledgment References

Power electronics for hybrid energy systems G. Konstantinou and B. Hredzak 6.1 Introduction 6.2 Classification 6.3 AC bus connected HES 6.4 DC-bus connected HES 6.5 DC-side integration of HES 6.5.1 Cascaded DC-connection 6.5.2 Series DC connection 6.5.3 Parallel DC connection 6.5.4 DC-side integrated hybrid energy storage systems 6.6 Three-port converters 6.7 DC DC converter based 6.8 High-frequency link 6.9 Neutral-point-clamped multilevel converters with multiple energy sources 6.10 Cascaded and modular multilevel converters 6.11 Solid-state transformers 6.12 Summary Acknowledgment References

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Photovoltaic power plant planning and modeling Julius Susanto 7.1 Introduction 7.2 Photovoltaic plant planning for hybrid microgrids 7.2.1 Load matching index 7.2.2 Photovoltaic utilization index 7.2.3 Solar irradiance variability index 7.3 Hybrid microgrid design and photovoltaic plant planning 7.3.1 Synchronous versus inverter-based grid forming 7.3.2 Centralized versus decentralized control 7.3.3 Centralized versus distributed generation 7.3.4 AC versus DC coupling 7.4 Special technical considerations for hybrid microgrids 7.4.1 Management of photovoltaic intermittency 7.4.2 Management of excess photovoltaic output 7.4.3 Frequency stability 7.4.4 System strength 7.5 Conclusion References Appendix: Standard photovoltaic plant planning considerations Site selection Photovoltaic plant layout Electrical system design Mounting system design Photovoltaic module and inverter selection Energy yield simulations Grid integration modeling Environmental and social impacts

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Wind power plant planning and modeling Leidy Tatiana Contreras Montoya, Mohamed Yasser Hayyani, Mohamad Issa, Adrian Ilinca, Hussein Ibrahim and Miloud Rezkallah 8.1 Chapter overview 8.2 Wind resource 8.2.1 Impact of the height 8.2.2 Temperature and altitude correction for air density 8.3 Types of wind turbines 8.3.1 Horizontal axis wind turbines 8.3.2 Vertical axis wind turbines 8.3.3 System elements 8.4 Wind energy production estimate 8.4.1 Power in the wind 8.4.2 Betz limit 8.4.3 Airfoil fundamental concepts

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8.4.4 Wind speed distribution (Weibull and Rayleigh) 8.4.5 Wind turbine energy production estimates 8.5 Wind turbine control and hybrid systems 8.5.1 Wind turbine control systems 8.5.2 Hybrid power systems 8.5.3 Particular operating conditions for the wind power plant 8.6 Environmental impacts of wind energy projects 8.6.1 Visual impact of wind turbines 8.6.2 Wind turbine noise 8.6.3 Bird and bat interaction with wind turbines 8.6.4 Other impact considerations 8.7 Economic and financing aspects of wind energy projects 8.7.1 Revenues and financing of wind energy projects 8.7.2 Economic evaluation of wind energy projects 8.8 Conclusion References

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Fuel cell and hydrogen power plants Himadry Shekhar Das, Md. Fahim F. Chowdhury, Shuhui Li and Chee Wei Tan 9.1 Chapter overview 9.2 Fuel cells 9.2.1 Principle of operation 9.2.2 Construction of fuel cell stack 9.2.3 Classification of fuel cell 9.3 Hydrogen-based power plants 9.3.1 Hydrogen generation processes 9.3.2 Large scale stationary power plants 9.3.3 Hybrid distributed generation systems 9.3.4 Combined heat and power systems 9.4 FC energy system modeling 9.4.1 Fuel cell 9.4.2 DC DC converter 9.4.3 Controller design 9.4.4 Simulation results 9.5 Conclusion 9.6 Nomenclature References

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Hybrid energy storage systems Hussein Ibrahim, Miloud Rezkallah, Adrian Ilinca and Mazen Ghandour 10.1 Chapter overview 10.2 Hybrid energy storage system configuration classification 10.2.1 Passive configuration 10.2.2 Semiactive configuration

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10.2.3 Series-active configuration 10.2.4 Parallel-active configuration 10.3 Control strategies for hybrid energy storage system configurations 10.4 Control of microgrid configuration based on solar photovoltaic wind turbine, and hybrid energy storage system 10.4.1 Control of wind turbine 10.4.2 Control of solar photovoltaic 10.4.3 Control of Ni Cd batteries 10.4.4 Control of SCs 10.4.5 Control of the interfacing inverter 10.5 Results and discussion 10.5.1 Performance at the DC bus 10.5.2 Performance at the AC bus 10.6 Conclusion References 11

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Control systems for hybrid energy systems Miloud Rezkallah, Ambrish Chandra, Hussein Ibrahim, Zoe Feger and Mohamad Aissa 11.1 Chapter overview 11.2 Configuration of HES-based MG 11.2.1 AC/DC hybrid-MG configurations based on two ESs 11.2.2 AC/DC hybrid-MG configurations based on three ESs 11.2.3 AC/DC hybrid-MG configuration based on four ESs 11.3 AC/DC hybrid-MG configuration under study 11.3.1 Operation modes of selected AC/DC hybrid-MG configuration 11.4 Control for AC/DC hybrid-MG configuration 11.4.1 Hierarchical control 11.5 Results and discussion 11.6 Conclusion References Microgrids and their control Farhad Shahnia 12.1 Introduction 12.2 Primary controllers of DDERs and BESs 12.2.1 DDER’s primary controller 12.2.2 Battery energy storage systems’ primary controller 12.3 Microgrid’s secondary controller 12.3.1 Dynamic power ratio adjustment 12.3.2 Droop curve adjustment 12.3.3 Selection of a suitable internal balancing inductance

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12.3.4 Corrective controller 12.3.5 Preventive controller 12.4 Network’s tertiary controller 12.4.1 Self-healing capability 12.4.2 Coupling of two microgrids 12.4.3 Coupling of more than two microgrids References

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Demand-side management Alain Aoun, Mazen Ghandour, Adrian Ilinca and Hussein Ibrahim 13.1 Chapter overview 13.2 Demand-side management 13.2.1 Demand-side management categories 13.2.2 Demand-side management stakeholders 13.2.3 Demand-side management drivers and benefits 13.2.4 Demand-side management cost-effectiveness 13.3 Demand response 13.3.1 Price-based demand-response programs 13.3.2 Incentive-based demand-response programs 13.3.3 Potential benefits 13.3.4 Limitation and barriers 13.4 Advanced demand-side management technologies 13.4.1 Smart loads and smart grids 13.4.2 Internet of Things 13.4.3 Blockchain-based demand-side management programs 13.5 Conclusion References

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Index

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List of contributors

Mohamad Aissa Department of Pure and Applied Sciences, Maritime Institute, Rimouski, QC, Canada Masoud Alilou Department of Electrical Engineering, Urmia University, Urmia, Iran Alain Aoun Department of Mathematics, Computer Science and Engineering, University of Quebec at Rimouski (UQAR), Rimouski, QC, Canada Aydın Boyar Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Nevsehir Haci Bektas Veli University, Nevsehir, Turkey Ambrish Chandra Department of Electrical Engineering, E´cole de Technologie Superieure (ETS), Montre´al, QC, Canada Md. Fahim F. Chowdhury Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States Leidy Tatiana Contreras Montoya University of Quebec at Rimouski, Rimouski, QC, Canada; Mathematics, Informatic & Engineering Department, University of Quebec at Rimouski, Rimouski, QC, Canada Himadry Shekhar Das Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL, United States Zoe Feger Technological Institute for Industrial Maintenance (ITMI), College of Sept-Iˆles, Sept-Iˆles, QC, Canada Mazen Ghandour Faculty of Engineering, Lebanese University, Hadat, Beirut, Lebanon Mohamed Yasser Hayyani University of Quebec at Rimouski, Rimouski, QC, Canada

xiv

List of contributors

B. Hredzak School of Electrical Engineering and Telecommunications, The University of New South Wales (UNSW Sydney), Sydney, NSW Australia Hussein Ibrahim Technological Institute for Industrial Maintenance (ITMI), College of Sept-Iˆles, Sept-Iˆles, QC, Canada Adrian Ilinca Department of Mathematics, Computer Science and Engineering, University of Quebec at Rimouski (UQAR), Rimouski, QC, Canada Mohamad Issa Department of Applied Sciences, Quebec Maritime Institute, Rimouski, QC, Canada Taskin Jamal Department of Electrical and Electronic Engineering, Ahsanullah University of Science and Technology, Dhaka, Bangladesh Ersan Kabalci Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Nevsehir Haci Bektas Veli University, Nevsehir, Turkey Yasin Kabalci Department of Electrical and Electronics Engineering, Faculty of ¨ mer Halisdemir University, Nigde, Turkey Engineering, Nigde O G. Konstantinou School of Electrical Engineering and Telecommunications, The University of New South Wales (UNSW Sydney), Sydney, NSW Australia Santiago Lain PAI+ Group, Energetics and Mechanics Department, Autonomous University of the West, Santiago de Cali, Colombia Shuhui Li Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL, United States Miloud Rezkallah Technological Institute for Industrial Maintenance (ITMI), College of Sept-Iˆles, Sept-Iˆles, QC, Canada; Department of Electrical Engineering, E´cole de Technologie Superieure (ETS), Montre´al, QC, Canada Sayedus Salehin Department of Mechanical and Production Engineering, Islamic University of Technology, Gazipur, Bangladesh Farhad Shahnia Discipline of Engineering and Energy, Murdoch University, Perth, WA, Australia Hossein Shayeghi Energy Management Mohaghegh Ardabili, Ardabil, Iran

Research

Center,

University

of

List of contributors

xv

Julius Susanto School of Engineering and Information Technology, Murdoch University, Perth, WA, Australia Chee Wei Tan School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia

Introduction to power systems

1

Ersan Kabalci1 and Yasin Kabalci2 1 Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Nevsehir Haci Bektas Veli University, Nevsehir, Turkey, 2Department of Electrical and Electronics Engineering, Faculty of Engineering, Nigde O¨mer Halisdemir University, Nigde, Turkey

1.1

Introduction

Different electrical quantities such as charge, voltage, current, resistance, and electric and magnetic fields will be studied in this chapter to understand the concept of electricity. Generally, it is found difficult to grasp the concepts of these electrical quantities by most students of science and engineering because these are entirely different from the normal things that we have around us. Electrical phenomena have a definite mysteriousness that we cannot directly derive from our direct experience and need to gain the knowledge from the powerful fundamental force of nature they represent. Electric charge is also considered as one of the basic physical quantities in addition to mass, distance, time, and temperature for measurement. The combination of these five quantities is used to represent several physical units. However, the electric charge is realized as an entirely isolated quantity from the other four. It is so because “charge” is hard to perceive as a physical phenomenon, whereas other physical quantities such as the size of an object can be imagined and sensed by its weight. Moreover, the electrical process is also involved in our bodies, but generally, we hesitate to apply the concepts of electrical quantities or forces. For example, these concepts can be practiced from our cell metabolism to neural impulses. Electric shock is usually considered as a recognizable example of electricity. An exceptional feeling is realized after the shock that sends such a strong wave of nervous impulses through our bodies. There are different other effects of electricity on our bodies like hair that rebelliously stands on end, a zap from a doorknob, and static cling in the laundry. Unfortunately, these types of effects are avoided to analyze in terms of electric power, where we can practically realize the effects of electricity, such as turning on a light bulb or rotating a motor. Every important phenomenon occurs like a ghost within pieces of metal. Therefore numerical and geometric illustrations with the help of substantial analogies from other areas of the physical world are utilized to form conception building of electricity [1]. The electric power industry is continuously striving for the prosperity, advancement, and technological improvements of humanity. The extraordinary increase in electric energy consumption is realized in the world for the last few decades. For example, electric energy sales have reached more than 400 times in the period Hybrid Renewable Energy Systems and Microgrids. DOI: https://doi.org/10.1016/B978-0-12-821724-5.00005-2 © 2021 Elsevier Inc. All rights reserved.

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Hybrid Renewable Energy Systems and Microgrids

between the 1970s and 2000s in the United States. As compared to all other forms of energy, the growth rate of electricity was 50 times greater during the same period. About 3 kW installed power capacity per capita is projected in the United States. The Pearl Street Station inaugurated the Edison Electric Illuminating Company of New York in 1881. The station had the capability of delivering steam to six-engine dynamo sets with the aid of four 250-hp boilers. A 110-V dc underground distribution network with copper conductors protected by a jute wrapping was implemented by Edison’s system. The first water wheel-driven generator was placed in Appleton, Wisconsin in 1882. The central stations flourished all over metropolitan areas because of the low voltage of the circuits that limited the service area. AC systems became realizable after the development of the transformer, formerly known as the “inductorium”. W. Stanley, who attained the American rights to the transformer from its British designers Gaulard and Gibbs, has installed the first applied ac distribution system in the United States at Great Barrington, Massachusetts, in 1866 for Westinghouse. 1000-V overhead lines were used for ac distribution earlier. In 1888 the invention of the induction motor by Nikola Tesla replaced dc motors, which accelerated the use of ac systems. In 1889 the first American single-phase ac system was installed in Oregon. In 1893, the first three-phase 2.3-kV system was developed by Southern California Edison Company. There were almost 20 electric companies in Philadelphia by 1895 with distribution systems operating at 100- and 500-V two-wire dc and 220-V three-wire dc. The system utilized frequencies of 60, 66, 125, and 133 cycles per second for its single-, two-, and three-phase ac operation, while feeders were operating at 1000
1200 and 2000
2400 V [2]. An interconnected power system comprises several important components such as generation subsystem, transmission subsystem, distribution subsystem, and the utilization subsystems. Explanations regarding these subsystems are presented by the following part of this chapter. One of the most important subsystems is the generation subsystem that is composed of generators and transformers. The three-phase ac generators, which may be also called synchronous generator or alternator, are considered a fundamental component of the electrical power systems. A synchronous generator has two simultaneously rotating magnetic fields. One of them is generated by the rotor that is driven by the direct current at synchronous speed. The other one is generated by armature currents in the stator windings. The task of the excitation systems is to supply dc current to rotor windings. In the previous systems, dc generators positioned on the shaft were used as the exciters. AC generators including rectifier systems (i.e., brushless excitation systems) are employed in modern systems. The excitation systems not only protect the generator voltage but also manage the flow of reactive power. Typically, an ac generator may produce a high voltage as much as 30 kV since they do not contain commutators. Hydraulic turbines or steam turbines are the origin of mechanical power. The energy source of those turbines may be coal, fuel, or gas. Generally, the steam turbines having generators with cylindrical rotors function at 3600 or 1800 rpm speeds. While the two-pole generators are operated at 3600 rpm, the four-pole ones are operated at 1800 rpm speeds. The other type of turbines works at lower speeds. The generators used in these turbines include generally salient type rotor with multipoles. When a typical power station is considered, a few generators are run in parallel for

Introduction to power systems

3

supplying the requested power and the connection of the generators is realized over the bus system. On the other hand, several alternative energy sources are taken into account for efficient usage of energy sources due to the depletion of fossil resources and the concerns regarding the environment. Solar power, wind power, geothermal power, tidal power, and biomass are some examples of alternative energy sources. Transformers, which are another component of the power generation systems, convert either voltage or current value from one level to another for transferring the power with high efficiency. When the losses of transformers are neglected, the transferred power from their primary to their secondary is the same. The losses in the power lines can be decreased by employing step-up transformers that enable power transmission over long distances. The generated voltage is generally limited to low voltage levels as much as 30 kV due to the insulation and practical application issues. While the step-up transformers are employed at the side of power transmission, stepdown transformers are exploited to decrease the voltage to appropriate levels at the end of transmission lines. From the generation side to the consumer side, several transformation processes are performed in electrical power systems. The main goal of the transmission systems is to deliver electricity from generation systems to the distribution system. In the United States, the American National Standards Institute (ANSI) has standardized the transmission voltage levels for the higher than 60 kV operating voltage as 69, 115, 138, 161, 230, 345, 500, and 765 kV line-to-line. The higher than 230 kV transmission voltage levels are generally called extra-high voltage. High voltage transmission lines are ended in the substations that are generally named as high-voltage substations or primary substations where the voltage level is decreased to provide compatibility with the following part of power systems. There are also special-purpose substations called switching stations that are tasked with enabling or disabling several circuits. Some parts of industrial customers may be directly fed by the transmission system. The subtransmission networks are a crucial part of the transmission system, which intends to connect high-voltage substations to the distribution substations over step-down transformers. Even though there is no obvious difference between voltage levels of transmission and subtransmission systems, it is assumed that the voltage levels of the subtransmission systems are from 69 to 138 kV. Similarly, some parts of the industrial customers may be supplied from the subtransmission system. To protect the line voltage of the transmission system, capacitor banks and reactor banks are generally established in the substations. The distribution system combines the distribution substations with the consumers. The primary distribution lines cover 4
34.5 kV. Small-scale industrial customers can be directly supplied by the primary feeders. The task of the secondary distribution network is to step down the voltage for the available use of commercial and residential consumers. The cables, the length of which does not exceed several hundred meters, provide power to individual consumers. The secondary distribution systems can supply consumers at different voltage levels (e.g., 240/120 V, single-phase, threewire; 208Y/120 V, three-phase, four-wire; or 480Y/277 V, three-phase, four-wire). When a typical house case is considered, the power is supplied from a transformer decreasing the primary feeder voltage to 240 or 120 V through a three-wire line. Overhead or underground cables are employed in the distribution systems.

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Hybrid Renewable Energy Systems and Microgrids

The loads in the power system can be classified as residential, commercial, and industrial. The industrial loads generally comprise mixed loads and induction motors. The mixed loads change according to voltage or frequency variations and constitute a significant part of the system load. Examples of commercial and residential loads are cooking, lighting, and heating systems. These loads are frequency independent and expend considerably small reactive power. The total value of the loads changes dynamically during the day and the requested power by consumers should be provided. The maximum load value in 24 h is named maximum demand and load factor (LF) has been defined to evaluate the generating plant/utility. For a certain time interval, the LF is defined as the ratio of average load to the peak load. The LF can be calculated for different time intervals such as daily, monthly, or annually. The LF can be expressed as given in the following equation [1]: LF 5

Average load Peak load

(1.1)

The daily LF can be derived by extending Eq. (1.1) for 24 h as: LFðDailyÞ 5

Average load 3 24 h Consumed energy in a day 5 Peak load 3 24 h Peak load 3 24 h

(1.2)

Similarly, the annual LF is expressed as follows: LFðAnnualÞ 5

Total annual energy Peak load 3 8760 h

(1.3)

In general, the peak loads of different load classes are different that enhances the complete system LF. It is desired that the system LFs of power plants are as high as possible for an economic operation. A typical system LF value ranges from 55% to 70%. Load estimation is a very crucial process for electric power systems to spread the whole stages of the system. To maintain the electrical power systems, several devices and systems should be included in the power system. Some preservative devices that are immediately linked to the devices are referred to as switchgear. Circuit breakers, instrument transformers, lightning arresters, and fuses can be given as examples of the switchgear components. The use of these systems is required to switch off the energy in the event of faults or control purposes. The monitoring transactions of the power systems are required to present a reliable and economic power system that is realized through a center called the energy control center (ECC). The ECC contains several computers connected to the Internet that are able to remotely perform all advanced signal processing procedures. Each ECC includes several control components that are composed of keyboards, pen, and monitor units. Also, the ECC systems may inform operators in the event if an unusual situation occurs. Efficient and reliable control of the electrical power systems can be carried out through the simulation tools and software. Furthermore, supervisory control and data acquisition systems are the assistants of the ECC [2].

Introduction to power systems

1.2

5

Fundamentals of electric power systems

This section of the chapter aims to provide a comprehensive review of the fundamental concepts, notations, and terminology regarding electrical power systems. Especially, important basics of three-phase circuits such as network expressions, phasors, instantaneous, and complex powers will be analyzed in detail. This part of the chapter will make information refresh quickly.

1.2.1 Basics of power in ac systems The power sources and loads are usually considered as the circuit elements responsible for the power delivery in real circuits. For example, in the distribution circuit, applications used in a house or several homes are responsible to supply power to a specific block. It is very important to connect all these loads in parallel such that each load can be operated independently. In this way, each load can utilize the same standard voltage but may draw different current depending upon specific function which defines the amount of power used up by each load. In the framework of power systems, according to an approximation, resistive loads in parallel are unpretentious by each other by supplying a constant voltage source. If an ideal situation is compromised, then interactive effects may happen. For instance, it is seen that different load in a system are also affected when a heavy load influence on the local voltage. However, elements in series share the same current; therefore independent operation of loads in series is not realizable. Moreover, the current flow to all the other elements will be not possible if any of the elements is damaged. In addition, the voltage across each load is equivalent to some portion of total voltage in the circuit, and the amount of power consumed by each load cannot easily be adjusted even if all elements are working. The series connection is useful in power systems when the considered elements signify sequential stages between power generation and consumption. Therefore the series load is generally utilized in a distribution line, a transmission line, and a generator. Since the whole system consists of a network with many links, these elements are also in parallel with several other loads. Yet, there exist a minimum number of elements in a series that establish a route from the power source to load. For instance, one should add all the contributions to resistance along the way if the resistance of the conductors between a distribution substation and a client is taken into account (generally owing to the radial layout of the distribution system there exists only one path). It is worth noting that every element has a certain dependence on other elements due to the series connection and cannot be adjusted. This can be understood in the framework of transmission constraints and extreme voltage drops because of high loads. Although it is known to all engineers that the elements will depend upon each other in the specific device if the same current is flowing in it, it causes a severe issue when legal and institutional measures concerning power systems have such devices under the sponsorship of separate groups. [1]

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Hybrid Renewable Energy Systems and Microgrids

1.2.2 Kirchhoff’s laws The current and voltage magnitudes of any circuit are analyzed using the wellknown Kirchoff’s laws which are Kirchoff’s votlage law (KVL) and Kirchoff’s current law (KCL). These laws are considered very powerful, instinctive, and universal in the application of circuit analysis. Kirchhoff’s laws provide the basic physical properties of electricity. These laws provide a simple and structural solution by converting complex circuit problems with various connections into sets of equations. According to the KVL, the algebraic sum of voltages around any closed loop in a circuit is always equal to zero. Essentially, this law defines the characteristic properties of “voltage” or “electric potential.” More precisely, according to this law, a potential is related to a specific point that is independent of the path by which a charge might arrive there. It also infers that if there exist three points (let A, B, and C), and the potential differences between two couplings are known (i.e., between A and B and between B and C), then the relationship between A and C can be established. To express the collective voltage between the initial and final point, KVL is applied when traveling from one point to another along a circuit by adding the potential differences or voltages along the path. Finally, the initial and final points become the same when passed through all the way around a closed path (i.e., no potential difference, “a zero difference”). This is similar to flowing water analogy. The voltage at any given point can be realized as altitude, whereas closed loop resembles a water fountain (closed system). The voltage “rise” is considered as a power source (battery) which represents a pump. The height of the fountain starts reducing when the water flows down from the top of the fountain may be in different steps and it comes to at end at the bottom. Similarly, the electric current flows “down” in voltage, maybe in some different stages or resistors, and ends at the “bottom” side of the battery. This concept is demonstrated by a series circuit shown in Fig. 1.1 that comprises a single battery and two resistors. It is also important to note that it does not matter which point is selected to describe the “zero” potential. In other words, the selection of the starting point does not matter. The algebraic sum of all the potential gains and drops across the whole loop should provide zero net gain [1]. According to the KCL, the algebraic sum of the currents entering and leaving at any branch point or node in the circuit is always equal to zero. This law is totally in accordance with conservation property which states that electric charge is neither created nor destroyed, and cannot be “stored” (in substantial quantity) within wires

Figure 1.1 A simple circuit representing Kirchhoff’s voltage law.

Introduction to power systems

7

Figure 1.2 A simple circuit representing Kirchhoff’s current law.

such that the total charge that enters into any conjunction point must also leave. It means that if there are three wires that are linked at one point but the current in two of them is only known then it is easy to find the current in the third one. The same water flowing analogy can be utilized for understanding here as well. The amount of water coming in must equal the amount going out at any point where three pipes are coupled (except there is a leak). Usually, positive or negative signs are allocated to currents entering and leaving the junction for calculation purposes, respectively. However, it is not important which one is considered positive, but the same assumption must be followed throughout the analysis. Thus the algebraic sum of all the currents across the junction is zero. This is explained in Fig. 1.2, where the KCL is applied on branch points, which verifies that the current entering into the battery equals the sum of currents flowing out of the two separate resistors [1]. Normally, electric power in the circuit is more important than the currents for the electric power systems expert. The product of voltage across an element and current passing through it tells the value of instantaneous power. Therefore it looks sensible to switch the current for power without any information loss. Some additional definitions are required to study the sinusoidal-steady-state performance of circuits. This concept can be understood with the help of a cosine representation of the waveforms [2]. At a certain constant frequency, two parameters are considered to characterize a sinusoidal voltage or current (i.e., a maximum value and a phase angle). A voltage has a maximum value Vmax and a phase angle δ in reference to the cosðωtÞ [3]. vðtÞ 5 Vmax cosðωt 1 δÞ

(1.4)

The effective value or root-mean-square (rms) value of the sinusoidal voltage is given as: Vmax V 5 pffiffiffi 2

(1.5)

Euler’s identity, ejϕ 5 cos ϕ 1 j sin ϕ, is usually used for the phasor representation of a sinusoid. Hence, the phasor form of the above voltage can be written as:   pffiffiffi  vðtÞ 5 Re Vmax ejðωt1δÞ 5 Re 2ðVejδ Þejωt

(1.6)

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Hybrid Renewable Energy Systems and Microgrids

pffiffiffiffiffiffi where j 5 21 and Re specifies “the real part” of voltage. Usually, the voltage can be represented in three different rms phasor forms—exponential, polar, and rectangular: V 5 Vejδ ðexponentialÞ 5 V+δ ðpolarÞ 5 V cos δ 1 jV sin δ ðrectangularÞ

(1.7)

It is simple to convert phasor from one form to another. Fig. 1.3 describes the phasor diagram converting from polar to rectangular form. Euler’s identity helps the transformation between exponential and rectangular forms. For instance, for the voltage: vðtÞ 5 169:7 cosðωt 1 60 Þ V

(1.8)

with a maximum value of Vmax 5 169:7 V and a phase angle of δ 5 60 in referenced to cosðωtÞ the polar form with the rms phasor representation is defined as: V 5 120+60 V

(1.9)

whereas for the current, iðtÞ 5 100 cosðωt 1 45 Þ A

(1.10)

pffiffiffi with a maximum value of Imax 5 100 A and an rms value of I 5 100= 2 5 70:7 A with a phase angle of 45 degrees can be expressed in the phasor form as: I 5 70:7+45 5 70:7ej45 5 50 1 j50 A

(1.11)

Fig. 1.4 summarizes the relationships between the voltage and current phasors for three types of passive elements (i.e., resistor, inductor, and capacitor). In this

Figure 1.3 Phasor diagram representation of polar to rectangular conversion.

Introduction to power systems

9

Figure 1.4 Ohm’s law and phasor diagrams of passive elements.

example the constant values of R, L, and C are supposed for sinusoidal-steady-state excitation. It is important to note that the lowercase letters v(t) and i(t) indicate instantaneous values of voltage and current, respectively, the rms values of them are represented by uppercase letters (i.e., V and I), whereas uppercase letters in italics (V and I) specify rms phasors voltages and currents. Generally, the voltage and current values indicate the rms values unless otherwise indicated [3]. If the impedance element is taken into account in phasor form (i.e., Z 5 Z+ϕ) for a sinusoidal voltage V ðtÞ given by: vðtÞ 5 Vm cos ωt

(1.12)

The instantaneous current can be expressed as: iðtÞ 5 Im cosðωt 2 ϕÞ

(1.13)

Then maximum current in terms of impedance can be represented as: Im 5

Vm jZ j

(1.14)

1.2.3 Instantaneous and complex power in ac systems The rate of an energy change over time is described by power. Watt is the unit of power which is equal to a joule per second. The value of power absorbed by an electrical load can be calculated through the instantaneous power that is defined by multiplying the instantaneous voltage and instantaneous current across the load. When the load voltage is as follows [3]: vðtÞ 5 Vmax cosðωt 1 δÞ

(1.15)

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Hybrid Renewable Energy Systems and Microgrids

While the instantaneous current is given as: iðtÞ 5 Im cosðωt 2 ϕÞ

(1.16)

Also, Im 5

Vm jZ j

(1.17)

Then, the instantaneous power for any element can be calculated as: pðtÞ 5 vðtÞiðtÞ 5 Vm Im ½cosðωtÞcosðωt 2 ϕÞ

(1.18)

The above equation can be further simplified as: pðtÞ 5

Vm I m ½cos ϕ 1 cosð2ωt 2 ϕÞ 2

(1.19)

For any complete cycle the average of cosð2ωt 2 ϕÞ term is zero and has no significance while calculating average power. Thus the average power is defined as: pav 5

Vm I m cos ϕ 2

(1.20)

pffiffiffi pffiffiffi Using the expressions Vm 5 2ðVrms Þ and Im 5 2ðIrms Þ, this average power in terms of rms value can be expressed as: pav 5 Vrms Irms cos ϕ

(1.21)

The power flowing in any network is defined as the product of the effective values of terminal voltage and current, and the power factor (PF) (i.e., the cosine of the phase angle Q). It is worth noting that this fact is applicable only to sinusoidal voltages and currents. If there exist reactance and resistance in a circuit, then a component of the current continuously carries the energy that is charged (stored) and discharged from the reactance at regular intervals. This stored energy contributes to the current in the circuit but does not affect the average power when being traveled to and from the magnetic field of inductance or the electric field of a capacitance. The active power also known as real power or true power is defined as the value of the average power consumed in a circuit, whereas the power that delivers the stored energy in reactive elements is named as reactive power. Active power is represented by P, while the reactive power is denoted by Q. These two terms are expressed as [2]: P 5 VI cos ϕ

(1.22)

Q 5 VI sin ϕ

(1.23)

Introduction to power systems

11

where V and I describe the rms values of terminal voltage and current, while the phase angle by which the current lags or leads the voltage is denoted by ϕ. The Q is considered as nonactive power and measured in reactive volt
ampere units (var). Hence, instantaneous power can be expressed as: pðtÞ 5 Vrms Irms ½cos ϕð1 1 cos 2ωtÞ 1 Vrms Irms sin ϕ sin 2ωt

(1.24)

It can be further simplified as: pðtÞ 5 Pð1 1 cos 2ωtÞ 1 Q sin 2ωt

(1.25)

Consequently, the average power and the amplitude of the exciting power are denoted by P and Q, respectively. The time variation of the different variables already discussed is illustrated in Fig. 1.5. In Fig. 1.6, after multiplying the rms values of current I with V, V cos ϕ, and Vsin ϕ, we get VI, P, and Q, respectively. Similarly, the product of I cos ϕ and Isin ϕ with V results in VI, P, and Q, respectively. This leads to a power triangle as a result a quantity is introduced known as the complex or apparent power and labeled as S. The apparent power is defined as: S 5 P 1 jQ 5 VIðcos ϕ 1 j sin ϕÞ

Figure 1.5 Voltage, current, and power in a single-phase network.

Figure 1.6 Phasor diagrams for power triangles.

(1.26)

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Hybrid Renewable Energy Systems and Microgrids

According to Euler’s identity above equation can be represented as: S 5 VIejϕ

(1.27)

S 5 VI+ϕ

(1.28)

or

In a more compact form, apparent power can be expressed as: S 5 VIT

(1.29)

Since voltage and current can be expressed in the following forms V 5 ZI and I 5 YV, complex power can also be defined as: S 5 ZIIT 5 Z jI j2

(1.30)

S 5 VYTVT 5 YTjV j2

(1.31)

Fig. 1.7 describes the series circuit where the total applied voltage to the circuit is equivalent to the sum of voltage drops across the individual impedances and represented as: V 5 I ðZ1 1 Z2 1 ? 1 Zn Þ

(1.32)

When IT is multiplied with the whole equation: S5

n X

Si

(1.33)

i51

The complex power of each element is calculated as: Si 5 jI j2 Zi

(1.34)

Eq. (1.33) defines the summation rule for complex powers and is also applicable to parallel circuits. Using this rule, the phasor diagram shown in Fig. 1.6 can be transformed

Figure 1.7 Phasor diagrams for power triangles.

Introduction to power systems

13

Figure 1.8 Complex power diagram (A) voltage and current phasors; (B) power phasors.

into complex power diagrams by simply exploiting the relation of complex power with voltage and current. The current lags the voltage by the phase angle of ϕ in an inductive circuit; therefore the conjugate of the current will lie in the first quadrant of complex plane as can be seen in Fig. 1.8A. We can get the complex power by simply multiplying the phasors diagram with V, which is illustrated in Fig. 1.8B. This power triangle provides a very useful relationship of the PF for any circuit [2]. cos ϕ 5

P j Sj

(1.35)

Next, we will examine instantaneous power absorbed by purely resistive, purely inductive, purely capacitive, and general RLC loads. We also aim to discuss the fundamental concepts of real power, reactive power, and PF. Lastly, the physical importance of real and reactive power will be presented. The current in purely resistive load will always be in phase with the load voltage, I 5 V=R, and it can be expressed as [3]: iR ðtÞ 5 IRmax cosðωt 1 δÞ A

(1.36)

where IRmax 5 Vmax =R while instantaneous power absorbed by the resistor is given as: pR ðtÞ 5 vðtÞiR ðtÞ 5 Vmax IRmax cos2 ðωt 1 δÞ   1 5 Vmax IRmax 1 1 cos½2ðωt 1 δÞ 2   5 VIR 1 1 cos½2ðωt 1 δÞ W

(1.37)

From the above equation the instantaneous power absorbed by the resistor is defined as the combination of an average value of Eq. (1.38) and VIR cos½2ðωt 1 δÞ terms [3]. pR 5 VIR 5

V2 5 IR2 R W R

(1.38)

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Hybrid Renewable Energy Systems and Microgrids

The current always lags the voltage by 90 degrees in a purely inductive load, IL 5 V=ðjXL Þ and iL ðtÞ 5 ILmax cosðωt 1 δ 2 90 Þ A

(1.39)

In the above equation, ILmax 5 Vmax =XL where XL 5 ωL represents the inductive load. Finally, the instantaneous power absorbed an inductor is defined as pL ðtÞ 5 vðtÞiL ðtÞ 5 Vmax ILmax cosðωt 1 δÞcosðωt 1 δ 2 90 Þ 1 Vmax ILmax cos½2ðωt 1 δÞ 2 90  2 5 VIL sin½2ðωt 1 δÞ W 5

(1.40)

According to this equation, the instantaneous power absorbed by the inductor is always a double-frequency sinusoid and will have zero average value. The current always leads the voltage by 90 degrees in a purely capacitive load, IC 5 V=ð2 jXC Þ and iC ðtÞ 5 ICmax cosðωt 1 δ 1 90 Þ A

(1.41)

In the above equation, ICmax 5 Vmax =XC where XC 5 1=ðωC Þ represents the capacitive load. Finally, the instantaneous power absorbed a capacitor is defined as pC ðtÞ 5 vðtÞiC ðtÞ 5 Vmax ICmax cosðωt 1 δÞcosðωt 1 δ 1 90 Þ 1 5 Vmax ICmax cos½2ðωt 1 δÞ 1 90  2 5 2VIC sin½2ðωt 1 δÞ W

(1.42)

According to this equation, the instantaneous power absorbed by the capacitor will always be a double-frequency sinusoid as well and will have zero average value. The load current in RLC circuits under the sinusoidal-steady-state excitation is given as: iðtÞ 5 Imax cosðωt 1 βÞ A

(1.43)

The instantaneous power absorbed by RLC load is then defined as: pðtÞ 5 vðtÞiðtÞ 5 Vmax Imax cosðωt 1 δÞcosðωt 1 βÞ 1 Vmax Imax fcosðδ 2 β Þ 1 cos½2ðωt 1 δÞ 2 ðδ 2 β Þg 2 5 VI cosðδ 2 β Þ 1 VI cosðδ 2 β Þ cos½2ðωt 1 δÞ 1 VI sinðδ 2 β Þ sin½2ðωt 2 δÞ

5

pðtÞ 5 VI cosðδ 2 β Þf1 1 cos½2ðωt 1 δÞg 1 VI sinðδ 2 β Þ

Introduction to power systems

15

In the last equation the terms I cosðδ 2 β Þ 5 IR and I sinðδ 2 β Þ 5 IX can be used to simplify the equation as: pðtÞ 5 VIR f1 1 cos½2ðωt 1 δÞg 1 VIX sin½2ðωt 1 δÞ 5 pR ðtÞ 1 pX ðtÞ

(1.44)

According to this equation, there are two components of the instantaneous power absorbed by the entire load in the circuit. pR ðtÞ is the power absorbed by the resistive component of the load, whereas pX ðtÞ defines the power absorbed by the reactive (inductive or capacitive) component of the load. The pR ðtÞ is similar to Eq. (1.37), where IR 5 I cosðδ 2 β Þ represents the component of the load current, which is in phase with the load voltage. The value obtained from the term ðδ 2 β Þ decides the actual phase angle between the voltage and current. The reactive component pX ðtÞ is the same as Eq. (1.40) or (1.42), where IX 5 I sinðδ 2 β Þ is the component of load current and is 90 degrees out of phase with the voltage. Eq. (1.44) also suggests that the instantaneous power pR ðtÞ absorbed by the resistive component of the load is always a combination of a double-frequency sinusoid and an average power P shown as [3]: P 5 VIR 5 VI cosðδ 2 β Þ W

(1.45)

This average power P is also known as real power or active power. All of these terms define the power term P indicated in Eq. (1.45). The instantaneous power pX ðtÞ absorbed by the reactive component of the load, also denoted as Q (reactive power), defined in Eq. (1.44) is always a doublefrequency sinusoid and having zero value of average power. Q 5 VI sinðδ 2 β Þ var

(1.46)

Although it has similar units as real power, practically “var” or volt
amperes reactive is used to define the unit of reactive power. The real and reactive power can be easily calculated using complex power expressions for the circuits functioning in sinusoidal-steady-state. If the voltage across any circuit element is V 5 V+δ while the current passing through it is represented by I 5 I+β, then the complex power S is defined as the product of the voltage and the conjugate of the current represented as: S 5 VIT 5 ½V+δ½I+β T 5 VI+δ 2 β 5 VI cosðδ 2 β Þ 1 jVI sinðδ 2 β Þ

(1.47)

where the phase difference (angle) between the voltage and current is represented by ðδ 2 β Þ. Thus S can take the following form if Eq. (1.47) is compared with Eqs. (1.45) and (1.46): S 5 P 1 jQ

(1.48)

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Hybrid Renewable Energy Systems and Microgrids

Figure 1.9 Representation of (A) load and (B) generator operation.

The magnitude of the complex power S 5 VI is known as apparent power. Although it has similar units as P and Q, practically “VA” or volt
amperes is used to define the unit of apparent power. The real power P is obtained by multiplying the PF term cosðδ 2 β Þ with the apparent power (i.e., P 5 VI  cosðδ 2 β Þ). Fig. 1.9 tells us about the behavior of the circuit element whether it absorbs or delivers power. The load convention is shown in Fig. 1.9A where the current flow in toward the positive terminal of the circuit element. Eq. (1.47) helps to calculate the complex power absorbed by the circuit element. According to this equation, P may have either a positive or negative value based on the phase difference value of ðδ 2 β Þ. The circuit element absorbs positive real power when P turns out to be positive on the other hand if the value of P is negative, the circuit element delivers positive real power or in simple words absorbs negative real power. In a similar fashion, for the positive value of Q, the circuit element absorbs positive reactive power while in case of negative Q, the circuit element delivers positive reactive power or it absorbs negative reactive power for Fig. 1.9A. The generator convention is shown in Fig. 1.9B, where the current flow out of the positive terminal of the circuit element. Eq. (1.47) helps to calculate the delivered complex power. For the negative values of P and Q, the circuit element delivers positive real and reactive power, respectively, while for the negative values of P and Q, the circuit element delivers negative reactive power, respectively [3].

1.3

Balanced three-phase systems

The following topics for balanced three-phase circuits are discussed in this section: Y connections, line-to-neutral voltages, line-to-line voltages, line currents, Δ loads, Δ
Y conversions, and equivalent line-to-neutral diagrams [3]. The balanced threephase systems are currently utilized almost in all of the electric power systems used in the generation, transmission, and distribution. The efficiency of generator copper and iron is enhanced using a three-phase operation. It was seen in the previous section that the power flow in single-phase circuits is pulsating. However, this issue is

Introduction to power systems

17

Figure 1.10 Y-connected three-phase system and phasor diagram.

solved in a three-phase system. Moreover, the three-phase motors not only start easily but also run smoothly throughout than single-phase motors owing to constant torque. Certain difficulties are realized if additional phases are included to further enhance the operating efficiency (i.e., use of polyphase systems instead of three phases). A balanced three-phase voltage system comprises three identical (in terms of magnitude and frequency) single-phase voltages where each phase is 120 degrees apart from the other. A schematic diagram of three single-phase voltage sources using Y configuration is displayed in Fig. 1.10A; also can be represented in Δ configuration. Fig. 1.10B illustrates the phasor diagram of each phase voltage [2]. If phasors rotate in counterclockwise (positive) direction with respect to the reference line at the angular frequency ω, then the sequence of positive maximum value occurrence will be for phases a, b, and c, respectively. In simpler words, the sequence of the phase voltage arrival will be a, b, and c, respectively while considering the phasor space. Thus abc (order, phase sequence, and rotation have the same meanings) is the phase sequence of three-phase voltage for Fig. 1.10. The order of the threephase system plays a vital role to decide the direction of a motor. For example, considering three-phase induction motors, this order defines whether the motor rotates clockwise or counterclockwise. The synchronous generators (also known as alternators) are basically three-phase machines. It is necessary to use at least three coils with a phase difference of 120 electrical degrees apart from one another in space to produce a set of three voltages having a phase displacement of 120 electrical degrees in time. For simplicity, each coil can be considered as a separate generator. Six wires are required to carry the power from the three generators. Therefore this system can be considered as the expansion of the single-phase circuits debated earlier. However, it is better to use a single wire for the return of all three generators instead of using a separate wire from each load to each generator. As a result, the current in the return wire will be Ia 1 Ib 1 Ic , and all of these will be cancel out while considering a balanced load. However, in the case of an unbalanced load, the value of the return current will be smaller than any of Ia ; Ib ; or Ic . This fact can be exploited to use smaller

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Hybrid Renewable Energy Systems and Microgrids

return wire than the other three. Therefore this configuration is also recognized as a four-wire three-phase system. It is much better to build a connection from the electrical system to the ground (equipotential surface at zero potential) to protect the system and safety. The neutral point of the generator is a reasonable point for grounding [2].

1.3.1 Balanced Y connection A voltage source with Y (Wye) connection, which supplies a balanced Y-connected load, is shown in Fig. 1.11. The neutral point of each phase is combined in this connection type. While the neutral point connection of the source is titled with n, the neutral point connection load is titled with N in Fig. 1.11. It is assumed that the three-phase source is an ideal source due to neglected source impedances. In addition, line impedances and neutral impedance are also ignored in the diagram shown in Fig. 1.11. Therefore the three-phase load is called balanced. The balanced term is used to describe that the load impedances in the whole phases are equal. As can be seen from Fig. 1.11, terminals of the source are entitled as a; b; and c, while the source voltages from line to voltages are entitled as Ean ; Ebn ; and Ecn . Similarly, the source is called balanced if magnitudes of the voltages are identical and the phases have a 120-degree phase difference between them. Balanced line-toneutral voltages for a three-phase system can be expressed as follows: Ean 5 10+0 Ebn 5 10+2120 5 10+240 Ecn 5 10+120 5 10+2240 V

(1.49)

where the reference phasor is Ean with 10 V magnitude. A positive phase (or abc) sequence is obtained if phase differences with 120 degrees are spread from Ean to Ebn , and then, from Ebn to Ecn . Conversely, a negative phase (or abc) sequence is obtained if phase differences with 120 degrees are spread from Ean to Ecn and then from Ecn to Ebn . It is clear that the defined voltages in Eq. (1.49) are positive phase sequence. The phasor diagram of the balanced positive phase sequence is illustrated

Figure 1.11 Y-connected three-phase system feeding balanced Y-connected load.

Introduction to power systems

19

Figure 1.12 Phasor diagram of balanced positive-sequence line-neutral voltages.

in Fig. 1.12. Line-to-line voltages are the voltages between phases entitled Eab ; Ebc ; and Eca . By applying a KVL in Fig. 1.11, it can be expressed as: Eab 5 Ean 2 Ebn

(1.50)

From Eq. (1.49), 2

pffiffiffi3 21 2 j 35 Eab 5 10+0 2 10+2120 5 10 2 104 2 0pffiffiffi 1 pffiffiffi pffiffiffi 3 1 j1 A 5 3ð10+30 Þ V Eab 5 3ð10Þ@ 2

(1.51)

Similar steps are followed for the calculation of other line-to-line voltages Ebc and Eca .  Ebc 5 E 2 10+120 pbnffiffiffi 2 Ecn 5 10+2120  5 3ð10+290 Þ V Eca 5 p Ecnffiffiffi 2 Ean 5 10+120 2 10+0 5 3ð10+150 Þ V

(1.52)

The calculated line-to-line voltages Eab ; Ebc ; and Eca are also balanced because of the satisfying required rule. When the line-to-line voltages are compared with the line-to-neutral voltages in a balanced connected three-phase system, it is pffiffiWye ffi evident that the line-to-line voltages are 3 times the others and lead by 30 degrees if positive-sequence sources are available. It can be expressed as: pffiffiffi Eab 5 pffiffi3ffi Ean +30  Ebc 5 p3 ffiffiffiEbn +30 Eca 5 3Ecn +30

(1.53)

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Hybrid Renewable Energy Systems and Microgrids

Figure 1.13 Positive-sequence line-neutral and line-line voltages in Y-connected three-phase system (A) phasor diagram, (B) voltage triangle.

The line-to-line voltages are visually compared with the line-to-neutral voltages in a balanced Wye connected three-phase system in Fig. 1.13. Every phasor starts at the beginning of the phasor diagram as shown in Fig. 1.13A, whereas the line-to-neutral voltages start from the corners and end in the middle of the voltage triangle that is entitled n for neutral n, as shown in Fig. 1.13B. Besides, positive-sequence voltages can be determined by following the clockwise sequence of the triangular corners abc. The Ean is the reference for both illustrations. The sum of balanced line-to-line voltages is zero due to the closed triangle structure. It is notifying that the sum of lineto-line voltages is zero in both balanced and unbalanced systems because they create a closed path around the corners of the triangular every time. On the other hand, the sum of line-to-neutral voltages is zero only in the balanced systems. In addition, the line currents can be derived through KVL equations as: Ean ZY Ebn Ib 5 ZY Ecn Ic 5 ZY Ia 5

(1.54)

When the impedance value of the Y-connected phases is ZY 5 2+30 Ω, the line currents can be calculated as: 10+0 5 5+230 A 2+30 10+2120 5 5+2150 A Ib 5 2+30 10+120 5 5+90 A Ic 5 2+30 Ia 5

(1.55)

Introduction to power systems

21

It is clearly seen from the calculated values that the line currents keep the balanced conditions. The neutral current In can be expressed by applying a KCL equation to the node N in Fig. 1.11. In 5 Ia 1 Ib 1 Ic

(1.56)

If the values of the line currents are taken into account, the following result is obtained: In 5 5+230 1 5+2150 1 5+90 0pffiffiffi 1 0 pffiffiffi 1 3 2 j1 2 3 2 j1 A 1 5@ A 1 j5 5 0 In 5 5@ 2 2

(1.57)

Fig. 1.14 shows the phasor diagram of line currents in a Y-connected balanced system. As can be seen from the figure, the line currents create a closed structure, and the sum of them is equal to zero that stands for the neutral current In . As mentioned earlier, the sum of balanced phasor sets is generally equal to zero due to the fact that every balanced phasor constitutes a closed loop. Therefore the value of the neutral current will be equal to zero for all neutral impedances if the balanced case of the system is kept, even if impedance value changes from the short circuit to the open circuit. In the event of unbalanced system conditions, which may be appeared due to voltage sources, line impedances, or load impedances, line currents are not balanced and a neutral current In may be available between n and N. The case illustrated in Fig. 1.15 is considered where a balanced Δ (delta) connected load is supplied by a three-phase Wye connected source. As can be seen from the figure, the load impedances ðZΔ Þ are selected with equal values to provide a balanced delta connection case, and they are titled by A; B; and C. The delta connection has not neutral. The load currents of this connection scenario can be determined as [3]: Eab ZΔ Ebc IBC 5 ZΔ Eca ICA 5 ZΔ IAB 5

(1.58)

Figure 1.14 Phasor diagram of line currents in a balanced Y-connected three-phase system.

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Hybrid Renewable Energy Systems and Microgrids

Figure 1.15 Y-connected three-phase system feeding balanced Δ-connected load.

When the impedance value of the delta connected phases is ZΔ 5 5+30 Ω, the load currents can be calculated as: 0 1 pffiffiffi 10+230 A 5 3:464+0 A IAB 5 3@ 5+30 0 1 pffiffiffi 10+290 A 5 3:464+2120 A IBC 5 3@ 5+30 0 1 pffiffiffi 10+2150 A 5 3:464+120 A IAB 5 3@ 5+30

(1.59)

In addition, the line currents can be calculated through the obtained results and the KCL as: pffiffiffi  Ia 5 IAB 2 ICA 5 3:464+0 2 3:464+120 5 p 3ðffiffi3:464+230 Þ ffi    Ib 5 IBC 2 IAB 5 3:464+2120 2 3:464+0 5 3pð3:464+2150 Þ ffiffiffi    Ic 5 ICA 2 IBC 5 3:464+120 2 3:464+2120 5 3ð3:464+90 Þ

(1.60)

When the obtained results are considered, it is clear that both of the line currents and load currents satisfy the balanced conditions. In other words, the sum of balanced load currents ðIAB 1 IBC 1 ICA Þ in the delta connection is equal to zero. The sum of the line currents ðIa 1 Ib 1 Ic Þ is always zero in the delta connected load case, regardless of the balance condition. As a final remark, it is obvious that when the line currents are compared with the load currents pffiffiffi in a balanced delta connected three-phase system, the line currents are 3 times

Introduction to power systems

23

Figure 1.16 Phasor diagram of line and load currents in a balanced Δ
load.

Figure 1.17 Balanced Δ-connected three-phase system feeding balanced Δ
load.

the others and lead by 30 degrees if positive-sequence sources are available. It can be given in Eq. (1.61) and can be illustrated by Fig. 1.16 [3]: pffiffiffi  Ia 5 p3 ffiffiffiIAB +230  (1.61) Ib 5 pffiffi3ffi IBC +230 Ic 5 3ICA +230

1.3.2 Balanced Δ connection A balanced Δ
Δ system is composed of the connection of the balanced source and the balanced load. A typical Δ
Δ connected system is illustrated in Fig. 1.17. Similar to previous connection schemes, we aim to define phase and line currents analytically. The phase voltage of this connection scheme can be described by assuming a positive sequence as [4]: Vab 5 Vp +0 Vbc 5 Vp +2120 Vca 5 Vp +120

(1.62)

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Hybrid Renewable Energy Systems and Microgrids

In this scheme the line voltages are equal to the phase voltages. When the value of line impedances is considered as zero, the phase voltages of this connection type are the same with the voltages across the impedances as shown in Fig. 1.17, which can be expressed as: Vab 5 VAB Vbc 5 VBC Vca 5 VCA

(1.63)

Thus the phase currents can be given as: Vab VAB 5 ZΔ ZΔ Vbc VBC IBC 5 5 ZΔ ZΔ Vca VCA ICA 5 5 ZΔ ZΔ IAB 5

(1.64)

By applying the KCL to the load side the line currents can be defined as follows: Ia 5 IAB 2 ICA Ib 5 IBC 2 IAB Ic 5 ICA 2 IBC

(1.65)

pffiffiffi In addition, each line currents is 3 times the phase currents as in Eq. (1.66) and phase currents lead the line currents by 30 degrees. IL 5

pffiffiffi 3I p

(1.66)

The source and load may be turned into their Y equivalents for analyzing the system through another method. It is important to note that ZY 5 ZΔ =3 is known. The conversion of a Δ-connected source to a Y-connected load will be presented in the next section.

Figure 1.18 Balanced Δ-connected three-phase source.

Introduction to power systems

25

A three-phase Δ-connected system can be constituted by properly arranging three single-phase sources, which is depicted in Fig. 1.18. The line and phase voltages of this type of connection are characterized as [2]:   j VL j 5  V p 

(1.67)

Even though the phase and line currents of this connection type are not the same, the KCL equation can be employed to determine the relationship between these currents. Similar to the Y-connected source system, the phasor diagram of this connection type can be illustrated as in Fig. 1.19 where the phase values of the currents are assumed by the following to keep balanced conditions. Iab 5 Ip +0 Ibc 5 Ip +2120 Ica 5 Ip +120

(1.68)

The current between the point a and the point a0 is labeled as Iaa0 . This current can be determined via Iaa0 5 Ica 2 Iab and its value is as follows: Iaa0 5

pffiffiffi 3Ip +150

(1.69)

Figure 1.19 Phasor diagram of phase and line currents in a balanced Δ-connected threephase system.

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Hybrid Renewable Energy Systems and Microgrids

In a similar manner, pffiffiffi Ibb0 5 pffiffi3ffi Ip +30 Icc0 5 3Ip +290

(1.70)

pffiffiffi Inpthis ffiffiffi connection type, each line current is 3 times the phase currents (i.e., IL 5 3Ip ). By assuming, a three-phased generator feeds a balanced load, the following expressions regarding phase voltages and currents can be given: pffiffiffi va ðtÞ 5 p2 ffiffiffiVp sin ωt vb ðtÞ 5 pffiffi2ffi Vp sinðωt 2 120 Þ vc ðtÞ 5 2Vp sinðωt 1 120 Þ

(1.71)

pffiffiffi ia ðtÞ 5 p2 ffiffiffiIp sinðωt 2 φÞ ib ðtÞ 5 pffiffi2ffi Ip sinðωt 2 120 2 φÞ ic ðtÞ 5 2Ip sinðωt 1 120 2 φÞ

(1.72)

where φ stands for the phase angle. The total power given to the load can be expressed as: p3φ ðtÞ 5 va ðtÞia ðtÞ 1 vb ðtÞib ðtÞ 1 vc ðtÞic ðtÞ

(1.73)

Also, the power expression can be expanded as:   p3φ ðtÞ 5 Vp Ip 3 cos φ 2 ½cosð2ωt 2 φÞ 1 cosð2ωt 2 240 2 φÞ 1 cosð2ωt 1 240 2 φÞ

(1.74) The last terms of the power equation are related to reactive power. If the reactive power is equal to zero, the expression turns into the following equation: p3φ ðtÞ 5 3Vp Ip cos φ

(1.75)

On the other hand, the total power expression can be determined in terms of the line voltage and current as: p3φ 5

pffiffiffi 3jVL jjIL jcos φ

(1.76)

The total instantaneous power has a constant value and its magnitude is three times the real power of each phase. In a three-phased system the reactive power may be ignored since the assumption of the Q value is equal to zero. The complex or apparent power ðSÞ of a three-phase system can be defined as: S3φ 5 3Vp Ip

(1.77)

Introduction to power systems

27

Also, the following expressions can be derived for the complex power: S3φ 5 P3φ 1 jQ3φ

(1.78)

P3φ 5 3jVP jjIP jcos φ

(1.79)

Q3φ 5 3jVP jjIP jsin φ

(1.80)

P3φ 5

pffiffiffi 3jVL jjIL jcos φ

(1.81)

Q3φ 5

pffiffiffi 3jVL jjIL jsin φ

(1.82)

The apparent power is generally used while determining nominal values of power systems and equipment such as transformers, generators, circuit breakers, and so on. On the other hand, horsepower rates and voltages values are employed while determining the three-phase motors [2]. A balanced Δ
Y system, which is shown in Fig. 1.20, comprises a source supplying a balanced and Y-connected load. The phase voltages of this connection, which are also equal to line voltages, can be derived as [4]: Vab 5 Vp +0 Vbc 5 Vp +2120 Vca 5 Vp +120

(1.83)

The line current can be derived from several methods. One of them is to write a KVL among the points aANBba loop in Fig. 1.20. Thus the following is obtained: Ia 2 Ib 5

Vp +0 ZY

(1.84)

Figure 1.20 Schematic diagram of balanced Δ-connected three-phase system feeding Y-load.

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Hybrid Renewable Energy Systems and Microgrids

Since Ia leads Ib by 120 degrees (i.e., Ib 5 Ia +2120 ), it can be expressed as: pffiffiffi  pffiffiffi 3 1 Ia 2 Ib 5 Ia ð1 2 1+2120 Þ 5 Ia 1 1 1 j 5 Ia 3+30 2 2

(1.85)

The equation can be simplified by considering Eq. (1.84) as: Ia 5

pffiffiffi Vp = 3+230 ZY

(1.86)

The solution can be expanded for the other line currents by remembering the Ib 5 Ia +2120 and Ic 5 Ia +120 . The phase currents and the line currents are the same. Another method to acquire the line currents is to change Δ connected source with its Y equivalent as shown in Fig. 1.21. It is known that phase voltages lag their corresponding line-to-line voltages by 30 degrees in a Y-connected source. Hence, phase voltages of equivalent Y-connectedpsource can be obtained by dividing line ffiffiffi voltages of the Δ-connected source by 3 and shifting phases by 230 degrees. The phase voltages are as follows: Vp Van 5 pffiffiffi +230 3 Vp Vbn 5 pffiffiffi +2150 3 Vp Vcn 5 pffiffiffi +90 3

Figure 1.21 Δ-Connected source transformation to a Y-connected system.

(1.87)

Introduction to power systems

29

When Zs is the impedance of Δ-connected source, the source impedance of each phase is Zs =3 the equivalent Y-connected source. As soon as the source is converted into the Y form, the system turns into a Y
Y system. Thus the equivalent singlephase circuit can be employed and, similar to Eq. (1.86), the line current of phase a is derived as follows: Ia 5

pffiffiffi Vp = 3+230 ZY

(1.88)

As a different approach, the Y-connected load can be converted into an equivalent Δ-connected load, and then, the voltages can be expressed as follows by considering the obtained connection as a Δ
Δ system: Vp VAN 5 Ia ZY 5 pffiffiffi +230 3 VBN 5 VAN +2120 VCN 5 VAN +120

(1.89)

As mentioned earlier, a Δ-connected load is desired rather than the Y-connected load. It is simpler to change the loads in any phase of the Δ-connected loads since the loads are separately connected to the lines. Nonetheless, the use of Δ-connected sources in practice is hard since any small inequality in the phase voltages leads to undesired circulating currents. A summary of the formulas for the different connection types is listed in Table 1.1. Those formulas can be easily derived by applying the KCL and KVL in the proper three-phase circuits [4]. It should be noted that understanding formula derivation is more useful than memorizing.

1.4

Per-unit system

The per-unit (pu) system is used to express the quantities of a system as fractions in a limited base unit which is widely used in power systems. The analytical computations are not changed from one side to another in a transformer or power systems due to simplified representation based on pu calculations. This approach facilitates analyzing the entire power system where a large number of generators, transformers, and loads are involved. A pu system is used to generate units for power, voltage, current, impedance, and admittance quantities. The main target of using a pu conversion is to convert large differences and values in absolute into base representations. In a pu system, generally power and voltage are selected base values by accepting the impedance and admittance. The quantities of the power system transformed to pu comprise multiples of selected base values. The numerical pu value of a selected quantity should be in the same dimension of the selected base quantity

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Hybrid Renewable Energy Systems and Microgrids

Table 1.1 Formula summary list for three-phase systems by assuming positive or abc sequence. Connection type

Phase voltages/currents 

Y
Y

Van 5 Vp +0 Vbn 5 Vp +2120 Vcn 5 Vp +120 Similar to line currents

Y
Δ

Van 5 Vp +0 Vbn 5 Vp +2120 Vcn 5 Vp +120 IAB 5 VAB =ZΔ IBC 5 VBC =ZΔ ICA 5 VCA =ZΔ Vab 5 Vp +0 Vbc 5 Vp +2120 Vca 5 Vp +120 IAB 5 Vab =ZΔ IBC 5 Vbc =ZΔ ICA 5 Vca =ZΔ Vab 5 Vp +0 Vbc 5 Vp +2120 Vca 5 Vp +120 Similar to line currents

Δ
Δ

Δ
Y

Line voltages/currents pffiffiffi Vab 5 3Vp +30 Vbc 5 Vab +2120 Vca 5 Vab +120 Ia 5 Van =ZY Ib 5 Ia +2120 Ic 5 Ia +120 pffiffiffi Vab 5 VAB 5 3Vp +30 Vbc 5 VBC 5 Vab +2120 Vca 5 VCA 5 Vab +120 pffiffiffi Ia 5 IAB 3+230 Ib 5 Ia +2120 Ic 5 Ia +120 Similar to phase voltages pffiffiffi Ia 5 IAB 3+230 Ib 5 Ia +2120 Ic 5 Ia +120 Similar to phase voltages

Ia 5

Vp +230 pffiffi 3ZY

Ib 5 Ia +2120 Ic 5 Ia +120

where it is titled normalized quantity comparing to the base value. The pu representation of any selected quantity is denoted as follows; pu 5

Actual value Base value

(1.90)

The five quantities involved in pu calculations are the current I, the voltage V, the complex power S, the impedance Z, and the phase angles. The angles are dimensionless, while the other four quantities are completely described by knowledge of only two of them. An arbitrary choice of two base quantities will fix the other base quantities. In case it is assumed that absolute values of Ib and Vb denote the base current and base voltage expressed in kiloamperes and kilovolts, respectively; the complex power in mega volt amperes (MVA) is obtained as the product of both quantities as seen in the following equation [2,5]: jSb j 5 jVb jjIb j 5 1 pu

(1.91)

Introduction to power systems

31

On the other hand, the base impedance is obtained by: jZb j 5

jVb j2 jVb j 5 5 1 pu j Sb j jIb j

(1.92)

Another calculation that is performed for base admittance quantity will be the reverse of base impedance as given by: jYb j 5

jSb j jIb j 1 5 5 1 pu 5 jZb j jVb j2 jVb j

(1.93)

In three-phase systems the power and voltage are calculated in pu values similarly to presented single-phase systems. The specific feature of three-phase systems requires to denote power in total instead of per phase and the voltage is calculated for line to line. The complex power S is the vectoral representation of active power P and reactive power Q as follows: P 5 Sp cos ϕ Q 5 S sin φ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffi 2 Sb 5 P 1 Q2 5 3Vb Ib

(1.94)

The base voltage, impedance, and admittance in three-phase power system are calculated as follows referring to (1.94): j Sb j jVb j 5 pffiffiffi 5 1 pu 3j I b j

(1.95)

 2 V  j Vb j jZb j 5 pffiffiffi 5 b 5 1 pu j Sb j 3j I b j

(1.96)

pffiffiffi jSb j 3jIb j 1 jYb j 5 5 5  2  5 1 pu jZb j j Vb j Vb

(1.97)

The line voltage and complex power of any power system are known in their nominal values. Therefore these quantities are essentially used for calculating the base values. The known complex power value is used in all sections of a power system where the other base values are to be calculated. A base voltage is selected as a reference value that determines all other corresponding voltages in the system. The ohmic impedance of power system ZΩ is determined regarding pu impedance as given in (1.96) by [5]: Zpu 9

ZΩ ZΩ jIb j ZΩ jIb jjVb j ZΩ jSb j 5 5 5 5 1 pu jZb j jVb j jVb j2 j Vb j 2

(1.98)

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Hybrid Renewable Energy Systems and Microgrids

and the admittance as follows: Ypu 9

jVb j2 1 5 1 pu 5 Zpu ZΩ jSb j

(1.99)

Example: It is assumed that a particular power system has the following base values: jVb j910 kV

jSb j91 MVA

and the following pu values of voltages, currents, powers, and impedances are requested to be calculated: 1. 2. 3. 4. 5. 6.

jV j915 kV V 5 15 1 j8 kV I 5 750 1 j150 A S 5 200 1 j300 kW and kVAR S 5 9:3 MVAR Z 5 80 1 j40 Ω

It is first required to calculate base current and base impedance for the solutions of requested values: jIb j 5

j Sb j 1 5 0:1 kA; 5 jVb j 10

jZb j 5

j Vb j 10 5 100 Ω 5 jIb j 0:1

Then the values are calculated as follows: 1. jV j 5 2. 3. 4. 5. 6.

15 10 5 1:5 pu 15 1 j8 V 5 10 5 1:5 1 j0:8 pu 1 j150 I 5 750100 5 7:5 1 j1:5 pu 200 1 j300 S 5 1000 5 0:2 1 j0:3 pu jSj 5 9:3 1 5 9:3 pu 80 1 j40 Z 5 100 5 0:8 1 j0:4 pu

1.5

and kVAR

Power generation and electric machines

The vast majority of the electricity used all over the world is generated by centralized power stations that are based on the large hydroelectric, thermic boiler, nuclear, or some other fossil fuel
based plants. The distributed generation concept has been introduced in the early 1990s to overcome some economic and technical demerits of centralized generation in terms of efficiency, accessibility, and sustainability. Both generation types are based on electromechanical conversion where any prime mover is required for driving the shaft of electricity generator [6
8].

Introduction to power systems

33

The operation principle of generators in an electricity generation system is described by two fundamental physical laws that are Faraday’s law and Ampere’s law. The prior defines generating the electricity due to the effect of a rotating magnetic field, while the latter describes the created magnetic field around a wire where an alternative current flows through. Both of these fundamental laws are essential to understand electricity generation at any power station equipped with electric machines operated as a generator, and left sections of power network including transmission, distribution, and consumption. These physical laws are described below for providing an understanding of the operation of the electric power generation system. Faraday’s law, as the first of these facts, represents the operating principle of electric machines in electromechanical transformation enabling them to act as motor or generator. It proves that the theory of any changing magnetic field generates a voltage on any conductor. This is the fundamental description of ac power generation in electrical machines, which is observed by putting a coil into a rotating magnet that results in generated voltage on the terminals of that coil. The generators are operated regarding this principle for generating voltage on the coil which is then supplied to the transmission and distribution system of the power network. The coils are called stator in a generator, while the source of a rotating magnetic field is described as a rotor that accommodates magnets. The rotational movement of the rotor produces a strong magnetic field passing to stator windings, and this field induces alternating current and voltage through the stator coils. Thus the amplitude of the generated voltage is proportional to the strength of the magnetic field produced in the rotor. The detailed description of the operating principle based on Faraday’s law is represented in the following part of this section [9]. The analytical representation of Faraday’s law is seen in (1.100): ð I d E  ds 5 2 B  da (1.100) dt s C where it states that the line integral of the electric field intensity E around a closed contour C will be equal to the time rate of change of the magnetic flux B passing through that contour. It is noted that in magnetic structures with windings of high electrical conductivity, it can be shown that the E field in the wire is very small and can be neglected. Therefore the left-hand side of (1.100) reduces to the negative of the induced voltage e at the winding terminals. In addition, the flux on the righthand side of (1.100) is dominated by the core flux ϕ. Since the winding (and hence the contour C) links the core flux N times, (1.100) is transformed to: e5N

dϕ dλ 5 dt dt

(1.101)

where λ denotes the flux linkage of the winding and is defined as λ 5 Nϕ. The flux linkages λ can also be related to the current i in the coil by the definition of inductance L through as λ 5 Li [2,10]. Ampere’s law states the current flowing through a coil generates a magnetic field around the coil. It describes the relationship of the produced magnetic field

34

Hybrid Renewable Energy Systems and Microgrids

and the current flowing through the coil is proportional. It also describes the relationship between rotor speed and frequency of the generated voltage at the terminals of the coil as represented in Faraday’s law. The rotational movement of the rotor generates a magnetic field inducing voltage at the terminals of the coil, and the frequency of the induced voltage is determined by the speed of the rotor. The analytical representation of Ampere’s law is described by the integral around a closed path of the component of the magnetic field tangent as follows: I B  dl 5 μ0 I

(1.102)

The constant μ0 is the permeability of free space which is given by μ0 5 4π1027 [2] The represented fundamental laws are also applicable in three-phase generator systems which are the backbone of power generation systems. In this situation, the coils are places with 120 degrees shifted to each other and generate three-phase voltages that are induced in stator windings by the magnetic field produced in the rotor. The magnetic field source can be permanent or electromagnets located in the rotor where the latter is widely used in large power plant generators [9].

1.5.1 The principles of electromechanical energy conversion The power generation system is based on synchronously operating power stations comprising many large generators. The generation infrastructure is installed with synchronous electric machines operating in parallel connection and reaching several thousand MVAs in the overall system. The synchronous generators that are based on constant speed and frequency operation are used for generating the electricity as described in Faraday’s law [2]. The interaction of electrical and mechanical systems through the power generation comprises electromagnetic fields and the induced energy is transferred from one to another system as the outcome of this interaction. It is essential to analyze the principles of the electromechanically operating system for understanding the power generation. The electromechanical system comprises the coupling field connecting the electrical and mechanical systems as shown in Fig. 1.22. In this architecture the electrical system is considered to be a lumped parameter system by neglecting the electromagnetic interference and operated with low frequency. The friction causes heat loss in the mechanical system and the electrical system dissipates the heat owing to the resistance of conductors. The magnetic fields cause Eddy current and hysteresis losses in the electrical system while the electrical fields generate dielectric losses. In cases, the total energy

Figure 1.22 Block diagram of a fundamental electromechanical transformation system.

Introduction to power systems

35

supplied by the electrical source is assumed to be WE while mechanical source energy is WM, the energy distribution is denoted with the following equations [10,11]: WE 5 We 1 WeL 1 WeS

(1.103)

WM 5 Wm 1 WmL 1 WmS

(1.104)

The WeS denotes the stored energy in electric or magnetic fields that are not connected with the mechanical system, while WeL is the heat losses that are produced by the resistance of conductor due to current flow in the electrical system as well the energy dissipated due to hysteresis, eddy and dielectric losses. The electrical energy transferred from the electrical system to the coupling field is shown with We, while the mechanical system energies are given in (1.104) in similar ways. The stored energy in moving elements of the mechanical system is shown with WmS, while the heat losses in the mechanical system are denoted with WmL and transferred mechanical energy with WM in the coupling field. In case the total energy transferred to the coupling field is defined with WF, then it is given by: WF 5 Wf 1 WfL

(1.105)

where Wf is the stored energy in the coupling field and WfL is the dissipated energy due to heat losses in the coupling field as eddy current, hysteresis, and dielectric losses. The overall energy of electromechanical system is represented in compliance with the conversation of energy law as follows: Wf 1 WfL 5 ðWE 2 WeL 2 WeS Þ 1 ðWm 2 WmL 2 WmS Þ

(1.106)

which can be rewritten as: Wf 1 WfL 5 We 1 Wm

(1.107)

The relationships between electrical and mechanical system over coupling field are seen in Fig. 1.23 where converting the electrical energy to mechanical energy is independent from the loss of energy in electrical or mechanical system WeL and

Figure 1.23 Transfer diagram of energy balance.

36

Hybrid Renewable Energy Systems and Microgrids

WmL. On the other hand, the energies stored in both fields WeS and WmS are not common to electrical and mechanical system. If the losses of coupling field are neglected, the field will be conservative and equivalence given in (1.108) will be obtained [11]. Wf 5 We 1 Wm

(1.108)

The methods and concepts represented in this section are related to many engineering solutions including electromechanical energy conversion systems. In addition to the energy balance described earlier, the quantitative introduction of the forces in electromechanical systems is represented as follows in derivations of forces and torques in magnetic field-based electromechanical systems. The Lorentz force law that is also known as the electromagnetic force is the combination of the electric and magnetic force on a point charge due to electromagnetic fields. A particle of charge q moving with a velocity v in an electric field E and a magnetic field B produces the force given by: F 5 qðE 1 v 3 BÞ

(1.109)

The F is in newtons, q in coulombs, E in volts per meter, B in Tesla, and v is in meters per second. The force in a pure electric field is determined with the charge of the particle and electric field relationship as in (1.110), while the force in pure magnetic field systems is given by (1.111): F 5 qE

(1.110)

F 5 qðv 3 BÞ

(1.111)

The force determined in the magnetic field is obtained with the magnitude of the charge on the particle and the magnitude of the B field as well as the velocity of the particle. In fact, the direction of the force is always orthogonal to the direction of both the particle motion and that of the magnetic field which is analytically indicated by the vector cross product v 3 B in (1.111). The magnitude of this vectoral product is equal to the magnitude product of v and B which is defined by the wellknown right-hand rule. The rule states that the thumb points the direction of v, the forefinger denotes the direction of B, and the palm of the hand represents the force F, which is orthogonal to the directions of both B and v. In the case when vast number of charged particles are moved, (1.109) is rewritten with charge density ρ which is calculated in coulombs per cubic meter as follows: Fv 5 ρðE 1 v 3 BÞ

(1.112)

where Fv denotes the force density in newton per cubic meter, while the current density is given in a similar way: J 5 ρv

(1.113)

Introduction to power systems

37

The J has the units of amperes per square meter in SI units. The force density of magnetic system is given by [10]: Fv 5 J 3 B

(1.114)

1.5.2 Generator operation of electric machines The induced voltage by a time-varying magnetic field is calculated by using the equation given in (1.101) (e 5 dλ/dt). The mechanical movement produces the flux linkage λ driving the electromagnetic energy conversion principle. The rotating machines comprise windings generating the voltages and magnetic components producing the magnetic field inducing voltage on the terminals of windings. A number of coils or windings are connected in serial and parallel to comprise the armature winding which is known as stator of ac synchronous or induction machines. On the other hand, a rotating mechanical system defined as rotor is used to induce voltage due to occurred magnetic field. In spite of general principles, synchronous and dc machines typically include a second winding carrying dc current for generating the main operating flux in the machine. This type of windings is defined as field winding that is located on stator of dc machine, while it is placed on the rotor of synchronous machine. The stator and rotor are made using ferromagnetic steel with good permeability materials in most of the rotating electrical machines. The ferromagnetic field increases the magnetic energy in the rotor and improves the efficiency of electromechanical system. The alternative flux of the armature induces current in the machine. Since the higher eddy currents can cause losses in these kinds of machines, the armature comprises thin and laminated ferromagnetic steels for decreasing the eddy current density. The variations of rotating machines are implemented in different structures including dc, synchronous, induction, permanent magnet, brushless, and so on. Any of these machines are built in different mechanical structures, but electromechanical energy conversion principles are the same as discussed earlier. In the induction machine the magnetic flux distribution is accomplished by the rotor and stator, while the dc machines differentiated in structure are also operated in the same physical principles [1,2,10,11]. The conventional ac machines used in electromechanical energy conversion are analyzed in two categories as synchronous and induction machines. The names imply their operation principles where the currents of rotor windings are directly fed from the stationary frame through a rotating contact in synchronous machines. On the other hand, the operation of induction machines is based on inducing current in the rotor windings by a function of the time variation of the stator currents and the motion of the rotor. The synchronous machine is the most widely used rotating electrical machine used as a generator in electromechanical energy conversion systems. The simple schematic diagram of a two-pole single-phase synchronous machine is shown in Fig. 1.24 where field windings, armature windings, and magnetic flux paths are placed on the figure. The armature windings of a synchronous machine are located on the stator while the field windings are on the rotor. The field windings are

38

Hybrid Renewable Energy Systems and Microgrids

Figure 1.24 Schematic diagram of a synchronous generator.

Figure 1.25 Waveforms of a single-phase synchronous generator: (A) flux density, (B) generated voltage.

excited by using a dc source connected to rotating slip rings over carbon brushes. The synchronous machine is convenient with a single and low-power field winding and multiple-phase high-power armature windings. The armature and field windings of a synchronous machine are illustrated on the stator and rotor in Fig. 1.24, respectively. The electromechanical energy conversion is performed by turning the rotor at a constant speed that is obtained from a mechanical source connected to the shaft. In case it is assumed that the armature windings are open circuit in this condition, the flux of the machine that is shown with dashed lines in the figure is generated by only the field winding. The resultant flux density B in the air gap is shown as a function of angle θa in Fig. 1.25A. Once it is assumed that the distribution of flux is sinusoidal and the rotor speed is constant, the coil voltage is obtained in sinusoidal as seen in Fig. 1.25B. The frequency of induced voltage in cycles per second will be identical with the speed of the rotor in revolutions per second. It is associated with the frequency of

Introduction to power systems

39

induced voltage and synchronized with mechanical speed which proves the operation of synchronous machine. Therefore a synchronous machine should be revolved at 3600 revolutions per minute for generating 60-Hz voltage. In spite of the synchronous generator shown in Fig. 1.24, many of the synchronous machines include more than two-poles as seen in Fig. 1.26 where four-pole single-phase synchronous generator is illustrated. The connections of field coils produce alternative polarity poles and result two complete cycles in the flux distribution circumference as seen in Fig. 1.27. The armature windings comprise two coils connected in series as a1, 2a1, a2, 2a2 that induced one half-cycle of flux density and a single revolution of rotor generates two cycles. Thus the frequency of generated voltage also will be twice the rotor speed in revolutions per second.

Figure 1.26 Schematic diagram of a four-pole synchronous generator.

Figure 1.27 Distribution of flux density in a four-pole synchronous generator.

40

Hybrid Renewable Energy Systems and Microgrids

The electrical frequency fe of the voltage generated in a synchronous machine is therefore defined as follows: fe 5

n Poles  Hz 60 2

(1.115)

where n denotes the mechanical speed in revolutions per minute, and n/60 gives the speed in revolutions per second. A salient-pole synchronous generator as shown in Figs. 1.24 and 1.26 is operated at low speeds comparing to its alternative in cylindrical-pole machines, and it is the characteristic of hydroelectric generators since the hydraulic turbines operate at relatively low speeds. However, the steam turbines and gas turbines are better to be operated at relatively high speeds, and turbine-driven alternators or turbine generators are commonly two- or four-pole cylindrical-rotor machines [10]. The armature current of a synchronous generator supplying electric power to a load creates magnetic flux wave rotating at synchronous speed in the air gap. This magnetic flux reacts with the field current flux, and an electromechanical torque is generated as a result of these two magnetic fields to align. The generated electromagnetic torque opposes rotation of generator, and an external mechanical torque should be applied from the prime mover to sustain rotation. This electromechanical torque is the fundamental mechanical source ensuring the synchronous generator to sustain converting mechanical energy to electric energy. The magnetomotive force (MMF) produced by the three-phase armature current of a synchronous machine is generally defined as armature-reaction MMF. The MMF of each phase is proportional to the corresponding current, and hence, the peak MMF is given by: Fmax 5 K  I m

(1.116)

where K is a constant depending on the winding distribution and the number of series turns in the winding per phase. The armature-reaction MMF is the wave rotating at synchronous speed and is directly opposite to phase a at the instant when phase a has its maximum current at t 5 0 as seen in Fig. 1.28 where the MMF waves of armature and field windings of a cylindrical-rotor generator are depicted. The dc field winding generates a sinusoidal F signal that lags 90 degrees to the phase. The magnetic field occurring at any point of the machine is the sum of two fluxes inherited from the field and armature windings. It is possible to analyze the sinusoids given in Fig. 1.28 by using phasor diagrams as seen in Fig. 1.29. Therefore the A and F waves can be included in the phasor diagram where the field flux Φf, armature flux Φar, and resultant flux Φr are represented in the figure. In case it is assumed that the air gap is uniform and there is no saturation, the expressed fluxes will be proportional to those of MMF waves F, A, and R, respectively. The figure denotes the accordance of the case that the armature current is in phase with the excitation voltage.

Introduction to power systems

41

Figure 1.28 MMF waves of armature and field windings of a cylindrical-rotor synchronous generator. MMF, Magnetomotive force.

Figure 1.29 Distribution of flux density in a four-pole synchronous generator.

The electrical equivalent model of synchronous machine with cylindrical-rotor is represented by an inductive reactance where the phasor diagram of armature and field fluxes and corresponding voltages are shown in Fig. 1.30. The field flux Φf is added to armature flux Φar in phasor diagram that yields the resultant flux Φr which is in phase with armature current Ia. The field flux generates the excitation voltage Ef which lags by 90 degrees to Φf. On the other hand, the armature flux Φar and resultant flux Φr generate 90-degree lagging armature voltage Ear and resultant voltage Er, which are given, respectively, by the following equations: Ear 5 2jxΦ I a

(1.117)

Er 5 Ef 2 jxΦ I a

(1.118)

42

Hybrid Renewable Energy Systems and Microgrids

Figure 1.30 The phasor diagram of a synchronous generator.

Figure 1.31 Electrical equivalent circuits of synchronous generator.

where 2j represents the 90-degree lagging. The electrical equivalent circuit generated in accordance with (1.118) is shown in Fig. 1.31 where the inductive reactance xΦ represents the armature-reaction effect that is known as magnetizing reactance of the machine. The terminal voltage of generator Vt is the difference between Er and voltage lost in the armature resistance ra and the leakage-reactance xl. The synchronous impedance Zs is obtained by the combination of xΦ, ra, and xl as follows: Zs 5 ra 1 jXs

(1.119)

X s 5 xl 1 xΦ

(1.120)

The introduced model is represented for a single-phase equivalent of synchronous generator with balanced loads. The model is also dealt in unsaturated situation that defines machine flux is operated in the linear region of its magnetization characteristic. The voltage equation of the synchronous machine is given by: Ef 5 Vt 1 Ia Zs

(1.121)

In the case of a synchronous generator with Ef and synchronous reactance Xs is coupled to a system with VE and reactive impedance jXE as shown in Fig. 1.32, the power characteristic of this system is represented as seen in the following equation: P5

Ef VE sin δ XS 1 XE

(1.122)

Introduction to power systems

43

Figure 1.32 Electrical equivalent circuits of synchronous generator with and external system.

Figure 1.33 Phasor diagram of salient-pole synchronous generator.

where P denotes the power transferred from synchronous generator to the systems. In spite of the cylindrical-pole synchronous generator, the phasor diagram of salient-pole machine is obtained by dispatching the armature current Ia into two components as direct axis and quadrature axis as seen in Fig. 1.33 which is drawn for an unsaturated salient-pole generator operating at a lagging PF. The direct axis component of armature current is denoted with id while the quadrature axis component is iq. The id is quadrature with the generated voltage Ef and opposite with armature-reaction flux Φad along the axis of the field poles. On the other hand, iq is in phase with the generated voltage Ef and generates armature-reaction flux Φaq in space-quadrature with the field poles. In an unsaturated generator the armaturereaction flux Φar is the vectoral sum of the components Φad and Φaq, and the resultant flux ΦR is the vectoral sum of Φar and the field flux Φf as seen in Fig. 1.33. The relationship between voltage and currents of synchronous machine is introduced in brief by limiting the fundamental principles. The power angle characteristic of salient-pole synchronous generator is summarized in single-line and phasor diagrams illustrated in Fig. 1.34. The single-line

44

Hybrid Renewable Energy Systems and Microgrids

Figure 1.34 Salient-pole synchronous generator with series impedance (A) single-line diagram, (B) phasor diagram.

diagram as seen in Fig. 1.34. comprises a machine connected to an infinite system with the voltage VE over a series impedance of reactance jXE. The resistance as shown in Fig. 1.31 is commonly neglected since it has a very small quantity. The phasor diagram of generator operation is illustrated in Fig. 1.34B where the dashed phasors denote the external reactance drop of components due to id and iq. The total reactances between the excitation voltage Ef and the bus voltage VE are given by: XdT 5 Xd 1 XE

(1.123)

XqT 5 Xq 1 XE

(1.124)

In case the bus voltage VE is dispatched to its direct and quadrature axis components, Vd 5 VE sin δ and Vq 5 VE cos δ are obtained in phase with id and iq, respectively. The per-phase power P delivered to the bus is: P 5 Id Vd 1 Iq Vq 5 Id VE sin δ 1 Iq VE cos δ

(1.125)

The direct and quadrature axis currents in the following forms can be used for rewriting Eq. (1.125) as follows: Id 5

Ef 2 VE cos δ XdT

(1.126)

Iq 5

VE sin δ XqT

(1.127)

P5

Ef VE sin δ V 2 ðXdT 2 XqT Þ sin 2δ 1 E XdT 2XdT XqT

(1.128)

Introduction to power systems

45

Figure 1.35 Power angle characteristic of a salient-pole synchronous generator.

Eq. (1.128) denotes the per-phase power, while the excitation voltage Ef and the bus voltage VE are line-to-neutral voltages and the reactance is in ohm values. The power angle characteristic given in this equation is illustrated in Fig. 1.35. The first component of the equation is the same as the expression written for a cylindricalrotor machine in Eq. (1.122), while the second component represents the contribution of the salient-pole machine. The synchronous machines either in cylindrical or in salient-pole structure comprise the vast majority of electricity generators placed in the power systems. Therefore it is expected that the represented structure and analysis methods on synchronous machines will be helpful for readers to understand the use of electrical machines in power generation systems.

References [1] A. von Meier, Electric Power Systems: A Conceptual Introduction, IEEE Press: WileyInterscience, Hoboken, NJ, 2006. [2] M.E. El-Hawary, Adapted and updated ed Introduction to Electrical Power Systems, IEEE Press; Wiley, Piscataway and Hoboken, NJ, 2008. [3] J.D. Glover, T.J. Overbye, M.S. Sarma, Power System Analysis & Design, sixth ed., Cengage Learning, 2017. [4] C.K. Alexander, M.N.O. Sadiku, Fundamentals of Electric Circuits, McGraw-Hill Education, 2001. [5] O.I. Elgerd, Electric Energy Systems Theory: An Introduction, McGraw-Hill Education, 1971.

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Hybrid Renewable Energy Systems and Microgrids

[6] F. El Zahraa Magdy, D.K. Ibrahim, W. Sabry, Energy management of virtual power plants dependent on electro-economical model, Ain Shams Eng. J. 11 (2020). Available from: https://doi.org/10.1016/j.asej.2019.11.010. [7] J.A. Momoh, Smart Grid: Fundamentals of Design and Analysis, Wiley, Hoboken, NJ, 2012. [8] K. Mahmud, B. Khan, J. Ravishankar, A. Ahmadi, P. Siano, An internet of energy framework with distributed energy resources, prosumers and small-scale virtual power plants: an overview, Renew. Sustain. Energy Rev. 127 (2020) 109840. Available from: https://doi.org/10.1016/j.rser.2020.109840. [9] S.W. Blume, Electric Power System Basics, Wiley-IEEE Press, 2016. [10] A.E. Fitzgerald, C. Kingsley, S.D. Umans, Electric Machinery, sixth ed., McGraw-Hill, Boston, MA, 2003. [11] P.C. Krause, O. Wasynczuk, S.D. Sudhoff, S. Pekarek, Analysis of Electric Machinery and Drive Systems, third ed., Wiley, Hoboken, NJ, 2013.

Centralized power generation

2

Ersan Kabalci1, Aydın Boyar1 and Yasin Kabalci2 1 Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Nevsehir Haci Bektas Veli University, Nevsehir, Turkey, 2Department of Electrical and Electronics Engineering, Faculty of Engineering, Nigde O¨mer Halisdemir University, Nigde, Turkey

2.1

Introduction

The centralized generation which is the conventional infrastructure in electric power generation and distribution systems is based on real-time delivery of electrical energy to consumers and loads. The electricity systems differ from storage systems such as water or gas distribution systems. Therefore the real-time concept describes the delivery of energy to any load is generated, transmitted, and distributed on the time of demand occurred. The generators start to produce the demanded energy according to the amount of load and release the generated energy to the transmission and distribution system for turning on the load. The electricity is generated in the power plants and transformed into high-voltage (HV) levels that are required to transmit the electricity to long distances with low losses. The power plants act as an energy converter benefiting from any type of energy source to generate electrical energy. The source energy used in the plant may be obtained from any of the heat, hydraulic, fossil, nuclear, geothermal, wind, solar, and many other sources suitable for electricity generation. The generated power is transformed to HV levels at station centers of generation plant and then transported to customers over transmission and distribution lines. At the distribution centers, delivered HV is transformed to medium voltage (MV) levels that are relatively low for use of distribution to consumer sites according to power requirements. The residential and commercial consumers require low-voltage (LV) distribution that is less than 1 kV and mostly 0.4 kV for three-phase systems, while the industrial consumers are supplied with MV or LV levels according to load sites and requirements. A complete schematic diagram of a power system including generation, transmission, distribution, and consumer sections is illustrated in Fig. 2.1. The fundamental principles of power generation systems have been described in Chapter 1, Introduction to Power Systems, where the fundamental laws of electricity, power generator based on electric machines, and power systems have been represented. The power can be defined as a function of voltage, current, and phase angle between them. Voltage denotes the potential energy difference between two nodes of an electrical system which is also known as electromotive force (EMF) in Hybrid Renewable Energy Systems and Microgrids. DOI: https://doi.org/10.1016/B978-0-12-821724-5.00002-7 © 2021 Elsevier Inc. All rights reserved.

48

Hybrid Renewable Energy Systems and Microgrids

Figure 2.1 Schematic diagram of centralized generation and power system.

electromechanical systems. The base unit of voltage and EMF is volt in the SI system. Although the voltage is not capable to do itself, it provides the potential force for doing a work due to its pushing or force effect. The voltage levels are defined as LV, MV, HV, and extra HV (EHV) in power systems. International Standard IEC 60038:1983 describes a set of standard voltages for use in LV and HV AC electricity supply systems. On the other hand, ANSI C84.1-1989 defines the 60-Hz voltage levels with the title of American National Standard Preferred Voltage Ratings for Electric Power Systems and Equipment. According to these standards, the 60-Hz systems are based on different voltage levels as listed in Table 2.1. The voltage levels are classified starting from residential consumers in LV to ultra-high level (UHV) which is the highest level between 120 V and 1100 kV, respectively. Both standards defined the voltage levels considering the generation, transmission, distribution, and consumer load types and requirements where ANSI C84.1 is more detailed compared to IEC 60038 as depicted in Table 2.1. The LV is widely used in residential and commercial consumers where the line voltage is lower than 1 kV, while the MV is limited between 1 and 35 kV. ANSI standard describes a number of voltage levels in MV range for three- and four-wire three-phase systems up to 69 kV instead of 35 kV of IEC 60038. The MV is mostly used in the distribution layer, while the transmission level is designed for transporting HV, EHV, and UHV levels as listed in the table [1,2]. A power plant comprises four main sections as three-phase generators that of the operating principles and fundamentals have been introduced in Chapter 1, Introduction to Power Systems, prime movers that actuate the generator and force it to sustain generating, operation center, and substation. The prime movers and energy sources of centralized generation are discussed in this chapter. The energy conversion of prime mover that is used for rotating the rotor of the generator can be achieved from steam turbine fed by coal, natural gas or nuclear, or hydroturbines fed by water. Despite the centralized generation, the distributed generation is accomplished by the use of wind, solar, geothermal, or biomass sources. The content of this chapter represents nuclear, hydro, and thermal power plants where steam and hydroturbines are used to generate a high level of energy in the context of

Centralized power generation

49

Table 2.1 Standard three-phase system voltages in IEC 60038 and ANSI C84.1-1989. Voltage level

IEC 60038 (kV)

ANSI C84.1-1989 Three-wire connection (kV)

LV

MV

Vrated , 1

1 , Vrated # 35

0.24 0.48 0.6 2.4 4.16 4.8 6.9

Four-wire connection (kV) 0.208 Y/0.12 0.240/0.12 0.48 Y/0.277

4.160 Y/2.4

8.320 Y/4.8 12 Y/6.93 12.47 Y/7200 13.2 Y/7.62 13.8 Y/7.97 20.78 Y/12 22.86 Y/13.2 23

HV

35 , Vrated # 230

EHV

230 , Vrated # 800

UHV

Vrated 5 1050 or 1200 (in the United States)

34.5 46 69 115 138 161 230 345 500 765 1100

24.94 Y/14.4 34.5 Y/19.92

EHV, Extra-high voltage; HV, high voltage; LV, low voltage; MV, medium voltage; UHV, ultra-high voltage.

centralized generation. The discussion on fossil and nuclear source-based power generation is thought to be essential for comparing the hybrid and renewable energy sources that are introduced in detail and represented in the remainder chapters of this book.

2.2

Hydropower power plant

Hydropower is a simple, safe, and efficient source used in centralized power systems with its renewable structure as a source of energy. Hydropower has crucial importance in power generation systems as balancing and regulating the whole power network in

50

Hybrid Renewable Energy Systems and Microgrids

addition to be a renewable energy source. It benefits from the kinetic energy of water in stored dams or run-of-the-river (RoR) constructions. Although the construction of a hydropower plant (HPP) is usually a hard objective, it enables to have very large power plants exceeding 1000 MW rated power. Therefore, in case they are not designed after detailed feasibility analyses, large HPPs may cause disruptive and challenging issues in the environment. The dams and reservoirs of large HPPs often cause discussions on the displacement of people, harmful effects to wildlife and habitats, and several other environmental arguments. These issues and effects are eliminated with intensive care at the planning and construction stages of HPPs that maintain electricity generation during all their life span of 50 or 60 years on average. HPPs generate electricity regarding its structure and determine the flow of water managed by a penstock, flume, or dam architecture for driving the hydroturbine. The flow of water causes slower rotation on hydroturbine comparing to faster steam turbines operated under high pressure. Moreover, hydroturbines are started faster and deliver the full-load power in a few minutes that are accomplished relatively slower in steam turbines. The HPPs are operated with a black start that states that the plant does not need any or little startup power is required in some cases. The HPPs are constructed in three architectures where the first one includes an accumulation reservoir and known as impoundment type plant, the second is operated as RoR and named as diversion type while the last one is pumped-storage HPPs. The impoundment plant is the most commonly used type of HPP where the plant utilizes a dam to accumulate the water of river in a reservoir. The released water of the reservoir forces the hydroturbine for rotating due to elevation difference, thus the generator is mechanically activated to generate electricity. The RoR or diversion type HPPs are constructed on a part of the river which does not have or has a limited reservoir. The water flow is supplied to a hydroturbine for generating electricity, while the excessive flood is supplied to the outlet without use. These types of HPPs require a suitable selection of construction site considering the constant supply of water. The pumped HPPs are constructed with a combination of an accumulation reservoir HPP with an auxiliary reservoir that is filled by pumped water from the outlet of the dam. The pumped HPPs are based on the energy storage principle owing to its reservoir located at the higher elevation and create potential energy for converting to electricity on demand [2 5]. The generated power in HPP depends on the density of water ρ, the flow rate of water ϕ, acceleration coefficient of gravity g, and the height h as given by: P 5 kρφgh

(2.1)

where k is the coefficient of efficiency determined by the type of turbine, plant design, and the dynamic flow and head conditions at which the turbine is operated [6]. The expression can be rewritten neglecting hydrokinetic devices as in the following equation [6]: P5

1 ρAv3 2

(2.2)

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Table 2.2 HPP (hydropower plant) categories according to power ranges [7]. Type of HPP

Power range (MW)

Micro Mini Small Large

0.001 0.1 0.1 1 1 10 .10

Table 2.3 Annual global hydropower generation rates [3]. Year

Global hydropower generation (TW h)

1973 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

1296 2808 2994 3121 3162 3288 3229 3516 3566 3756 3874 3983 3978

where A is the cross-sectional area of the flow, and v is the velocity of water flow. The HPPs are constructed in various power ranges and classified into four categories as listed in Table 2.2. The HPPs are implemented in a different range of power rates as micro, mini, small, and large starting from a few kWs to many MWs [7]. The largest five HPPs are listed as Three Gorges Dam located in China with 22,500 MW rated power capacity which is followed by Itaipu Dam on the border of Brazil-Paraguay with 14,000 MW rated power capacity, Xiluodu Dam in China with a rated power of 13,860 MW capacity, Belo Monte in Brazil with 11,233 MW capacity, and Simon Bolivar Hydroelectric Plant with 10,235 MW rated power in Venezuela. Annual global electricity generation of HPPs is summarized in Table 2.3 according to inherited data from International Energy Agency (IEA) reports. Although the annual generation depends on the rainfall, the increased number of installed HPPs increases the annual generation as in the table. The most recent generation capacity obtained from the report denotes 3978 TW h in 2015, while it was 1296 TW h in 1973 [3].

2.2.1 Reservoir-based hydropower plants and dams The vast majority of HPPs are based on reservoir types and dams. The main objective of a dam is to create the required reservoir of HPP, and constructing the energy

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storage infrastructure. Thus the water capacity is controlled by the reservoir during high flow or arid periods, and the dam can also control the flood risks. The balancing feature of hydropower systems facilitates to regulate intermittent operation features of renewable energy systems connected to the centralized generation network. The construction schematic of reservoir type HPPs is similar to RoR plants in terms of intake, penstock, and turbine structures. Three types of dams widely used in reservoir HPPs are embankment (earth fill or rockfill), concrete, and arch dams which of the simplest one is embankment dams. The construction of embankment type dams comprises a foundation wall covered with earth or rocks on both sides as in Fig. 2.2A. The main drawback of this type of dam is vulnerability to waterflood or excessive reserve that may accelerate the eroding on the walls of the foundation. The solution to eroding of embankment dams is improved by concrete gravity dams where the foundation is constructed by concrete as its name implies, and it is much more resistant against the eroding force of water in the reservoir. The sectional

Figure 2.2 Cross-sectional schematics of dam types: (A) embankment type, (B) concrete gravity type, (C) arch type [3].

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Figure 2.3 Operating principle of a pumped-storage plant [11].

view of the concrete gravity dam is illustrated in Fig. 2.2B, while the schematic view of the arch type dam is shown in Fig. 2.3C. The arch type dams can only be built in both sided rock ravines or canyons in a bowed configuration where both sides connecting to the ravines are bent into the rocky surfaces. It can be constructed in any type of dam and decreases the required material in construction. It allows to build the highest and the strongest dams depending on the geographical structure on the site [2,3,8].

2.2.2 Pumped-storage hydropower In a pumped-storage plant, electrical energy obtained from a utility grid is turned into hydraulic energy at off-peak time intervals thanks to pumping water from a storage unit located on a lower level to a storage unit located on a higher level. This transaction is referred to as the charging process. Later, the hydraulic energy is converted back to electrical energy at on-peak time intervals as in a traditional HPP, and this transaction is referred to as the discharging process [9,10]. These operation modes of a pumped-storage plant are illustrated in Fig. 2.3. This type of power plant is generally employed for two objectives. One of them is to ensure reactive power, whereas the other is to satisfy peak loads. There exist three different approaches in pumped-storage plants. First comprises two components, which are a pump-turbine and an electrical machine that can be operated in both motor and generator modes. The second approach is based on the use of a turbine, a pump, and an electrical machine that can be functioned in both motor and generator modes. The third one comprises four elements where a turbine is used to drive a generator, a pump, and a motor [9].

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The oldest pumped-storage plant in the world is the Koepchenwerk that has been built from 1927 to 1930 in Herdecke, Ruhr, Germany, whereas the largest one is Vianden pumped-storage plant in Vianden, Luxembourg whose construction has been started in 1959. A modern pumped-storage plant that was built between 1997 and 2004 is Goldisthal in Thuringia, Germany. Even though this type of power plants was generally considered extremely expensive, they were exploited throughout the world due to their advantages of renewable support and peak power adequacy. Operation flexibility of pumped-storage plants and diversity of additional services not only ensures better use of several renewable energy sources but also presents a more effective and stable electric power system. Furthermore, the utilization of this technology in power systems provides several superiorities that can be sorted out as mitigating generational variability and physical availability, better demand response opportunity, reduction of imbalances originated from scheduling problems, reducing transmission congestion, and enhancing the reliability of the system. Moreover, primary frequency regulation can be realized in both charging and discharging operation modes [8,9,12].

2.2.3 Hydraulic turbines Hydraulic or water turbine is accepted as the core of any HPP since it converts the mechanical power obtained from water into rotation on the shaft of a generator and accomplishes electricity generation. The kinetic energy of water in the reservoir is transferred to the turbine over the penstock, and the efficiency of the generation process is closely related to turbine type and features. Although turbines of HPP can be classified according to several ways, the most commonly accepted classifications are based on three major criteria as the direction of flow, pressure of water, shape, and orientation of the turbine [8,13,14]. The hydraulic turbines are classified into three groups—axial, radial, and mixed flow turbines—where the most widely known axial turbine types are Kaplan as in Fig. 2.4 and Propellor turbines, while the Pelton is a well-known radial flow turbine. On the other hand, Francis turbines are listed under the mixed flow turbines category owing to its feature meeting requirements of axial and radial flow turbines. Francis overcomes the flow direction that is neither axial nor radial with its radial water inlet and axial water outlet configuration. Another group of turbines is described as crossflow turbines that of widely known models are Banki turbine or Ossberger turbine. The flow of water is supplied to the blade ring which delivers rotational energy to upper and lower blades, and a sliding valve controls the flow rate. The turbines are classified as impulse turbines and reaction turbines according to the pressure change in water flow. In the impulsive turbine operation the flow of water does not change during the flow through the rotor, and the flow is controlled by nozzle. The most common type of these turbines is Pelton turbine that of the operational schematic is shown in Fig. 2.5. This turbine can provide remarkable efficiency of up to 95%. In the reaction type turbines the pressure of water changes regarding the flow path on the turbine rotor. The reaction is produced by this change in the speed of water and pressure changing the rotational speed of the rotor. Kaplan and Francis turbines as in Figs. 2.4 and 2.6 are the most popular types of reaction turbines.

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Figure 2.4 Schematic diagram of a Kaplan turbine [16].

Figure 2.5 Schematic diagram of a Pelton turbine with runner and PCD [17]. PCD, Pitch circle diameter.

The Kaplan turbine has been developed in the early 1900s and named by its inventor Viktor Kaplan. The turbine using the changing water pressure can be placed at various elevations of the penstock and may have multiple blades up to eight. The efficiency of Kaplan turbines can change between 80% and 95% [13]. The Francis turbine comprises a runner and fixed vanes up to 18 in design. The water pressure is applied to the top of the runner which results in rotation of the turbine. The flow is directed in radial entrance in the Francis turbine, while it is exhausted in the axial direction at the output. The directional rotation of flow drives the turbine blades and provides acceleration. The cross-sectional schematic of this turbine as in Fig. 2.6 illustrates the operation [15]. The Ossberger turbine is known

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Figure 2.6 Top and side views of Francis turbine [18].

Figure 2.7 Ossberger turbines: (A) horizontal orientation, (B) vertical orientation. Source: Ossberger GmbH Co (From T. Ghosh, M. Prelas, Energy Resources and Systems: Volume 2: Renewable Resources, Springer, 2011. ,https://doi.org/10.1007/978-94-007-1402-1.).

as a cross-flow turbine that uses curved vanes on a centrifugal runner. The most important contribution of cross-flow turbines is their operation allowing the water flow to pass through the blades twice. The water flow is transferred from outside to the inside of the blades at the first pass, while the second pass is completed at the inside. The Ossberger turbines are designed in both horizontal and vertical structures as in Fig. 2.7A and B, respectively. The overall efficiency of Ossberger turbines is around 80% with rated power of lower than a few MWs [15]. The comparison of the introduced hydraulic turbines is represented in Fig. 2.8 regarding drop height and flow rates that of turbines can operate efficiently. Pelton turbines have a higher drop elevation exceeding 1000 m, while Francis turbines have higher drop elevations compared to Kaplan and Ossberger turbines. On the other hand, Pelton turbines can be used up to 70 m3/s, which is relatively lower than Francis and Kaplan turbines that are operated up to 900 m3/s flow rates of water.

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Figure 2.8 Operating areas for different water-powered turbines.

The Francis and Kaplan turbines can be operated up to 500 MW in design, while the rated power capacity of the Pelton turbine is around 250 MW in a single unit. On the other hand, Ossberger turbines are suitable to be used in mini and small HPPs with their maximum unit capacity up to 2 MW.

2.3

Thermal power plants

This section describes the thermal power plants based on coal and gas-fired fuels. Both types of fired thermal plants use steam turbines that are used to produce the kinetic energy rotating the electricity generator.

2.3.1 Coal-fired power plants Energy has an indispensable role thanks to worldwide economic development. One of the main energy sources to generate electrical power is coal. Even though coal is being replaced by natural gas, solar, and wind energy in some countries, especially in the United States, it will probably continue to be an important energy source for electrical power generation [19]. Since it is the main energy source, it has been widely used for producing the electricity up until now. Approximately 64.7% of the worldwide energy production is realized by fossil fuels in 2017; 58.9% of this (9723.4 TW h) is based on coal-fired electricity production. As a result of this intense usage, it causes to reveal large amounts of carbon dioxide (CO2) that is a major ingredient causing global warming [20]. Therefore the power plants based on coal-fired are under enormous pressure from various environmental issues. Due to environmental issues, the utilization of renewable energy should be increased [4].

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Figure 2.9 Schematic illustration of a coal-fired power plant [22].

The main components of a typical coal-fired power plant are illustrated in Fig. 2.9. This plant is mainly composed of coal preparation section, coal burning and steam producing section, turbine, heat rejection, control of emissions, water treatment section, and waste processing section. These components are explained in the following. This kind of power plants contains two major paths that are combustion path and steamwater path. In the combustion path the coal is first stored by the handling system, and then, the coal is prepared for the burning process in the boiler (i.e., steam generator). The related air system of the plant pumps compressed air to the boiler with an air fan. The steam generator containing the air preheater burns the mixture of combustible and air, regains the heat, and produces the steam. After the flue gas is separated from the boiler, it is passed through some pollution control devices to eliminate fly ash, nitrogen oxides, and sulfur oxides. Later on, it is transferred to the stack through the induceddraft fan. In the second path, water is evaporated by a boiler to supply high-pressure and high-pressure steam to a generator set generating electric power. The boiler can heat the steam again after the steam goes through sections of the multilevel turbine system. Afterward, the steam is sent to the condenser for extracting the residual heat. Finally, various pumps and heaters are exploited to boost the pressure and temperature of the water before it is returned to the boiler. The cooling towers radiate the heat, which is absorbed by the condenser, into the atmosphere [21,22].

2.3.2 Gas-fired power plants A steam turbine is also used in the conversion of thermal energy in natural gas into electrical energy. The structures of a natural gas-fired boiler and a coal-fired boiler are analogous, and these boiler types generally employ similar systems in the energy production

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Figure 2.10 Schematic diagram of a gas-fired power plant. Source: From Zhengzhou Boiler Co., Ltd. (P. Breeze, Chapter 1—An introduction to gasfired power generation, in: P. Breeze (Ed.), Gas-Turbine Power Generation, Academic Press, 2016, pp. 1 7. ,https://doi.org/10.1016/B978-0-12-804005-8.00001-X.).

process. The schematic diagram of a typical gas-fired power plant is illustrated in Fig. 2.10. When a gas-fired power plant is considered, natural gas is burned in a furnace with a controlled amount of air. The heat produced by the combustion process is then obtained by both pipes inside the walls of the furnace and special pipe bundles installed on the way of hot gases coming out of the furnace. To increase the steam as much as possible, the produced heat is aimed to be obtained as much as possible. Therefore the air coming out of the boiler chamber should be at a very low temperature. Previous versions of the boilers had low efficiency; in other words, they were able to produce steam with low temperature and low pressure. However, thanks to the advances in material technology, it has become possible to build plants that can produce steam with much higher pressure and higher temperature. While the temperature in a coal-fired boiler furnace may be up to 1700 C, the temperature in a gas-fired furnace is slightly lower than this value. The efficiency is the main motivation of the desire to constantly reach higher temperatures and pressure conditions [23].

2.3.3 Gas-turbine principle A contemporary gas turbine (also referred to combustion turbine) is composed of a rotary engine, which allows taking energy from the combustion gases. These gases

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are either the air or combustion output of fuel and air. The main characteristics of a gas turbine can be listed as follows [24]: G

G

G

It contains a compressor that is used to increase air pressure. It includes combustion chamber(s) where fuel is sprayed from the compressor via compressed air to realize the combustion process. It contains a gas turbine where high-temperature outputs of the combustion process obtained from the combustor are passed through and enlarged.

The components of a typical gas turbine are depicted in Fig. 2.11, where an upstream compressor is immediately connected to a downstream turbine. In addition, a combustor (or a combustion chamber) is placed between the upstream compressor and the turbine. The compressed air through the compressor is applied to the combustor. The energy is obtained after the air and fuel mixture is burned in the combustor unit. As a result of the combustion process at a stable pressure, the air temperature rises. The resultant gases with high temperature, which leaves from the combustion chamber, are directly applied to the blades of the gas turbine. The applied gases result in rotating the turbine and this effect also provides power to the compressor. The gases are then fed to supplementary turbine blades that are tasked with returning to atmospheric pressure and expediting hot exhaust gases to produce stronger thrust. The energy can be obtained in one or any combination forms of compressed air, thrust, and shaft power. Therefore this form of energy can be efficiently utilized in electrical generators and vehicles such as ships, trains, aircraft, and tanks [24]. The first advancement of gas turbines was the intention of developing jet engines that can be employed in military applications, and this approach continued from the 1940s to the 1960s. On the other hand, the gas turbines can be classified into two categories—industrial and aero-derivative. The first group of these turbines is particularly utilized for constant power generation and the production capacity of an individual turbine unit can reach up to 450 MW. Capital costs per established kilowatt of such turbines are lower than others. Other superiorities of this type of

Figure 2.11 The structure of a typical gas turbine [24].

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Figure 2.12 An industrial gas-turbine structure employed in electric power generation. Source: With permission from Siemens. Siemens SGT5 4000F 300 MW gas turbine (P. Breeze, Chapter 4—Gas turbines, in: P. Breeze (Ed.), Gas-Turbine Power Generation, Academic Press, 2016, pp. 31 42. ,https://doi.org/10.1016/B978-0-12-804005-8.00004-5.).

turbines can be sorted out as being more robust, requiring less maintenance, being more convenient for uninterrupted base-load operation thanks to longer maintenance intervals, low compression ratio as much as 16:1, and the ability to operate without an external fuel gas compressor. The drawback of this turbine type is being low efficiency and much heavier. An industrial gas-turbine structure exploited in power generation is depicted in Fig. 2.12 [25]. The other type of turbines is originally developed for use in aircraft as jet engines, and then, they are employed to run electrical generators. The weight of engines is very critical in aviation applications, while it is not one of the most important parameters in industrial applications. Although they offer advantages of lightweight and efficiency in a thermal manner, they are generally more costly than that of the gas turbines produced for constant applications. Presently, the production capacity of an individual aero-derivative turbine unit can reach up to 50 MW, and they can present a compression ratio up to 30:1 if an additional fuel gas compressor ensuring high pressure is employed. A gas-turbine structure exploited in a jet engine is indicated in Fig. 2.13 [25]. Currently, the development of the aero-engine carries on not only in military aviation applications but also in commercial applications. In addition, it is important to note that types of liquid and gaseous fuels that can be used in gas turbines are many. Natural gas, heavy fuel oil, methane, crude oil, and distilled jet fuel can be given as examples [24].

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Figure 2.13 A cross section of a gas turbine used in a jet engine. Source: From Wikimedia (P. Breeze, Chapter 4—Gas turbines, in: P. Breeze (Ed.), GasTurbine Power Generation, Academic Press, 2016, pp. 31 42. ,https://doi.org/10.1016/ B978-0-12-804005-8.00004-5.).

2.4

Nuclear power plant

Although some catastrophic accidents such as Chernobyl and Fukushima nuclear disasters, the atomic energy and its use in nuclear power plants are an essential component of the global energy portfolio. Many countries are still installing, rebooting, and planning to construct new nuclear power plants. China which is the biggest global energy consumer is installing 24 new reactors, while the United States is considering to construct 5 new reactors as the second major consumer. Furthermore, Japan has constructed its recent nuclear reactor after the Fukushima accident in 2015 [26]. Nuclear energy is in use for more than 50 years where 29 countries are operating 440 nuclear reactors, and 15 countries are constructing a total of 65 new nuclear power plants. It has been noted that nuclear power plants were generating 14% of global electricity in 2009, while it has been decreased to 10% in 2019, but 18% of the electricity of Organisation for Economic Co-operation and Development (OECD) countries [27]. It is expressed by the World Nuclear Association that nuclear plants supplied 2563 TW h of electricity in 2018, up from 2503 TW h in 2017. France gets around three-quarters of its electricity from nuclear energy, Hungary, Slovakia, and Ukraine get more than half from nuclear, while Belgium, Sweden, Slovenia, Bulgaria, Switzerland, Finland, and the Czech Republic get onethird or more. South Korea normally gets more than 30% of its electricity from nuclear, while in the United States, United Kingdom, Spain, Romania, and Russia, about one-fifth of electricity is from nuclear. Japan is used to relying on nuclear power for more than one-quarter of its electricity and is expected to return to somewhere near that level. In 2018, the United States has generated 805 TW h energy from nuclear, while France has generated 379 TW h. A summary of global power generation and amount of generated energy at nuclear power plants have been listed in Table 2.4 where it covers data from 2004

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Table 2.4 Annual global electricity generation by nuclear power plants [28]. Year

Global generation (TW h)

Global nuclear generation (TW h)

Proportion of annual production (%)

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

17,450 18,239 18,930 19,771 20,181 20,055 21,431 22,126 22,668 23,322 23,816 24,255

2738 2768 2793 2719 2731 2697 2756 2584 2461 2478 2535 2571

15.7 15.2 14.8 13.7 13.5 13.4 12.9 11.7 10.9 10.6 10.6 10.6

Figure 2.14 Schematic diagram of a typical nuclear power plant.

to 2015. The total generation of nuclear power plants was 2738 TW h in 2004 which was 15.7% of the total global electricity generation of 17,450 TW h. Although the generation rates of nuclear plants have fluctuated around 2700 TW h until 2011—the year Fukushima accident has occurred, it has decreased to 2400 TW h in a few years. The increment of global power generation has decreased the overall ratio of nuclear power generation to 10.6% [28]. The catastrophic power of nuclear energy experienced in World War II and the hydrogen bomb has attracted the attention of researchers for using this energy for electricity generation. Scientists turned their attention to harnessing this energy for the generation of electricity. The first plants that were developed utilized the easier to initiate and control the uranium fission process. A typical nuclear fission power plant design is shown in Fig. 2.14. The operation of a nuclear power plant is managed at

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two sections—the reactor and turbine. The nuclear energy is provided by the atomic components where protons and neutrons determine the energy level. In case the nucleus of any atom of elements such as Uranium-235 (U-235) is split, a serious manner of energy is released as a result of the process that emits a high amount of heat. It is used to produce steam for driving the turbine of a generator placed in the nuclear power plant. Two methods used to produce heat and steam are known as fission and fusion. Both methods are applied in the reactor section as in Fig. 2.14 where fission splits a large number of nuclei atoms in the reactor core and produced heat is used to obtain steam for driving the generator. On the other hand, the fusion method is based on conjoining atoms with small nucleus to comprise larger atoms releasing heat energy. The fusion reactors are not being used in the generation of electricity due to technical difficulties in overcoming the natural repulsion of positive charge protons, but the fission process based on heavy elements such as U-235 is widely used. The fission of uranium starts a reaction chain that should be a controlled release in the reactor which is accommodated in the containment section. The controlled heat-producing process is accomplished by using neutron absorbent materials that are located in the fuel assemblies. The control elements called control rods are pushed into the core for decreasing the produced heat due to higher absorption of neutrons, or pulled out to increase chain reaction and the produced heat. In the fission process, many of the nuclear power plants known as light water reactors use regular water process for cooling the reactor. The water absorbs the heat for the boiling, and steam is then fed to a steam turbine which generates the electricity [2,29,30]. Two types of light water reactor designs—the pressurized water reactor (PWR) and the boiling water reactor (BWR)—are noted in Ref. [2] in which PWR nuclear power plants are approximately 70% more than BWR in the United States. The nuclear energy is accepted as reliable and steady sources in electricity generation due to its detailed control structure. In addition, fuel costs are relatively low compared to other sources, and the capacity factor is the highest one among others. It is a remarkable alternative to fossil fuel based plants in centralized generation. The fission, fusion, and nuclear reactor types are introduced in the following sections.

2.4.1 Nuclear fission The nuclear fission reaction can be seen naturally in many large atom elements such as the U-235 that is the fuel used in all nuclear power plants. U-235 has 92 protons and it has two isotopes—U-234 and U-238. The odd number of neutrons in U-235 makes it easier to split compared to other isotopes. Once the neutrons are fired to the atom, U-235 atom absorbs the kinetic energy and transformed to U-236 for a moment. The energized nucleus of U-236 gets vibrated by the absorbed neutrons and causes it to split into two equal-size atoms [28,29]. The fission reaction and progress stages are illustrated in Fig. 2.15. The split atom process seen in stage C may produce gamma ray under excessive energy. The electrostatic repulsion over nuclear reaction produces fission fragments as seen in the last stage of fission [31].

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Figure 2.15 The fission process [31].

2.4.2 Fusion Another nuclear reaction is fusion which combines the atoms instead of splitting as in fission, and it is seen in energy generation of sun and stars. The fusion process occurred in the sun combines hydrogen atoms for producing deuterium that defines heavy hydrogen atoms, and deuterium and hydrogen atoms fuse for producing helium with the released energy. The reaction occurs at extremely high temperatures up to 15 million  C and under high pressure. The fusion process achieved in the sun conditions cannot be provided on the earth, but the fusion reactors can generate electricity by using more favorable conditions. It enables to have a reaction between deuterium and tritium which are two isotopes of hydrogen. The deuterium 2 3 1 H is naturally found in water with small amounts, while tritium 1 H is produced from lithium. These two isotopes react for producing the helium and energy as given in the following equation [28,32]: 2 1H

1 31 H 5 42 He 1 n 1 @18 meV

(2.3)

2.4.3 Nuclear fission reactors The nuclear power plant reactors are mostly based on light water treatments that are known as PWR, BWR, and advanced BWRs. Besides, some other types such as Canada Deuterium Uranium (CANDU) reactor based on HWR and gas-cooled reactors also exist in commercial nuclear power plants. This section represents a brief introduction to these reactors.

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2.4.3.1 Boiling water reactor The BWRs that are commonly constructed in the United States, Europe, and Japan are characterized by almost identical technical designs and are based on the use of ordinary or light water as its cooler and moderators. In the schematic diagram of the BWR reactor in Fig. 2.16, the water is allowed to boil under the pressure up to 75 bar, and the temperature can rise up to 286 C in the core of the reactor. The produced steam at the end of the boiling process is used to drive the turbine of generator and then condensed and returned to the core for reuse. In the recycle process of condensed steam, water is preheated up to 150 C as shown in the lower left-hand side of Fig. 2.16. The control rods of the reactor core should be kept in the liquid medium since the reactor core is produced for using water as neutron moderator. In case the returned steam is not liquidized, insufficient collisions occur with water and cause to increase in the energy of neutrons that decreases the fission rate and nuclear reaction is interfered. Therefore the liquid water is pumped by using high-volume jet pumps to surround the control rod. The steam pressure and temperature of BWR are relatively low compared to a coal-fired power plant, while the steam turbine is generally very large. The BWRs can have capacities up to 1400 MW with an efficiency of around 33% [28,33,34].

2.4.3.2 Pressurized water reactor The PWRs were first developed as power plants for nuclear submarines in the 1950s. The reliable and steady operation of PWR has triggered its experimental

Figure 2.16 BWR [33]. BWR, Boiling water reactor.

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Figure 2.17 Schematic of a PWR. Source: US Nuclear Regulatory Commission (P. Breeze, Chapter 17—Nuclear power, in: P. Breeze (Ed.), Power Generation Technologies, third ed., Newnes, 2019, pp. 399 429. ,https://doi.org/10.1016/B978-0-08-102631-1.00017-1.).

applications in nuclear power plants in Russia and the United States. The PWR type reactors are the most widely used one in around 300 nuclear power plants with efficiency up to 33% and rated power up to 1000 MW. Modern PWRs are constructed regarding the same technical principles by a number of manufacturers in different countries. The schematic diagram of a PWR is shown in Fig. 2.17 where the core is fulfilled with light water and pressure can be increased up to 150 bar enabling the water to be heated up to 325 C without boiling. The primary water cycle is dope with borate for absorbing the neutrons in the starting stage of the new fuel cycle. The closed-loop operation of PWR removes any radioactive emission, and therefore the PWR number in the operation is three times higher than BWR. Besides the radioactive isolation, the advantages of PWR can be stated as its capability of operating at higher temperatures and pressure, and stable operation compared to other designs. The boiling process is not permitted in the reactor vessels and it provides more constant water density in the core. The reactor design is complicated compared to BWR due to high pressure and operating temperature, and vessels resisting the high pressure increase the cost of PWR. Moreover, the power generation of PWR is achieved faster than the cooling process of the water that can cause damage to fuel rods [2,28,33,34].

2.4.3.3 The pressurized heavy-water reactor (Canada Deuterium Uranium) The heavy-water reactors (HWRs) have been developed in Canada, United Kingdom, Germany, India, Japan, and South Korea where the most widely known is CANDU type of Canada. The pressure vessel structures are similar to PWR in terms of construction and water is used as cooler and moderator in HWRs. The heavy water is maintained up to 100 bar pressure and water reaches 290 C temperature without boiling in

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Figure 2.18 Schematic diagram of CANDU reactor [28]. CANDU, Canada Deuterium Uranium.

CANDU reactors. The pressure tubes as in the schematic diagram in Fig. 2.18 use heavy water with pressurized heavy-water coolant. The produces heat is transferred to a heat exchanger for light water system with a generator, while the secondary systems control the steam turbine in a similar way as in PWR [28,35]. The CANDU reactor has been developed in Canada for eliminating the requirement of enriched uranium. Therefore the CANDU reactor has attracted many interests all over the world due to its use of unenriched uranium in its natural form. The heavy water described the form of water that comprised deuterium instead of hydrogen in light water. The deuterium which is two times heavy isotope of regular hydrogen element converts light water to heavy water which rarely exists in natural water. One of the most important advantages of CANDU reactor is the refueling process without shutting down the reactor which increases the overall performance and efficiency of the power plant. The control rods made by zirconium alloy are horizontally located and the reactor is fueled with uranium oxide pellets. Although the CANDU reactor has been developed by Atomic Energy of Canada, many countries including Argentina, India, South Korea, and Pakistan are operating these reactors [35].

2.4.3.4 Gas-cooled reactors The rising temperature or decreasing pressure does not cause any change in the phase of gas which makes it providing safety in nuclear reactor cooling. The change of temperature or pressure in a reactor can be easily determined and gas coolants

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are maintained without any intermittence during these changes or any faulty conditions. Moreover, the risks of fuel coolant interactions are eliminated due to its physical structure [36]. The advanced gas cooler reactors (AGRs) have been developed by British researchers for using low-enriched uranium oxide fuel and a small diameter architecture in the 1950s [36]. The Magnox reactor that is operated without enriched uranium requirement is based on graphite moderator, and carbon dioxide for heat transfer was one of the first designs of British researchers. The generated heat is transferred to the heat exchanger that is used for boiling the water and driving the steam turbine as in Fig. 2.19. The channels of graphite moderator are composed of magnesium alloy that implies the name of Magnox reactor. AGR is the second generation of these reactors with some modifications in operation as uranium oxide pellets contain slightly enriched uranium fuel at 2% rate and housed in zirconium alloy rods. The temperature of primary cooling system can reach up to 650 C and can operate at a gas pressure of 4.2 Mbar, while the secondary cooling system is allowed to be operated up to a temperature of 540 C and a pressure of 16 Mbar. AGR plants are implemented up to 630 MW rated power at a unit until now, and the overall plant efficiency is noted around 41% with seawater cooling. The whole primary cooling system of AGR plants comprising a reactor core, gas circulators, and steam generators is constructed in a prestressed concrete reactor pressure vessel [28,34].

Figure 2.19 Schematic diagram of an AGR [28]. AGR, Advanced gas cooler reactor.

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References [1] B. Brown, Section 4: System voltage considerations, A Comparison of Circuit Breakers and Fuses for Low-Voltage Applications Data Bulletin, Schneider Electric, 2007. [2] S.W. Blume, Electric Power System Basics for the Nonelectrical Professional, IEEE Press, 2017. [3] P. Breeze, Chapter 8—Hydropower, in: P. Breeze (Ed.), Power Generation Technologies, third ed., Newnes, 2019, pp. 173 201. Available from: https://doi.org/ 10.1016/B978-0-08-102631-1.00008-0. [4] H. Liu, R. Zhai, K. Patchigolla, P. Turner, Y. Yang, Performance analysis of a novel combined solar trough and tower aided coal-fired power generation system, Energy 201 (2020) 117597. Available from: https://doi.org/10.1016/j.energy.2020.117597. [5] V.S. Lopes, C.L.T. Borges, Impact of the combined integration of wind generation and small hydropower plants on the system reliability, IEEE Trans. Sustain. Energy 6 (2015) 1169 1177. Available from: https://doi.org/10.1109/TSTE.2014.2335895. [6] A.W. Wood, G. Sreckovic, 5.12—The sustainability of Pacific Northwest hydropower generation in the context of nonstationarity and renewable energy growth, in: R.A. Pielke (Ed.), Climate Vulnerability, Academic Press, Oxford, 2013, pp. 177 194. Available from: https://doi.org/10.1016/B978-0-12-384703-4.00514-1. [7] P. Breeze, Chapter 2—The hydropower resource, hydropower sites and types of hydropower plants, in: P. Breeze (Ed.), Hydropower, Academic Press, 2018, pp. 13 21. Available from: https://doi.org/10.1016/B978-0-12-812906-7.00002-8. [8] M. Ko¨tter, J. Mathur, Introduction to Hydro Energy Systems: Basics, Technology and Operation, Springer-Verlag, Berlin, Heidelberg, 2011. Available from: https://doi.org/ 10.1007/978-3-642-20709-9. [9] G.T. Bitew, M. Han, S.A. Mekonnen, S. Patrobers, Z.W. Khan, L.K. Tuan, Pumped energy storage system technology and its AC DC interface topology, modelling and control analysis: a review, J. Eng. 2019 (2019) 705 710. Available from: https://doi. org/10.1049/joe.2018.8379. [10] S. Rehman, L.M. Al-Hadhrami, Md.M. Alam, Pumped hydro energy storage system: a technological review, Renew. Sustain. Energy Rev. 44 (2015) 586 598. Available from: https://doi.org/10.1016/j.rser.2014.12.040. [11] T.M. Letcher, Storing Energy: With Special Reference to Renewable Energy Sources, Elsevier, Amsterdam, 2016. [12] R. Raja Singh, T. Raj Chelliah, P. Agarwal, Power electronics in hydro electric energy systems a review, Renew. Sustain. Energy Rev. 32 (2014) 944 959. Available from: https://doi.org/10.1016/j.rser.2014.01.041. [13] V. Quaschning, Hydropower plants wet energy, Renewable Energy and Climate Change, IEEE, 2010, pp. 191 209. Available from: https://doi.org/10.1002/9781119994381.ch9. [14] H.-J. Wagner, J. Mathur, Introduction to Hydro Energy Systems, Springer, Berlin, Heidelberg, 2011. Available from: https://doi.org/10.1007/978-3-642-20709-9. [15] T. Ghosh, M. Prelas, Energy Resources and Systems: Volume 2: Renewable Resources, Springer, 2011. Available from: https://doi.org/10.1007/978-94-007-1402-1. [16] K. Stenhouse, J. Hanania, J. Donev, Energy education—Kaplan turbine., n.d. ,https:// energyeducation.ca/encyclopedia/Kaplan_turbine#cite_note-4. (accessed 30.05.20). [17] T. Nigussie, A. Engeda, E. Dribssa, Design, modeling, and CFD analysis of a micro hydro Pelton turbine runner: for the case of selected site in Ethiopia, Int. J. Rotating Mach. 2017 (2017) 1 17. Available from: https://doi.org/10.1155/2017/3030217.

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[18] P. Breeze, Chapter 4—Hydropower turbines, in: P. Breeze (Ed.), Hydropower, Academic Press, 2018, pp. 35 46. Available from: https://doi.org/10.1016/B978-012-812906-7.00004-1. [19] D.M. Martı´nez, B.W. Ebenhack, T.P. Wagner, Chapter 5—Electric power sector energy efficiency, in: D.M. Martı´nez, B.W. Ebenhack, T.P. Wagner (Eds.), Energy Efficiency, Elsevier, 2019, pp. 129 160. Available from: https://doi.org/10.1016/B978-0-12-812111-5.00005-6. [20] K. Goto, K. Yogo, T. Higashii, A review of efficiency penalty in a coal-fired power plant with post-combustion CO2 capture, Appl. Energy 111 (2013) 710 720. Available from: https://doi.org/10.1016/j.apenergy.2013.05.020. [21] J.G. Speight, Coal-Fired Power Generation Handbook, Wiley, Wiley. Com. (n.d.). Scrivener Publishing; Wiley, 2013. [22] B.G. Miller, 5—Anatomy of a coal-fired power plant, in: B.G. Miller (Ed.), Clean Coal Engineering Technology, second ed., Butterworth-Heinemann, 2017, pp. 231 260. Available from: https://doi.org/10.1016/B978-0-12-811365-3.00005-3. [23] P. Breeze, Chapter 1—An introduction to gas-fired power generation, in: P. Breeze (Ed.), Gas-Turbine Power Generation, Academic Press, 2016, pp. 1 7. Available from: https://doi.org/10.1016/B978-0-12-804005-8.00001-X. [24] D.K. Sarkar, Chapter 7—Gas turbine and heat recovery steam generator, in: D.K. Sarkar (Ed.), Thermal Power Plant, Elsevier, 2015, pp. 239 283. Available from: https://doi.org/10.1016/B978-0-12-801575-9.00007-X. [25] P. Breeze, Chapter 4—Gas turbines, in: P. Breeze (Ed.), Gas-Turbine Power Generation, Academic Press, 2016, pp. 31 42. Available from: https://doi.org/10.1016/B978-0-12804005-8.00004-5. [26] Q. Wang, R. Li, G. He, Research status of nuclear power: a review, Renew. Sustain. Energy Rev. 90 (2018) 90 96. Available from: https://doi.org/10.1016/j.rser.2018.03.044. [27] C. Karakosta, C. Pappas, V. Marinakis, J. Psarras, Renewable energy and nuclear power towards sustainable development: characteristics and prospects, Renew. Sustain. Energy Rev. 22 (2013) 187 197. Available from: https://doi.org/10.1016/j.rser.2013.01.035. [28] P. Breeze, Chapter 17—Nuclear power, in: P. Breeze (Ed.), Power Generation Technologies, third ed., Newnes, 2019, pp. 399 429. Available from: https://doi.org/ 10.1016/B978-0-08-102631-1.00017-1. [29] B.F. Towler, Chapter 7—Nuclear energy, in: B.F. Towler (Ed.), The Future of Energy, Academic Press, Boston, MA, 2014, pp. 135 159. Available from: https://doi.org/ 10.1016/B978-0-12-801027-3.00007-5. [30] D.M. Tagare, Nuclear power generation, Electric Power Generation: The Changing Dimensions, John Wiley & Sons, Ltd, 2011, pp. 153 171. Available from: https://doi. org/10.1002/9780470872659.ch8. [31] R.L. Murray, K.E. Holbert, Chapter 6—Fission, in: R.L. Murray, K.E. Holbert (Eds.), Nuclear Energy, seventh ed., Butterworth-Heinemann, Boston, MA, 2015, pp. 89 99. Available from: https://doi.org/10.1016/B978-0-12-416654-7.00006-X. [32] R.L. Murray, K.E. Holbert, Chapter 7—Fusion, in: R.L. Murray, K.E. Holbert (Eds.), Nuclear Energy, seventh ed., Butterworth-Heinemann, Boston, MA, 2015, pp. 101 106. Available from: https://doi.org/10.1016/B978-0-12-416654-7.00007-1. [33] Chapter 12—Nuclear power plant design, in: G.J. Suppes, T.S. Storvick (Eds.), Sustainable Nuclear Power, Academic Press, Burlington, VT, 2007, pp. 319 351. Available from: https://doi.org/10.1016/B978-012370602-7/50029-6. [34] G. Kessler, Sustainable and Safe Nuclear Fission Energy: Technology and Safety of Fast and Thermal Nuclear Reactors, Springer-Verlag, Berlin, Heidelberg, 2012. Available from: https://doi.org/10.1007/978-3-642-11990-3.

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[35] N. Tsoulfanidis (Ed.), Nuclear Energy: Selected Entries From the Encyclopedia of Sustainability Science and Technology, Springer-Verlag, New York, 2013. Available from: https://doi.org/10.1007/978-1-4614-5716-9. [36] B. Barre´, Chapter 22—Gas-cooled reactors, Handbook of Nuclear Engineering, Springer Science 1 Business Media LLC, 2010, pp. 2712 2748.

Distributed generation and microgrids

3

Hossein Shayeghi1 and Masoud Alilou2 1 Energy Management Research Center, University of Mohaghegh Ardabili, Ardabil, Iran, 2 Department of Electrical Engineering, Urmia University, Urmia, Iran

3.1

Introduction

In the last decade the issue of microgrid (MG) has been introduced for better managing a complex power network so that the extensive distribution system is divided into multiMGs. Thus the total power system operates properly if each of MGs is managed efficiently. Indeed, the MG is a power network in a small size. In other words, the MG has both the production and consumption sides of the power network. Therefore distributed generation (DG) units are one of the important and necessary devices of MGs [1,2]. Distributed energy sources supply the load of the MG in most of the time. Moreover, the MG can sell electrical power to the upstream network when the produced electricity of DGs is more than the demand of the MG. On the other hand, the MG has to buy energy from the upstream network when DGs cannot supply the load of the MG. Thus distributed energy resources have the ability for increasing the reliability and flexibility of the MG. Diesel generator, microturbine, fuel cell, wind turbine, and photovoltaic panels are samples of DG units that can be used in MGs. Distributed energy sources improve the efficiency of the MG. Of course, the optimum place and size of distributed energy resources have a high effect on the performance of both energy sources and the MG [3,4]. Therefore selecting the best place and capacity of DG units in the MG is so important subject in the studies of MGs. Improving the indices such as active and reactive losses, voltage variation, voltage stability, an hourly variation of produced power, reliability, stability, environmental, and economic parameters of the MG are the sample of objective functions that can be used to optimize the DG units in the MG [5,6]. In the last years, finding the best site and size of DG units in the power network and MG has been studied by some researchers. For instance, in Ref. [7], the recent optimization procedures to find a solution for the problem of selecting the optimal location and capacity of the renewable energy sources were reviewed by Abdmouleh et al. Moreover, various parameters of DG units such as environmental issues, economic profits, technologies, technical advantages, and regulatory drivers were also analyzed in this paper. In another study, Ahmed and Hasan introduced a new technique based on particle swarm optimization algorithm to optimally allocate the DG units in a radial network for reducing the difference between the voltage levels of two buses of the system [8]. The load flow method including bus Hybrid Renewable Energy Systems and Microgrids. DOI: https://doi.org/10.1016/B978-0-12-821724-5.00006-4 © 2021 Elsevier Inc. All rights reserved.

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injections and branch currents and branch current and bus voltage matrixes of the system was used for analyzing the load flow. In another study a multiobjective method based on economic index, efficiency index, and energy reliability model was presented to optimize the multi-DG units including wind turbine, photovoltaic and fuel cell technologies [9]. An advanced method based on Hammersley point set method was used to multiobjective optimize the various DG technologies. In Ref. [10] a competent optimization approach based on the intelligent algorithm was established for selecting the optimal site and size of distributed energy units. Minimizing the reactive power losses and maximizing the voltage profile of the power network are the considered purposes of the DG allocation. So in this chapter, the optimization of the place and capacity of the multidistributed resources are studied in the MG as a multiobjective problem to improve the performance of both DG sources and MG. The renewable and nonrenewable technologies including wind turbine, photovoltaic panel, diesel generator, microturbine, and fuel cell are the energy sources of the proposed method for optimizing. Moreover, the nonlinear load model based on voltage and frequency parameters is also discussed for better pondering the optimization method in the more pragmatic operational conditions of the MG. The multiple objectives are to minimize loss index including active and reactive losses and promote the voltage stability of the MG. A multiobjective gray wolf optimizer (MOGWO) is applied to minimize the considered indices and find the nondominated solutions. Then, a selecting way based on a fuzzy method is used to choose one of the nondominated particles from the Pareto optimal solutions as the best site and capacity of energy sources. Ultimately, the proposed optimization method is implemented on the sample MG that is equal to the IEEE 69-bus distribution system. The results show the proper performance of the optimization procedure in increasing the efficiency of the MG.

3.2

Microgrid

In the last years the concept of MG has been introduced for better controlling the total power network. In other words, an overall grid is divided into the number of MGs to increase the reliability, constancy, control, and the performance of the grid. The ability to inject the electricity into the system is one of the important differences between the MG and the usual distribution network. Therefore an MG has loads and also some energy sources such as distributed energy resources. Both the production and consumption sides of the MG should be managed optimally based on the technical, economic, and environmental indices of the network for improving the performance of the MG [11]. Totally, an MG is a group of produced devices and consumers within clearly considered electrical borders that operate as a single controllable unit with considering the technical indices of the overall power network. Moreover, the MG can be operated in both connected and islanded modes; it means that the MG can have/ haven’t a connection with the power network. A sample MG with its connections is demonstrated in Fig. 3.1.

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75

Wind turbine

Photovoltaic

Microgrid

Upstream grid Other microgrids

Diesel generator

Microturbine Fuel cell

Figure 3.1 A sample microgrid with its connections.

Hence, MGs are utilized in the power network for improving the local reliability and flexibility of electric power systems so that the total grid is operated efficiently if each of MGs is managed and operated optimally. Although MGs can act in islanded condition, MGs in grid-connected one have a useful ability for purchasing electricity from the upstream grid when the demand of consumers is more than the produced power of local renewable and nonrenewable DG technologies. Moreover, the grid-connected MG can inject its extra energy into the grid or other MGs. In this chapter, the grid-connected MG is studied.

3.3

Distributed generation

DG units are one of the important technologies of MGs because the local production of electricity is the main proviso for calling a system as an MG. The produced power of distributed energy resources improves the reliability and independence of the MG. The distribution company of MG uses the energy of these technologies for supplying the demand of the MG and selling energy to the upstream network or other MGs. Of course, the distribution company has to buy energy from the upstream grid when the utilized DG units cannot provide the demand of the MG.

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DG units have technical, economic, and environmental advantages for both MG and power network. Of course, allocating these energy resources with optimum size in the best site affects the performance of them. Technical advantages of DG units consist of several issues such as decreasing the dependence of the MG on the power of the upstream network, increasing the voltage profile, and improving the stability of the power network. The major technical benefits of DG units are presented in Fig. 3.2. Saving transmission and distribution costs and reducing the consumption of world fuel are the samples of economic benefits of distributed energy resources. Fig. 3.3 demonstrates the major economic advantages of this type of energy source. Based on environmental aspects such as reducing greenhouse gases, decreasing noises, and saving the natural sources for other applications, renewable DG units are more useful than nonrenewable ones [12,13]. Totally, DG units can be divided into nonrenewable and renewable units. The output power of nonrenewable DGs depends on their primary fuel and demand of the system; so their power is stable. But the output power of renewable technologies is unstable due to their dependence on the weather conditions. The following sections are dedicated to the description and modeling of nonrenewable energy sources including diesel generator, microturbine, and fuel cell and also renewable technologies including wind turbines and solar panels [12
15].

Improved system stability Enhanced system reliability

Increased energy efficiency

Improved voltage stability

Increased voltage profile

Technical advantages of DG units

Improved power quality

Decreased microgrid dependence Reduced power losses

Figure 3.2 The major technical advantages of distributed generation units.

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77

Economic

Improved productivity

Decreased operation and maintenance costs

benefit of DG units

Deferred investments for facilities upgrades

Reduced market prices

Decreased costs of reserve requirements

Reduced health care costs

Figure 3.3 The major economic benefits of distributed energy sources.

Figure 3.4 The sample diagram of diesel generator.

3.3.1 Diesel generator A diesel generator utilizes a diesel engine and electric generator to generate electrical energy. Liquid fuels or natural gas are usually used as the primary fuel of the diesel generator. Totally, a diesel generator works based on air compression and the fuel. First, the air is blown into the generator until it is compressed. Subsequently, the proper fuel of diesel generator is injected. The combination of air compression and subsequent injection of the fuel will contribute to generate the heat that triggers the inflammation of the fuel. In this way the diesel generator starts combustion and causes the generator to start up. Thus the generator starts to produce the necessary electrical energy to be distributed according to the needs of the MG (loads) connected to the diesel generator. A sample diagram of diesel generator is shown in Fig. 3.4. The diesel generator can produce electrical energy according to the demand of the MG. The output power of a diesel generator can be changed based on the

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Bus

Bus

(

Load , )

Diesel generator , ) (

= =

− −

Figure 3.5 The active and reactive power of a bus with diesel generator.

load variation of the MG. The diesel generator has the ability to simultaneously inject both active and reactive powers. Hence, in the mathematical equations of the system, the diesel generator is considered as a PQ bus that injects both active and reactive power into the system. Fig. 3.5 demonstrates the detail of the active and reactive power of a bus of the MG in the presence of a diesel generator. In this chapter the power factor of the diesel generator is considered equal to 0.85.

3.3.2 Microturbine Microturbine is one of the useful technologies of nonrenewable units. Overall, the microturbine is a small turbine like a jet engine that has the capability to operate on different gas fuels and liquid ones. This small turbine is linked to an electric generator. The combination of a small turbine and electric generator in the presence of power electronic devices and control equipment is called microturbine. The microturbine utilizes the high-velocity gases that are exhausted from the combustor. The compressor and the electric generator of the microturbine should be mounted on the same shaft of the turbine. Fig. 3.6 demonstrates a sample diagram of microturbines. Microturbines are a technology of combustion turbine for producing both heat and electricity on a relatively small scale. Based on their generation procedure, they can be divided into two classes: unrecuperated and recuperated types. In unrecuperated microturbines, compressed air is combined with fuel and burned under constant pressure conditions during a simple cycle. Recuperated microturbines utilize a sheet-metal heat exchanger. It can recover some of the heat from an exhaust stream and transfers it to the incoming air stream, boosting the temperature of the air stream supplied to the combustor. Further exhaust heat recovery is used in a cogeneration process. Although unrecuperated microturbines have lower efficiency of about 15%, they have lower capital cost, higher reliability, and more heat available for cogeneration than another one. The efficiency of recuperated technologies is in the range of 20%
30%. The not-nature-dependent behavior of microturbine, like other nonrenewable technologies, helps the operator of the MG for providing the load of the grid in urgently times using local energy resources instead of buying energy from the

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Figure 3.6 The sample diagram of a microturbine.

Bus

Bus

(

Load , )

Micro-turbine ( , )

= ≤

− ≤

Figure 3.7 The active and reactive power of a bus with microturbine.

upstream network. Moreover, the microturbine has the unique ability to simultaneously produce electricity and heat. Moreover, the microturbine has the ability to inject both active and reactive power into the MG. In the mathematical equations of the MG, this type of nonrenewable energy source is modeled as a constant voltage bus model. The detail of the active and reactive power of a bus of the MG in the presence of a microturbine is shown in Fig. 3.7.

3.3.3 Fuel cell A fuel cell is a nonrenewable DG technology that turns the chemical energy into the electrical energy. It can provide highly reliable electricity so that it produces almost no pollutants. The efficiency of fuel cells is more than 85%. Fuel cells have two electrodes—anode and cathode. The reactions between these electrodes

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Figure 3.8 The sample diagram of a fuel cell.

cause to produce electricity. Every fuel cell also has an electrolyte, which carries electrically charged particles from one electrode to the other, and a catalyst, which speeds the reactions at the electrodes. Hydrogen and oxygen are the basic fuels of fuel cells. The injected electricity of the fuel cell into the MG returns to the fuel cell because of the way electricity behaves. In other words, the electrical current should return to it after powering the MG due to completing an electrical circuit. Fig. 3.8 shows the sample diagram of fuel cells that are used in the MGs. This technology produces only water and heat in its generating procedure. Thus the fuel cell can be said as an environment-friendly technology. Indeed, the operational process of a fuel cell consists of hydrogen and oxygen that are passed through anode and cathode of it, respectively. At the anode part the hydrogen molecules are divided into electrons, which pass through the electrolyte membrane, and protons, which cause to generate electric current and heat. On the other hand, the protons, electrons, and oxygen combine to produce water molecules at the cathode. It is worth mentioning that the technology of a fuel cell is different from the technology of a battery because a fuel cell can continuously provide the electrical energy for as long as fuel is available, while in the storage system the electrical energy comes from chemicals already present in the battery. The fuel cell produces just active power; hence, it is modeled as a P bus model in the mathematical equations of the MG. Fig. 3.9 shows the detail of the active and reactive power of a bus of the MG in the presence of a fuel cell.

Distributed generation and microgrids

Bus

Bus

(

Load , )

81

Fuel cell ) (

=

− =−

Figure 3.9 The active and reactive power of a bus with fuel cell.

Figure 3.10 The sample diagram of wind turbines.

3.3.4 Wind turbine Renewable wind energy is approximately available in most parts of the world during the year. For this reason, it is one of the proper sources for producing electricity. The technology that is used to convert wind energy into electrical energy is called the wind turbine. The operational procedure of a wind turbine is based on a simple principle. The energy in the wind spins two or three blades around a rotor (turbine). The turbine is connected to the main shaft, which turns a generator for producing the electricity. A sample diagram of the wind turbine is shown in Fig. 3.10.

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Bus

Bus

(

Load , )

Wind turbine ( , )

= = −(

− +

)

Figure 3.11 The active and reactive power of a bus with wind turbine.

Wind turbines work based on a simple principle called wind. The wind turns the blades of the wind turbine and causes to spin rotor and generator. Thus the electrical power is produced. Totally, wind turbines are divided into two categories—horizontaland vertical-axis turbines. The horizontal-axis turbines usually have three blades and operate upwind. The turbine of this type pivots at the top of the tower so the blades face into the wind. The vertical-axis turbines are omnidirectional. It means that they do not need to be adjusted to point into the wind to operate. The horizontal technologies are more popular than vertical ones. Wind speed and swept area of the turbine affect the produced electrical energy of the wind turbine. Moreover, air density and power coefficient affect the power of wind turbine. It is worth mentioning that it uses reactive power to inject active power due to its induction generator. Therefore wind turbine can be modeled as a PQ model with changeable reactive power in the mathematical equations of the MG. The consumed reactive power of this renewable technology is presented in the following equation: QWT 5 2 ð0:5 1 0:04PWT 2 Þ

(3.1)

where PWT and QWT are the injected active power and consumed reactive power of the wind turbine, respectively. The detail of the active and reactive power of a bus of the MG in the presence of a wind turbine is demonstrated in Fig. 3.11.

3.3.5 Photovoltaic panel Photovoltaic is one of the popular technologies of renewable DG units, especially in the MGs. The photovoltaic panel is a solar system that utilizes solar cells or solar photovoltaic arrays to turn directly the solar irradiance into electrical power. In other words, photons of light are absorbed in photovoltaic arrays and thus electrons are released in the panel. When they are captured in photovoltaic arrays, the electric current is produced in the panel. The sample diagram of photovoltaic panels is demonstrated in Fig. 3.12. Basically, the photovoltaic panel works based on the sunlight. The light from the Sun falls onto a photovoltaic panel and creates an electric current through a process called the photovoltaic effect. Each panel generates a relatively small amount of electricity, but panels can be connected together to produce higher amounts of energy as a solar array. The produced electricity of photovoltaic panels is in the form of direct

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83

Figure 3.12 The sample diagram of a photovoltaic panel. Bus

Bus

(

Load , )

Photovoltaic panel ) (

=

− =−

Figure 3.13 The active and reactive power of a bus with photovoltaic panel.

current that can be used in many electronic devices such as phones and laptops. Of course, it is better that the solar electricity to be converted from direct current to alternating current using an inverter. Thus the alternating current of photovoltaic panel can be used to power local electronic devices or be injected into the MG for use elsewhere. Photovoltaic panels are the practical choice for providing the electricity demand of remote areas and the MGs due to the availability of solar energy approximately all points of the world. The produced power of photovoltaic panels is related to the level of solar irradiance, the area, and efficiency of the panel. Moreover, a photovoltaic panel can only produce active power. Hence, in the mathematical equations of the system, it is simulated as a P model. Fig. 3.13 shows the detail of the active and reactive power of a bus of the MG in the presence of a photovoltaic panel.

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Hybrid Renewable Energy Systems and Microgrids

The load model of the microgrid

The formulations of the load model of the MG have a considerable influence on the results of power system studies. Totally, load modeling of the MG refers to the mathematical representation of the relationship between the active and reactive powers in load buses and some important parameters of the system such as voltage and frequency. The load model of the power system can be considered as two main categories—static (constant) and dynamic (variable) models. The level of active and reactive demand of all buses are usually considered the constant in conventional load flow analysis of power network, whereas the amount of the bus’s voltage and system’s frequency has a significant impact on the real active and reactive demand of the power network. Therefore the best result cannot be achieved by considering a constant load pattern. For this reason, in this research, the variable load model is also assumed for evaluating the optimization algorithm in the more pragmatic condition of operating in the MG. As mentioned previously, the load model of the MG is supposed as sensitive to voltage and frequency. The considered voltage-frequency-dependent active and reactive powers are calculated by Eqs. (3.2) and (3.3) [16]. The flowchart of applying the considered load model is shown in Fig. 3.14.
APbus 5 APbus

0


RPbus 5 RPbus

0

Vbus VB Vbus VB

kAP

kRP

v

v





1 1 kAP

f ð fMG

2 fMG

0Þ

1 1 kRP

f ð fMG

2 fMG

0Þ





(3.2)

(3.3)

In Eqs. (3.2) and (3.3), APbus 0 and RPbus 0 are the active and reactive powers of each bus at nominal voltage, while APbus and RPbus are the active and reactive powers of each bus at actual voltage. The parameters VB and Vbus are the nominal and actual voltage of each bus, respectively (VB 5 1 Pu). The parameters fMG 0 and fMG , respectively, show the nominal and actual frequency of the MG. Moreover, kAP f , kRP f , kAP v , and kRP v demonstrate the frequency and voltage slops for active and reactive powers.

3.5

Optimization algorithm

Distributed energy resources have considerable benefits for the MGs. To achieve all advantages of them, it is important to allocate properly sized distributed energy resources at optimal sites. For this reason the optimization of location and size of different technologies of distributed energy sources is investigated as a multiobjective issue in this chapter. Minimizing the loss index, which consists of active and

Distributed generation and microgrids

85

Applying the effect of voltage on the active power

_

× 1+

×

_

Finding the voltage of buses using the load flow based on the load model

microgrid

_

−

_

_0

_

Selecting the load type of the

(

_

Applying the effect of frequency on the active power

Choosing the frequency of the microgrid based on the load model (

)

)

_

Applying the effect of voltage on the reactive power ×

Applying the effect of frequency on the reactive power × 1+

_

−

_0

Figure 3.14 The flowchart of applying the variable load model.

reactive parts, and improving the voltage stability as the voltage index are the objective functions of the problem which are optimized simultaneously with considering some constraints. An intelligent algorithm based on the multiobjective whale optimization algorithm and fuzzy decision-making method is considered to simultaneously optimize the capacity and place of various types of distributed energy resources in the MG. The formulation of objective functions and constraint and also the used intelligent algorithm for allocating the energy sources in the MG are explained completely as in the following subsections.

3.5.1 Objective functions The power loss indicator is one of the influential parameters of the MG because the majority of the produced electricity is wasted in the MG or distribution system which is connected directly to the consumers. Another significant technical indicator of the MG is the voltage stability because the voltage of the system’s buses should be controlled in the allowable levels to have an optimal operation of the MG. Consequently, the indices of loss and voltage are considered in this chapter for optimal allocating the distributed energy resources.

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Mathematically, the main objective function of the optimization procedure is demonstrated by the following equation: Main objective function: minfIPL ; IVI g

(3.4)

where IPL and IVI show the power loss and voltage indices that are mathematically formulated in the following.

3.5.1.1 Loss index In this study the considered power loss indicator is the combination of active and reactive power losses of the MG. Mathematically, it is calculated by the following equation: IPL 5 CAL LAP 1 CRL LRP

(3.5)

where LAP and LRP are the quantity of active and reactive power losses of the MG in per-unit, respectively. Moreover, CAL and CRL show the penalty coefficients. The following equations are utilized to calculate the active and reactive power losses of the system:  2 Pnb   j51 Rej Cuj LAP 5 Pn (3.6)  2 b Rj Cuj0  j51

 2   j51 Xj Cuj  2 Pnb   j51 Xj Cuj0 Pn b

LRP 5

(3.7)

In these equations, Rej and Xi are the resistance and reactance of branch j, respectively. nb demonstrates the number of branches in the MG. Cuj0 and Cuj are the current of branch j before and after allocating the DG unit.

3.5.1.2 Voltage index The voltage stability of the MG is considered as the voltage index in this chapter. This indicator shows the capability of the MG to keep the voltage of buses at suitable levels. The voltage index is presented mathematically by the following equation: IVI 5

1 2 Vst 1 2 Vst 0

(3.8)

where Vst 0 and Vst are the voltage stability of the grid before and after operating the DG units, respectively.

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For calculating this index, first, the stability of all buses based on voltage is calculated by Eq. (3.9). Then, the minimum amount of all buses is selected as the voltage stability of the MG.  4 n  2 o Vst b2 5 Vb1  2 4 APb2 Xj 2RPb2 Rej n    o 2 2 4Vb1  APb2 Rej 1 RPb2 Xj b2 :2; 3; . . .; NB

(3.9)

where Vst b2 is the amount of voltage stability of each bus (b2 ) of the MG. NB shows the number of buses in the MG and Vb1 is the voltage of the bus b1 . The parameter APb2 is the sum of the active demand of all buses beyond the bus b2 plus the active power consumption of node b2 itself plus the sum of the active power loss of all the branches beyond the node b2 . The parameter RPb2 is the sum of the reactive demand of all buses beyond the bus b2 plus the reactive power consumption of node b2 itself plus the sum of the reactive power loss of all the branches beyond the node b2 .

3.5.2 Constraints There are the following constraints, including DG constraint and voltage of buses constraint, during the implementation of the optimization procedure for selecting the best site and size of distributed renewable energy resources in the MG.

3.5.2.1 Distributed generation constraint The produced power of each type of distributed energy resources should be in allowable size as the following range: ESP mi # ESP # ESP ma

(3.10)

where ESP mi and ESP ma demonstrate the minimum and maximum power of each type of energy source technology for producing the electricity, respectively. The parameter ESP is the produced power of the considered type of DG unit.

3.5.2.2 Voltage of busses constraint The following equation demonstrates the allowable bounds of voltage for the busses of the MG: Vmi # Vj # Vma

(3.11)

where Vj is the voltage of bus j, whereas Vmi and Vma are the lower and upper bounds of the voltage, respectively.

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3.5.3 Intelligent algorithm In this chapter the used intelligent algorithm for multiobjective optimizing the site and capacity of the multidistributed energy units consists of MOGWO and fuzzy process. In the first step the MOGWO algorithm is used to simultaneously optimize the technical goals including the loss and voltage indices and create the optimal Pareto-front. Second, the fuzzy process is applied to choose the optimal particle, which is equal to the best site and size of distributed energy resources in the MG, from the nondominated particles.

3.5.3.1 Multiobjective gray wolf optimization algorithm Intelligent algorithms are usually inspired by existing natural behaviors of nature. The optimization method of MOGWO is inspired by the gray wolves. Gray wolves that belong to the Canidae family are regarded as apex predators. This means that gray wolves are at the top of the food chain. They would prefer to live together as shown in Fig. 3.15. The multiobjective gray wolf optimization algorithm is inspired by the social behavior of wolves. In this metaheuristic algorithm the optimal answer is considered as α wolf, while β is the second answer and δ shows the third one. The rest of the particles are supposed as ω wolves. The optimization method of the MOGWO is managed by α, β, and δ so that the ω wolves follow alpha, beta, and delta wolves for reaching the best result. The position updating of search agents (ω wolves) based on the position of optimal wolves is demonstrated in Fig. 3.16. So in this algorithm, the position of each particle is updated using the following equation [17]: Pðit 1 1Þ 5 PGW ðitÞ 2 CAUCD

Figure 3.15 Living of gray wolves in groups.

(3.12)

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Figure 3.16 Position updating of search agents (ω wolves) based on α, β, and δ in MOGWO algorithm. MOGWO, Multiobjective gray wolf optimizer.

where   CD 5 CCU PGW ðitÞ 2 PðitÞ

(3.13)

CA 5 2caUra1 2 ca

(3.14)

CC 5 2ra2

(3.15)

where PGW and P show the position vector of the prey and the position vector of a gray wolf, respectively. Vectors of CA and CC are the coefficients. Moreover, ca linearly decreases from 2 to 0 during the iterations while vectors of ra1 and ra2 are random in [0 and 1]. The extra detail of the multiobjective gray wolf optimization algorithm is available in Ref. [17].

3.5.3.2 Fuzzy method After creating the optimal Pareto-front by applying the MOGWO the fuzzy procedure runs to select the best compromise answer which represents the optimal

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amount of loss and voltage indicators equal to the optimal site and capacity of the various types of distributed energy resources in the MG. In the fuzzy decision-making method the best result is selected in three steps [18]: 1. The membership values of each particle based on various technical indices are calculated by the following equation: 8 > 1 > > < IAj ma 2 IAj p Fvj p 5 IA ma 2 IA mi j j > > > : 0

IAj p # IAj mi IAj mi , IAj p , IAj ma

(3.16)

IAj ma # IAj p

where IAj mi and IAj ma demonstrate the minimum and maximum boundary of indicator j, respectively. IAj p shows the amount of particle p based on the objective function j. 2. The following equation is utilized to calculate the total membership value of each nondominated particle which is in the Pareto-front: PNI

p j51 Fvj Fv 5 PNP PNI p p51 j51 Fvj p

(3.17)

3. Each particle that has the highest amount of total membership value is selected as the best compromise answer.

Therefore the complete method for optimizing the site and capacity of multidistributed energy resources in the MG is shown in Fig. 3.17. This figure presents that the best result is selected in four sections—input details of the problem, initial steps, multiobjective gray wolf optimization steps, and fuzzy decision steps.

3.6

Numerical results

In this part the optimization procedure for simultaneously finding the place and capacity of distributed units is applied to the sample MG which is equal to the IEEE 69-bus Standard system. Fig. 3.18 demonstrates the single diagram of the MG. For better judging the quality of results the upper production power of all technologies of DGs is considered equal to 2.5 MW. Moreover, all buses of the MG have the ability for allocating the DG units. As mentioned earlier, the variable load pattern is also supposed in this chapter. The voltage-frequency coefficients of different load models of the MG including constant, residential, industrial, and commercial are presented in Table 3.1. Moreover, the base frequency of the MG is 1 Pu, while 0.98 Pu is considered as the real frequency of the system [16].

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Start Input details of the problem including:

• • • • •

The line and bus data of the considered microgrid Details of various types of distributed generations Details of different load models of the microgrid Select the type of distributed generation unit Select the load model of the microgrid

Initial steps including:

• • • •

Create random particles Run the radial load flow in the microgrid Evaluate the constraints Calculate the amounts of objective functions

Multiobjective gray wolf optimization steps including:

• • • • • • •

Select the alpha, beta, and delta particles Update position of particles based on MOGWO method Run the radial load flow in the microgrid Evaluate the constraints Calculate the amount of objective functions Update the iteration counter Repeat above steps until reaching the maximum iteration

Fuzzy decision steps including:

• Calculate the membership values of each particle • Calculate the total membership value of each particle • Select the best particle with the highest amount of total membership value • Determine the best location and size of DGs • Calculate the amount of loss and voltage indices of the microgrid End

Figure 3.17 Flowchart of the optimization of location and size of multidistributed generation units in the microgrid.

For better evaluating the influence of various technologies of distributed units on the technical indices and the efficiency of the MG, the optimization process is analyzed through seven scenarios. The following case studies are conducted: Case 1. The base case of the MG Case 2. Allocating of a wind turbine Case 3. Allocating of a photovoltaic panel Case 4. Allocating of a diesel generator

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Figure 3.18 The single diagram of the sample microgrid equal to the IEEE 69-bus system.

Table 3.1 Voltage-frequency coefficients of different load models [16]. Coefficients

kAP kRP kAP kRP

v v f f

Amount of coefficients in various load models Constant

Residential

Industrial

Commercial

0 0 0 0

1.7 2.6 1.0 21.7

0.1 0.6 2.6 1.6

0.6 2.5 1.5 21.1

Case 5. Allocating of a microturbine Case 6. Allocating of a fuel cell Case 7. Allocating of all available DG technologies including wind turbine, photovoltaic panel, diesel generator, microturbine, and fuel cell Hence, the proposed method is applied to various cases for finding the optimal site and capacity of the considered type of DG technology in the MG. As mentioned earlier, first, the MOWOA is utilized to simultaneously improve the loss and voltage indices. The optimal Pareto-fronts in the different cases are shown in Figs. 3.19
3.24. After optimizing the technical indices, the fuzzy procedure is used to select the best particle from the optimal Pareto-front. The optimal site and capacity of various energy sources in the different cases are presented in Table 3.2. The analysis of the MG structure is more useful for more clear understanding the impact of the optimization procedure on the optimal allocating of DG units in the MG. The consumers of the MG consume 3801.59 kW and 2693.6 Kvar based

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Figure 3.19 The obtained Pareto-fronts in case 2—wind turbine.

Figure 3.20 The obtained Pareto-fronts in case 3—photovoltaic panel.

on the load details of the system. Totally, the MG can be divided into three load sections: 1. Buses before the bus with the number 33 that consume about 30% of all demand of the MG. 2. Buses after the bus with the number 47 that consume about 20% of all demand of the system. 3. The connected subbranch to the bus with the number 33 that consumes about 50% of all demand of the MG. Moreover, the bus with number 42 is the most consumed bus of the MG; its demand is 1244 kW and 888 Kvar.

Based on the network structure, the connected subbranch connected to the bus with the number 33 is one of the optimal sites to operate distributed energy

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Figure 3.21 The obtained Pareto-fronts in case 4—diesel generator.

Figure 3.22 The obtained Pareto-fronts in case 5—microturbine.

resources. As can be seen in Table 3.2, DG units are located on the bus 42, which has the highest amount of the consumption, in cases 2
6 to improve the technical indices of the MG. In case 7, one of the DG units is also placed on this bus. Therefore it can be said that the location of DG units is selected optimally based on the network structure. In the following, this claim is pondered by evaluating the technical indices of the MG before and after allocating the energy sources. The amounts of power loss indices including the active loss and the reactive loss of the MG in the different cases are demonstrated in Table 3.3. The active and reactive losses are decreased by 28%
90% in allocating the different technologies of DG units. The improvement of the loss indicator in the different cases can be shown in Fig. 3.25. Based on the type of DG units, the diesel

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Figure 3.23 The obtained Pareto-fronts in case 6—fuel cell.

Figure 3.24 The obtained Pareto-fronts in case 7—all available DG units. DG, Distributed generation.

generator has the highest influence on this index, whereas the wind turbine has the lowest impact on it. The voltage index of the MG is also improved considerably after applying the proposed method. Table 3.4 presents the details of the voltage index in various cases. The voltage stability of the MG is increased by 54%
79% in different cases. Fig. 3.26 shows clearly this improvement in allocating the different technologies of distributed units in the MG. For better evaluating the impact of DG technologies the variations of buses’ voltage of the MG in different cases are demonstrated in Fig. 3.27. According to the results of this figure, the variations of voltage become more linear and near

Table 3.2 The site and capacity of energy units in the different cases. Type of used distributed generation unit

Wind turbine Photovoltaic panel Diesel generator Microturbine Fuel cell DG, Distributed generation.

Location (no. bus) Capacity (MW) Location (no. bus) Capacity (MW) Location (no. bus) Capacity (MW) Location (no. bus) Capacity (MW) Location (no. bus) Capacity (MW)

Details of DG units in different scenarios Case_1

Case_2

Case_3

Case_4

Case_5

Case_6

Case_7













42 2.0164











42 2.3705











42 2.1392











42 2.1511











42 2.3217

4 0.1021 25 0.4765 42 1.6274 33 1.1048 29 1.0592

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Table 3.3 The details of loss index of the microgrid in the different cases. Amount of loss indices in various scenarios

Active Loss (MW) Reactive Loss (Mvar)

Case_1

Case_2

Case_3

Case_4

Case_5

Case_6

Case_7

0.2249

0.1629

0.0915

0.0263

0.0266

0.0901

0.0216

0.1021

0.0740

0.0432

0.0152

0.0153

0.0426

0.0114

100.00% 90.00% 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% Case 1 Case 2 Case 3 Case 4 Case 5 Case 6 Case 7

Figure 3.25 The effect of different cases on the improvement of loss index.

Table 3.4 The details of voltage indicator of the microgrid in the various cases. Number of cases

Voltage stability (Pu)

Case_1

Case_2

Case_3

Case_4

Case_5

Case_6

Case_7

0.6833

0.8548

0.8878

0.9014

0.9018

0.8858

0.9313

to nominal voltage after utilizing the proposed method. Based on the type of DG units, the microturbine has the highest impact on the voltage stability, whereas the wind turbine has the lowest effect on this index of the MG. The different performance of DG technologies is related to their produced power and load flow modeling. After evaluating the proposed method in the MG with the constant load pattern, it is evaluated in the MG considering the consumption pattern sensitive to voltage and frequency. The details of the technical indices of the MG in the

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90.00% 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% Case 1

Case 2

Case 3

Case 4

Case 5

Case 6

Case 7

Figure 3.26 The improvement of voltage index in the different cases (%). Case 1

Case 2

Case 3

Case 4

Case 5

Case 6

Case 7

1.04 1.02 1 0.98 0.96 0.94 0.92 0.9 0.88 0.86 0.84 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69

Figure 3.27 The variation of buses’ voltage of the microgrid in different cases (Pu).

different cases and load models are presented in Table 3.5. For better showing the impact of DG units and load models on the efficiency of the MG the variations of loss and voltage indicators in various conditions are shown in Figs. 3.28 and 3.29, respectively. As can be shown in Figs. 3.28 and 3.29, the initial technical indices of the MG are varied in a range of 0.05 Pu when the load model of the system is changed, whereas they are varied in a range of 0.005 Pu after utilizing the DG units by the proposed method. This improvement, which is about 90%, causes that the performance of the MG will not change much when the load pattern of some buses turns to another one. Totally, it can be said that the optimization procedure has a high efficiency in optimizing the site and capacity of the distributed energy units in the MG so that the technical parameters of the network are modified considerably after utilizing the DG units by the proposed method.

Table 3.5 The amount of indices of the microgrid in the different cases and load models of the system. Type of load model

Active loss (MW)

Reactive loss (Mvar)

Voltage stability (Pu)

Constant Residential Industrial Commercial Constant Residential Industrial Commercial Constant Residential Industrial Commercial

Number of cases Case_1

Case_2

Case_3

Case_4

Case_5

Case_6

Case_7

0.22495 0.16636 0.19467 0.18091 0.10216 0.07698 0.08867 0.08312 0.68331 0.72437 0.70241 0.71134

0.16292 0.15766 0.15318 0.15576 0.07402 0.07156 0.06928 0.07069 0.85479 0.86069 0.86164 0.86003

0.09155 0.09457 0.08891 0.09393 0.04325 0.04442 0.04154 0.04403 0.88784 0.88955 0.89401 0.89037

0.02633 0.02521 0.02633 0.02592 0.01521 0.01474 0.01464 0.01491 0.90143 0.90558 0.90913 0.90569

0.02659 0.02543 0.02667 0.02619 0.01529 0.01481 0.01476 0.01499 0.90176 0.90586 0.90946 0.90601

0.09001 0.09258 0.08698 0.09183 0.04269 0.04366 0.04081 0.04322 0.88579 0.88781 0.89201 0.88851

0.02166 0.02244 0.02151 0.02241 0.01146 0.01193 0.01141 0.01189 0.93127 0.93187 0.93743 0.93322

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Case 1

Case 2

Case 3

Case 4

Case 5

Case 6

Case 7

0.2 0.18 0.16 0.14 0.12 0.1 0.08 0.06 0.04 0.02 0 Constant load

Residential load

Industrial load

Commercial load

Figure 3.28 The variation of loss index in the various cases and load modes (Pu). Case 1

Case 2

Case 3

Case 4

Case 5

Case 6

Case 7

0.35 0.3 0.25 0.2 0.15 0.1 0.05 0 Constant load

Residential load

Industrial load

Commercial load

Figure 3.29 The variation of voltage index in the various cases and load modes (Pu).

3.7

Conclusion

Distributed energy resources have the ability for improving the performance of the MG if they are located in the best site with the optimal size. For this reason the multiobjective optimization of the place and size of the various types of DG units was investigated in the MG. The renewable and nonrenewable technologies including wind turbine, photovoltaic panel, diesel generator, microturbine, and fuel cell were the energy sources of the proposed method. For better judging the quality of the proposed optimization procedure in a more pragmatic operational condition of the MG, the location and size of the DG units were also evaluated in the presence of a nonlinear load pattern sensitive to frequency and voltage. The multiple objectives were the loss and voltage indices that were optimized simultaneously by the combination of the MOWOA and the fuzzy process. Ultimately, this method was tested on the sample MG.

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The numerical results demonstrate that the proposed method can properly optimize the location and capacity of distributed energy resources in the MG so that the technical indicators of the MG are modified considerably after operating the DG units by the proposed method. In all cases, the DG units are located near the load center to better distribute the power in the grid and improve the parameters of the network. The loss indicator of the MG is reduced about 28%
90% in various cases. The voltage index of the MG is also reduced by about 54%
79% in various scenarios. Based on the technology of the MGs, nonrenewable units have a better performance than renewable ones. The proposed optimization procedure has also the appropriate efficiency in the MG with a nonlinear load model so that the range of variation of the technical indices of the MG is reduced significantly when the load model of the system changes to another model. Ultimately, it can be said that the proposed method considerably improves the efficiency of the MG.

References [1] H. Shayeghi, M. Alilou, B. Tousi, Multi-objective optimization of demand side management in the presence of DG and demand response, Int. J. Ind. Electron. Electr. Eng. 6 (2018) 1
7. [2] M. Alilou, D. Nazarpour, H. Shayeghi, Multi-objective optimization of demand side management and multi DG in the distribution system with demand response, J. Oper. Autom. Power Eng. 6 (2018) 230
242. [3] Alilou M., B. Tousi, H. Shayeghi, Multi-objective optimization of operational schedule of DG units in the presence of demand response, in: Iranian Conference on Renewable Energy & Distributed Generation-ICREDG, 2018. [4] H. Shayeghi, A. Ghasemi, M. Moradzadeh, M. Nooshyar, Simultaneous day-ahead forecasting of electricity price and load in smart grids, Energy Convers. Manage. 95 (2015) 371
384. [5] H. Shayeghi, M. Alilou, Application of multi objective HFAPSO algorithm for simultaneous placement of DG, capacitor and protective device in radial distribution network, J. Oper. Autom. Power Eng. 3 (2015) 131
146. [6] H. Shayeghi, E. Shahryari, Integration and management technique of renewable energy resources in microgrid, Energy Harvesting Energy Effic. 37 (2017) 393
421. [7] Z. Abdmouleh, A. Gastli, L. Ben-Brahim, M. Haouari, N. Al-Emadi, Review of optimization techniques applied for the integration of distributed generation from renewable energy sources, Renew. Energy 113 (2017) 266
280. [8] A. Ahmed, S. Hasan, Optimal allocation of distributed generation units for converting conventional radial distribution system to loop using particle swarm optimization, Energy Procedia 153 (2018) 118
124. [9] H. Chen, C. Yang, K. Deng, N. Zhou, H. Wu, Multi-objective optimization of the hybrid wind/solar/fuel cell distributed generation system using Hammersley sequence sampling, Int. J. Hydrogen Energy 42 (2017) 7836
7846. [10] U. Sultana, A. Khairuddin, A. Mokhtar, N. Zareen, B. Sultan, Grey wolf optimizer based placement and sizing of multiple distributed generation in the distribution system, Energy 111 (2016) 525
536.

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[11] A. Cagnano, E. Tuglie, P. Mancarella, Microgrids: overview and guidelines for practical implementations and operation, Appl. Energy 258 (2020) 114039. [12] L. Mehigan, J. Deane, B. Gallacho´ir, V. Bertsch, A review of the role of distributed generation (DG) in future electricity systems, Energy 163 (2018) 822
836. [13] R. Viral, D. Khatod, Optimal planning of distributed generation systems in distribution system: a review, Renew. Sustain. Energy Rev. 16 (2012) 5146
5165. [14] A. Ehsan, Q. Yang, Optimal integration and planning of renewable distributed generation in the power distribution networks: a review of analytical techniques, Appl. Energy 210 (2018) 44
59. [15] A.E. Maczulak, Renewable Energy: Sources and Methods, Infobase Publishing, 2010. [16] M. Alilou, M. Farsadi, H. Shayeghi, Optimal allocation of renewable DG and capacitor for improving technical and economic indices in real distribution system with nonlinear load model, J. Energy Manage. Technol. 2 (2018) 18
28. [17] S. Mirjalili, S. Saremi, M. Mirjalili, L. Coelho, Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization, Expert. Syst. Appl. 47 (2016) 106
119. [18] M. Alilou, V. Talavat, H. Shayeghi, Simultaneous placement of renewable DGs and protective devices for improving the loss, reliability and economic indices of distribution system with nonlinear load model, Int. J. Ambient Energy 40 (2020) 877
881.

Renewable energy systems

4

Leidy Tatiana Contreras Montoya1, Santiago Lain2, Mohamad Issa3 and Adrian Ilinca4 1 Mathematics, Informatic & Engineering Department, University of Quebec at Rimouski, Rimouski, QC, Canada, 2PAI 1 Group, Energetics and Mechanics Department, Autonomous University of the West, Santiago de Cali, Colombia, 3Department of Applied Sciences, Quebec Maritime Institute, Rimouski, QC, Canada, 4Department of Mathematics, Computer Science and Engineering, University of Quebec at Rimouski (UQAR), Rimouski, QC, Canada

4.1

Chapter overview

The aim of this chapter is to describe and address the key elements of renewable energy sources, thus providing a solid foundation for assessment. To this end, it introduces the physical foundations and the technological bases. Furthermore, key figures are elaborated which allow these options to be categorized according to the demands of the energy system. To ensure a clear, comprehensible, and straightforward presentation of the various sources of renewable energy, individual chapters detailing each of them are presented. This chapter addresses the resource characterization and assessment, the process of producing electricity from solar radiation [photovoltaic (PV)], wind, hydropower, and biomass. In addition, a short section is included in harnessing ocean energies. The most important data regarding resource use and availability are presented. We discuss the information and parameters that allow a renewable project development as well as economic and environmental assessment. It provides, from the viewpoint of each renewable resource, the required data to evaluate the opportunity of an energy production project for various applications.

4.2

Photovoltaic power generation

Solar power requires converting the radiant energy from the Sun into electricity using PV or concentrating systems. As sunlight hits the surface of the PV cell, some of the photons are absorbed and the solar cell releases electrons used to generate electric current, that is, electricity [1]. Bell Telephone researchers developed the first practical PV cell in 1954. In the late 1950s, PV cells were used to power US space satellites. By the late 1970s, PV panels were supplying electricity to remote locations without electric power lines. Most of the PV panels installed in the United States are connected to the grid on homes, buildings, and Hybrid Renewable Energy Systems and Microgrids. DOI: https://doi.org/10.1016/B978-0-12-821724-5.00013-1 © 2021 Elsevier Inc. All rights reserved.

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Figure 4.1 Solar PV capacity and additions, top 10 countries, 2018. PV, Photovoltaic. From: REN21, Renewables 2019 Global Status Report, REN21 Secretariat, Paris, 2019.

central-station power plants since 2004. Technological advances, lower costs for PV systems, and various financial incentives and government policies helped to greatly expand PV use since the mid-1990s, not only in the United States but around the world [2]. At least 32 countries, covering each continent, had a combined potential of 1 GW or more by the end of 2019 (see Fig. 4.1). Solar PV has played a significant and increasing role in the generation of electricity in several countries, including Honduras (12.1%), and Italy and Greece (both about 8.2%), and, by the end of 2018, one in five Australian households generated at least some of their electricity with solar energy [3].

4.2.1 Principles of solar radiation The Sun is the source of solar energy on Earth. The Sun is located at 1.50 3 1011 m from the Earth and has a diameter of 1.39 3 109 m. The solar constant (Gc), defined as the flux of solar energy incident on the surface oriented normal to the sunrays, has a mean value of 1353 W/m2. Nevertheless, the solar constant is the flux incident at the outer edge of the atmosphere. For terrestrial applications, what is relevant is the solar flux incident on the surface of the Earth, after the sunrays have passed through the atmosphere [4]. Thus only a part of the energy incident from the Sun, the one received as radiation on the Earth’s surface, can be absorbed and converted into different utilizable forms of energy. Their wavelengths correspond to the optical spectral range from 0.3 to 5.0 μm [5]. Once the solar radiation enters the atmosphere, different processes alter its magnitude and direction [4]. Various mechanisms are involved: G

Diffusion: Diffusion is a displacement of radiation from its initial radiation angle without energy transfer and therefore without energy loss [4,5]. Two forms of diffusion processes appear, Rayleigh and Mie. Rayleigh diffusion involves particles with a radius significantly

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105

smaller than the wavelength of the incident light (e.g., air molecules), while the Mie diffusion involves particles with a radius equal or larger than the wavelength of the incident light (e.g., aerosol particles). Furthermore, Mie diffusion can turn into diffraction as the larger the particles that diffuse the sunlight, the more they diffuse into a forward direction [5]. Absorption: Absorption is the conversion of solar radiation into other energy forms, that is, solar radiation is converted into heat during this process [5]. Such absorption can take place in aerosol, cloud, and precipitation particles. In addition, it is possible that a selective absorption, that is, some gases existing within the atmosphere, can absorb selected spectral and wavelength ranges of solar radiation [e.g., ozone (O3), water vapor (H2O), and carbon dioxide (CO2)].

The global radiation (Gg) is the sum of direct radiation (Gb) and diffuse radiation (Gd) (Eq. 4.1) that are the result of the diffusion processes previously described. Direct radiation is the radiation that has traveled a straight path from the Sun, and it is incident on a particular spot. On the other hand, diffuse radiation is the radiation produced by diffusion in the atmosphere and thus indirectly reaches a particular point on the surface of the Earth, for example, atmospheric counter-radiation and radiation reflected by the neighborhood [5]. It is important to note that PV arrays use both direct and diffuse radiation, and these are strongly dependent on weather conditions. In sunny weather with clear skies, about 10%
20% of the radiation is diffuse, while in cloudy weather with a lack of bright sunshine, most of the incident radiation is diffuse [6]. Gg 5 Gb 1 Gd

(4.1)

The understanding of the position of the Sun relative to the Earth is important for the design of a PV system. The most effective tool is the sun path lines or sun path chart. The measurement and interpretation of this method start with the consideration of the geometric relationship between the Sun and the Earth as an elliptic orbital plane in which the Earth rotates at an angle of 23.45 degrees. Besides, the relationship of the Sun to a point on the surface of the Earth is described by the solar altitude angle (α), and the solar azimuth angle (as) as can be seen in Fig. 4.2. The altitude angle is the angle between a line collinear with the Sun and the horizontal planes. The azimuth angle is the angle between a due-south line and the projection of the site-to-sun line on the horizontal plane [4]. Fig. 4.3 shows the sun path chart calculated for Montre´al (Canada), using the tool developed by the University of Oregon [7]. The y-axis represents the altitude or solar elevation, measured from 0 degree on the horizon to 90 degrees directly overhead. The x-axis represents the solar azimuth. The center of chart is 180 degrees or due south. The intersection of the Sun’s altitude and azimuth on the graph shows the Sun’s position in the sky, also, the chart shows the solar time (vertical lines in the chart). Analyzing in detail Fig. 4.3, the blue lines in the chart are different for each day and season of the year. The summer solstice (June 21) has almost 14 h of daylight, while the winter solstice (December 21) has 8 h. That means almost 6 h less of solar radiation and therefore less amount of energy that can be generated with solar panels. Also, the shading of direct solar radiation by mountains, buildings, and trees can be considered, including the elevations of the obstacles [5].

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Figure 4.2 Solar azimuth angle and solar altitude angle definitions.

Figure 4.3 Sun path chart for Montre´al (Canada). Source: By permission of the University of Oregon (Solar Radiation Monitoring Laboratory)— http://solardat.uoregon.edu.

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Other useful tools to obtain the solar path chart are available in Refs. [8,9]. It is important to point out that in all of these tools, the altitude and azimuth angles are not fundamental but are functions of the location (the latitude), the time (the solar hour angle), and the solar declination. The principles and equations behind the different software that give rise to the generation of the sun path chart are described below. The solar declination (δs), defined as the angle between the Sun and the plane of Earth’s equator, varies between 223.45 degrees and 123.45 degrees and has the same numerical value as the latitude at which the Sun is directly overhead at solar noon on a given day. The solar declination can be approximated as [4]:  360ð284 1 nÞ δs 5 23:45 sin 365 




(4.2)

where n is the Julian day. The Julian day is the number of the day as measured sequentially from January 1 (Julian day 1) to December 31 (Julian day 365). The solar declination as a function of the Julian day is illustrated in Fig. 4.4. The solar hour angle (hs) is based on the 24 h required for the Sun to move 360 degrees around the Earth (see Eq. 4.3). The motion is 15 degrees per hour with 2180 # hs # 1 180 . The solar hour angle at solar noon is defined as zero, with values east of south (morning) being negative and values west of south (afternoon) being positive. In this case the solar noon is defined as the time when the azimuth angle is zero and the Sun’s altitude angle is the maximum for a given day [4]. hs 5 ðlocal hour 2 12 hÞ  15

Figure 4.4 Declination angle as a function of Julian date.

(4.3)

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Considering the above, the following equation allows estimate the solar altitude angle (α) which is related to the declination (δs), the latitude (L), and the solar hour (hs): sinðαÞ 5 sinðLÞsinðδs Þ 1 cosðLÞcosðδs Þcosðhs Þ

(4.4)

The solar azimuth angle (as) is given by the following equation: sinðas Þ 5

cosðδs Þ  sinðhs Þ cosðαÞ

(4.5)

The generation of a sun path line for a particular day using Eqs. (4.4) and (4.5) is easy for a latitude greater than the solar declination. Throughout the year the zenith angle (θ) that forms the vertical of a place with the direction of the Sun’s rays varies according to Eq. (4.6) in the summer solstice, to Eq. (4.7) in the winter solstice, being θ 5 L at equinoxes. Therefore, for a surface to receive solar radiation perpendicularly (e.g., solar panel), the surface will have to be tilted at an angle equal to that formed by the vertical of the place with solar radiation (see Fig. 4.5), this angle is represented by β. θ 5 L 2 δs

(4.6)

θ 5 L 1 δs

(4.7)

In addition to the zenith angle the orientation with respect to the equator or azimuth is also important. At the equator, as 5 0, in the northern hemisphere the orientation is in south’s direction, and in the southern hemisphere the orientation is in north’s direction. The solar irradiance incident on a horizontal plane at the edge of the atmosphere, which forms an angle θ between the vertical (zenith) and the direction of radiation, is defined by the following equation:    360  n G0 ð0Þ 5 Gc 1 1 0:033 cos cos θ 365

(4.8)

Figure 4.5 Surface normal to the solar radiation: (A) at noon of the winter solstice, (B) at noon of the equinoxes, (C) at noon of the summer solstice.

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The sum of solar irradiance must be calculated considering that θ changes throughout the day (see Eq. 4.9): G0d ð0Þ 5

h π i 24  3600 Gc ε0 2 ωs  sin δs  sin L 2 cos δs cos L sin ωs π 180

(4.9)

where    360  n ε0 5 1 1 0:033cos 365

(4.10)

ωs 5 2 arccosð2tanðδs ÞtanðLÞ

(4.11)

Based on the global irradiance, the direct and diffuse component of solar radiation in a horizontal plane can be obtained. The following equation allows to determine such components on an inclined plane (β): Gðβ Þ 5 Gb  RB 1 Gd 

1 1 cos β 1 2 cos β 1 ρ  Gg  2 2

(4.12)

where ρ is surface reflectivity (e.g., 0.2 by default) and RB is defined by the following equation:   ωss π=180 ½signðLÞsin δs sinðjLj 2 β Þ 1 cos δs cosðjLj 2 βÞsin ωss   RB 5 ωss π=180 sin δs sin L 1 cos δs cos Lsin ωs

(4.13)

ωss 5 max½ωs ; 2 arccosð 2 ½signðLÞtan δs tanðabsðLÞ 2 β ÞÞ

(4.14)

From a statistical analysis of results obtained by Eqs. (4.12)
(4.14) for different inclinations at a particular site, it is possible to identify the optimum inclination angle (β) of a static surface that allows the solar resource to be maximized. Three configurations are possible [10]: 1. Inclination angle equal to the latitude of the site: this is the typical inclination adopted for systems used throughout the year. 2. Inclination angle equal to the latitude of the site minus 15 degrees: this is the typical inclination adopted for seasonal systems used in summer. 3. Inclination angle equal to the latitude of the site plus 15 degrees: this is the typical inclination adopted for seasonal systems used in winter. Also is used in autonomous systems (with batteries) located in isolated places (e.g., Nordic populations) where these months are critical for PV systems.

4.2.1.1 Measuring radiation To characterize the solar resource, several measuring instruments are available. Relative and absolute instruments are generally differentiated. In the absolute instruments, the radiation is absorbed on a blackened surface which converts it into

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heat, as consequence, there is an increase in surface temperature and an amount of heat per time unit is released by heat transfer in the instrument or into the air by thermal radiation. In this case the resulting temperature increase is a measure of the energy radiation [5]. Otherwise, a relative instrument can be used to calibrate such an absolute instrument [5]. Types of absolute instruments are the pyranometer (see Fig. 4.6) and the compensation pyrheliometer (see Fig. 4.7), and one example of relative instrument is the actinometer. A pyrheliometer is used to measure solar radiation coming directly from the Sun and a small portion of the sky around the Sun at normal incidence [6]. In this device, sunlight enters through a window (in the 0.3
3 μm range) to a “thermopile” which consists of two blackened thin manganese surfaces. One surface is exposed to direct solar radiation, which heats it up. The other surface, without incident solar radiation, is heated with electrical energy to the same temperature as the exposed surface. The heat output is proportional to the square of the electrical current is therefore equal to the

Figure 4.6 Pyranometer. From: T.S. Hukseflux, Pyranometer, 2020. ,https://www.hukseflux.com/uploads/styles/ xsmall/public/product/SR20-D2-pyranometer-1webXLv1401.jpg?itok 5 t9Vrwm0u. [11].

Figure 4.7 Pyrheliometer. From: T.S. Hukseflux, What is a pyrheliometer?, 2020. ,https://www.hukseflux.com/ applications/solar-energy-pv-system-performance-monitoring/what-is-a-pyrheliometer..

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Figure 4.8 Schematic illustration of a pyranometer (A) and a pyrheliometer (B). From: J. Wide´n, J. Munkhammar, Solar Radiation Theory, Uppsala University Publication: Uppsala University, 2019.

energy absorbed by the radiation [5]. Pyrheliometers are usually calibrated to the incident radiation and are designed in a way that enables direct radiation to occur only on the receiving surface (see Fig. 4.8B). According to the WMO (World Meteorological Organization) convention, all modern pyrheliometers use the same field of view, characterized by an opening half-angle of 2.5 degrees. It means that the measurement of direct solar radiation includes some “circumsolar” radiation [12]. On the other hand, a pyranometer is used to measure global radiation (diffuse plus direct), this device also contains a thermopile as a receiving surface. In this case the measure is obtained by a difference of pressure due to the temperature difference caused by heating of the receiving surface. To avoid atmospheric influences on the measuring process the thermopile is protected with a spherical dome made of different materials, in accordance with the spectral range to be measured (e.g., glass for long-wave radiations fluxes and polyethylene for short-wave radiations) [5]. Pyranometers are mainly aligned horizontally as can be seen in Fig. 4.8A. Besides, a pyranometer can be used to measure only diffuse radiation by attaching a shading ring to block direct radiation [6]. Global radiation is measured at numerous sites. The measurements are made hourly, daily or monthly, the mean values are added up over one year and the long-term mean values are estimated. The previous process helps in the construction of maps as the one in Fig. 4.9 which shows the global horizontal irradiation distribution on Earth. Fig. 4.9 reveals that the highest global irradiation occurs mainly at the Equator, especially in the deserts and mountains located there, while the lowest values of global irradiation were found in the zone close to the north pole. This kind of maps can only give an approximate evaluation of what is to be expected within a larger region. Locally, the solar radiation can vary from country to country, as seen in Fig. 4.10, the maximum irradiation that can be found in Canada is 1387 kW h/m2 and only in few locations, while in the sub-Saharan Africa this value is 2556 kW h/m2

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Figure 4.9 Distribution of global horizontal irradiation worldwide. Adapted from: ©2019 The World Bank. Global Solar Atlas 2.0, Solar resource data (Solargis, Global horizontal irradiation, 2019. ,https://solargis.com/maps-and-gis-data/download/world. [13]).

Figure 4.10 Distribution of global horizontal irradiation in Canada. Adapted from: ©2019 The World Bank. Global Solar Atlas 2.0, Solar resource data (Solargis, Global horizontal irradiation, 2019. ,https://solargis.com/maps-and-gis-data/download/world.).

(see Fig. 4.11). The reason for the higher supply in Africa is the proximity to the equator. This solar atlas helps to identify the PV potential in some places, especially where isolated populations need a solution for their energy needs.

4.2.2 Photovoltaic cell fundamentals It is important to understand the atomic theory and semiconductor theory to understand the functioning of the PV cell. An atom is composed of protons, neutrons, and electrons. The protons and neutrons are located in the nucleus, while the electrons are positioned in

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orbital or bands around the nucleus. All inner bands must be filled before any outer band can be partially filled. The energy of an electron is determined by its location in one of the different bands. The number of electrons in the outermost band determines the chemical characteristics of the element. The remotest band an electron can occupy and maintain its association with the atom is called the valence band [4]. If the valence band is filled, the element is chemically inert. If electrons in an unfilled valence band are lightly bound, they may attach to a nearby atom; in this case the atom that accepts the electron is charged negatively, while the donor atom charged positively. Such sharing of electrons results in a covalent bond [4]. Nevertheless, the electrons in the valence band can become energetic and jump to the conduction band that is far away from the nucleus. The difference in energy between an electron in the band and one in the conduction band is called the bandgap energy [4,5]. Electrons in the conduction band require only a small amount of energy to move away from the atom, which is responsible for heat and electrical conduction. Such amount of energy is measured in electron-volts (eV 5 1.6 3 10216 J) and is different for each type of materials, that is, insulators materials whose valence bands are full have high bandgap energies (.3 eV), conductors materials whose valence bands are relatively empty have low bandgap energy, and semiconductors materials (e.g., silicon, germanium, gallium arsenide) whose valence bands are relatively full have bandgap energy ,3 eV [4]. A chemically pure semiconductor, at low temperatures, acts as an insulator, but as the temperatures increases, electrons are released from their chemical bond and lifted to the conduction band, becoming conductors with increasing temperatures [5]. An example of semiconductor used in PV systems is silicon. Pure silicon is called an intrinsic semiconductor (i.e., beyond a certain temperature the electrons in the valence band are released and reach the conduction band), but if a small amount of impurities, usually called the dopant, is combined with the pure silicon, an extrinsic semiconductor results (i.e., the combination of different atoms will allow to increase the conductivity of semiconductor [5] and thus enhances the flow of electrons and holes [4]). There two types of semiconductor doping [4,5]: 1. n-type doping: the dopant has more electrons than the base material in the valence band, which means that the semiconductor seems to have an excess of electrons even though the semiconductor is neutral [4]. 2. p-type doping: the dopant has fewer electrons in the valence band than the base material; as a result, the semiconductor appears to have a deficit of electrons, or an excess of “holes,” although it is electrically neutral [4].

The two doping should not simultaneously be done, since the effects of acceptation and donors cancel each other [5]. In Fig. 4.12, there is a diagram of both doping processes for silicon; as can be seen, the atoms that are frequently used are phosphorus and boron. The PV effect requires a p
n junction with an incident photon (see Fig. 4.13). If the photon incident is sufficiently energetic to displace a valence electron, that electron will jump into the conduction band and initiate a current flow. To force an electron to move from the valence band to the conduction band, the electron must

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Figure 4.11 Distribution of global horizontal irradiation in sub-Saharan Africa. Adapted from: ©2019 The World Bank. Global Solar Atlas 2.0, Solar resource data (Solargis, Global horizontal irradiation, 2019. ,https://solargis.com/maps-and-gis-data/download/world.).

have at least the bandgap energy [4]. The energy of a photon is given by the following equation: E 5 hν

(4.15)

where h is the Planck constant (6.625 3 10234 J) and v the frequency. The frequency, wavelength (λ), and speed of light (c 5 3 3 108 m/s) are related in the following equation: ν5

c λ

(4.16)

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Figure 4.12 Semiconductor doping.

Figure 4.13 Structure of a photovoltaic cell. From: U.S. Energy Information Administration, Solar explained: photovoltaics and electricity, 2019. ,https://www.eia.gov/energyexplained/solar/photovoltaics-and-electricity.php..

Combining Eqs. (4.15) and (4.16), it is possible to conclude that the smaller the wavelength, the greater the excess energy of the photon. The low efficiency of PV devices is also attributable to this principle and to additional conversion inefficiencies associated with photon wavelengths less than those corresponding to the exact

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Table 4.1 Theoretical and measured efficiencies of photovoltaic cells. Material

Theoretical (%)

Measured (%)

Si, silicon CdTe, cadmium telluride CdS, cadmium sulfide CuInSe2, copper indium diselenide GaAs, gallium arsenide GaP, gallium phosphide InP, indium phosphide

24 21 16 26 24 17 23

18 7 7 19 11 1 3

Adapted from: B.K. Hodge, Alternative Energy Systems and Applications, first ed., John Wiley & Sons, 2010.

Figure 4.14 Best research-cell efficiencies. From: NREL, Best research-cell efficiencies, 2019. ,https://www.nrel.gov/pv/assets/pdfs/ best-research-cell-efficiencies.20191106.pdf. [15].

bandgap energy [4]. The above can be appreciated in Table 4.1; however, the PV cells are part of active research programs and the materials and its efficiencies are constantly changing [3]. Thus Table 4.1 is complemented with Fig. 4.14 that was developed by the US National Renewable Energy Laboratory in 2019. The efficiencies of several PV cells and the research entities involved are shown in Fig. 4.14. Four categories are listed: crystalline silicon cells, single-junction GaAs, multijunction cells, thin-film and emerging technologies. The single-junction GaAs and multijunction cells are less common due to their high cost but are ideal for use in concentrated PV systems and space applications [14]. According to Fig. 4.14, over the last decade, the increase of efficiency in the PV cell has been augmented. However, the increase in efficiency of some technologies has been more significant, as is the case of “multijunction cells” that have reached efficiencies above 30%. The previous figure is summarized in Table 4.2, to show the efficiencies of PV cells in 2019. Regarding CIGS and CdTe, these technologies have an important element to

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Table 4.2 Efficiencies of photovoltaic cells in 2019. Material

Category

Efficiency (%)

Four junctions Three junctions Two junctions Thin film Perovskite/Si tandem Single crystal Silicon heterostructures (HiT) Single crystal Perovskite cells ClGS (copper indium gallium diselenide) Multicrystalline CdTe (cadmium telluride) Thin-film crystal

Multijunction cell

39.2 37.9 32.8 29.1 28.0 27.8 26.7 26.1 25.2 23.4 22.8 22.1 21.2

Single-junction GaAs Emerging PV Single-junction GaAs Crystalline Si cells Crystalline Si cells Emerging PV Thin film Crystalline Si cells Thin film Crystalline Si cells

Source: Adapted from NREL, Best research-cell efficiencies, 2019. ,https://www.nrel.gov/pv/assets/pdfs/bestresearch-cell-efficiencies.20191106.pdf..

be considered, namely, that they contain rare and toxic elements such as cadmium which require additional precautions during manufacturing and eventual recycling [14]. In the case of single-junction GaAs, it has a similar crystal structure to that of monocrystalline silicon, but with alternating gallium and arsenic atoms. This combination has a higher light absorption coefficient and wider bandgap, which means that it is more efficient than those made of silicon; in addition, GaAs cells can operate at much higher temperatures without considerable performance degradation [14]. Multijunction cells have two or more junctions layered on top of each other, allowing energy to be collected from multiple portions of the spectrum. Light that is not absorbed by the first layer will travel through and interact with subsequent layers, the same happens with GaAs cells. These multiple layers present in GaAs and multijunction cells make them very expensive to produce, and only commercially viable for concentrated PV systems and space applications [14]. Finally, the perovskite cells are constructed from organic compounds of lead and elements such as chlorine, bromine, or iodine; they are relatively cheap to produce but they are currently limited by a short lifetime [14]. Fig. 4.13 can be appreciated for the basic structure of a PV cell, which consists of p-conducting base material and n-conducting layer on the top. A metallic contact covers the entire backside of the cell, while the irradiated side is equipped with a finger-type contact system to minimize shading losses. Also are used the total insulation and translucent lead layers. In addition, an antireflecting coating can be applied to reduce reflection losses on the cell surface [5]. The efficiencies indicated for PV cells apply for determined standardized measuring conditions, also called “standard test conditions,” due the power output of a solar cell depends on spectral light composition, temperature, and irradiation intensity [5]. These conditions are listed in Table 4.3, and the power generated by solar cells under these conditions is referred as peak power.

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Table 4.3 Standard test conditions. Parameter

Value

Radiation Solar cell temperature Spectral distribution of the irradiation according to AM

1000 W/m2 25 C 1.5

AM, Air mass.

Figure 4.15 Cell, module, and array.

4.2.2.1 Solar module The fundamental element in PV system is the solar module or also called solar panel, which is the assimilation of individual cells, and modules are also assembled into arrays [4,5]. Fig. 4.15 schematically illustrates the sequence. Cells in PV modules are arranged in series and parallel configurations according to the same rules as series and parallel direct current (DC) circuits. For the identical components placed in a series arrangement the voltages are applied at constant current, thereby the voltage increases (see Fig. 4.16A); and for identical components placed in parallel, the currents are introduced at constant voltage resulting in increased current (see Fig. 4.16B) [5]. Solar cells can therefore be arranged to meet specified voltage and power requirements. Nonetheless, to ensure proper operation of the module under realistic conditions, such as failure of an individual cell or shading of portion of the module, in-series elements in the arrays are usually provided with blocking diodes to prevent reverse current flow and other inefficiencies [4].

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Figure 4.16 Behavior of cells in series and in parallel: (A) two identical cells in series and (B) two identical cells in parallel. From: B.K. Hodge, Alternative Energy Systems and Applications, first ed., John Wiley & Sons, 2010.

It is important to mention that the nominal short-circuit current also depends on cell technology, the quality of the used material, the manufacturing process, as well as mainly the size of the cell. As the short-circuit current is linearly proportional to the solar irradiance, it is also linearly proportional to the effective surface of the cell. Also, the rated power of the PV modules depends on the total number of cells [5]. The solar module also includes embedding materials that help to protect the cells against atmospheric impacts, ensure a defined upper voltage level and maximum amperage, respectively. Embedding of cells, as well as edge sealing, is subject to very high requirements that must be achieved throughout the overall technical lifetime of 20
30 years and even longer. For instance, the temperature fluctuation must range from 240 C to 180 C, the cells must be protected against any kind of humidity (e.g., rain, condensation), mechanical deterioration associated to hailstones or wind loads with gusts of 50 m/s needs to be prevented, high insulation strength needs to be ensured, and the materials must not be afflicted by bacteria nor be eroded by animals (e.g., birds) [5].

4.2.3 Photovoltaic systems As mentioned in the previous sections, a PV system allows performing a conversion process of photons from solar radiation into electricity. This technology has shown great developments. According to Ref. [16], some reports indicate that this type of energy conversion into electricity could produce energy to more than 4 billion people in the world, which is almost half of humanity by 2030. Here are some of these systems, which are classified according to the connection type, and can be divided into: 1. grid-independent PV systems or off-grid systems, 2. grid-connected PV systems, and 3. PV hybrid systems.

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The components of the PV systems depend on the type of connection. A gridindependent system comprises PV modules, batteries or storage system, charge controller, inverter, cabling, and protections. The grid-connected systems consist of PV modules, inverter, protections, and, in some cases, transformer. In both PV configurations a monitoring system can be included to send power supply data to remote equipment for data acquisition and control. Below is the definition of these components: G

G

Charge controller: it is a device that directs the output of the PV array to the load and to battery recharging when electricity is generated; when the array is not generating, it extracts electrical energy from the battery for the load. It also provides the capability to direct battery power to an inverter for alternating current (AC) loads [4]. This device is important for a safe and reliable battery operation, because they protect the battery against deep discharge, prevent premature aging, and are responsible for the charging strategy avoiding overcharge [5,16] Battery: it is a rechargeable device that converts potential chemical energy into electrical energy, storing the energy obtained during sunlight hours to be used in night hours [16]. The typical parameters considered when choosing batteries are the voltage, the charge capacity, the cycle capability, and the depth of discharge. Voltage (V) and charge capacity ðA hÞ are used to determine the energy storage/retrieval potential of the battery (see Eq. 4.17) [4]. Capacity also depends on the discharge current, the temperature, and the defined end-of-discharge voltage [5]. Energy stored in battery 5 Voltage 3 charge capacity ðW hÞ

G

(4.17)

The nominal voltage is defined by the materials participating within the electrochemical reactions and defines also how many modules have to be connected in series to obtain the voltage level needed by the consumer [5]. Cycle capability refers to the number of charge/discharge cycles expected from the battery. The depth of discharge specifies how much (in percentage) of the stored energy at full capacity may be extracted without damage to the battery (should not exceed 60%
80%). Batteries for PV applications require both cycle capability and depth of discharge high [4], they also have a limited technical lifetime and have to be replaced several times over the entire lifetime of PV system which increase the total cost of the system [5]. The batteries used in PV are, primarily, nickel
cadmium batteries, nickel
metalhydride batteries, lead
acid batteries, lithium-based battery systems, and capacitor (bilayer capacitor). However, small-scale and hybrid systems are usually equipped with conventional batteries [5]. Inverter: When operating in off-grid systems, it converts DC into the appropriate AC power required by commercially available appliances. When is used in grid-connected systems, inverters are required to transfer the properties of the electrical energy produced by the PV systems into properties similar to theses of the grid [5]. To properly select an inverter for a PV solar system, it is necessary to know the amount of DC power required per day to supply the AC load, and this is determined by the inverter’s efficiency. If the system operates with a low-efficiency inverter, the required amount of DC will be higher than the system using a high-efficiency inverter, so it is important that the inverter is not only reliable but also high efficiency, that is, between 95% and 98% [16]. These devices are classified according to output voltage curves, which are rectangular and sinusoidal waves [5,16]. The first ones have a simple circuit; they are low cost but can generate harmonics in the system which implies an increase in energy losses and are

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121

generally used in some hundred volts. The last ones are more expensive but can be used for a wide range of load [16]. Mounting systems: in the case of freestanding installation, mounting and installation systems must be adapted to the respective site conditions regarding material and soil conditions. Special mounting systems are available for roof and fac¸ade installation, but it is important to consider architectural, esthetical, and cooling reasons. The latter is the most important as an increase in the temperature of panel results in a power reduction [5]. To take advantage of the greatest amount of solar radiation, the mounting system is always dependent on optimum angles of inclination and orientation. Regarding optimum inclination and orientations, there are tracking systems available such as: single axis tracking around the horizontal rotation axis, single axis tracking around the polar axis, single axis tracking around the vertical rotation axis for inclined mounted modules, and two axes tracking. G

G

G

G

Due to the increasing costs, tracking systems in PV systems tend to become less attractive, require more space, have higher maintenance costs, and require more expensive mounting equipment. However, for sites with a high proportion of direct radiation, this situation can be different [5].

4.3

Wind power generation

Wind energy is the kinetic energy available in the air masses moving around the globe, which in turn is an indirect form of solar energy: the solar radiation absorbed into the atmosphere causes temperature and pressure differences. As a result, the air masses move and accumulate kinetic energy. This energy can be converted into mechanical energy or electric power generation and used for several purposes [17]. In this section, we will focus on electric power generation. Wind energy has captured a great deal of media attention in the same way as solar energy, exhibiting the fastest growth of all nonconventional renewable energy sources in recent years without taking into account the hydropower (see Fig. 4.17). As can be appreciated in Fig. 4.18 at the end of 2018, the world power capacity for wind energy was almost 600 GW, and the top five of countries that have an important energy production from wind are China, the United States, Germany, India, and United Kingdom. The aims of this section are the development of the basic principles of wind energy and the provision of a quantitative understanding of wind energy.

4.3.1 Wind resource Solar energy drives the wind, which is then dissipated due to turbulence and friction at the Earth’s surface. The Earth’s atmosphere can be considered a giant duct, and if energy is taken out at one location, it is not available elsewhere [18]. Wind turbine power varies with the cube of wind speed. But the wind is variable in terms of speed, direction, and altitude; hence, the power extracted by wind turbines has to take into account the site-specific wind conditions [4]. Table 4.4 shows the variations in space and time of atmospheric wind.

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Figure 4.17 Estimated renewable energy share of global electricity production, end-2018. From: REN21, Renewables 2019 Global Status Report, REN21 Secretariat, Paris, 2019.

Figure 4.18 Renewable power capacities in the world (2018). From: REN21, Renewables 2019 Global Status Report, REN21 Secretariat, Paris, 2019.

Table 4.4 Time and space scale for atmospheric motion. Name

Time

Length (km)

Example

General circulation Synoptic scale Mesoscale Microscale

Weeks to years Days to weeks Minutes to days Seconds to minutes

1000
40,000 100
5000 1
100 ,1

Trade winds, jet streams Cyclones, hurricanes, typhoons Thunderstorms, land-sea breezes, tornadoes Turbulence

Source: ©2009 From Wind Energy: Renewable Energy and Environment by (Vaughn Nelson). Reproduced by permission of Taylor and Francis Group, LLC, a division of Informa plc. This permission does not cover any third party copyrighted work which may appear in the material requested. User is responsible for obtaining permission for such material separately from this grant.

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If the wind speed is doubled, the power is increased eight times, for instance, the wind power at 10 m/s is eight times the power at 5 m/s. As a conclusion, the high-speed winds contain a lot of power, but they are also associated to damage to structures; for this reason, wind turbines do not extract all the available energy at high wind speeds. All wind turbines have some means of control, or they would be destroyed in high winds [18]. The wind power is proportional to the density of air, which for standard conditions (sea level, 15 C) is 1.225 kg/m3. Air density is a function of temperature (T) and pressure (p). Applying the ideal gas law can be expressed by Eq. (4.18) [17]; high temperatures and low pressures reduce the density of air, and vice versa, but the major factor that affects air density is the change in pressure with elevation. If only the elevation (H) is known, air density can be estimated by Eq. (4.19) [18]: p p 5 3:4837 RT T   ρ 5 1:226 2 1:194 3 1024 H ρ5

(4.18) (4.19)

The annual mean wind speed is usually used as a first estimation of wind power potential (power/area), which can be estimated from the mean hourly speeds or other measurements of wind speed [17,18]. However, this will underestimate the wind power. The frequency distribution of the wind speeds defined as the duration of each wind speed value (or a wind speed histogram giving the number of observations within each wind speed range) is important for a better estimation of the wind power potential for any time period at a determined place [18].

4.3.1.1 Wind shear The atmospheric boundary layer is the lowest part of the atmosphere and its characteristics, such as velocity, temperature, and relative humidity, are directly influenced by its contact with the Earth’s surface [17]. As said earlier, the wind speed varies with height and, in the boundary layer; this variation is called vertical wind shear or vertical profile of wind speed. In this case the speed is assumed to be zero on Earth’s surface and increases as the height in the boundary layer augments [17,18]. The power law for wind shear is: U 5 U0

 α z z0

(4.20)

where U and U0 are the wind speeds at z and z0 heights, respectively, and α is the wind shear exponent, which is around 1/7 (0.14) for a stable atmosphere; however, α will vary, depending on elevation, time of day, season, nature of terrain, wind speed, or temperature [17,18]. Another method used to represent the wind shear is the log law or logarithmic profile which is expressed by Eq. (4.21); there, z0 is the surface roughness length which characterizes the roughness of the ground terrain (see Table 4.5), κ the von

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Table 4.5 Values (approximate) of surface roughness length for various types of terrain. Terrain description

z0 (mm)

Very smooth, ice or mud Calm open sea Blown sea Snow surface Lawn grass Rough pasture Fallow field Crops Few trees Many trees, hedges, few buildings Forest and woodlands Suburbs Centers of cities with tall buildings

0.01 0.20 0.50 3.00 8.00 10.00 30.00 50.00 100.00 250.00 500.00 1500.00 3000.00

From: J.F. Manwell, J.G. McGowan, A.L. Rogers, Wind Energy Explained: Theory, Design and Application, second ed., John Wiley & Sons, 2010.

Karman’s constant and U the friction velocity, which is determined by Eq. (4.22) where τ 0 is the surface value of the shear stress [17]. U ðzÞ 5

  U H ln z0 κ

rffiffiffiffiffi τ0 U 5 ρ

(4.21)

(4.22)

The determination of vertical wind shear is an important design parameter since it directly determines the productivity of a wind turbine on a tower of a certain height and it can strongly influence the lifetime of a turbine rotor blade [17]. The blade fatigue is influenced by the cyclic loads resulting from rotation through a wind field that varies in the vertical direction. In general, wind speeds increase with height, and the energy capture capability depends on the rotor diameter [1]. That is the reason for the latter improvements in wind turbines are focused on rotor and hub height (see Fig. 4.19).

4.3.1.2 Wind direction Changes in wind direction are due to the general circulation of atmosphere, on an annual basis (seasonal) to the mesoscale (4
5 days). The seasonal changes of prevailing wind direction could be as little as 30 degrees in trade wind regions to as high as 180 degrees in temperate regions. Conventionally, wind direction is illustrated by a graph called “wind-rose diagram” which indicates the percentage of winds from that direction [18].

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Figure 4.19 Increase in rotor dimension over time. From: K. Rohrig, V. Berkhout, D. Callies, M. Durstewitz, S. Faulstich, B. Hahn, et al., Powering the 21st century by wind energy—options, facts, figures, Appl. Phys. Rev. 6 (3) (2019) 031303. https://doi.org/10.1063/1.5089877 [19].

The circular shape of the wind rose shows the direction from which the winds came and the length of each spike around the circles shows how often the wind blew from that direction. For example, the wind rose in Fig. 4.20 shows that during this particular sampling period the wind blew from the Northeast 23% of the time, and from the South-South-West 14% of the time, etc. The coarse line in each spike provides details on the wind speed from each direction. Using the same example, the longest spike shows the wind blew from the Northeast at speed of 12 m/s about 23% of the time.

4.3.1.3 Turbulence The wind will vary according to location and time and be influenced by landscape, vegetation, and obstacles. This phenomenon is known as turbulence that is caused by dissipation of kinetic energy in wind into thermal energy by eddies or gusts. Over time periods of an hour or more, turbulent wind may have a relatively constant mean, but over shorter times (minutes or less), it may be unpredictable [17]. The previous concepts can be represented by Eq. (4.23), where the short-term mean wind speed (U) refers to mean wind speed averaged over some (short) time period (Δt) which is usually taken to be 10 min but can be as long as an hour. The variable u is the longitudinal component of wind speed in the prevailing wind direction, and u~ is the superimposed fluctuating wind of zero mean [17]. 1 U5 Δt

ð Δt u dt; 0

u 5 U 1 u~

(4.23)

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Figure 4.20 Wind-rose example.

Instantaneous turbulent winds are not necessarily constantly observed; they are simply measured at some relatively high rate. In this case the short-term mean wind speed can be expressed in sampled form as [17]: U5

NS 1 X ui NS i51

(4.24)

where NS is the number of samples during each short-time interval, and ui is the sequence of turbulent wind. The basic measure of turbulence is the turbulence intensity (see Eq. 4.25) which is defined as the ratio of the standard deviation (σu) of the wind speed to the mean wind speed (U). Both the mean and standard deviation are calculated over a time period, by convention in wind energy engineering equal to 10 min. σu TI 5 ; U

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u NS u 1 X σu 5 t ðui 2U Þ2 NS 2 1 i51

(4.25)

In general, the highest turbulence intensities occur at the lowest wind speeds, but at a given location the lower limiting value may depend on the particular terrain characteristics and conditions of the region [17].

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4.3.1.4 Wind speed histograms A wind speed histogram shows the number of hours (or whatever time period is used) the wind blew at each wind speed class. Usually, winds speeds are sampled at 1 Hz and averaged for 1 h; the results are presented for 1 year to obtain a representative annual value. Histograms are constructed from the data as shown in Table 4.6; in this case an average air density of 1.1 kg/m3 was used to calculate the average wind power potential. From Table 4.6, it can be concluded that the average wind speed was 8.2 m/s and the average wind power potential was 466.73 W/m2 [18]. It is important to mention that these histograms must be made from information obtained at hub height; usually, the measuring instruments are located at 10 or 50 m. The information can be also presented as a graphic (see Fig. 4.21) where the wind speed and its frequency are related.

Table 4.6 Data for calculation of wind speed histogram. Wind speed (m/s)—Uj

Hours

Frequency (fj)

fj 3 Uj

f j 3 U3j

0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5 12.5 13.5 14.5 15.5 16.5 17.5 18.5 19.5 20.5 Sum

54 146 353 487 617 747 844 950 949 940 801 702 486 302 175 85 52 32 22 12 4 8760

0.0062 0.0167 0.0403 0.0556 0.0704 0.0853 0.0963 0.1084 0.1083 0.1073 0.0914 0.0801 0.0555 0.0345 0.0200 0.0097 0.0059 0.0037 0.0025 0.0014 0.0005 1.0000

0.0031 0.0250 0.1007 0.1946 0.3170 0.4690 0.6263 0.8134 0.9208 1.0194 0.9601 0.9216 0.6935 0.4654 0.2897 0.1504 0.0979 0.0639 0.0465 0.0267 0.0094 8.2143

0.0008 0.0563 0.6296 2.3836 6.4183 14.1875 26.4593 45.7513 66.5302 92.0014 105.8513 121.8783 108.3583 84.8211 60.9029 36.1335 26.6656 19.5776 15.9013 10.1574 3.9338 848.5995

Power/area (W/m2)

466.7297

Source: ©2009 From Wind Energy: Renewable Energy and Environment by (Vaughn Nelson). Reproduced by permission of Taylor and Francis Group, LLC, a division of Informa plc. This permission does not cover any third party copyrighted work which may appear in the material requested. User is responsible for obtaining permission for such material separately from this grant.

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Figure 4.21 Wind speed histograms.

4.3.1.5 Duration curve Wind data can also be represented by a speed
duration curve (Fig. 4.22), which is a plot of cumulative frequency. This graphic is made by subtracting 100 from the percentage of cumulative frequencies. The percent duration can be converted to number of hours in a year by multiplying with 8760 (hours/year). This curve allows estimating the periods during which the wind speed is above a given value. Nevertheless, a histogram does not give a time sequence of data nor does a wind speed
duration curve indicate the length of calm periods [18].

4.3.1.6 Wind speed distributions Sometimes, data is not available due to different factors such as location, poorly calibrated instruments; then the wind speeds can be predicted from one or two parameters. The most used distributions are Rayleigh and Weibull. However, they give poor estimation for low mean wind speed situations; but at higher wind speeds, both give good results for many locations [18]. The Rayleigh distribution depends only on the mean wind speed (U); hence, the probability density function is given by [17,18]:   "   # π U π U 2 pð U Þ 5 exp 2 2 U2 4 U

(4.26)

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Figure 4.22 Wind speed
duration curve.

The Weibull distribution is better for regions with steady winds, such as the trade winds. This distribution is based on two parameters and, thus, can better represent a wider variety of wind regimes. The two parameters are a shape factor (k) and a scale factor (c). Both parameters are functions of mean wind speed and standard deviation [17,18]. The Weibull probability density is given by: "  #   k21 k U U k pð U Þ 5 exp 2 (4.27) c c c Fig. 4.23 shows the comparison between both distributions with measured data. These results were obtained by Bilgili and Sahin [20]; as it can be seen, both distributions are similar to measured data which allows to conclude that any of the distributions is valid to represent the frequency of occurrence of wind speeds. There are methods to determine k and c from U and σu. Table 4.7 summarizes the expressions proposed by different authors. The k parameter is dimensionless, and as the value of k increases, the curve has a sharper peak, indicating that there is less wind speed variation, usually is near 2. The c parameter has the same units as the speed and controls the most probable speed (or mode); the larger the scale parameter, the higher the mode, and the lower the probability of a given speed less than the mode [4,17,18].

4.3.1.7 Wind atlas Access to or lack of information on wind resources is an important barrier to the development of wind energy projects. Particularly in isolated regions and

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Figure 4.23 Example of probability density distribution calculated using Rayleigh distribution and Weibull distribution. From: M. Bilgili, B. Sahin, Investigation of wind energy density in the southern and southwestern region of Turkey, J. Energy Eng. 135 (1) (2009) 12
20. https://doi.org/10.1061/(ASCE)0733-9402 (2009)135:1(12), with permission from ASC. This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil Engineers. Table 4.7 Approximations to estimate the shape and scale factor in Weibull distribution.

Justus (1978) Lysen (1983)

Shape factor (k)

Scale factor (c)

For 1 # k , 10 21:086 k 5 σUu

c 5 Γð1 1U 1=kÞ Requires use of the gamma function c 5 ð0:56810:433=kÞ2ð1=kÞ U

developing countries the wind atlas is typically used (available in https://globalwindatlas.info/). Fig. 4.24 shows the global average wind speed at 100 m height. The atlases provide a general description of the wind resource within a large area (mesoscale) but were typically unable to provide enough information for the detailed examination of candidate sited for wind development (microscale). However, recent wind atlases with high resolution (as fine as 200 m) have been produced. These are designed to quantify the wind resource of a given location, even at different heights above the ground [17]. However, these maps should be used with caution due to the different measurement methods (instrumentation, location, height), the wind shear exponent used, the distribution used, etc. [18].

4.3.1.8 Wind measurement and instrumentation There are three general types of instrumentation for wind measurements [17,18]: G

Instruments used by national meteorological services: the data obtained by this method are almost worthless for determining wind power potential, due to the location (cities, airports,

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Figure 4.24 Wind resource map—mean wind speed. Adapted from: Obtained from the “Global Wind Atlas 3.0,” a free, web-based application developed, owned, and operated by the Technical University of Denmark (DTU). The Global Wind Atlas 3.0 is released in partnership with the World Bank Group, utilizing data provided by Vortex, using funding provided by the Energy Sector Management Assistance Program (ESMAP). For additional information: https://globalwindatlas.infobtained.

G

G

buildings, and control towers), there are few stations, the quantity of data is small (one per day or even monthly averages), and lack of calibration after installation. Instruments designed specifically for determining the wind resource. Instruments for high sampling rates in determining gusts, turbulence, and inflow winds for measuring power curves, stress, fatigue, etc., for wind turbines.

The use of specialized instruments will imply higher costs in the implementation of wind energy projects, especially in small projects. However, it is necessary that any wind energy project has its resource measurement station, so it is possible to opt for analog and nondigital options [18]. In general, wind energy application uses the following types of meteorological sensors: G

G

G

G

anemometers to measure wind velocity, wind vanes to measure wind direction, thermometers to measure the ambient air temperature, and barometers to measure the air pressure.

Wind instrumentation systems consist of four major components: sensors, transducers, signal conditioner, and recorders. The sensors will react to changes in the environment, for example, the cup in an anemometer reacts to the force of the wind. A transducer converts energy from one form to another; in the previous case the mechanical motion of the anemometer is converted to an electrical signal. The signal conditioner supplies power to the sensor when required, receives the signal from the sensor, and converts it to a form that can be used by a recorder for display. And finally, the recorder will store and/or display the data obtained [17]. Besides the components of the instrumentation system, it is recommended that the data obtained satisfy five characteristics: accuracy and precision, error, reliability, repeatability, and reproducibility. The accuracy refers to the mean difference between the output of the instrument and the true value of the measured variable.

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Precision refers to the dispersion about that mean. Error is the difference between the indication and the true value of the measured signal. The reliability is a measure of the likelihood that it will continue to perform within specified limits of error for a specified time under specified conditions. And reproducibility is defined by the closeness of agreement among measurements of the same quantity where the individual measurements are made under different conditions [17].

4.3.1.8.1 Wind speed measuring instrumentation The wind speed measuring can be made by different types of instruments which can be classified according to their principles of operation [17]: G

G

G

G

G

Momentum transfer: cups, propellers, and pressure plates Pressure on stationary sensors: Pitot tubes and drag spheres Heat transfer: hot wires and hot films Doppler effects: acoustics and laser Special methods: ion displacements, vortex shedding, etc.

Some of the most used instruments are described as follows: G

G

G

G

Cup anemometers: they use their rotation, which varies in proportion to the wind speed, to generate a signal. The most used design corresponds to three cups mounted on a small shaft. These anemometers can be mechanical, electrical, or electronic, and photoelectric. However, the mechanical type is more used in remote areas because it does not require a power source [17]. It is important to mention that environmental factors can reduce the reliability of the anemometers. For instance, dust stored in the bearings increases friction, reducing wind speed readings; the same happens in the ice presence, if the anemometer freezes its rotation will be reduced, or it will stop completely, causing errors in the measurement until the sensor is completely defrosted. Heated cup anemometers can be used, but they require a significant source of power [17]. In conclusion, the reliability for cup anemometers depends on calibration and service visits. And the frequency of these visits depends on the site environment and the value of the data [17]. Propeller anemometers: these devices use the wind blowing into a propeller to turn a shaft that drives an AC or DC generator, or a light chopper to produce a pulse signal. In a typical horizontal configuration the propeller is kept facing the wind by a tail-vane, which also can be used as a direction indicator. This type of anemometer faces the same reliability problems in measurement as does the cup anemometer [17]. Sonic anemometers: they use ultrasonic sound waves to measure wind speed and direction. Wind velocity is measured based on the time of flight of sonic pulses between pairs of transducers [17]. Acoustic Doppler sensors (SODAR): SODAR or sound detection and ranging is a remote sensing system, that is, can make measurements without placing an active sensor at the point of measurement, which can be installed in onshore or offshore applications. Remote sensing is used mainly for meteorological and aerospace purposes but recently has been used for wind siting and performance measurements. With these devices, the wind profile is measured by sending vertically and at a small angle to the vertical acoustic pulses, the acoustic energy scattered back to the ground is then collected by microphones. The reliability in this kind of sensors can be affected by fluctuations due to wind shear, temperature, and humidity gradients [17]. Some issues associated to these devices are noise in populated sites, locations with easy access for maintenance, and noncomplex terrains, and it may not work correctly in events of high wind speeds (above 18 m/s). This fact is important for the power curve determination that requires measurement of wind speeds up to the cut-out wind speed, typically around 25 m/s [17].

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133

Laser Doppler Sensors (LIDAR): LIDAR or light detection and ranging is, as SODAR, also classified as remote sensing device. In this case a beam of light is emitted, the beam interacts with the air and some of the light is scattered back to the LIDAR. The returned light is analyzed to determine the speed and distances to the particles from which it was scattered. In the same way as SODAR, LIDAR technology has been widely used in meteorological and aerospace applications [17].

4.3.1.8.2 Wind direction measuring instrumentation Wind direction is normally measured via the use of a wind vane. A conventional wind vane consists of a broad tail that the wind keeps on the downwind side of a vertical shaft. The friction at the shaft is reduced with bearings; hence, the vane requires a minimum force to initiate movement, usually at wind speeds on the order of 1 m/s. In the same way as with cup and propeller anemometers, environmental problems (blowing, dust, salt, ice) affect the reliability of wind vanes [17]. Generally, wind vanes produce signals either by contact closures or by potentiometers. The accuracy obtained from potentiometers is higher than that obtained from contact closures, but the latter are less expensive [18].

4.3.1.8.3 Vegetation indicators Vegetation can indicate regions of high wind speed where there are no measurements available or the data is unreliable. Deformation or flagging of trees is the most common indicator [18]. Fig. 4.25 shows one of the methods used to estimate the wind speed from tree deformity, also known as Griggs
Putnam index of deformity. The use of trees as an indicator of wind speed is subject to several practical limitations. The deformation should be viewed perpendicularly to the prevailing wind direction, to take into account the full effects of flagging and throwing. Trees selected as indicators therefore need to be well exposed to the prevailing winds. Rarely do trees in a forest extend far enough above the canopy to be in the undisturbed airflow by the other trees. However, isolated trees or those in small, widely spaced groups should be favored as wind speed indicators [18].

4.3.2 Wind potential assessment (siting) Similar to solar energy, wind power can have a variety of applications that will depend largely on where they are installed. These include hybrid power systems, offshore wind energy, installations in severe climates, special purpose applications, energy storage, and fuel production. As with PV, wind power can also support a grid (grid connected) or work in isolation (grid independent or off grid).

4.3.2.1 Hybrid power systems A hybrid power system is the one that contains multiple generators (solar, wind, or fuel), storage or power converters. In this system the power sources and any system loads may strongly influence each other [17]. This is also known as “Distributed generation.” Commonly, energy grids are designed to accept power flow in one direction; however, when large amounts of power generation are added, the remainder of the

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Figure 4.25 Griggs
Putnam index of deformity. From: B.M. Meneses, A. Lopes, An integrated approach for wind fields assessment in coastal areas, based on bioindicators, CFD modeling, and observations, Theor. Appl. Climatol. 128 (1
2) (2017) 301
310. https://doi.org/10.1007/s00704-015-1707-4 [21].

supply from the local utility can become more variable, and in some cases, generation may be sufficient to send power in the other direction [17]. An example of a grid-connected system is illustrated in Fig. 4.26, where three sources are complementing each other, for example, PV plant, combined heat and power (CHP) plant, and wind turbine. In some cases the hybrid power systems are connected to small or independent grids, in such cases, wind
diesel power systems or wind
solar power systems can be found. Wind turbines integration takes place in grids where the voltage or frequency may not be able to remain relatively constant under all conditions, that is, to help stabilize those grids [17]. Some issues, such as wind turbine design, the amount of wind penetration, the inclusion of other renewable power sources, the amount of energy storage, and the existence of any load management, are essential at the moment to design a hybrid power system [17]. For instance, some isolated grids powered by diesel generator provide power for only a part of the day to conserve fuel. Some have large voltage swings because of one

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Figure 4.26 Distributed generation system. From: A. Ehsan, Q. Yang, Optimal integration and planning of renewable distributed generation in the power distribution networks: a review of analytical techniques, Appl. Energy 210 (2018) 44
59 [22].

or two significant loads on the system, as a sawmill or a fish-processing plant. As a result, these grids are susceptible to disruption due to interconnected loads and generation; in this case, wind power plays an important role since it can generate the energy necessary to compensate the loads [17]. The above requires the grid be balanced which is achieved with an adequate energy storage and/or controllable loads; energy storage could provide power for periods when wind power is less than the load, but when the wind power is greater than the load, energy storage, and controllable loads could provide sinks for excess power [17]. Some examples of the last ones are wind power using blade pitch control, optional loads, and water pumping.

4.3.2.2 Offshore wind energy As the name implies, offshore wind energy refers to electricity produced by wind turbines installed in the ocean or in lakes. The principal motivation to develop this type of wind energy is the lack of available land with good resource for new turbines [17]; besides, considering that large percentage of the population lives along the coasts, offshore wind could be a renewable resource located close to population centers [1]. In the last decade, several countries have focused their efforts in wind production on offshore wind resources, as can be appreciated in Fig. 4.27.

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Figure 4.27 Wind power offshore global capacity by region (2008
18). From: REN21, Renewables 2019 Global Status Report, REN21 Secretariat, Paris, 2019.

Offshore projects have several attributes [17]: (1) greater area available, (2) proximity to cities and other load centers, (3) generally higher wind speeds, (4) lower intrinsic turbulence intensities, and (5) lower wind shear. However, there are some challenges that need to be considered [1,17]: (1) higher cost due to specialized installation, equipment, and more expensive support structures; (2) more difficult working conditions; (3) decreased availability due to limited accessibility for maintenance; and (4) necessity for special corrosion prevention measures. The International Electrotechnical Commission (IEC) has developed the documentguide IEC 61400-3-1:2019 that outlines the minimum design requirements for fixed offshore wind turbines.

4.3.2.2.1 The offshore wind resource The key factor that makes offshore wind energy attractive is the wind resource. Up to approximately 50 km from shore, the average wind speed increases, exhibits less shear and less turbulence, which is associated to the smooth water surface in comparison with land surface [17]. Because the wind speed is higher, the productivity increases; for example, Fig. 4.28 shows the wind speed map for Bretagne, a region located in France, at a height of 50 m: the velocity onshore is about 5.50 m/s, at the close coast is 7.25 m/s and offshore is 8.50 m/s. These data imply that a wind turbine located in the coast and offshore would produce 56% and 73% more energy, respectively, than onshore. The decrease of wind shear means that the tower height can be lower offshore than onshore. However, this reduced height could be compensated by the support structure required below the water level [17]. The reduced turbulence can be both positive and negative. Positive in the sense that wind turbines generally perform better in less turbulent winds and are less exposed to fatigue. Negative because less turbulence means that downstream wakes take longer to recover so there should be greater spacing between wind turbines [17].

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Figure 4.28 Wind speed map for Bretagne (France) at a height of 50 m. Adapted from: Obtained from the “Global Wind Atlas 3.0,” a free, web-based application developed, owned and operated by the Technical University of Denmark (DTU). The Global Wind Atlas 3.0 is released in partnership with the World Bank Group, utilizing data provided by Vortex, using funding provided by the Energy Sector Management Assistance Program (ESMAP). For additional information: https://globalwindatlas.infobtained.

In the offshore case the roughness length (z 0) from Eq. (4.21) must be changed to take into account the special conditions of water surface. In this case the Charnock model is often used for modeling the change in sea surface roughness length which varies as a function of wind speed and the distance from shore [17]: z 0 5 AC

U2 g

(4.28)

where g is the gravitational constant, AC is the Charnock constant, and U is the friction velocity. The surface shear stress from friction velocity depends on the waves’ height. Another method to estimate the roughness length employs the following function of wave parameters [17]:  4:5 z0 HS 5 1200 HS LP where HS is the significant wave height and LP is the peak period wavelength.

(4.29)

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As said earlier, the turbulence intensity is lower not only because the smaller surface roughness but also due to lower vertical temperature gradients, related to high specific heat of water, which results in relatively constant water temperature [17].

4.4

Hydroelectric power generation

Hydropower is the electricity derived from the potential energy of water captured when moving from higher to lower elevations or by the energy available in the water stream. It is characterized by its variability associated to weather patterns (e.g., places with dry and rainy seasons) and other local conditions. However, it is an attractive, cost-effective option for grid balancing and services, adding further value to its generation assets [3]. At the end of 2018 the worldwide hydropower capacity was 1132 GW, which constitutes 31% of the global capacity (see Fig. 4.29). Fig. 4.29 also shows the top 10 countries with major hydropower capacity. Over the last decade, this form of energy supply has had an increase due to its easy integration with other sources of renewable energy, such as solar PV and wind power. However, this integration relies on renewable energy technologies and local conditions; for instance, most of the hydropower plants may need modernization schemes due to how long they have been operating [3]. Categories of hydropower projects include run-of-the-river, reservoir-based capacity, low-head in-stream technology (also known as hydrokinetic energy), and ocean energy (which is divided into tidal and wave energy). The use of these different types of hydropower projects will depend on the available resource in the place of interest.

Figure 4.29 Hydropower global capacity, shares of top 10 countries and rest of the world, 2018. From: REN21, Renewables 2019 Global Status Report, REN21 Secretariat, Paris, 2019.

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The water resource is mainly governed by the hydrological cycle. This global cycle is fed by water evaporation from the oceans and, among others, the plants and continental waters. The evaporation is also related to the incident solar energy, which will increase or decrease the water evaporation rate and determine the amount of water available in reserves such as rivers, lakes, groundwater [5]. The evaporated water is transported into the atmosphere by global and local winds as water vapor and then precipitates either in the form of rain, snow, soft hail, or dew. A little less water falls over the oceans than is evaporated. This results in correspondingly higher rates of precipitation on land and a net importation of water from the oceans to the continents. The resulting precipitation feeds snowfields, glaciers, streams, rivers, lakes, and the groundwater [5]. Finally, the precipitation rate will determine which water resource use is suitable for a specific place.

4.4.1 Conventional hydroelectric power For years, hydroelectric power
produced using dams have been known as the most environment friendly of all sources of electricity and, because of that, are the most widely type of hydropower used in the world. Compared with other technologies, hydroelectric dams result in larger capital investments and construction time, but once complete, hydroelectric power has the lowest operating costs. However, in recent times, the environmental impacts of hydroelectric power were seen as less positive and more invasive [4]. It consists of harnessing the potential energy in falling water and using conventional mechanics to convert that energy into electricity [5]. The water flows within a stream or a river from a higher site to a lower site due to gravitation. Hence, at both sites the water is characterized by potential and kinetic energy, which are different from each other. To identify this energy difference we apply Bernoulli’s equation. Assuming a stationary and friction-free flow with incompressibility, the equation is written as follows [4,5]: p 1 ρw gh 1

1 ρ U 2 5 const: 2 w w

(4.30)

where p is the hydrostatic pressure, ρw the water density, g the acceleration of gravity, h the head, and Uw the velocity of water flow. To determine the available energy head (hutil) of a particular section of a stream or river, Eq. (4.30) can be transformed into: p ρw g

1 hutil 1

1 Uw2 5 const: 2 g

(4.31)

Applying Eq. (4.31) at both sites, the pressure differences, the differences in height, and the different flow velocities of the water can be obtained. However, when applying this equation, we need to bear in mind that it is an idealized form that does not consider any actual losses. Therefore head losses due to friction must be subtracted [4,5]. Differences in height can generally be used as the utilizable head (hutil) in a first rough estimation due to the relatively small differences in pressure and velocity. This assumption is the base to determine the power from a respective water supply.

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Eq. (4.32) allows  to obtain the power of the water (Pw) from the product of volumetric flow rate q_w and utilizable head (hutil). Large heads can generally be achieved in mountainous areas, whereas in lowland areas, the volumetric flow is dominant [5]. Pw 5 ρw gq_w hutil

(4.32)

Rainfall plays an important role in hydropower. For instance, a certain amount of water can be held back in times of high precipitation (Vreservoir), becoming a reservoir that can supply energy. The power depends on the period required for emptying the reservoir. Consequently, the stored energy (Ew) is defined by the size of the reservoir and thus by the stored water volume and the utilizable head [5]: Ew 5 ρw ghutil Vreservoir

(4.33)

The reservoirs are in line with the topographical conditions, which mean that the storage of water in different regions varies considerably. In the high mountains, for example, conditions are generally quite favorable. It is important to mention that, sometimes, the reservoirs supply drinking and domestic water in addition to serve for the energy production. Often both aspects are interconnected [5]. Regarding Fig. 4.30, if the energy balance between two reference points upstream (1) and downstream (5) of a hydroelectric power station is set forth, the Bernoulli equation (Eq. 4.31) can be written as follows: 2 2 2 Uw;1 Uw;5 Uw;5 p1 p5 1 h1 1 5 1 h5 1 1ξ 5 const: ρw g 2g ρw g 2g 2g

(4.34)

The terms of Eq. (4.34) are defined as pressure energy (p/ρwg), potential energy (h), kinetic energy ðUw2 =2gÞ, and energy losses ðξUw2 =ð2gÞÞ. ξ is the loss coefficient, pi and Uw,i represent the pressure and the flow velocity at the corresponding reference points, respectively. The energy lost is therefore part of the rated power that is converted by friction into ambient heat and cannot therefore be used technically [5]. The energy losses are classified in friction losses and minor losses. For the first, values for the friction factor and fully rough friction factor are needed. The Moody diagram is often used to present the functional dependence of the friction factor (f), on the Reynolds number and the relative roughness (ε/D). However, some expressions are also used depending on the flow regime, see Eq. (4.35) for laminar flow and Eq. (4.36) for turbulent flow [4]. f5

ρUD μ

(4.35)

0:3086   i2 1:11  log ε=3:7D 1 6:9=ReD

(4.36)

64 ; ReD

f5h

ReD 5

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Figure 4.30 Physical correlations in a hydroelectric power station. From: M. Kaltschmitt, W. Streicher, A. Wiese, Renewable Energy: Technology, Economics and Environment, Springer Science & Business Media, 2007.

Minor losses are associated to all the accessories available in the pipeline (valves, elbows, etc.) and are expressed as the product of tabulated equivalent lengths and the fully friction factor (fT), the asymptotic value of the friction factor for a given relative roughness and is expressed by the following equation [4]: 0:3086 fT 5 h  1:11 i2 log ε=3:7D

(4.37)

4.4.1.1 Measuring precipitation Common precipitation measuring devices represent tiny drainage areas for which the water balance is drawn. The most used instruments are the pluviometer and pluviograph. The pluviometer measures the precipitation collected during a certain period. This is done by a cylindrical gauging vessel, where every increase of the level equals a certain precipitation height (Fig. 4.31). On the contrary, the pluviograph records the retention. This can be done by a float gage inside the collecting basin or by permanent weighing of this basin [5].

4.4.1.2 System components A hydroelectric power station, depending on the scale, normally consists of a dam, an intake, a penstock, in some cases a headrace, plus the powerhouse and tailrace

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Figure 4.31 Scheme of a pluviometer.

(see Fig. 4.31). The flow is transmitted to the turbine through the intake structure, the headrace, and penstock. Subsequently, the flow goes through the draft tube into the tailrace [5].

4.4.1.2.1 Dam, weir, or barrage The dam must bank up the water to allow regulated water supply to the power plant from the impounded water source. This way, a stream natural head is centralized in one place. The reservoir with a certain water capacity is upstream of the barrage or dam. In periods of low water the dam and spillway must be able to handle flooding and hold the water level at the appropriate height [5]. Dams can take the form of fixed or movable weirs, whereas barrages always have movable gates. For instance, in the case of small hydroelectric power station, very low-head plants and run-of-the-river plants must use barrages or weirs with movables gates. As a result, the upstream water level stays stable and the turbine can be regulated; if the inflow to the impoundments reaches the turbine capacity discharge, the gates have to be partially opened to allow the excess of water entry directly into the riverbed without further increasing the headwater level [5]. For plants where the headwater level does not have to be kept constant, dams without movable parts are also applicable. In fact, using such a fixed dam designed hydraulically to transmit the design flood while retaining a certain upstream water level greatly reduces the investment cost [5]. Finally, Tyrolean weirs are used for steeper streams and high heads without impoundment to divert the water from the bottom. Such Tyrolean weirs consist of a grid or a perforated plate sunk into the riverbed bottom conveying the water to a channel below. There are ecological benefits if Tyrolean weirs do not stretch over the whole width of the stream and then part of the water stays in the river [5].

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4.4.1.2.2 Intake The intake structure is the connection between the headwater and penstock or turbine. Usually, at the entrance of the intake, there is a screen responsible for keeping floating debris out of the plant. In addition, there are stoplogs and a quick action stop valve. The stoplogs allow the hydroelectric plant to be drained during maintenance work. And in the event of an accident, the quick action stop valve stops the inflow [5]. In this first component of the system a partial conversion of energy takes place. From reference point 1 to reference point 2, the potential energy transforms into kinetic energy; hence the Bernoulli’s equation becomes:   U2 p1 p2 1 h1 5 1 h2 1 1 1 ξIS w;2 ρw g ρw g 2g

(4.38)

The water density (ρw) can be considered constant, up, and downstream of the intake structure. The energy losses (ξIS) at the intake are due to the flow resistance on the screen and are shown in Fig. 4.32 by the drop in the energy line at reference point 1. As a result, the losses in this structure manifest as a decrease in the pressure level [5].

Figure 4.32 Application of different turbine types. From: M. Kaltschmitt, W. Streicher, A. Wiese, Renewable Energy: Technology, Economics and Environment, Springer Science & Business Media, 2007.

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4.4.1.2.3 Penstock The penstock connects the intake structure to the turbine (reference point 2 to reference point 3 in Fig. 4.31). Here, the potential energy is converted into pressure energy. There is also a loss of energy (represented by the coefficient ξPS in Eq. 4.39), but in this case, it is related to the friction in pipes which, in turn, depends on the pipe diameter, the flow velocity, the surface roughness of the penstock, and it increases proportionally to the length of the conduit. Again, the Bernoulli equation can be rewritten as follows [5]:   U2 U2 p2 p3 1 h2 1 w;2 5 1 h3 1 1 1 ξPS w;3 ρw g 2g ρw g 2g

(4.39)

Increasing the penstock diameter will reduce friction losses and increase the power of the turbine. It is important to note that low-head power stations do not have a penstock, so water flows directly from the intake into the turbine [5].

4.4.1.2.4 Turbines This component is responsible for converting pressure energy into mechanical energy (reference point 3 to reference 4 in Fig. 4.31). The conversion losses are described by the turbine efficiency (ηturbine) which is defined as the power at the turbine shaft and the available hydraulic power between the turbine intake and the draft tube outlet, including turbine outlet losses. The following equation describes the portion of usable waterpower that can be converted to mechanical energy at the turbine shaft Pturbine [5]: Pturbine 5 ηturbine ρw gq_w hutil

(4.40)

where hutil is the usable head at the turbine and the turbine losses are differentiated as volumetric losses, losses due to turbulence, and friction losses. Two types of turbines are used in this kind of power plant, these are reaction and impulse turbines, owing to the specific head and flow speeds (see Fig. 4.32) and the resulting changing water pressures and speed conditions [5]. G

G

Reaction turbines: these turbines convert the potential energy of water mainly into pressure energy that is transferred to the turbine blades, where it is converted into rotation [5]. Some examples of reaction turbines are Francis, propeller, Kaplan, and Straflo (abbreviation of “straight flow”) turbines (see Fig. 4.33). These turbines can manage high flow rates at low heads [4]. Impulse turbines: in impulse turbines the potential and the pressure energy of water is converted completely into kinetic energy. This energy is then transferred to the turbine which converts it into mechanical energy. The pressure before and after the turbine is the same; it is roughly the same as the atmospheric pressure [5]. Impulse turbines are Pelton turbine and crossflow turbines (see Fig. 4.34).

The inlet velocity in this type of turbines depends on the elevation difference available: the larger the difference, the higher the velocity. Consequently, impulse turbines are designed for dams with high available heads [4].

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Figure 4.33 Diagrams of reaction turbines: (A) Francis, (B) propeller, and (C) Kaplan.

Figure 4.34 Diagrams of impulse turbines: (A) Pelton and (B) crossflow.

Depending on the turbine type and size, efficiencies between approximately 85% and 93% are achieved at the design discharge and head. Since the turbine is designed for a specific discharge, the efficiency is dependent on the respective availability of water. The different performance curves for the main turbine types are shown in Fig. 4.35. According to Fig. 4.35, Pelton turbines have a reasonable performance even if they operate at only 20% of their maximum discharge, while the propeller turbine should not be operated at less than 60% of their design flow because its performance will be less than 50% [5]. As it may be observed in Fig. 4.36, the parameter used to classify the turbine is the ratio of flow to design flow or the “specific speed.” The usual practice of this parameter is to define the specific speed of the turbine in terms of the power and the available head as shown in the following equation [4]: NSP 5

ω

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Pturbine =ρw ðghutil Þ5=4

at maximum efficiency

(4.41)

where ω is the turbine speed. The specific speed, when expressed at the maximum efficiency operating point, determines the global device geometries for optimal operating

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Figure 4.35 Efficiency curve of different turbines types. From: M. Kaltschmitt, W. Streicher, A. Wiese, Renewable Energy: Technology, Economics and Environment, Springer Science & Business Media, 2007.

Figure 4.36 Typical diversion-type power plant. From: W. Zhao, W. Guo, L. Zhao, Q. Li, X. Cao, X. Tang, Influence of different types of small hydropower stations on macroinvertebrate communities in the Changjiang River Basin, China, Water 11 (9) (2019) 1892 [23].

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conditions. Pelton turbine, for example, is used for low values, Francis turbine for moderate specific speeds, and Kaplan turbine for high specific speeds [4].

4.4.1.2.5 Outlet Usually, the reaction turbines use a draft tube to improve the use of head. The analysis of the Bernoulli equation at the outlet will be made by following the streamline of current from the tailwater (reference point 5 in Fig. 4.31) to the turbine outlet (reference point 4 in Fig. 4.31). In this case the remaining kinetic energy in the water is loss through turbulence. This appears in Fig. 4.31 by the drop of the energy line at reference point 5. As shown in Fig. 4.31 the turbine outflow and the draft tube outlet are at the same height; therefore the Bernoulli equation can be simplified and written as in the following equation: U2 U2 p4 p5 1 w;4 5 1 w;5 ρw g 2g ρw g 2g

(4.42)

From Eq. (4.42), we may conclude that since the cross section of the flow at the end of the draft tube is bigger than that directly behind the turbine, Uw,5 has to be less than Uw,4. The draft tube causes the flow velocity to be reduced before entering the tailwater. Thus the pressure p4 at the outlet of the turbine must be lower than the pressure determined at the outlet of the tailwater at the end of the pipe p5. Eventually, this leads to a decrease in losses due to turbulence and thus a better head utilization [5].

4.4.1.2.6 Overall system The losses in a hydroelectric plant are mainly located at the intake structure, the penstock, and the outlet. In the light of all the previous analyses the actual usable waterpower (Pw,act) is calculated by removing the various losses from the theoretical waterpower (i.e., losses in all the components); this can be described with the following equation [5]: " Pw;act 5 ρw gq_w

2 2 2 Uw;2 Uw;3 Uw;5 2 ξ PS 2 ðh1 2 h5 Þ 2 ξIS 2g 2g 2g

# (4.43)

As a result, losses are dependent on the flow velocity and can consequently be minimized with an optimized plant design and layout. The power that can eventually be produced at the turbine shaft is a function of the real waterpower available and the performance of turbines [5].

4.4.1.2.7 Regulation The regulation, in this type of hydroelectric power generation, depends on the mode of operation, that is, isolated operation always requires frequency regulation. A controlling device maintains the grid frequency at varying loads. Nonetheless, it is not possible to control the flow or the headwater at the same time in this type of

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operation. However, water level or flow regulations are common for grid-connected operation. The turbine opening (guide vane or runner blades) is opened enough to maintain the desired headwater level or to maintain the desired flow through the turbines. The turbines and generators always rotate at constant rotational speed, in line with the grid frequency [5]. The hydropower plants using modernized controls and communication systems can modulate output and provide passive storage and system balancing functions to better integrate variable generation (solar PV or wind power) [3].

4.4.1.3 Classification of hydroelectric plants Hydroelectric power plants, including dams or reservoirs, can be classified into low, medium, and high head. The distinctions between the different types are not clear; in practice, there are several combinations and mixed types. Significant aspects of the different types are briefly discussed in the following subsections.

4.4.1.3.1 Low-head plants Low-head plants are characterized by a generally large flow and relatively low heads up to approximately 20 m. In most situations, low-head plants harness the flow of a river without storage. Hence, depending on the configuration of the energy project, two types may be distinguished: diversion-type and run-of-the-river power plant [5]. G

G

Diversion-type plant: in this case the water is drained from the river at a dam, then is redirected through the headrace or a pipeline to the power station, and transferred back to the riverbed at the end of the tailrace (see Fig. 4.36). The so-called instream flow remains in the original riverbed. At the time of its construction, it is important to consider both ecological and economic aspects [5]. Run-of-the-river plant: in this case the water is redirected from the river but without the use of a dam, that is, the intake is directly in the river. The water is then transported through a canal or a pipeline to the powerhouse. Finally, the turbinated water is returned to the riverbed (see Fig. 4.37). The generation of energy in this type of hydropower plant depends on the seasonal flow of the year (i.e., precipitation rate). Its construction can be simpler and shorter than those including dams [24].

4.4.1.3.2 Medium-head plants The medium-head plants consist of a dam and a powerhouse at their base. As a result, these plants use the dam’s head, which can vary from 20 to about 100 m in height. Appropriate reservoir management gets the average discharges used by the turbines [5] (Fig. 4.38).

4.4.1.3.3 High-head plants High-head plants have a head of between 100 and 2000 m (max.). They can be found in low and high mountain ranges and are usually designed with a reservoir to store the water. In contrast with low-head plants the flow rates are relatively low; therefore in this configuration the power is a result of high heads. Given that the available water mostly comes from very limited drainage areas, often the effort

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Figure 4.37 Typical run-of-the-river power plant. From: A. Mejbel Ali, S. Saadoon Algburi, M. Abdelmajed Aljaradin, Design optimization of a hybrid hydro-wind micropower system for rural communities, J. Eng. Sustain. Dev. 22 (02 Part 5) (2019). Retrieved from ,http://jeasd.uomustansiriyah.edu.iq/index.php/jeasd/article/view/577. [25].

Figure 4.38 Typical medium-head power plant.

needed to collect water in the reservoirs is important. To fill the dam, usually, small streams are redirected from parallel valleys into the valley with the reservoir [5]. Fig. 4.39 shows the typical configuration of a high-head power plant. It can be seen that the water from the reservoir is conveyed through tunnels or low-pressure pipes

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Figure 4.39 Typical high-head power plant.

using a so-called surge tank that allows a reduction of the water hammer effect and pressure fluctuations that occur when starting and shutting the plant down; from there it flows to the turbine through penstocks or high-pressure tunnels. The powerhouse is often far away from the reservoir in the lower valley of the main river [5]. For some areas the reservoirs are also filled with the water from snowmelt in spring and summer to produce electricity in the following winter to cover the peak demand. The higher the available head, the smaller the reservoir which still provides the same amount of energy [5].

4.4.1.4 Operation behavior In the operation behavior of the turbines, two cases are important to keep in mind [5]: G

G

Power generation decreases with decreasing flow: the corresponding head increases as the tailwater reduces due to the smaller flow. The plant must be shut off at a certain point if the discharge is very low, as turbines cannot operate with flows below a certain minimum. Under these conditions, no power can be produced if the turbines are properly dimensioned; this only happens a few days of the year. Power generation decreases with increasing discharge: the turbine cannot process discharges that exceed the design capacity. The power output is reduced because with an increase in the discharge, the difference between the head and tailwater level decreases. The additional flow must be separated and cannot be used to produce energy. Power production is no longer possible in the worst case, because the height difference between the head and tailwater is too small. This is typically the case when the dam or barrage gates are opened (i.e., during flooding).

4.4.2 Hydrokinetic energy Water currents such as oceans and rivers are a vast and almost unexploited source for renewable energy generation. Over 300 hydrokinetic projects are currently in

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progress worldwide, divided into four types of technologies: ocean wave, tidal stream, ocean current, and river hydrokinetic. In relation to applications in marine environments, these technologies have been developed for large scale, while the river applications are for the small scale [26]. Hydrokinetic energy that can be used from rivers has a strong dependence on volumetric flow or “runoff” from a specific area, which is strongly determined by precipitation. As a result, the water flow is drained into streams and rivers. The runoff is concentrated here and therefore the flowing water bodies are potential sites for the use of hydropower for the supply of energy [5]. High precipitation level implies a relatively high density of flowing water bodies. Furthermore, the mountainous nature of the site will increase the potential for the use of the energy stored within rivers and lakes [5]. Fig. 4.40 shows the behavior of the flow in the Elkhorn River in Nebraska (the United States); as it can be seen, the daily mean flows can sometimes vary enormously. These changes are related to the season, for example, spring, when snow melts and heavy rainfalls

Figure 4.40 Measured daily mean runoff for the Elkhorn river for 1 specific year. From: J. Szilagyi, F.E. Harvey, J.F. Ayers, Regional estimation of base recharge to ground water using water balance and a base-flow index, Ground Water 41 (4) (2003) 504
513. Retrieved from ,https://ngwa.onlinelibrary.wiley.com/doi/abs/10.1111/j.1745-6584.2003.tb02384.x?casa_token 5 l-npcQn43owAAAAA%3AT2-QlAv22SjTdPCz-aTkB5PcKAwD6T_ixwclvNNOa4ztq8YngW1FM FVY9kM6PWkGXqgK0OzrOGg7LQ. [28].

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occur [5], or in the case of countries located in the central and eastern equatorial Pacific, to the ENSO (El Nin˜o Southern Oscillation) which is characterized by two opposite phases: “El Nin˜o” and “La Nin˜a.” “El Nin˜o” is characterized by a reduction in rainfall over Indonesia while rainfall over the tropical Pacific Ocean increases. “La Nin˜a” is characterized by an increase in rainfall over Indonesia and a decrease in rainfall over the tropical Pacific Ocean [27]. Hydrokinetic energy is very attractive due to its predictability, high energy density (about 1000 times more than air currents), it does not require reservoirs or dams, and its low environmental impacts (without emissions or noise, only visual impacts) [29]. Then, fully submerged turbines can harness the kinetic energy using the principles of the lift and drag aerodynamic theory, same as in the case of wind rotors. Therefore, because the generated power is proportional to the density of the fluid (see Eq. 4.44), hydrokinetic turbines have a greater capacity for generating energy compared with wind turbines [5,26]. PW 5

1 ρ Arot Uw3 2 w

(4.44)

where ρw is the water density, Arot the rotor cross section, and Uw the water speed. Additional advantages are the following [26]: relatively low initial construction costs (as opposed to conventional hydropower technologies), fast implementation, uninterrupted power generation if the current flows continuously (avoiding energy storage system), and modular design allowing hydrokinetic energy to be easily scalable. Those benefits are very desirable in isolated communities for supporting public services. As in wind turbine, the performance of a hydrokinetic turbine is also defined by the nondimensional parameter known as tip speed ratio (TSR or λ) which is defined as the tangential blade tip velocity over incident fluid velocity (see Eq. 4.45). TSR 5

RΩ Uwa

(4.45)

where R is rotor radius and Ω is the angular speed.

4.4.2.1 Runoff measuring Measuring the flow of running waters can be made by three different methods: measuring the stream velocity, measuring the water level, and measuring tracer concentration [5]. G

G

Measuring stream velocity: the flow rate is defined as the integral of the stream velocity across the cross section of the stream. If the cross section is known, a horizontal arranged propeller driven by the current measures the flow speed. Measured values are used to calculate the flow. Besides, the propeller, magnetic induction gauges, or acoustic Doppler can be used for the same purpose [5]. Measuring water level: if the relation between the water level and the flow (e.g., from measurements, hydraulic calculations, or experimental data) is available, measuring the

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153

water level with slat, floating, or other level gauges is enough. This method of calculating the water level is ideally suited for continuous monitoring of level data and flows for several years because of its simplicity [5]. Measuring tracer concentration: in this method a tracer (e.g., salt or dye) is added to the river upstream. Downstream, the concentration of the added substances is measured. Assuming that the concentration of the tracer remains almost constant, that is, there is no tracer reaction with the water, and the flow is stationary, the flow can be calculated on the basis of a tracer balance [5].

4.4.2.2 Hydrokinetic energy exploitation systems Hydrokinetic energy conversion systems provide the facility for harvesting water flow energy without the need for large dams, impoundments, channels, or stream divergence as in traditional hydroelectric centrals. Hydrokinetic systems are designed for use in waterways, either naturally occurring (rivers, estuaries, marine currents) or artificially built [26]. The transformation of the kinetic energy present in a fluid stream into usable electrical energy is carried out in two steps. The first step consists of capturing the kinetic energy of the current using a turbine which converts it into mechanical energy at rotor shaft; in the second step the mechanical energy is transformed into electrical energy by the generator. On the other hand, since hydrokinetic turbines are aimed to micro- or pico generation, which means that not many resources have been invested to optimize them, their development is still not mature enough; for such reason, their design has been inspired by the analogy with wind turbines. Nonetheless, some empirical work has been conducted in this type of turbines dealing with portable devices, anchoring systems, debris protection, maintenance, and easy and quick deployment infrastructures [26,29]. There are two types of turbines that are usually used in hydrokinetic energy applications: axial turbines and crossflow turbines. In the first type the rotor shaft is parallel to the fluid stream, while in the second case, the rotor axis is perpendicular to the water current and generally appears as cylindrical rotating structures [26,30]. The last ones are also classified into vertical and horizontal axis. The horizontal axis is the most worldwide used due to its higher efficiency and low starting torque; however, they are more complex to build [26].

4.4.2.2.1 River current Along the history, rivers have played an important role in the development of all civilizations. With almost no exception, all the world’s major cities have a river in their proximity. Rivers were not only a source of freshwater but also provided food and transportation. On the other hand, in developing countries, much of the population without access to energy inhabit areas crossed by rivers; thus an effective and economical mechanism for extracting energy from these water currents will become a breakthrough in the rural energy generation scenario and represent a significant improvement in the quality of life of such population [26]. In that order of ideas, an initiative to implement hydrokinetic energy ended in 1978 in the so-called Garman Turbine aimed at pumping water and irrigation. Its initial design consisted of a fully submerged vertical axis turbine suspended from a

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Figure 4.41 Parts of an inclined axis hydrokinetic turbine. From: S. Laı´n, L.T. Contreras, O. Lo´pez, A review on computational fluid dynamics modeling and simulation of horizontal axis hydrokinetic turbines, J. Braz. Soc. Mech. Sci. Eng. 41 (9) (2019) 375. https://doi.org/10.1007/s40430-019-1877-6.

floating barge anchored to a post on the river bank; however, the design soon evolved to use an inclined horizontal axis turbine mounted on a similar floating and mooring system as shown in Fig. 4.41 [26]. River current turbines are defined as “low-pressure run-of-the-river ultra-lowhead turbine that will operate at the equivalent of less than 0.2 m of head” [30]. The resulting generated power in such energy conversion is expressed as [26]: PT 5

1 ρ Arot Uw3 ηCP 2 w

(4.46)

where CP is the mechanical power coefficient and η the global efficiency of the transmission and generator systems. River turbines have the potential of producing higher energy compared to a wind turbine of the same size, which can be described as follows: standard design wind speeds of wind turbines are within the range of 11
13 m/s, while river turbines may be designed for rated water speeds within the range 1.75
2.25 m/s or may be larger [26,29,30]. Another advantage is that as the river is unidirectional, there is no need for rotor yawing [29,30]. River size restricts the use of hydrokinetic systems, particularly if a large-scale system is intended to be introduced. Some of the characteristics that need to be considered are the complex river structure, shallow water level, and irregular stream beds. Therefore, from a practical point of view, it is recommended that this type of systems are implemented in man-made channels such as those devoted to irrigation or in those behind dam turbine way out [26,30].

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The cross-sectional area, depth, velocity, and other related characteristics should be known to predict the potential power in a river. The energy of the river is generally analyzed in the catchment based on feasibility analysis for the dam construction. There are only a small number of studies on the energy capacity of river regionally and globally, and the global river repositories are insufficient to generate a worldwide river energy resource assessment for hydrokinetic applications [29].

4.4.2.2.2 Marine current This method consists of exploiting the high- and low-tide stream, and therefore the water motion caused by low and high tides. In such currents, there are only small differences in pressure as a result of relatively slow current speeds and large current cross sections. Therefore turbines suitable for low- and high-tide streams (e.g., Savonius, Darrieus) must be implemented [5]. According to Eq. (4.44), it is sensible to exploit the low- and high-tide stream if sufficiently high current speeds are available [5]. In fact, thanks to the higher density of salty water, marine currents carry more energy density than river currents which explains why there are substantially more technologies proposed for marine than for river applications [26,29,30].

4.4.3 Wave energy Wave energy is a source of energy, caused primarily by wind energy, which is an indirect result of intermittent solar radiation, and the motion of the Earth [4]. Such waves contain both potential and kinetic energy [5]. The power calculation from waves is still not explained sufficiently due to the complexity and stochastic progression of waves, and several types of wave turbines have been designed based on different working principles [29]. One approximation is considered the usual description of a traveling wave [4]:  y 5 a sin

2π 2π x2 t λ T

 (4.47)

where a is the amplitude or crest, λ is the wavelength which is the distance of a complete cycle in the x-direction, and τ is the period; y is the height above mean sea level, t is the time, and x is the horizontal coordinate. Since the wave is periodic, when t 5 τ the wave shape is identical to the shape at t 5 0 [4]. The previous nomenclature is presented in Fig. 4.42. Although the wave motion is in the x-direction, with a speed of c 5 λ/τ, the motion of the water is not the same as the motion of the wave. The water rotates in place with an elliptical path in the plane of the wave propagation [4]. As said earlier, the total wave energy is the sum of potential and kinetic energies. The potential energy density per unit area is estimated with Eq. (4.48) and the kinetic energy density per unit area is estimated using Eq. (4.49). Hence, the total

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Figure 4.42 Wave nomenclature.

energy is expressed as shown in Eq. (4.50) and the power density is the energy density times the frequency (f) as shown in Eq. (4.51) [4]. PE 1 5 ρw a2 g A 4

(4.48)

kE 1 5 ρ w a2 g A 4

(4.49)

TE 1 5 ρw a2 g A 2

(4.50)

PD 1 5 ρw a2 gf ; A 2

f5

1 T

(4.51)

Another approximation is explained by Yuce and Muratoglu [29]. In this study the power in a typical wave source is presented rather than the extraction principle of energy from each wave converter. The wave power density (W/m2), which is the energy per wave period, is given by the following equation: Pdensity 5

pga2 2T

(4.52)

Power per meters of wave front and crest length are: Pwave front 5 Pcrest length 5

1 ρ g2 a2 T 8π w 1 ρ g2 H 2 T 32π w

(4.53)

(4.54)

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Approximate power per unit of wave front (kW/m) for irregular waves is: Pwave front D 0:42 HS TP

(4.55)

where T is the period (s), H the wave height (m), HS the significant height (m), and TP the peak wave period. Wave energy generation systems can also be used for coastal defense as wave energy systems turn the energy from the ocean into electricity and thus extract this energy from the ocean. In these conditions the wave energy is not only reflected or dissipated. Therefore a good combination of power generation and coastline protection may also improve the economic attractiveness of wave energy exploitation [5]. The global theoretical gross power resources are projected to be up to 80,000 TW h/year. According to recent analyses, it is expected to be theoretically (i.e., technically) recoverable up to 750 TW h/year. The World Energy Council suggests that, if the wave energy systems are significantly improved, the technical recoverable wave energy will be increased to 2000 TW h/yea. [29].

4.4.3.1 Wave energy exploitation systems To convert the dispersed movement of waves into mechanical power, several hydraulic and pneumatic power conversion systems have been developed. Those devices have reciprocating and rotating parts to use hydrodynamic lift force created by the flow over a hydrofoil or lifting structure producing high torque and lowspeed output. Their size, orientation, and distance from the shore are variables, and they can be installed at the bottom of the ocean, shore mounted, or floating. Each wave energy conversion device extracts a certain amount of power from the wave resource in accordance with its efficiency [29]. For example, the principle of converting the motion of sea waves into mechanically useful motion is not difficult. By “inverting” a piston engine’s concept the motion of a body floating on waves (replacing the piston) will cause a shaft to rotate by means of a rod drive, while the rotating shaft in turn drives a generator [5]. Some specifications are required to improve the technical reliability of supplying electricity at a reasonable cost using wave energy [5]: 1. Hydraulic optimization is necessary to achieve high electrical efficiencies. For instance, if only the upward and downward wave motions are exploited, 50% of the energy contained inside the wave is wasted. 2. The power plant needs to be designed to withstand the “wave of the century.” For example, if the plant is designed to harness waves of 1 m, it must also withstand waves of 10 times that height, which means 10 times more energy contained in the wave. However, this precaution results in additional design costs. 3. The power plant must be designed very reliably even under unfavorable operation conditions.

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4.4.3.1.1 Tapered channel wave energy conversion device system The TAPCHAN (tapered channel wave energy conversion device) system consists of the transport of seawater over the beach by breakers or swells into a raised reservoir via a converging inclined channel [4]. This tapered channel concentrates waves of different frequencies coming from different directions and simultaneously converts the kinetic wave energy into potential energy. As a result, the water level rises, and the seawater eventually spills over the narrow end of the channel into the reservoir whose water level is located several meters above the average sea level. From this storage reservoir the seawater, accumulated at a higher energetic level due to the difference in height, can flow back to the sea via a turbine (see Fig. 4.43) [5]. One of the drawbacks of this system is that only a small amount of the initial wave energy (i.e., from deep water) can be used due to shallow water impact. Nonetheless, the advantages are summarized as follows [5]: G

G

G

G

The operation of the system is easier than most of the other wave energy systems, due to the leveled drainage of the storage reservoir and the applied low-pressure turbine. Longer technical lifetime as the system components are not exposed to open sea conditions. In addition, as its location is on the shore, maintenance can be carried out easily. The plant is built using reinforced concrete elements; thus the plant can withstand bad weather conditions (wave of the century). It is also ideal for fish farm operations as fresh seawater is continuously conducted into the storage reservoir.

Figure 4.43 Operating principle of a TAPCHAN system. TAPCHAN, Tapered channel wave energy conversion device.

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4.4.3.1.2 Oscillating water column system OWC system also called “oscillating water column” is considered the first wavepowered generator. Already applied in 1910, it consisted of exploiting the air compressed inside a cave on a rocky shore. Current technologies are aimed at using the wave motion in artificially build chambers [4,5]. OWCs are usually mounted at the bottom of the sea, near a steep coast. The energy of moving water waves penetrates a wide chamber through an opening below the average seawater level, transmitting the wave frequency to the surrounding water column. As the water level is moving up and down the air volume is “breathed” in and out above the water surface inside the OWC plant. Subsequently, by means of a turbine (e.g., wells turbine), the kinetic energy of the air which is pulled in or blown out is partially converted into electric power (see Fig. 4.44). Optimum power generation is achieved if the frequency of the oscillation system consisting of inflow, water column, air quantity, turbine, and outflow corresponds to the frequency of the advancing waves [5]. The turbines and generators used in OWC systems can operate in two ways: one direction and alternating directions. Nearly all systems were based on the one direction method which consists of converting the cyclical oscillating flow into cyclical pulsating flow. However, today this method has been replaced by the alternating directions. The latter consists of a turbine designed to operate in one direction even when the direction of the airflow changes. As a result, low-efficiency crossflow turbines (e.g., Wells turbines, see Fig. 4.45) are used. Plants with Wells turbines installed use valves that must be opened and closed during a period of about 10-s undersea conditions.

Figure 4.44 Schematic representation of an OWC power plant. OWC, Oscillating water column.

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Figure 4.45 Schematic of the Wells turbine. From: E. Lakzian, Improved design of wells turbine for wave energy conversion using entropy generation, Meccanica 51 (2015). ,https://www.researchgate.net/publication/ 284435214_Improved_Design_of_Wells_Turbine_for_Wave_Energy_Conversion_Using_ Entropy_Generation/figures?lo 5 1:Researchgate. [31].

Figure 4.46 Schematic of Pelamis Wave Power.

4.4.3.1.3 Pelamis Wave Power The Pelamis Wave Energy Converter is an articulated semisubmerged device consisting of cylindrical parts connected by hinged joints (see Fig. 4.46). The motion is generated by the wave powers rams pumping high-pressure fluid into hydraulic powering electrical generators. The power is fed to shore through a transmission line. Several devices can be connected together to form a wave energy farm [4].

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4.4.4 Tidal energy The tides are the periodic alteration of the water level in the ocean due to the rotation of the Earth, the gravitational forces of the Moon and the Sun [4,5]. The alteration in the open sea is characterized by height differences slightly above 1 m. But, in the mainland, backwaters are generated at the shoreline, so that maximum water level changes by 10 m or more due to resonance or funneling [5]. Tides are described based on their schedule and range. The range is the difference in elevation between high and low tide, and the schedule includes the incidence dates of the high and low tides. Fig. 4.47 shows the daily cycle, the monthly tidal cycles, the tidal range variations, and the relationship between the Moon’s phases with the spring and neap tides. The daily cycle is also known as “lunar day” and has 24 h, 50 min length. During this time the tides rise and fall twice (12-h, 25-min cycle), which means that high and low tides do not occur at the same time every day, but advance 25 min per cycle, that is, 50 min every lunar day [4]. With regard to the range fluctuations, it can be noted that the range is not constant and varies over the lunar month, with the highest range occurring at the time of new and full moons (so-called spring tide) and the lowest range occurring at the first and third quarter moons (so-called neap tide). The average ranges at a given location are complicated but predictable, which means that the control strategies to optimize tidal energy benefits are more involved than strategies for hydroelectric facilities [4].

4.4.4.1 Tidal energy exploitation systems There are two different methods for producing electricity in relation to tides: use of the potential energy of backwaters by a tidal power station or exploitation of tidal streams. Both methods will be briefly listed in the explications below [5].

Figure 4.47 Typical tidal ranges.

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4.4.4.1.1 Tidal power station The energy production from tides is in theory identical to hydroelectric power. A reservoir is filled at high tide and drained at low tide. As in many hydroelectric systems, a turbine can be used in both charging and discharging processes to produce electricity [4]. There are two different methodological approaches: single- and double-basin system. Single-basin system consists of a bay isolated from the open sea by a dam but remains connected to the sea via a floodgate and a turbine (see Fig. 4.48). The turbine and floodgate control ensure that water can enter the bay only through the floodgate and can only leave the bay through the turbine. The simplicity of the turbine design is an advantage. And the most evident disadvantage is that only shorttime periods will produce electricity [4,5]. To avoid this disadvantage, it is possible to use a crossflow turbine for power generation. The floodgate can be built to accelerate the inflow and outflow of water when the difference between the basin and the sea is very low (e.g., high and low tide). This design ensures power generation over much longer time periods but implies higher costs that can be overcompensated with the higher possible energy provision [5]. The double-basin system consists of two basins connected with turbines and to the sea through floodgates (see Fig. 4.49). The turbine is either built into a dam or into a connecting channel located between the two basins. Inflow and outflow of

Figure 4.48 Schematic representation of a single-basin system.

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Figure 4.49 Schematic representation of a double-basin system.

water are regulated as water enters one basin at high tide and exits at low tide through the other basin after passing via the corresponding turbines. Energy production is more frequent and can be performed without interruptions. This does, however, require more space and thus increase the investment costs [4,5]. The energy available on a tidal basin is governed by Eq. (4.56). If the surface area variation with elevation in the basin is small, then A is sensitively constant and the energy available is proportional to the surface area and the square of the tidal range (R). The energy indicated in Eq. (4.56) is based on a low-tide data; therefore the energy can only be obtained when discharged to that data [4]. 1 Eavailable 5 gρwa AR2 2

(4.56)

Considering the above, two protocols are associated with tidal discharge strategies. Protocol 1 consists of discharging a simple tidal basin rapidly, so the conditions of Eq. (4.56) are satisfied. Hence, the extracted energy would be the maximum. However, this results in large “spikes” of electrical power that occur at high and low tides. Protocol 2 consists of a modulated simple tidal basin that slowly discharges the water, then the low-tide data will increase, and the high-tide elevation and the available energy would decrease. Although the extracted energy is lower, protocol 2 requires smaller turbines and results in a more uniform power delivered to the grid or for local use [4]. Tidal systems are attractive in regions where the tidal range is high, and the coastal geometry permits the development of large-surface-area tidal pools [4]. Many tidal power

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plants operate worldwide. South Korea’s Sihwa Lake Tidal Power Plant has the highest capacity for producing electricity at 254 MW. With 240-MW generation capacity, the oldest and second-largest operating tidal power plant is in La Rance, France. The next largest tidal power plant is at Annapolis Royal in Nova Scotia, Canada with a capacity of 20 MW. China, Russia, and South Korea all have smaller tidal power plants [32].

4.4.4.1.2 Tidal stream The difference between tidal stream energy and marine current energy is that it originates from the Moon’s gravitational action at the ebb and flow of a tide. Due to its high reliability and predictability, it is a very attractive form of energy, similar to one from the rivers, in addition is not dependent on weather. It also has the potential to reduce visual as well as noise emissions and is considered to be one of the most promising energy resources [26]. Tidal stream conversion systems operate in the same way as river turbines, but their dimensions are normally greater, they are moored rigidly and take advantage of the tide periodic motion. This technology has been targeted by several research projects and commercial applications, which means that it is more mature than river turbines. Therefore experience gained from tidal energy systems is essential to improve river turbine technology [26].

4.5

Biomass power generation

Biomass has been in use since humans first started to burn wood to cook food and keep it dry. Biomass technology includes a wide variety of energy technologies using renewable plant material, or phytomass, derived from photosynthesis as a feedstock for the production of solid, liquid, and gaseous biofuels, or used directly as a heat and electricity-producing energy source. Examples of sources of biomass and its uses for energy: G

G

G

G

Wood and wood waste—burned to heat homes, manufactured process fuel in industry, and generated electricity; Farm crops and waste materials—burnt as fuel or converted to liquid biofuels; Garbage food, yard, and wood waste—branded to generate electricity in power plants or converted into biogas in landfills; Animal dust and human wastewater—converted to biogas, which can be combusted as fuel.

4.5.1 Biomass fundamentals 4.5.1.1 Biomass photosynthesis Biomass fuels are derived from green plants absorbing solar energy and so treating it as chemical energy by reducing atmospheric carbon dioxide in photosynthetic form (Fig. 4.50). Plant leaves are biological solar collectors, while stems, branches, and roots are the equivalent of batteries that store complex carbon compounds that are rich in energy. Elementary analysis indicates that wood and grass are about 50% carbon. The average photosynthetic efficiency of converting solar energy into

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Figure 4.50 In the process of photosynthesis, plants convert radiant energy from the Sun in the form of glucose or sugar into chemical energy: 6H2 O 1 6CO2 1 Radiantenergy ! C6 H12 O6 1 6O2 .

energy stored annually in organic carbon compounds ranges from less than 0.5% in temperate and tropical grasslands to about 1.5% in humid tropical forests [33]. Though seemingly quite low, the worldwide annual storage of photosynthetic energy in terrestrial biomass is enormous, representing around 10 times the world’s annual energy use [34]. This annual energy storage reflects the diversity and adaptability of terrestrial plants, from the Polar Regions to the tropics, in many different climate zones.

4.5.1.2 Biomass sources Forest biomass used for bioenergy production comes from many sources: G

G

G

G

G

trees of harvestable age but not suitable for woodlots; material from thinning stand; harvest residues; trees killed by fire, disease, and insect disturbances; and planting trees grown primarily to provide biomass for bioenergy conversion.

Industrial forest-process by-products provide another biomass outlet for bioenergy use. Hardwood residues (such as sawdust, bark, and chips) from harvesting and milling operations and pulp residues (such as lignin-rich “black liquors”) leftover from the pulping process are among those by-products. For biomass to be effectively directed towards energy production the biomass must be supplied at a competitive cost and its use for this purpose must have minimal impact on the environment. Under conventional management practices, corn stover, wheat straw, and other crop residues often have greater economic value because they are left on the land to restore nutrients, reduce erosion, and stabilize soil structure. Currently, the major biomass sources used are corn and sugarcane to produce bioethanol and rapeseed for biodiesel production. The fuel and energy yield varies with the type of material used for the plant. It is the highest in the biodiesel and

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bioethanol sectors for oil palm and sugarcane, respectively [35]. A wide range of cellulosic materials such as grassy crops, wood plants, forest, and agricultural byproducts (including wood residues, stems, and stalks) and urban waste are the socalled second-generation feedstock for which conversion technologies are still experimental. Brazil is the fastest rising country for bioethanol. As the principal feedstock, it uses sugarcane. Brazil is the only large-scale example of a mature biofuel industry that made bioethanol cost-effective for customers, meeting 40% of its fuel needs for vehicles [35,36]. The United States is the second-largest producer of bioethanol. Their ethanol is made of corn grain and provides just under 2% of the total fuel needs of automobiles [35,36]. These two countries represented about 71% of the bioethanol production worldwide in 2004 (calculated from Worldwatch Institute et al. [35] and Brown and Plan [36]). Regarding biodiesel production, Europe is the leader with Germany providing about 55% of the total biodiesel production in 2005, followed by France, which has a 15% share (calculated from Worldwatch Institute et al. [35]).

4.5.1.3 Potential energy crop production opportunities and challenges in the growing demand for biomass Raising the world’s biomass energy resources dramatically would entail the development of high-yield crops committed to bioenergy conversion. The most environment-friendly dedicated crop production systems will be perennial plant production, using genetically superior materials, built on previously cropped land, and maintained as crops. Perennials such as annually harvested grasses or short rotational trees harvested over a 3
10-year period reduce soil disturbance, increase soil carbon build-up, provide wildlife habitat, and generally require less chemical and water inputs for a given production level. For areas where genetically superior content is cultivated on land with plenty of exposure to sunlight and water, the energy crop yields will be highest. In the wet tropics and subtropics, as well as in irrigated desert regions, yields in the range of 20
30 t/ha/year are feasible with grasses and trees (e.g., eucalyptus and tropical grasses, and hybrid poplars in irrigated Pacific Northwest of the United States). Without irrigation, temperate woody crops would be expected to yield 9
13 t/ ha/year of harvestable biomass, through several experimental trials have demonstrated yields of improved genotypes of poplars in the range of 12
20 t/ha/year and best yields up to 27 t/ha/year [37]. Temperate shelterbelts and tropical agroforestry plantings could also make a big contribution to the energy resources of biomass. The primary energy resource potential of turning 10%
15% of cropland into energy crops was projected to range from a low of 18 to a high of 49 EJ, considering a range of possible yields. An additional 25
100 EJ are projected to be available from the conversion of grasslands and degraded areas to the worldwide output of biomass energy resources [37]. To date, most of the biomass used in forests to generate bioenergy and bioproducts has come from the by-products of manufacturing processes in the forest industry, with added value to what otherwise waste residues. But the growing

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interest in bioenergy and bioproducts has also led to an increased interest in expanding the availability of biomass for energy use. One idea that receives considerable attention is the removal of biomass from the forest on a large scale. However, such an approach is questionable from a forest management perspective. For one, increased removal of forest biomass (e.g., by more intensive removal of biomass from harvested sites) must be balanced against other forest resource uses—from timber harvesting and recreation to collecting nontimber forest products (such as maple sirup, wild berries, and medicinal plants) and providing wildlife habitat. It is also a concern how such accelerated removal could affect the longterm sustainability of forests? Forests provide large-scale ecological services that are essential to the long-term ecosystem health. Such programs include nutrient supplementation, soil safety, water and air purification, and carbon sequestration. Scientists are carrying out research to determine how much biomass can be safely removed from the forests by tree species and by habitat type while maintaining healthy ecological functions. The knowledge obtained from now ongoing studies should help forest managers better understand the drawbacks of biomass harvesting. Certain areas of ongoing research include developing methods to increase forest biomass more rapidly, harvesting it more effectively and improving transport, processing and storage systems.

4.5.1.4 Forest biomass energy plantations Plants explicitly designed to “grow biomass” for the production of bioenergy provide many advantages over natural forests: G

G

G

G

G

G

Typically, forest-growing trees take 40
100 years to mature. Those grown in use-specific plantations—that is, to generate suitable biomass for bioenergy conversion—generally grow in 3
15 years’ time. Having the ability to grow biomass in plantations close where bioenergy is to be used offers significant reductions in the supply cost. Surplus or marginal agricultural land that is not economically or biologically viable may be well suited for forest biomass crops, providing additional cash crops for landowners and farmers. Improvements to cultivation systems improve the biomass productivity of these “dedicated energy crops” plantations. Today, for example, improved fertilization, irrigation, and control of pests allow Canadian plantations to produce 10
15 oven-dry tons of biomass per hectare per year. Silver maple and several species of fast-growing poplars, willows, and alder were tested for suitability in that yields in Canada’s forest biomass plantations. Achievements include: A program in eastern Ontario that demonstrated the possibility of developing and growing hybrid poplars from genetically improved stocks to generate biomass sustainably. A Newfoundland project where European willows yielded 8
12 oven-dry tons per hectare per year. Spectacular growth of individual willow clones using intensive cultivation was also demonstrated on nursery plots. Trials at the Botanical Gardens in Montreal that demonstrated successful establishment and maintenance techniques to improve the growth and yield of several species of short rotation, such as poplar and willow. In addition, the use of dried municipal sludge as an environmentally safe source of fertilizer has been explored.

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Modified agricultural equipment and current European machinery have performed most successfully for the efficient harvesting of biomass from energy plantations.

4.5.2 Biomass characteristics 4.5.2.1 Composition of plant biomass The chemical composition of the biomass of plants varies between species. However, generally speaking, plants are made of roughly 25% lignin and 75% carbohydrates or sugars. The proportion of carbohydrates is made up of many sugar molecules bound together in long chains or polymers. There are two distinct categories: cellulose and hemicellulose. The lignin fraction is composed of nonsugar type molecules that serve as a glue that holds the cellulose fibers together. The typical values for the composition of straw, softwoods, and hardwoods are provided in Table 4.8.

4.5.2.2 The energy content of biomass Typically, a fuel’s calorific value is expressed as a high heating value (HHV) and/ or low heating value (LHV). The difference is caused by the heat associated with water evaporation in the chemical combustion process. The difference between the two heating values is dependent upon the fuel’s chemical composition. The HHV refers to the maximum possible energy released during full oxidation of a fuel unit. That requires the thermal energy recaptured from all combustion products by condensing and cooling. The LHV was developed in the late 1800s when it became clear that water vapor or sulfur oxide condensation in smokestacks contributed to corrosion and exhaust system degradation. Because flue gases of sulfur-rich coal were technically impossible to condense, the heat below 150 C was considered to be of no practical use and thus excluded from energy considerations. With regard to combustion—and the other thermo-chemical processes—the most significant property of biomass feedstocks is the moisture content, which determines the fuel’s energy content. Table 4.9 shows the evolution of the lower heating value of wood (LHV, in MJ/kg) as a function of the moisture content.

4.5.2.3 Physical characteristics At processing plants, physical properties are critical for the design and handling of biomass fuels and feedstocks. Compared with fossil fuels, woody biomass has a relatively low bulk density in raw state. Bituminous coal or crude oil, for example, Table 4.8 Typical qualities for straw, softwood, and hardwood compositions.

Straw stalks Softwood Hardwood

Cellulose (%)

Hemicellulose (%)

Lignin (%)

40 45 42

45 25 38

15 30 20

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Table 4.9 Potential ranges of humidity content for selected biomass resources. Biomass resource

Moisture content (wt.%)

Industrial fresh wood chips and sawdust Industrial dry wood chips and sawdust Fresh forest wood chips Chips from wood stored and air-dried Waste wood Dry straw

40
60 (wb) 10
20 (wb) 40
60 (wb) 30
40 (wb) 10
30 (wb) 15 (wb)

Table 4.10 Proximate analysis of solid fuels. Fuel

Ash content (%)

Moisture (%)

Volatiles (%)

Heating value (HHV GJ/Mg)a

Anthracite coal Bituminous coal Subbituminous Softwood

7.83 2.72 3.71 1.00

2.80 2.18 18.41 20.00

1.3 33.4 44.3 85.0

30.90 34.50 21.24 18.60

a

HHV, Higher heating value; GJ, Gigajoule; Mg, Megagram.

has a volume of 30 dm3/GJ, while solid wood has around 90 dm3/GJ. In chip form the volume increases to 250 dm3/GJ for hardwood species and 350 dm3/GJ for coniferous species. Straw has even less energy density, ranging from 450 dm3/GJ for large round bales to 1
2 m3/GJ for chopped straw, similar to bagasse. Reducing the size of biomass resources is often more difficult than with minerals because the materials are naturally strong fibers, and it is correspondingly difficult to produce uniform particle size feedstocks. Due to the high chemical reactivity of the biomass materials, the size reduction problem is often mitigated in combustion and gasification. However, if it is required to penetrate chemicals into the biomass structure or to establish access for biological agents, as in biomass acid and enzymatic hydrolysis, a higher degree of size reduction will be necessary. The energy requirements for size reduction are proportional to the surface exposure and increase in inverse proportion to the average length cube, so while small amounts of energy are needed at particle sizes of several millimeters, the energy penalty is significant at the size of 100 μm.

4.5.2.4 Chemical characteristics 4.5.2.4.1 Proximate analysis The proximate research benefit is that it determines the fuel value of the biomass material as received, offers and estimates the criteria for ash handling and water removal, and explains some of the characteristics in burning (Table 4.10). Highly volatile fuels, such as biomass, typically need advanced combustor designs to cope with rapid gas evolution. Coals with very high fixed carbon need to be burned on a grate because if they are not pulverized to a very small size, they can take a long time to burn out.

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Biomass energy content is always recorded for dry material; however, most woody crops are harvested in a green state with up to 50% of their mass as water. Two specific conventions for recording the heating value are in use. HHV refers to the energy released in combustion when condensing the water vapor resulting from the combustion, causing latent heat evaporation as a result. LHV records the energy released if the water vapor stays in a gaseous state. HHV and LHV are similar to pure carbon, which creates only carbon dioxide when burned. Of hydrogen, for each molecule of hydrogen, one molecule of water is formed and the HHV is 18.3% higher than the LHV. The HHV is 11.1% higher than the LHV for the combustion of one methane molecule (CH4).

4.5.2.4.2 Polymeric composition Technologies for the conversion of biomass that rely on the action of microorganisms are very sensitive to the ultrastructure and polymeric composition of the feedstocks. A complex series of biochemical events occurs in ethanol production and anaerobic digestion in which the polymers are broken down into smaller units and hydrolyzed to create simple sugars, alcohols, and acids that can be converted into fuel molecules. Wood, grasses, and straws are collectively referred to as lignocellulosic and consist mainly of lignin, cellulose, and hemicellulose fibers. Cellulose, hemicellulose, and lignin are various polymers of carbon
hydrogen
oxygen with different energy content and chemical reactivity. Usually, lignocellulosic contains cellulose (40%
50%) and hemicellulose (25%
30%) together with lignin (20%
30%), some extractive and inorganic materials. Cellulose is depolymerized and hydrolyzed to glucose, a six-carbon sugar (C-6), while hemicellulose is a complex mix of precursors consisting mainly of five-carbon sugar (C-5) with xylose, a major product. Lignin is a complex polymer, based on monomeric units of phenyl propane. C9 monomer is usually replaced by methoxy groups on the aromatic ring, occurring predominantly in forms of syringil (dimethoxy) and guaiacyl (monomethoxy) for which the ratio is a signature of grasses, hardwoods, and softwoods. Other major components of plants that contain carbon, hydrogen, and oxygen polymers which are also used for energy include: G

G

G

starches which make up the bulk of cereal grains, as well as tuber starch such as manioc and potatoes; lipids formed by oil-bearing plants such as soybeans, rape, or palms, which may be diesel fuel substitutes when esterified with simple alcohols such as methanol and ethanol; and simple sugars produced by sugar beet in tropical temperature climates, or sugarcane, which can be fermented directly into alcohols.

4.5.2.5 Heat value One of the most important characteristics of a fuel is the heat value, or the amount of heat available in a fuel (kJ/kg). For a given type of fuel the heat value is mostly a function of the chemical composition of the fuel. As mentioned earlier, the heat value can be expressed in one of two ways: the higher or lower heating value. The HHV is the total amount of heat energy in the fuel, including the energy in the exhaust gases found

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Figure 4.51 Heat quality of different combustibles (dry oven). 1 MJ, if your pot is 100% full, is enough energy to brew about 10 cups of coffee. It is equal to 1000 BTU, too.

in the water vapor. The LHV does not include the energy incorporated in the water vapor. The HHV is usually the correct value for biomass combustion, although some manufacturers may use the LHV instead, which may lead to confusion. Some biomass materials tend to get more energy per unit of weight than others. The difference between species, however, is often no greater than the naturally occurring differences within one species. The heat content of a fuel type can vary significantly depending on the climate and soil in which the fuel is grown, as well as other conditions. As a result, the energy content of a biomass fuel should be thought of as a range rather than a fixed value. Fig. 4.51 shows the spectrum typical of some rising fuels. The most notable pattern in these data is that wood (which has lower ash content) appears to have a heat value slightly higher than field crops. Several characteristics affect biomass fuel output including heat value, moisture level, chemical composition, and fuel size and density. Some features can be noticeably different from fuel to fuel. Furthermore, there may be major natural variations of a given type of fuel. Combustion equipment for managing this variety of properties can and should be planned.

4.5.3 Biomass conversion into useful energy 4.5.3.1 Prime mover systems and fuels Power generation takes place in prime movers, a technical term to describe the generation of electricity by engines of all kinds connected to alternators: (1) steam engine, (2) steam turbine engine, (3) internal combustion engine, (4) gas turbine engine, and (5) Stirling engine. Each of these involves the processing of the raw biomass to a certain degree and then its use in the prime mover. Fuel cells will eventually replace the prime mover and alternator requirement by generating electricity directly from hydrogen fuels derived from biomass. The steam cycle uses combustor and boiler combinations to

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produce steam, requiring a reduction in the size of the fuel and the elimination of physical pollutants. The high-pressure vapor is then expanded by a small-scale steam engine or by a larger scale turbine. The performance of conversion technologies for combustion steam boiler systems depends on a very large scale—a small steam engine or turbine would not exceed 10%. Over a transport radius of 10
80 km, however, usually enough biomass is available to run a device within the range of 10
50 MW of electricity production. At that scale, current Rankine cycles operate within the range of 25%
30% efficiency and therefore require around 0.75
1 t of dry biomass to generate 1 MW h of electricity [38]. The thermal output of industrial and power generation boilers ranges in size from 100 to 300 MW. The major boiler types are pile burners, grate boilers, suspension-fired boilers, fluidized beds, and fluid beds that circulate. Biomass energy, when converted into renewable liquid or gaseous fuels, can be used in internal combustion engines and gas turbine systems. Installed capacity for generating waste power in the United States currently approaches 1 GW [39]. Biogas’s energy content usually is 20
25 MJ/N m3, or about 50%
60% of natural gas. Fuel gases can also be generated by thermal gasification; the gasifying agent is usually air when conducting this at small scales. Biomass-fired plants have the biggest problems in storing and preprocessing the coal. This is true of small grate-fired plants as well as massive suspension-fired plants. Drying the biomass before it is combusted or gasified increases the overall efficiency of the plant, but in many situations, it may not be economically viable. Exhaust systems are used to vent toxic by-products of combustion. Controls on pollution could include a cyclone or multicyclone, a baghouse, or an electrostatic precipitator. The primary function of this equipment is particulate matter management and is listed to estimate the cost and productivity of energy. Precollectors, cyclones, and multicyclones can be used to extract larger particles upstream of a baghouse (fabric philter) or electrostatic precipitator. Furthermore, pollution controls might be needed for unburned hydrocarbons, nitrogen oxides, and sulfur, depending on fuel properties and local, state, and federal regulations.

4.5.3.2 Cofiring of biomass in coal-fired power plants Cofiring biomass consists of burning biomass in coal-fired power plants along with coal. Cofiring will play an important role in growing the use of biomass in power generation and reducing greenhouse gas (GHG) emissions because it needs only a relatively modest incremental investment to retrofit existing coal plants or build new cofired plants. Cofiring provides many advantages compared with power plants that burn 100% biomass, including lower capital costs, higher efficiency, increased economies of scale, and lower electricity costs due to the larger size and superior performance of conventional coal power plants. Actually, some 230 electricity and CHP plants use cofiring, with a capacity of 50
700 MW, mostly in Northern Europe and the United States. Today, cofiring at CHP plants is the most economical choice to leverage the potential of biomass energy for both electricity and heat production.

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4.5.3.3 Cofiring technologies There are three main approaches to cofiring: G

G

G

Direct cofiring: the easiest, cheapest, and commonest method is direct cofiring. Biomass can either be milled together with the coal (typically less than 5% in terms of energy content) or premilled, and then moved to the same boiler separately. Standard or separate burners can be used, with the second option allowing for more flexibility regarding the type and quantity of biomass. Indirect cofiring: this is a less common process in which a gasifier transforms the solid biomass into a fuel gas which is then burned in the same boiler as coal. Though more expensive due to the additional technical equipment (the gasifier), this method makes it possible to use a greater variety and higher percentages of biomass. Gas cleaning and filtering are required before burning to eliminate gas impurities, and the ashes of the two fuels remain separate. Parallel cofiring: requires a separate biomass boiler that supplies steam during the same vapor cycle. This method allows for high percentages of biomass and is commonly used in large pulp and paper facilities to enable the use of paper production by-products such as bark and waste wood.

Today, more than 20% of biomass is technically feasible in terms of energy content [40]. Substitution of more than 50% of coal can also be done, depending on the plant setup and the cofiring technology chosen [41]. In most cases, however, cofiring rates are below 5% and only for around a dozen coal-fired plants worldwide surpass 10% on a continuous basis [42]. The cofiring combination also depends on available type of boiler. Fluidized bed boilers typically can substitute higher levels of carbon with biomass than pulverized coal-fired or grate-fired boilers [43]. Nonetheless, the combustion of pulverized fuel is much more common and a 100% conversion from coal to biomass has been demonstrated in some different pulverized coal-fired installations.

4.5.3.4 Performance and costs A cofired coal/biomass power plant’s net electrical output ranges from 36% to 44%, depending on plant technology, capacity, quality, and biomass share. While a 20% cofiring (as energy content) is currently feasible and more than 50% is theoretically possible, the average share of biomass today is below 5%, and on a continuous basis seldom reaches 10%. A large share of biomass means lower GHG emissions. It is projected that 1%
10% of biomass cofiring in coal-fired power plants will reduce CO2 emissions by 2035 from 45 to 450 million tons per year if no biomass is accounted for upstream emissions. Nonetheless, a high share of biomass entails technical issues such as obtaining enough biomass and possible combustion problems such as slagging, fouling (which limits heat transfer), and corrosion. The total cofiring costs are responsive to the position of the plant, and the main cost factor is the feedstock for biomass. The investment cost to retrofit a coal-fired power plant for cofiring is in the range of USD 430
500/kW for cofiring plants, USD 760
900/kW for separate feed plants, and USD 3000
4000/kW for indirect cofiring. Such prices are still far lower than the cost of 100% dedicated biomass power plants. The cost of the biomass fuel depends on the type of biomass, the amount exchanged, and the location.

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4.5.3.5 Sustainability, potential, and barriers Replacing 10% of the global coal-fired capacity by cofiring would result in a biomass capacity of around 150 GW. In comparison, today’s cofiring efficiency is estimated at 1 in 10 GW and the total installed biomass capacity amounts to some 62 GW. Therefore a great potential for cofiring exists, but a significant increase will raise the question of biomass supply, which can also be used for the production of biofuels and biomaterials. Although estimates of biomass resources vary widely, practical evaluations should only account for sustainable biomass, that is, resources that do not compete with food production and that do not require changes in land use with negative environmental and climate impacts. On that basis, the Intergovernmental Panel on Climate Change (IPCC) predicts an annual global sustainable biomass energy potential of 100
300 EJ, mainly based on agricultural and forestry residues and lignocellulosic feedstock. The use of biomass energy can add value to developing and emerging-country forestry and agricultural sectors. Recently, the production and trading of biomass from Latin America, Africa, and Asia have increased significantly, while longdistance transport reduces the gain of biomass use. Furthermore, to ensure the sustainability of biomass production, international cooperation is required. UNFCCC (United Nations Framework Convention on Climate Change) has recognized biomass cofiring based on residues and waste as a technology for reducing GHG emissions so that countries can sell carbon credits linked to their cofiring projects. Certain cofiring initiatives include CO2 emissions trading schemes (e.g., the European ETS), the elimination of fossil-fuel subsidies, incentives to turn power plants into cofired CHP plants, and compulsory cofiring quota schemes. Across EU countries (Austria, Denmark, Finland, the Netherlands, Sweden, and the United Kingdom) and the United States, assistance policies are in place. Emerging economies with large agricultural waste production and coal-based energy (e.g., China and India) are also well placed for cofiring implementation.

4.6

Conclusion

Continued climate change issues have made clean energy sources an important component of the global energy consumption mix. Renewable energy technologies could reduce GHG emissions by replacing fossil fuels in the electricity generation. Due to some negative and irreversible externalities in traditional energy production, renewable energy supply technologies and demand need to be promoted. To reduce unit cost of generation, power generation using renewable energy sources should be increased. Energy consumption depends on a number of factors including economic development, population, energy prices, weather, and technology. While renewable energy has been used for many centuries as a major source of energy in the past, it currently represents only a small percentage of the world’s total primary energy. According to the C2ES report, solar generation is projected to climb from 11% of total US renewable generation in 2017 to 48% by 2050, making it the fastest growing electricity source.

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Electricity use by industry will account for a growing share of the global demand for electricity over the next two decades. The growth rate of electricity demand is expected to be higher than that of other energy sources (e.g., liquid fuels, natural gas, and coal). The Fukushima Daiichi incident has been a turning point in calling for renewable energy sources. With no catastrophic threats, renewable energy is today considered a more attractive source than nuclear power plants. Wind, solar, and hydro energy are primary renewable sources in many countries.

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5

Taskin Jamal1 and Sayedus Salehin2 1 Department of Electrical and Electronic Engineering, Ahsanullah University of Science and Technology, Dhaka, Bangladesh, 2Department of Mechanical and Production Engineering, Islamic University of Technology, Gazipur, Bangladesh

5.1

Introduction

The world is gradually moving toward using more renewable energy (RE) resources for electricity generation. The public awareness of shifting away from carbon-heavy energy sources to environment-friendly green energy sources is highly motivated by the urge to reduce greenhouse gas (GHG) emissions to the environment [1]. This helps to reduce global warming, which causes adverse effects on the climate surrounding us. As human civilization is also evolving at a faster rate, the world is experiencing a rise in the overall gross domestic product (GDP). As the GDP is rising along with the increase in population, this will lead to a substantial increase in energy and electricity demand [2]. To address the increasing demand, the electricity generation and distribution systems are getting modernized day by day. Although the fossil fuel resources, for example, crude oil, gas, and coal, are dominating the energy mix and dictate energy markets, the world energy outlook provides a precise idea on how the increasing penetration of renewable energy sources (RESs) will have a higher share in the fuel mix within 2040 [2]. For the last few decades, the scientific community has been expressing keen interest in the development of RE systems to abate the environmental effects [3,4]. RE systems utilize energy conversion from RE resources, such as solar energy, wind energy, biomass energy, geothermal energy, and hydro energy for generating electricity. Despite tremendous development in technology, RESs suffer significantly from their inherent stochastic nature along with higher energy prices [5]. Also, the electricity that can be produced from such energy sources varies greatly with locations. Some of these energy sources, for example, wind energy, have diurnal variation, whereas energy can only be extracted in the daytime from the sun and thus energy storage option is imperative for their applications. The energy density of these sources is another concern for their use to supply the electricity in places having limited space. This leads to a delicate situation for renewable-based standalone power systems. No single alternative is absolute and therefore a hybrid renewable energy system (HRES) (a combination of multiple resources) is always preferable as this offers resource diversity, enhanced energy security and individuality, system redundancy, and long-term sustainable development [6]. Hybrid Renewable Energy Systems and Microgrids. DOI: https://doi.org/10.1016/B978-0-12-821724-5.00010-6 © 2021 Elsevier Inc. All rights reserved.

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This chapter is organized as follows: Section 5.2 introduces the RE-based hybrid power system. Section 5.3 describes the photovoltaics (PV) diesel battery system overview. Section 5.4 presents a holistic planning approach for the PV diesel battery hybrid system. Section 5.5 discusses the integration of shortterm PV forecasting into the hybrid system Sections 5.6 and 5.7 provide the discussion on the share of other renewable sources in the energy mix and conclusion, respectively.

5.2

Renewable energy-based hybrid power system

HRESs provide a solution to many challenges that a single RES-based standalone power system poses. HRES could be referred to as an electricity-generating system having more than one energy source, at least one of them being an RES. A HRES may consist of RE conversion technology, conventional energy conversion technology (often fossil fuel), and energy storage devices [7]. By combining two or more RE generation technologies in an HRES utilizes the appropriate use of their respective operational characteristics to obtain higher efficiency than a single-source energy system [8]. A hybrid power system is usually a low-voltage power distribution system that can operate as an off-grid system. The cost of energy (COE), reliability, power quality access to remote or islanded areas, energy efficiency, extractable clean RE, and climate change mitigation are the key factors for the rapid deployment of HRESs [9]. Fig. 5.1 shows a basic schematic diagram of HRESs where electricity is supplied to the energy storage device from conventional and RESs, which will then be supplied to the load as required.

Figure 5.1 General schematic diagram of a hybrid renewable energy system with energy storage.

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HRESs have the potential to provide reliable electricity to the end users, especially in microgrids operating in remote localities [10]. Diesel is often incorporated in HRESs to supply partial load demand due to its reliable nature and economy of scale [11]. Having different intermittent RESs in the hybrid RESs can complement each other when optimized, from technical and economic perspectives, for a particular load scenario. This type of energy system can, in fact, be used to provide the electricity with lower cost, high system efficiency, and lower environmental emission [10]. Apart from urban areas, billions of people are living in remote, distant areas from cities worldwide [12]. Expansion of central electricity network is not economically feasible as people are dispersed throughout these areas and tend to have a low energy demand. Electricity generation in these remote systems mainly comes from local or imported resources (e.g., diesel, natural gas), which are easier to operate and maintain and have low initial investment cost [9,13]. However, the operating cost of hybrid power systems is higher than traditional power plants, and as a finite source, fossil fuels are costly and polluting the environment. RE [e.g., solar PV, solar thermal, wind, biomass, hydro] based electricity generations being environment-friendly and sustainable resources are becoming more attractive [9,14]. So, utilities operating in remote off-grid areas are now interested in integrating RE-based electricity generation in their network and looking for a costeffective, technically sound solution. A comprehensive planning approach incorporating all technical and nontechnical aspects is a key requirement to accelerate RE integration into HRESs. Alongside, system integration and proper application of modern technologies are recommended to address the technical challenges of RE integration into these systems.

5.3

PV diesel battery system overview

Solar PV systems are the fastest growing off-grid renewable energy technology (RET) deployed in remote and rural areas where hybrid energy systems are familiar. This rapid growth is also supported by the continuous technological advancements and rapidly decreasing cost of these technologies. Wind and solar generation has doubled in the last five years [15]. PV system uptake is also motivated due to the abundance of solar irradiance available in most parts of the world [9]. The decreasing price of PV systems and parallelly the fluctuating price and supply chain uncertainty of fossil fuel are also playing significant roles in this case. PV systems are considered as a variable source of electricity due to its variability behavior throughout the day. In case of a high level of PV penetration, this variability nature poses potential threats to power systems like deterioration of power quality, compromised system efficiency, and amplified complexities [16]. In the maximum of the implementation cases in hybrid systems around the world, the integration of PV systems is meant to reduce the pressure on fossil fuel to generate electricity. A certain amount of fuel-based generation may be

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maintained to form the grid of the system that supports the system inertia. Random cloud movements induce PV output fluctuation and call for special attention and planning for high PV share in the generation mix. In the literature, it has been well documented that PV diesel-based hybrid systems are technically and economically feasible in remote off-grid communities worldwide [17,18]. Therefore the integration of PV systems and energy storage devices like batteries into hybrid systems is gaining popularity among the relevant stakeholders. As storage technology, the battery storage systems are offering improved solutions to many of the technical challenges posed by the variable output of PV systems. Generally, low spatial diversity of PV systems in a small area leads to very high PV power variability compared to dispersed PV systems. An isolated or island community can be fully or partially shaded or unshaded by fast-moving clouds in a time span ranging from a few seconds to a few minutes [19]. Hence, to accommodate this variable nature of power output, there is a need to ensure power supply quality and network stability. Therefore to offer sufficient flexibility, battery storage must be introduced to provide an adequate buffer against short-term PV power fluctuations [19]. A survey-based study among the academics, industry experts, and energy consultants revealed that diesel PV battery systems have superior advantages over the diesel PV or diesel-only systems for remote off-grid communities, in general [20]. A well-planned hybrid energy system incorporating a comprehensive generation dispatch strategy provides a sensible evaluation of fuel and cost savings. An application of a load-following diesel generator dispatch strategy showed in a study that during winter, the PV diesel battery hybrid system could achieve 73% 77% and during summer, around 80.5% 82% fuel savings, compared to the diesel-only scenario [21]. An Indonesian study showed that in an island, the simultaneous gradual reduction of diesel generator output and increasing output from PV battery systems results in low initial capital expenditure [22]. Salas et al. have reviewed that, when the PV penetration is more than around 20% (medium and high PV penetration cases), energy storage is recommended for a robust system operation [23].

5.3.1 Technical and nontechnical challenges Integration of PV systems into the generation mix of a hybrid system associates with various technical and nontechnical challenges. The primary nontechnical challenges in integrating PV systems are mainly associated with the system planning and design, operation, and maintenance, which are essentially all localized issues and thus differ from location to location [24,25]. The technical issues are recognized globally, which include voltage and frequency fluctuation, harmonics, voltage flickering and imbalance, switching off electrical equipment, system blackouts, islanding detection, electromagnetic interference, etc. [26]. Electricity generation in remote regions utilizing renewable and sustainable power sources faces different adversities, including a high forthright expense [25]. Despite these adversities, utilities are keen on coordinating RE-based generation into their systems and are searching for financially feasible solutions for the potential adversities. Therefore

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comprehensive new plans and development strategies are adopted to facilitate the up taking of renewable resources by adequately addressing the techno-economic challenges. The system operational strategies and technical constraints of the components have a significant impact on the PV hosting capacity of the network [26]. The distributed PV hosting capacity calculation does not only depend on voltage, frequency, and thermal limit of the associate distribution lines but also other factors, for example, the amount of the system’s operating reserve, annual load profile, annual generator loading profile, PV systems’ diversity and fluctuation factors, step load capacities of generators, and generator start and runtime. Solar PV system output variability tends to deteriorate the power quality issues in the network. Power quality issues are referred to as the voltage and frequency fluctuations, harmonics, and flickering problems. As PV systems lack mechanical inertia and do not include any local governor, it becomes difficult for hybrid systems having high PV penetration to maintain system resiliency and stability. In large, secure networks, the frequency response is managed by the rotating masses, for example, synchronous generators, reciprocating internal combustion engines with generators. Hence, in a hybrid system having high shares of PV systems, the converter control systems must be adjusted to provide the response previously obtained from directly connected rotating masses. Due to the nature of PV systems’ output, it is often termed as an uncontrolled source of electricity generation system [16,27,28]. As the stakeholders and electricity networks are implementing more and more PV systems into the hybrid system’s architecture, utilities are encountering the challenges of integrating high share of PV generation into the network. Hybrid systems in remote areas being different from metropolitan or urban networks regarding the behavioral and operational characteristics and pattern of load demand are prone to technical challenges. Batteries having fast frequency response (FFR) features and other desired control capabilities can provide “synthetic inertia” by functioning as a synchronous generator in the network and can act as “virtual generator” to provide frequency support. The FFR batteries can offer grid-forming and black start capability in a network [29]. Usually, the off-grid hybrid systems around the world possess a similar nature of characteristics. Be the system exists in Asia, Africa, or Australia, the operational philosophy and managerial measures represent this perception [9,26]. Nevertheless, technical challenges due to PV battery integration vary between systems of developed countries and developing countries due to many nontechnical factors such as socioeconomic and political conditions. Load profile also varies from community to community; hence system design and planning tend to differ. Some of the common technical barriers faced by the utilities operating the hybrid systems are as follows [26]: G

G

G

G

Reliability and power quality due to comparatively weak networks. Issues occurred from PV output variability. Inadequate resource and data monitoring facilities. Generation scheduling during evening peak hours when PV output is not available.

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G

G

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Network protections issue due to distributed renewable resources. Continuously maintaining the operational and safety standards.

Implementation of RE-based hybrid systems in off-grid areas is challenging due to various reasons ranging from understanding the technology and application to neglecting the environmental gains over business profits. Some of the identified nontechnical barriers of establishing RE-based hybrid systems in remote communities are as follows [26]: G

G

G

G

Lack of technical knowledge and awareness in customers regarding renewable technology. Long-term energy costs are often of very low importance for sensitive small and medium industries where quick and continuous generation is of significant concern. Prominent industries in remote areas (e.g., mining, oil, and gas) are more concerned regarding the system reliability rather than emissions and fuel-saving initiatives through the utilization of renewable resources. The typical remoteness factors: distance from the nearest urban area, supply chain management issues, comparatively lower economic activities, education quality, availability of facilities, life standard, etc.

To illustrate this, the following are some of the nontechnical barriers to uptake more PV battery-based hybrid systems in communities and industries. These challenges sometimes pose indirect impacts on some technical challenges in remote communities [26]. G

G

G

G

System reliability: it is a significant consequence of reliable supply stream of energy resource. In self-dependent off-grid industries, it is a noteworthy concern, as the interruption may cost millions of dollars per annum. Hence, FFR battery-based technologies can be deployed more to overcome the issues arising from a high level of PV penetration cases. Energy cost: in some cases, due to the product manufacturing requirement, industries prefer uninterrupted electricity supply, be it a costly option using imported fossil fuel, against the utilization of the cheaper option of the RE-based hybrid environmentally friendly system. However, PV-based systems provide more fuel security and less exposure to fuel vulnerability arising from fuel price volatility. Careful planning of hybrid systems in these industries requires developing a business model that adopts a reliable and cost-effective control mechanism in the system operational philosophy. System lifespan: the payback period of PV-based hybrid systems could be more than the overall project period of an industry in a remote region. Hence, from an economic point of view, industries do not prefer this option. However, the “modular” nature and a reasonable salvage value of these sources can be a great solution to tackle this specific issue. Hosting capacity: due to remoteness factors and usage of smaller size systems, some industries and communities may find the hybrid option unfeasible. Proper management of the network and careful coordination of the resources facilitate high PV hosting capacity in the system.

The main technical challenge raises in the network due to the output variability of PV systems. Solar variability is somewhat more predictable than the wind speed variability and solar has default “bell” shaped intensity during daylight hours, with

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clouds causing dropouts when occurring [26]. The irradiance fluctuation causes PV systems’ output to ramp-up or down abruptly. “Ramp rate” is the term used to characterize PV output variability. Due to the lack of mechanical inertia, the PV output ramp rates are usually higher compared to those of the wind power system. Thus the ramp rates of the fossil fuel-based online generators have to match the ramp rates of net load fluctuations. Net load is the net demand seen by the online generators. When the ramp rate becomes high, technical issues such as voltage and frequency deviations may occur in the system. Loschi et al. demonstrated that the output of a large PV plant could vary up to 30% 40% of its capacity per minute due to the presence of clouds in the sky [30]. Dense clouds made impacts on PV output by 50% 80% within a particular area and output may vary from a few kilowatt (within few seconds) to hundreds of megawatt in a utility-scale plant (within a few minutes) [31]. For example, in the TKLN (Ti Tree, Kalkarindji, and Lake Nash communities) sites in NT, Australia, the output variation was observed 80% within 6 s only [32]. PV-based systems in Alice Springs, Australia showed high ramp rate events where 66% of power drops were recorded in 10 s of interval [16]. The number and severity of ramp rate events govern the intensity of flexibility offered by a network. Generation scheduling is profoundly affected due to the ramp rate escalation. However, due to geologically dispersed installations arising from high PV penetration cases, the issue of high ramp rates reduces due to an averaging effect. The output variability impacts also depend on different operating characteristics of the systems’ components, the device configuration, and geographical influences. The temporal resolution of the PV output variability has impacts on power quality, reserve calculation, and unit commitment. The relation between the timescale and the variability (intermittency) impact is shown in Table 5.1 [26]. Literature studies show that hybrid systems may suffer from the following technical issues due to an increasing level of PV generation into the network [26]: G

G

G

G

G

G

Voltage regulation and flicker. The ramp rate of conventional generators. Spinning reserve coverage or back up to cover loss of PV output. Reverse power flow from PVs Frequency deviation. Harmonics.

Islanding detection/unintentional islanding.

Table 5.1 Power system impacts of intermittency. Timescale of intermittency

Potential impact on power system

Seconds Minutes Minutes to hours Hours to days

Power quality Regulation reserves Load following Unit commitment

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5.3.1.1 Voltage impact Voltage fluctuations are the main power quality issue faced by the PV-based hybrid systems owing to a short-term solar irradiance variation. This can also activate excessive automated tap changing on distribution transformers, which affects the life span of the transformer and increase annual maintenance costs. System-wide brownouts may occur due to extended under-voltages in the network arising from very high PV output drop. On the other hand, sustained high voltages have unfavorable impacts on the life span of network equipment. “Voltage fluctuation” arising from PV systems can be classified into voltage imbalance and voltage rise, leading to reverse power flow [29]. Network characteristics also influence the power quality issue as voltage rise happens when there is a high impedance between the PV output and the nearest pole-top transformer. When PV output increases rapidly in a weak system due to the movement of cloud, the voltage at the point of common coupling increases and vice versa. The impact of voltage variation is more profound in low voltage feeders compared to the medium voltage feeders as the value of X/R is higher in the latter case. The issue of reverse power flow occurs from voltage imbalance in the feeder owing to an imbalance between PV generation and demand in the feeder. Inverter controlled reactive power mechanism can help alleviate the voltage issues locally. Hence, modern-day inverters are capable of operating at a power factor less than unity leading or lagging for the voltage support at the point of common coupling [26].

5.3.1.2 Harmonics The total harmonic distortion (THD) is a commonly used index worldwide to describe harmonics-related power quality scenarios in both the power transmission and distribution networks and is defined as the ratio of the sum of powers of all harmonic components to the sum of the power of the fundamental frequency [26]. Studies have less evidence of cases where voltage harmonic distortion went severe due to variabilities occurring from PV systems. However, reduced real power requirements can lead to high THD ratio [33,34]. An analysis found that the current THD is reduced below 20% when PV power generation is higher, but it may increase up to 95% during the early morning and evening hours when generation is comparatively lower [35]. Subsequently, shading effects contribute to higher current THD values.

5.3.1.3 Impacts from PV inverters PV inverter settings and control can have an immense impact on the permissible PV capacity (hosting capacity) and penetration levels in a remote network, such as volt/var and volt/watt control [26]. According to the IEEE 1547 standard for interconnecting distributed energy resources with electric power systems, inverters should have the voltage regulation capability by offering reactive power support, but the action is proposed to be at the utility’s preference. The inverters

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should be able to offer the following modes of reactive power control mechanism: G

G

G

G

Constant power factor Voltage-reactive power Active power-reactive power Constant reactive power

IEEE 1547 also allows the inverters for frequency support during over- and under-frequency events. To support, during over-frequency cases, inverters are required to provide droop response. During under-frequency support, inverters are required to provide droop response if power is available. The utilities can adjust the values of the dead bands, droop parameter, and response time according to the necessity. The mutual interaction between PV inverter settings and the PV output settings has essential effects on the PV penetration level. For example, the AS/NZS 4777.2 standard followed in Australia allows the network-tied PV inverters to maintain power quality at the point of common coupling and thus providing the necessary network support. The system control strategy takes action regarding the magnitude and direction of reactive power flow when inverters control voltage at the point of common coupling. Inverter-based sources add negligible amounts of fault current in a faulty situation, which has less adverse impacts on protective devices. A gradual power cut from inverters is techno-economically preferable than an abrupt power cut during network frequency issues [26]. The technical services that could be provided by the inverters deployed in hybrid systems are as follows: G

G

G

G

G

G

Reactive power compensation, Fault ride through, Power quality improvement (voltage, frequency), Reduction of network capacity utilization, Anti-Islanding operation, Black start capability.

Energy storage technologies assist in maintaining the resiliency and the stability of the network. FFR battery management system reacts immediately within a fraction of a second. Slow responsive energy storage devices are less costly but do not meet the requirement to respond immediately to mitigate the short-term production consumption fluctuation [26]. The hybrid power system has to deal with various types of technical and nontechnical challenges while integrating higher shares of PV generation into the generation mix. This calls for the implementation of real-time coordination and robust network control mechanism. Technical issues such as voltage and frequency fluctuations, reactive power consumption and injection, system operating reserve regulation, management of the battery energy storage system needs to be monitored on a real-time basis, and dynamic control of network assets are essential to accommodate the unavoidable variabilities posed by the PV systems. Besides, hybrid system operators must skillfully take care of the issues of managing ancillary services

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required to enhance system flexibility, study the modeling and experimental analysis of control strategies, and quantifying the economic and performance advantage of mitigating PV variability to improve the operation of PV-based hybrid system.

5.4

Holistic planning approach for PV diesel battery system

Proper planning is always a prerequisite for the desired success of any commercial project. Electric power systems that are hybrid in nature is a complicated kind of project that comprises several types of generation resources and load types must go through a robust planning approach. This kind of planning is a multidimensional problem that consists of varied objectives and complex constraints, for example, selection of resources, technology, associated economics. Most electrification planning has considered only mathematical modeling and power systems simulation, which briefly portrays the consideration of mono-objective optimization via simple linear programming and enumerative optimization [6]. It is always a challenge to come up with an optimal plan for a hybrid system and address the technical, economic, environmental, and social issues of a community energy need. Several criteria cannot be measured by monetary values, such as job creation, social acceptance, impacts on physical and mental health. The decision-making process for such systems is complicated, demanding to address multiple factors and criteria. Hence, during strategy planning, one criterion sometimes gets preferred over others but may not offer the best solution. In this context, the planning of hybrid systems calls for a holistic approach to be undertaken [9]. This approach includes different types of analysis but not only confided to prefeasibility analysis, multi-criteria decision analysis (MCDA), techno-economic analysis, and technical analysis. A coherent and comprehensive planning approach is also a prerequisite to power and energy projects that facilitates “affordable and clean energy for all” to address the United Nations Sustainable Development Goals [36]. MCDA is a practical, judicious, and sensible approach and is helpful when a decision-making dilemma arises from multiple and probably conflicting objectives [37,38]. Thus there is a need to identify the appropriate MCDA approach that will look for the most critical and standard universal criteria and subcriteria for optimal planning of hybrid systems consisting of diesel-driven synchronous generators, PV, and battery energy storage systems. The approach will: (1) assess the hierarchy of essential criteria for the planning of hybrid system architecture, and (2) evaluate the best hybrid system configuration for a specific site considering all constraints. There have been many techniques proposed for MCDA. The analytical hierarchy process (AHP), first introduced by Saaty, has been found as the best value measurement model and thus the most commonly used technique for MCDA for electrical systems planning [39,40]. Jamal et al. have demonstrated the application of AHP-based MCDA approach for a remote Australian site [6,20]. The extension of the electricity grid is not a

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feasible option for electrifying many remote areas of Australia, due to low population density and sparse population distribution. Many of these remote communities rely on diesel generators for electricity supply. Operating diesel generators poses uncertainty of energy supply in those areas, for example, the supply of fuel faces various supply hindrances during the wet season in Northern Australia [6]. The uncertainty of fuel price also affects the energy supply. These uncertainties lead to lack of energy security. Abundant solar irradiance in these areas, the falling costs of PV systems and gradual development of the associated technologies have made PV systems an attractive addition to the fossil fuel-run systems. PV systems facilitate remote area electricity supply with significantly less environmental impact compared to diesel-based systems. However, procurement and installation of PV systems have a higher capital cost. The remote utilities need to consider all sorts of economic, environmental, and technical issues and constraints for an optimal system plan for various sizes of remote communities. It is also demonstrated in the IEA-PVPS Report T9-13:2013 that PV hybrid systems are technically and economically feasible as a hybrid off-grid power supply system for remote and rural communities worldwide [41]. In this section, a methodical approach will be discussed that seeks the opinion of experts for the planning of a PV diesel battery hybrid power system. Then power system optimization and techno-economic analysis will be discussed, followed by an integrated approach of expert opinion and software analysis using the MCDA method and finally considering a technical analysis to check several dynamic situations to study the power quality issues.

5.4.1 Addressing stakeholders’ opinion Many of the hybrid systems around the world are in remote and rural areas. People in these areas are usually considered as the disadvantaged society from many perspectives. It is always a challenge to introduce new electricity-generating technology in these areas due to social barriers. Proper and secured handling of modular equipment, for example, PV panels, batteries, and wires can sometimes be very challenging due to lack of proper knowledge, understanding, and vandalism tendency in some areas. In many of the cases, government policies frequently change regarding the investment and regulations in the renewable sector, which has a big impact on the uptake of the technologies. Proposing an optimal plan for a hybrid system is also challenging due to the consideration of different social criteria such as job creation, social acceptance, and impacts on physical and mental health, which actually cannot be measured by monitory values. Considering these aspects, there is a necessity to include the economic, social, and environmental facts into the planning methodology of a hybrid system. MCDA, in this context, could be a judicious approach. The MCDA approach can be utilized to combinedly assess various stakeholder’s observations and opinions, expert’s views, policymakers’ decisions, and consumers’ perspectives. All these entities share divergent viewpoints and interests. Hence, this section looks into the application of the AHP-based MCDA methodology, which will quantify all these different views by a

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mathematical modeling approach and then tries to find out the priority criteria for optimal electrification planning of a hybrid system. For this, the question that was asked to the academics, experts, and independent consultants were “What are the primary concerns for optimal planning of a hybrid system?”. They were also asked to rank these concerns in terms of criteria. The experts were informed about the sources of the hybrid system—PV systems, diesel generator, and battery energy storage system. All the evaluations for a range of experts were then quantified using the AHP method to obtain the hierarchy of the criteria and subcriteria. The minimum number of experts’ responses required to conduct the study was determined using the following parameter values [42]. Z-score 5 1.645, when the confidence level is considered as 90% Standard deviation; StdDev 5 0:5 And margin of error 5 6 10% According to this equation, the total sample size has been calculated as 68. Adequate data collection was performed within around a month. Then the AHP methodology was utilized to produce pairwise matrices among the criteria, subcriteria, and alternative options (energy sources) and that results in a ranking of criteria, subcriteria, and alternatives for the hybrid system. These criteria have been chosen based on an extensive literature review. Most of the researchers have mentioned four broad criteria of economic, environmental, technical, and social to be considered when evaluating a remote area microgrid. To make the decision-making more comprehensive and robust, each criterion is also divided into several subcriteria, as listed in Table 5.2 [20]. The expert views and opinions regarding these criteria are universal and applicable to any off-grid remote communities in the world. Fig. 5.2 presents the diversity of the respondents that shows 63.4% of the respondents belong to the academic group, 29.6% to the industry expert group, and the rest 7% are in the consultant group. The results of the questionnaire are exclusive as experts represent various expertise, countries, and multicultural background [20]. In some of the cases, the final decision on criteria ranking was similar for all experts; however, some fascinating features were observed that represent different types of interpretation of the experts from their perspective. The highest-ranked Table 5.2 Considered criteria and subcriteria. Main criteria

Subcriteria

Technical

System reliability, renewable share, peak shaving, and generator efficiency Net present cost, capital cost, operation and maintenance cost, and fuel price CO2 emission, Noise pollution, and Land use Job creation, social acceptance, and remoteness factor

Economic Environmental Social

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Figure 5.2 Diversity of the survey respondents; (A) percentage of respondents belonging to each expert group, (B) distribution of participants between developed and developing countries, and (C) continent-wide distribution of respondents [20].

subcriteria are the capital cost, CO2 emissions, and the system reliability, respectively, for economic, environmental, and technical criteria. Academics and industry experts ranked all the subcriteria in the social criteria group similarly; however, the consultants weighted the remoteness factor as the most crucial factor to be considered from a social perspective. Fig. 5.3 presents the radar charts portraying the weightings of every subcriterion marked by each expert group [20]. Finally, the criteria weightings are depicted in Fig. 5.4 [20]. As seen from this figure, the weighting of the economic criterion outweighs other criteria by all expert groups. The intensity of importance for the economic criterion is 48.7%, 58%, and 69.9% for the academic, industry expert, and consultant groups, respectively. It is also seen from this figure that experts are less concerned about the environmental criterion when planning a remote area islanded microgrid.

5.4.2 Power system optimization and techno-economic analysis There are well-demonstrated projects around the world to showcase the excellence of hybrid systems in off-grid areas. Various researchers have also contributed to

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Figure 5.3 Weightings of each subcriterion scored by various expert groups for criterion: (A) economic, (B) environmental, (C) technical, and (D) social [20].

Figure 5.4 Overall weightings of all criteria scored by each expert group (in percentage) [20].

this area and expressed that based on the availability of resources in general, PV diesel battery hybrid systems provide an environment-friendly, sustainable, and techno-economic comprehensive solution [43 45]. It is evident from these studies that hybrid PV diesel battery systems are providing a technoeconomically feasible and sound solution to the power demand in the off-grid areas. Power system optimization and techno-economic analysis meet all the technical

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constraints and fulfill the optimization objectives to allocate available resources to meet the load demand optimally. This section will discuss such a study for an Australian off-grid hybrid system and will demonstrate the procedure in brief. The study considered a small centralized PV system in a diesel-run system and a small amount of energy storage, which acts as power smoother during the transient periods occurring from PV output variability [29]. The objective functions of the optimization and techno-economic analysis are to minimize the energy levelized cost and maximize the RE share while meeting all load demand. Diesel generators used to be the primary source of electricity in the system and the stakeholders have shown interest to integrate more PV systems for a greener power system. The combination of PV systems with diesel generators can benefit these networks from an economic and reliability point of view. Diesel generator and PV systems would operate in parallel as PV outputs are variable, which may create stability issues if it runs as a standalone PV system. Fig. 5.5 shows various possible configurations of PV diesel battery hybrid system: (1) two diesel generators, the centralized PV and the centralized battery storage systems; (2) two diesel generators, PV systems are both centralized and decentralized, whereas battery is centralized; (3) two diesel generators, PV and batteries are both centralized and decentralized; (4) two diesel generators, both the PV and battery are decentralized. To demonstrate, a PV diesel battery system shown in Fig. 5.5(A) has been chosen. The optimization approach aims to reduce diesel consumption and thus promote RE into the hybrid system. The operator aims to increase the PV penetration as much as possible meeting all the objective functions and technical constraints. The PV penetration level is defined as the ratio between the average power output from PV arrays to the average primary load and is expressed in “%”. The battery energy storage device considered can buffer the variable PV output [29]. When a significant amount of PV output fluctuates rapidly, it may take a few minutes for a

Figure 5.5 Various possible configurations of PV diesel battery hybrid system. PV, Photovoltaics.

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Table 5.3 System configuration obtained from system optimization and techno-economic analysis [44]. System configuration: PV diesel PV penetration range: 9.96% 33.47% Optimal PV size: 289.17 kW Cost of energy: AU$0.348 AU$0.358/kW h

System configuration: PV diesel battery PV penetration range: 9.96% 33.47% Optimal PV size: 289.17 kW Battery: 16 32kW h Li-ion Battery autonomy: 2.46 4.92 min Cost of energy: AU$0.318 AU $0.346/kW h PV annual energy share: 21.87%

Environmental savings: 218 tonnes of COx, 5 tonnes of NOx, and 0.45 tonnes of SOx. PV, Photovoltaics.

new diesel generator to turn ON and synchronize with the network. During that short period, the FFR Li-ion batteries can immediately respond and support the network frequency; otherwise, system instability will occur, which may lead to a blackout. The study considered the following parameter values while conducting the power system optimization study using the software tool HOMER Pro [29,44]. Table 5.3 presents the system configuration. G

G

G

G

G

G

G

G

G

G

G

Diesel capacity: 875 kV A (700 kW with 0.8 power factor)—5 nos. of generators Load: annual system peak load—580 kW, monthly average load—275 kW, annual load factor—47% Diesel minimum loading and runtime: 40% and 30 min, respectively Operating reserve—100% PV output 1 10% average hourly load Solar irradiance: annual average irradiance—5.83 kW h/m2 per day Battery pack: 6 V lithium-ion 1 kW h DC bus voltage: 24 V Costs: o Diesel generator capital cost—AU$1000/kW, replacement cost—AU$900/kW, operation, and maintenance cost AU$0.025/h, o Solar PV capital cost AU$1352/kW, replacement cost AU$1000/kW, operation and maintenance cost AU$50/year, o Battery capital cost AU$145/unit, replacement cost AU$130/unit, operation and maintenance cost AU$20/year, o Converter capital cost AU$800/kW, replacement cost AU$700/kW and operation and maintenance cost AU$0/year. Battery minimum state of charge: 30% Economics: expected inflation rate 2.20%, expected nominal discount rate 5% Project lifetime: 20 years

The optimization study attempted to find an optimal solution for the system operator as per the requirement. In this example, the requirement was to include a battery for smoothing purpose only. However, if the objective function denotes that battery share will be higher, and it will take part in energy dispatch during PVabsent times, then the optimization result will no more be the same. The system

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operator should be able to analyze the environmental impact from the optimization outcome and also perform a sensitivity analysis for several scenarios to find out the best possible configuration as per the requirement. This optimization and technoeconomic analysis are thus one of the prerequisites for a hybrid system planning.

5.4.3 Integrating software-based analysis Before making the final decision, one of the last steps is to integrate the expert’s opinion and the techno-economic analysis outcome to propose a suitable solution where all possible technical and nontechnical factors are addressed. The blending of expert judgments and computer-aided power system techno-economic analysis makes the planning and decision process more unique, coherent, sensible, and transparent. Sections 5.4.1 and 5.4.2 have separately discussed the mathematical modeling of the stakeholder’s opinion and the power system optimization and technoeconomic analysis, respectively. This section will now discuss the approach on how to integrate these two separate approaches into one new decision-making approach. The AHP-based MCDA technique will be applied here as well to model the required matrices and thus creating a weight factor-based hierarchy among the alternatives. This approach produces a robust method of planning a PV diesel battery hybrid system by addressing all the critical technical and nontechnical decisionmaking factors. The PV diesel battery hybrid system is designed and configured considering the Australian market trend as per the data provided in Section 5.4.2 (data obtained in the year 2017) [6]. In this case study, the PV systems are integrated with the diesel-based generation system to primarily increase the renewable share into the generation mix and consequently reduce the diesel fuel consumption, but not to replace the whole fossil fuel generation capacity. A single diesel generator is always kept online due to the grid-forming capability of the synchronous generators, which is also aided by the incapability of PV system’s lack of mechanical inertia and output variability characteristics due to weather factors. Battery storage is used to buffer the variable PV output for a smoother diesel loading and to add Table 5.4 Details of the optimal system configuration for different alternatives [6].

Total diesel capacity Total PV capacity Total battery capacity Levelized cost of energy Annual diesel consumption CO2 emission reduction PV, photovoltaics.

Diesel only

PV diesel

PV diesel battery

620 kW AU$0.273/kW h

560 kW 290 kW AU$0.266/kW h

560 kW 440 kW 180 kW h AU$0.246/kW h

Approx. 572,000 L -

Approx. 447,000 L 23%

Approx. 377,000 L 25.5%

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energy storage facility to be used during lengthy interruptions from PV output. Table 5.4 shows the configurations and the results obtained from the optimization and techno-economic analysis study. The analysis considered the maximum possible renewable share into the generation mix. For the PV diesel configuration, the average PV penetration level was around 24%, whereas due to battery integration, the level raised to around 35%. Fig. 5.6 shows the complex relationship tree among the criteria, subcriteria, and alternatives [6]. The experts’ opinions were sought regarding the criteria ranking and later on computer-aided software simulation provided with the power system optimization results and techno-economic analysis. These two features produce separate pairwise matrices and the AHP methodology is applied to finally combine all the weighted scores for each of the subcriterion and criterion and consequentially the overall alternatives’ ranking was obtained. Fig. 5.7 presents the obtained performance scores of each of the subcriteria from the techno-economic analysis (Table 5.4) using the AHP method [6]. The higher the score of a subcriterion for an alternative, the better the alternative is for that subcriteria compared to another alternative. From Fig. 5.7 it is observed that “capital cost” shows the highest score for the diesel-only alternative and the lowest score for the PV diesel battery option, which indicates that the diesel-only option is a better alternative compared to other alternatives in terms of capital cost requirement. Fig. 5.8 displays the expert-wise alternative preference score [6]. From the scores, it can be stated that all experts unanimously conveyed that PV diesel battery system is the best alternative. Both the academic and industry experts’ opinions portray that the diesel generator-only option is the second-best option among the alternatives. However, the consultant group carries a different opinion. According to this group, the second-best alternative is the PV diesel generator-based hybrid system.

Figure 5.6 Relationship between the objectives, criteria, subcriteria, and alternatives [6].

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Figure 5.7 Performance weight of each subcriteria for each alternative (obtained from simulation results) [6].

Figure 5.8 Expert-wise alternative preference [6].

All types of experts share equal importance in this analysis. Employing the AHP-based MCDA approach and the score shown in Figs. 5.3, 5.4, 5.7, and 5.8, the overall score computed for the PV diesel battery is 0.411; for the diesel-only

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Table 5.5 Calculation of the final score for each of the alternatives considering all experts’ score [6]. Alternatives (power source)

Expert group

Given weight

Overall weighted score

Diesel-only

Academica Industrya Consultanta

0.325 0.358 0.257

0.108 0.119 0.086 Overall score

PV diesel

Academic Industry Consultant

0.274 0.275 0.277

0.091 0.092 0.092 Overall score

PV diesel battery

Academic Industry Consultant

0.313

0.400 0.367 0.466

0.276 0.133 0.122 0.155

Overall score

0.411

PV, Photovoltaics. a All experts share equal weightage (0.333).

case, it is 0.313; and 0.276 for the PV diesel option. For the PV diesel generator case, it is 0.276. Table 5.5 shows the overall scores of the alternatives. This MCDA-based mathematical modeling suggests that PV diesel battery hybrid system configuration would be the most preferable option in that specific Australian site. This methodology does not only satisfy all the technical and nontechnical constraints and barriers but also address the experts’ opinion on criteria prioritization for hybrid system planning. Though the preferred system configuration may incur a high capital cost, in the long term the cost is gradually reduced as the levelized COE is the lowest among the other options. Increasing the share of renewables into the generation mix in hybrid systems benefit the community and industry by lowering the fuel dependency and associated transportation and storage costs. Communities become self-reliant, economic activities improve, and better social and environmental influences improve daily life. This overall approach can be used for more numbers of criteria and alternatives, given that any competent computer-aided software tool is available to assess the alternatives against the chosen criteria and subcriteria.

5.4.4 Power quality analysis The next step of the system planning is to conduct a scenario-based power quality analysis. For this, a computer-aided power system simulation tool, DIgSILENT Powerfactory, was employed, and the advanced functions of RMS/EMT simulation were utilized. The simulation considered the same electric network demonstrated in Sections 5.4.2 and 5.4.3. The case-based scenario considered that the PV systems are decentralized, and the diesel generators and battery system are in the central

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generation station. Each of the 170 kV A diesel generators was connected to 0.415 kV bus. The system frequency is 50 Hz. All the generators would share the load equally and are not allowed to run below the permitted 40% minimum loading (56 kW). Three distribution feeders (e.g., named X, Y, Z) were considered having load amount of 115, 115, and 350 kW, equaling to 580 kW. Three distribution lines are of 5, 5, and 15 km, respectively. For economic dispatch, the primary objective function was to maximize the PV penetration satisfying all technical constraints at the lowest life-cycle cost and minimize the excess electricity. Before running the power quality analysis, a technoeconomic analysis was conducted for ensuring the economic dispatch of the resources. The analysis resulted in providing the specific configuration for the hybrid system: the PV capacity in feeders X, Y, and Z is 100, 100, and 174 kW, respectively; diesel capacity of 700 kW; 140 kW (cumulative for decentralized PV) and 200 kW (bidirectional for battery) inverter capacity and 100 kW h Li-ion battery capacity. The simulation considered a 20-year long project and the resultant COE was AU$0.303 [29]. In this scenario-based case analysis, high level of PV penetration is considered. RMS time-domain stability analysis was conducted within a time frame of 3 min to see the impact of the PV-induced disturbance on the grid. All different situations in each case were simulated within this short time frame. A valid load flow initializes the simulation. Different functions govern the initial conditions of system factors along with controller units and mechanical components. These initial stipulations represent the steady-state working point at the commencing of the simulation, fulfilling the necessities that the derivatives of all state variables of loads, machines, and controllers are zero [29]. To study the system dynamics, a net load fluctuation profile was generated and employed. Two cases were considered to study the frequency instability issue. The cases are as follows: Case 1: No: of diesel gen 5 varies; PV 5 rated capacity; Battery 5 rated capacity&Load 5 fluctuating:

Case 2: No: of diesel gen 5 varies; PV 5 rated capacity; Battery 5 nil&Load 5 fluctuating:

The technical simulation results are demonstrated in Figs. 5.9 and 5.10. Each figure presents the generators’ active power in megawatt (Fig: 9 (A) and Fig. 10 (A)), battery active power in per unit and SOC in % (Fig: 9 (B)), feeder loads in megawatt (Fig: 9 (C)), and frequency of the generation bus in hertz (Fig: 9 (D) and Fig. 10 (B)). The controller consists of a frequency droop controller with droop setting of 5% for all the generators. There was no integral and secondary controller considered for frequency stability. Feeder “Z” was disturbed with a net load fluctuation (maximum of 57% of net load decrement) as can be seen from Fig. 5.9. It is observed that the FFR Li-ion batteries react immediately to frequency fluctuations

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Figure 5.9 Results for Case 1.

Figure 5.10 Results for Case 2.

due to short-term load and PV power fluctuations. Voltage control and voltage stability study were beyond the scope of this study [29].

5.5

Integrating PV forecasting mechanism

The variability characteristics of PV systems are caused by the diurnal and seasonal development of different sorts of clouds within the sky [19,46]. Introducing higher

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PV penetration thus requires PV forecasting to tackle the PV output variability issues [22]. PV generation varies within a vast range of its yield, within a short time (seconds to minutes) due to diverse cloud effects. Thick clouds may have effects on lowered output from PV by 30% 80% in a particular area. The affected output may vary from few kilowatts to megawatts ranges. The use of highresolution forecasted data could mitigate the ramp events in the system and thus the PV hosting capacity can be proliferated. Therefore appropriate PV forecasting technology identification and application is a dire need now for PV-based hybrid power system. Without the application of PV forecasting, the current remedies to tackle the PV fluctuation are to conservatively determine the hosting capacity, curtailing PV output, using batteries to compensate the ramp, and allocating higher spinning reserve from the operating generators. Utilities integrating the short-term PV forecasting will benefit from shrinking the extra cost incurred from all these conventional managerial measures.

5.5.1 PV forecasting technologies PV power fluctuation and variability have significant impacts on energy storage requirements, renewable share in generation mix, and power quality mitigation techniques. Higher PV penetration could be expedited if the output variability could be forecast precisely [19]. Solar PV forecasting is not a new concept in power systems operation. Distinctive strategies are utilized for solar PV forecasting depending on the application level, forecast horizon, and cloud conditions [19]. The prediction of cloud locations significantly influences the accuracy of PV forecasts. If the location detection is aided with regional meteorological and historical data, the performance significantly improves. Short-term PV fluctuations are more complex to accommodate and more troublesome to forecast compared to the long-term fluctuations. Higher forecasting accuracy ensures lower costs related to control of spinning reserve and flexible generator operation and maintenance [19]. Solar irradiation in a site depends on the extraterrestrial radiation, PV array declination, insolation rate, climate conditions (air and ground temperature, humidity, evaporation, atmospheric particles, etc.), albedo from the ground, the presence of clouds, optical depth, geographic location, etc. [19]. Along with the irradiance measurements, the relations among the PV system size, cloud size, cloud movement and speed, site topology, and other factors need to be considered while forecasting. The irradiance on the PV panel (mainly the global horizontal irradiance for flat panels) and the temperature at the back of the PV panel are the two variables that have significant impacts on PV forecasting [19]. Considering all these features, there are primarily three types of forecasting mechanisms, viz., satellite imagery, sky imagery, and the numerical weather prediction (NWP) method. One of the most recent PV forecasting mechanisms is the ground-based sky imagery mechanism. This enables the acquisition of high-resolution future irradiance data. It has some proven advantages over other well-known typical approaches, such as the NWP, satellite imaging, and statistical-based methods [47]. Combined with additional

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measurements of solar irradiance, cloud heights above the ground, information regarding basic geometrical considerations and supported with machine learning and maps of the local surface, a very high accuracy irradiance prediction can be executed through ground-based sky imagery mechanism [47]. Table 5.6 presents the fundamental differences between these three methods [20,48].

5.5.2 Short-term PV forecasting using sky imagery mechanism Sky imagery technology is a ground-based method to monitor the movement of clouds. It offers comparatively precise, high resolution, and very short-term forecasting capability [49]. Schmidt et al. have elaborately described the advantages of using sky imagers as a tool for short-term PV forecasting and have described the whole irradiance forecasting mechanism considering cloud movements in a wide area [49,50]. Sky imagers are one of the inexpensive solutions to modern-day precise forecasting challenges [50]. One of the recent research studies conducted by Jamal et al. applied a short-term PV forecasting mechanism using sky camera technology in a PV diesel battery hybrid power system to provide enhanced control addressing the PV output fluctuations due to cloud movements [19]. In this research, it is well demonstrated that this forecasting mechanism is supporting the utility to offer better confidence to integrate high shares from PV generation in hybrid systems. Short-term PV using sky imagery technology enables the utility to: G

G

G

G

G

predict future long-term and short-term fluctuations of PV output, manage battery storage effectively (e.g., charging discharging cycle) to handle the high PV power ramp rates, allocate high PV share into the overall generation mix, manage the operating reserve in a conservative way that would eventually reduce the COE, adjust properly the ancillary services required to handle the power quality issues.

Table 5.6 Different methods of PV forecasting. Method

Sky imagery

Satellite imagery

Numerical weather prediction

Sampling rate Spatial resolution Spatial coverage

30 s 10 100 m 3 8 km radius 10 s to min

15 min 1 km 65 S 65 N

1h 2 50 km Worldwide

5h

10 days

Ramps, regulation Statistical

Load following Statistical

Unit commitment, regional power estimation Physical

Forecast horizon (max) Application area Approach

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The study conducted by Jamal et al. and Schmidt et al. installed a sophisticated hardware setup to assess the performance of the short-term PV forecasting [19,47]. The hardware setup was installed at the University of Oldenburg (53.15232 N, 8.166022 E) in Oldenburg, Germany. The experimental setup is presented in Fig. 5.11 [47]. The essential equipment and features of this setup were as follows: G

G

G

G

G

G

The sky imager—network camera Vivotek FE8172V, equipped with a fisheye lens. Camera specification—a circular fisheye frame in a 1920 3 1920 pixels image plane and a full 180 degree view field, but the maximum field of view for the camera was set to 160 degree ( 6 80 degree of the zenith angle). Wind mast, sun tracker with diffuse horizontal irradiance and direct normal irradiation sensors, pyranometer, PV module. Strategy—taking images every 10 s from sunrise to sunset. Automation using Python-based interface. Computer station for data acquisition, installed with image processing software and Python-based software tool to compute irradiance forecast.

The images taken from the sky imagery device were processed by the image processing tool and then the forecasting algorithm was implemented to obtain 1-min ahead PV forecasting for a 25-min horizon. This is a single point irradiance measurement mechanism where any geographic diversity impact of solar irradiance was overlooked.

5.5.3 Developing a proprietary forecasting tool The success of PV forecasting in a hybrid system solely depends on the control mechanisms adopted by the operator. The generation scheduling, allocation of the

Figure 5.11 (A): Top view of energy meteorology station at the University of Oldenburg. Measuring equipment for three main horizontal irradiance components are located together with a sky imager above the staircase of the building. A PV station with wind measurements is located about 19 m nearby. A triangle of GHI measurements with photodiodes for cloud motion estimation is also located on the rooftop. (B): The sensors, camera, and the PV module used in the experimental setup [19,47].

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operating reserve, battery charging discharging signals, management of net load, application of demand-side management, selection of utility software tool, managerial advice—all these interact together to justify the application of PV forecasting. Most well-known and commercially available microgrid and RE-based system simulation software tools used for energy flow modeling do not accurately simulate minute-level system operation. For example, HOMER Pro by HOMER Energy [51], RETScreen Expert by Natural Resources Canada [52], and the System Advisor Model by the US National Renewable Energy Laboratory [53] can perform system simulations at an hourly resolution. Therefore Jamal et al. developed and proposed an energy flow simulation tool for incorporating short-term PV forecasting in a PV diesel battery hybrid system to assess the benefits of as short as 1min resolution PV forecasting for a specific system [19]. To improve the PV penetration level, it is essential to offset the uncertainties of the PV output variability as much as possible using high-resolution data computation. Diesel generators having a few hundred kilowatts capacity usually take roughly around a minute to go to full load from a cold start. This calls for an assessment of 1-min-level generator scheduling to ensure adequate system operating reserve. On the other hand, the PV forecast period should be adapted to the system size. Smaller sized power systems have higher PV fluctuation probabilities and thus need higher resolution forecasting. Therefore the data computation resolution of a 1-min window for PV forecasting and diesel battery dispatch represents a reasonable approach. The forecasting resolution is as high as 1 s, but it is averaged to 1 min for better computing performance [19]. Fig. 5.12 shows the logical sequence followed for this application. The 1-min ahead irradiance forecast data acquired from the image and irradiance processing software are applied to the energy flow simulation [19]. A distinguishing feature of the tool is that it takes 1-min data as inputs to simulate 1-min output. The specific inputs are system load profile of 1min resolution, PV array information, diesel generator capacity, battery capacity, and location-specific information. The diesel generators and the battery banks are operated in automated control mode. PV systems are assumed to be distributed on the rooftops of the community buildings and households. An automatic “Power Station Management System” communicates with “Generator Supervisory Systems” on each of the generators and the battery controller and schedules them to supply the consumer loads. There is no advisory control imposed on the distributed PV systems [19]. Some significant advantages of the forecasting tool identified are as follows [19]: G

G

It uses as input a minute-level resolution data from various available sources. The tool provides insight into the issues that need to be addressed on a minute-by-minute basis to apply them to the control mechanism of the power system to assess benefits. Short-term PV forecasting can forecast as little as 1-min ahead solar irradiance levels. Therefore the application requires a tool that can simulate minute-level resolution power flows. The tool achieves this and computes the overall fuel consumption, fuel savings, and operational reserve requirements for any period considered.

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Raw Image Clear Sky Library

Cloud Detection

Lens Function

Image Undistortion

Cloud Base Height

Cloud Mapping

Sun Position

Shadow Mapping

Image Analysis

Image captured in every 10 minutes

Irradiance Analysis

Resolution of 1second

Measurements Clear Sky Data

Surface Irradiacne

Cloud Motion Surface Irradiance

Additional Input to Output -1

Power Flow Simulation Tool

Forecasts are generated in 1-second resolution but averaged to 1-minute. Maximum forecast horizon is of 25-minutes.

1-minute resolution solar irradiance forecast data and Details of PV array

Data processing and Power flow simulation

Output -1

Irradiance Forecast

Determination of diesel loading, savings and energy balance

1-minute resolution power flow of generation resources and determination of operational reserve

Output -2

Figure 5.12 Stages and strategies of the approach leading to tool development [19]. G

The dispatch algorithm can be customized, and every step is visible and transparent. This is an outstanding feature of the tool. A techno-economic analysis is recommended before using this tool to learn about the economic configuration of any power supply system.

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However, by their inherent nature, energy flow models have limitations. These are: o They do not consider reactive power, frequency, or voltage as technical parameters in making any decisions. o The line loss calculation is omitted while dispatching power to the loads. o They assume that the speed of completing a charging/discharging cycle has no negative impact on battery operation and lifetime. o They do not consider cost functions as a decisive factor.

G

Several case-based strategies using the tool have been demonstrated in ref. [19]. Three system configurations are each assessed in relation to three forecasting strategies when PV systems are integrated. The base case scenario has been chosen as the diesel-only operation scenario for the hybrid system (Table 5.7). All technical constraints have been adhered to in every time instant while the tool is running. This helps the system to reduce the potential number of events where the N 1 1 redundancy criterion is violated. In addition, this methodology followed to prepare the tool used a strategy that calculates the maximum PV hosting capacity when the forecasting mechanism is not applied. It thus assesses the excellence of 1-min ahead Table 5.7 System configuration for application of PV forecasting [19]. Case no.

System configuration

PV forecast strategy

1

Diesel-only (base case scenario)

N/A

2

PV diesel generator

G

G

G

3

PV diesel generator battery battery 5 100 kW h

G

G

G

PV diesel generator battery battery 5 50 kW h

G

G

G

No forecasting 1-min ahead PV forecasting Perfect forecasting No forecasting 1-min ahead PV forecasting Perfect forecasting No forecasting 1-min ahead PV forecasting Perfect forecasting

Remarks G

G

G

G

G

G

G

Diesel generators are employed to meet the net load and try to maintain adequate operating reserve. Diesel generators are employed to meet the net load and try to maintain adequate operating reserve. PV system hosting capacity is affected by PV forecast strategy. Battery is not employed. Diesel generators are employed to meet the net load and try to maintain adequate operating reserve. PV system hosting capacity is affected by PV forecast strategy. Battery is employed, which comprises battery-regular reserve and battery-emergency reserve (dual-battery system).

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PV forecasting by increasing the PV hosting capacity and therefore evaluates the system performance in terms of fuel savings and PV share in the overall generation mix. The results obtained from the forecasting tool indicate that different days exhibit differing PV penetration level and thus differing fuel savings potential [19]. The addition of short-term PV forecasting improves the system performance by allowing much more prosumer PV capacity to be installed, leading to much higher PV generation in a hybrid system. The tool can demonstrate precisely the benefits of having 1-min ahead PV forecasting compared to no forecasting. When clouds cause a sharp drop in PV power, 1-min ahead PV forecasting allows far more uncontrolled prosumer PV capacity to be installed without running short of operating reserve. The demonstrated cases also showcased that PV penetration level went double as forecasting was applied and that saved around 540 L of diesel per day for a small hybrid power system of an annual peak load of 580 kW only (Section 5.4.2). For this specific community demand profile, it was revealed that battery storage achieved additional fuel savings and reduced average number of online generators [19]. The results have also exhibited that larger battery size does not necessarily mean significant fuel savings. Instead, a techno-economic analysis is required to justify the extra investment on the extra size of the battery storage system. Fuel savings potential also depends strongly on the available energy resource dispatch and the control strategies used to operate the system. Care should also be taken to accommodate PV forecast error as the error range varies from day to day, depending on weather conditions. Finally, the assessment of hybrid energy system performance using this robust tool demonstrated precisely the significant success of the shortterm PV forecasting application, which benefits the system with significantly increased PV hosting capacity.

5.6

Share of other renewable resources in the energy mix

Although HRESs consisting of solar PV, diesel generators, and batteries, there are other configurations that utilize other RESs. Wind diesel battery systems were suggested as a probable solution to provide electricity to consumers living in remote areas, even in medium wind-potential areas, considering the installation and operation cost. An integrated numerical algorithm provided a detailed energy balance analysis for the systems. The systems were optimized for three different windpotential areas with an emphasis on reduced diesel oil usage and battery bank dimensions [54]. Li et al. have studied different battery technologies for standalone wind diesel battery HRES for Lanzhou, a cold region in China. Wind diesel ZB (zinc bromine) battery systems were found to be providing electricity for $0.528/kW h [55]. Compressed air energy storage was used for storing energy in wind diesel HRES in a northern Canadian village and was found to be

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advantageous. This storage technique was offered 60% 70% conversion rate for a complete charge discharge cycle for strong stochastic wind fluctuation. The system was found to be effective considering the economics, GHG reduction, and efficiency [56]. A study combined techno-economic optimization with energy scenario while comparing solar PV diesel battery hybrid system with a wind diesel battery system for providing electricity for 1000 people in Kutubdia Island, Bangladesh. A methodological framework utilizing two different modeling softwares sequentially, HOMER and RETScreen, was proposed for the analysis of the RE systems. The study suggested that solar PV diesel battery hybrid system has a low payback period of 5.4 years compared to 11.2 years for the wind diesel battery hybrid system [57]. Sebastia´n and Alzola have used a Ni MH battery system for energy storage for a wind diesel hybrid system due to their high-power capability, the absence of harmful substances, less maintenance, and robustness. They have modeled the systems and analyzed the performance for the variation in load and wind speed. The integration of the battery energy storage significantly improved the performance of the system comparing with no storage system [58]. Kazem et al. has evaluated different configurations of a hybrid system for Masirah Island, Oman to obtain a techno-economically viable solution. As high solar irradiance is observed across Oman and the wind potential in this island is one of the highest in the country, they studied the feasibility of different energy systems: only diesel generator, wind diesel battery hybrid system, PV diesel battery hybrid system, and PV wind diesel battery hybrid system. The PV wind diesel battery hybrid system was found to be the most costeffective configuration for the study [59]. In another study, solar PV diesel battery hybrid system was designed for a remote locality of Bangladesh and compared with a diesel-only system to provide the load demand. The results revealed that although the electricity cost for these two systems differs by a smaller amount ($0.461/kW h for PV diesel battery hybrid system and $0.423/kW h for diesel-only system), the CO2 emission is expectedly higher for diesel-only system (44,870 kg/year) compared to the other system (20,786 kg/year) [60]. Saheli et al. have analyzed the performance of a different hybrid system for domestic usage in Manitoba, Canada including PV wind diesel battery hybrid system, wind diesel battery hybrid system, PV diesel battery hybrid system, PV wind battery hybrid system, and diesel battery hybrid system. Among all these hybrid systems, PV wind diesel battery hybrid system was found to be the most optimal configuration and COE was found to be $0.444/kW h [61]. In another study, a diesel generator was integrated with PV wind battery hybrid system to ensure uninterrupted power supply in six different Algerian climate zones. The results indicated that the electricity cost greatly depends on the quality of RE potential in the study locations [62]. Performance analysis was carried out for a PV wind diesel battery hybrid system to provide electricity in a remote area in Malaysia. The designed hybrid system was found to reduce 25% GHG emission per year compared to conventional power plants [63]. Two different configurations of hybrid systems, PV biomass hybrid system and wind biomass hybrid system, were modeled for 20 families in a remote area in

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India. Biogas was derived from the cow dungs and human waste in the village and supplied to a biogas generator. The study also showed the wind biomass hybrid system to be more expensive in electricity generation than the former one [64]. A grid-connected wind biogas hybrid system was designed for different locations and different wind turbine capacities. The results suggested that the system is not economically feasible without community feed-in-tariff [20]. In another study, a solar biogas hybrid system was designed and modeled as a polygeneration system in a village in China where domestic garbage was utilized by converting them to biogas. Solar energy was used as one of the heating sources for the biogas conversion process. The payback period was found to be between less than 4 years and 5.54 years [65]. A PV biogas diesel battery hybrid system was modeled to provide electricity for 200 families in an island in Bangladesh utilizing the locally available energy resources. Very few shops in the island use small rooftop PV, the study aimed at expanding the energy system by providing electricity to more people. The designed hybrid system is expected to provide electricity with a COE of $0.217/kW h [11]. Hydro resources are also often utilized when designing a hybrid system. A pico hydro biogas battery hybrid system was modeled to provide electricity in remote villages in Cameroon where the hydro resources were utilized from river Mungo and compared with a solar PV biogas battery hybrid system. The later configuration was found to generate electricity with a COE of $0.395/kW h compared to a COE of $0.352/kW h for the former configuration [66]. Solar PV biogas battery hybrid system was compared with a biogas battery hybrid system for a location in India. The solar PV biogas battery hybrid system was found to have a COE of $0.476/kW h where the biogas battery hybrid system was comparatively expensive with a COE of $0.550/kW h. However, both these systems had a negligible environmental impact [67]. Habibullah et al. have analyzed the technical and economic challenges of a microgrid consisting of solar PV, wind energy, and biogas generators. In this study, solar and wind energy sources were considered as primary sources of energy, whereas biogas was used to provide additional demand [68]. PV wind biogas battery hybrid system was modeled to provide electricity in a refugee camp in the Chad Sudan border as a part of emergency energy module. The electricity provided will be used to provide water purification and vaccine cooling along with the lighting demand in the common area in the camp. The levelized cost of electricity was for the modeled hybrid system was found to be $0.275/ kW h [69].

5.7

Conclusion

Electricity networks in off-grid areas, where the primary energy resources are diesel and natural gas, add to global warming and provide relatively less reliable electricity. Integration of RETs has been proven to be techno-economically feasible for these systems. However, integrating variable RE resources introduce technical

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challenges that need to be investigated and mitigated to improve system stability and reliability. There exist extensive literature and research work on the optimal design and planning; and control of hybrid systems. But, a holistic planning approach for hybrid systems consisting of addressing technical and nontechnical challenges, focussing on worldwide stakeholders’ opinion, simulating system optimization and techno-economic analysis, power quality analysis, and finally integrating a modern technology that assists in mitigating the power output variability issues of renewables offers an innovative methodology. This chapter has thus presented a discussion on the significant technical and nontechnical challenges for integrating higher PV shares into a hybrid system; discussed a novel method to integrate technical, economic, social, and environmental aspects into the system planning; analyzed the power quality issue of frequency instability of an isolated remote hybrid system due to power output variability characteristics of a renewable resource; and finally investigated the application of the short-term PV forecasting method to improve PV integration into PV diesel battery hybrid energy system. Though the article explicitly considered solar PV as the only RES to demonstrate the holistic planning approach, it is applicable for any kind of hybrid system irrespective of the nature and number of resources. One section of this article has thus discussed various types of hybrid system configurations where multiple numbers of RE resources were introduced. Finally, this chapter has presented a conceptual framework for the planning of hybrid systems. The learning outcomes can be summarized as follows: G

G

G

G

G

G

G

G

A comprehensive planning approach is required to improve the PV integration and enable PV diesel battery hybrid system to accept higher shares of PV generation into the overall energy mix. Deterioration of power quality and lack of operating reserve are the two primary technical challenges that need to be addressed in a hybrid system. Nontechnical challenges such as various aspects of economic, social, and environmental factors need to be included in the planning of hybrid system. Analysis result shows, economic criteria should be given more priority over technical criteria during the planning phase of a hybrid power system. An AHP-MCDA-based novel planning approach reveals that, the integration of high levels of PV penetration and battery storage technology into a diesel-run power system is techno-economically feasible in a remote isolated area. This design also addresses several important environmental and social aspects. Grid-integrated Li-ion-based FFR batteries can offer synthetic inertia when the grid is exposed to severe net load fluctuations. This allows increasing the solar PV hosting capacity in a hybrid system without compromising frequency stability. The application of short-term PV forecasting offers a beneficial solution to address the technical challenges of high PV penetration in hybrid systems with favorable impacts on generator scheduling and operating reserve. In doing so, significant diesel fuel savings and positive environmental impacts can be achieved. Li-ion-based battery storage can complement sky imagery-based short-term PV forecasting mechanism regarding economic benefits and technical advantages.

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Acknowledgment The authors would like to acknowledge the scholarly contributions of Dr. Tania Urmee, Dr. G.M. Shafiullah, Dr. Martina Calais, Adjunct Prof. Craig Carter, Dr. Farhad Shahnia (all from Murdoch University, WA, Australia), Dr. Thomas Schmidt and Dorothee Peters (both from the DLR Institute of Networked Energy Systems, Oldenburg, Germany) in the methodology development of the research. The authors would also like to thank Murdoch University, Australia; University of Oldenburg, Germany, and DLR Institute of Networked Energy Systems, Oldenburg, Germany for permitting to conduct a fully funded research.

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[53] National Renewable Energy Laboratory System Advisor Model (SAM) general description (Version 2017.9.5). 2019, 2019. [54] J.K. Kaldellis, G.T. Vlachos, Optimum sizing of an autonomous wind diesel hybrid system for various representative wind-potential cases, Appl. Energy 83 (2) (2006) 113 132. [55] C. Li, et al., Techno-economic performance study of stand-alone wind/diesel/battery hybrid system with different battery technologies in the cold region of China, Energy 192 (2020) 116702. [56] H. Ibrahim, et al., Study and design of a hybrid wind diesel-compressed air energy storage system for remote areas, Appl. Energy 87 (5) (2010) 1749 1762. [57] S. Salehin, et al., Assessment of renewable energy systems combining techno-economic optimization with energy scenario analysis, Energy 112 (2016) 729 741. [58] R. Sebastia´n, R.P. Alzola, Simulation of an isolated wind diesel system with battery energy storage, Electr. Power Syst. Res. 81 (2) (2011) 677 686. [59] H.A. Kazem, et al., Optimum design and evaluation of hybrid solar/wind/diesel power system for Masirah Island, Environ. Dev. Sustain. 19 (5) (2017) 1761 1778. [60] S. Salehin, M.M. Rahman, A.K.M.S. Islam, Techno-economic feasibility study of a solar PV-diesel system for applications in northern part of Bangladesh, Int. J. Renew. Energy Res. 5 (4) (2015). [61] M.A. Saheli, et al., Performance analysis of a photovoltaic/wind/diesel hybrid power generation system for domestic utilization in winnipeg, manitoba, canada, Environ. Prog. Sustain. Energy 38 (2) (2019). [62] D. Saheb-Koussa, M. Haddadi, M. Belhamel, Economic and technical study of a hybrid system (wind photovoltaic diesel) for rural electrification in Algeria, Appl. Energy 86 (7) (2009) 1024 1030. [63] S.A. Shezan, et al., Performance analysis of an off-grid wind-PV (photovoltaic)-dieselbattery hybrid energy system feasible for remote areas, J. Clean. Prod. 125 (2016) 121 132. [64] S. Mishra, C.K. Panigrahi, D.P. Kothari, Design and simulation of a solar wind biogas hybrid system architecture using HOMER in India, Int. J. Ambient. Energy 37 (2) (2016) 184 191. [65] D. Wang, et al., Simulation of a solar-biogas hybrid energy system for heating, fuel supply, and power generation, Int. J. Energy Res. 41 (13) (2017). [66] E.M. Nfah, J.M. Ngundam, Feasibility of pico-hydro and photovoltaic hybrid power systems for remote villages in Cameroon, Renew. Energy 34 (6) (2009) 1445 1450. [67] R. Sharma, S. Goel, Stand-alone hybrid energy system for sustainable development in rural India, Environ., Dev. Sustain. 18 (6) (2016) 1601 1614. [68] M. Habibullah, et al., Economic challenges of hybrid microgrid: an analysis and approaches for rural electrification, in: AIP Conference Proceedings, 2017. [69] S. Salehin, et al., Designing of an Emergency Energy Module for relief and refugee camp situations: case study for a refugee camp in Chad-Sudan border, in: 2011 World Congress on Sustainable Technologies (WCST), 2011.

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G. Konstantinou and B. Hredzak School of Electrical Engineering and Telecommunications, The University of New South Wales (UNSW Sydney), Sydney, NSW Australia

6.1

Introduction

Hybrid energy systems (HESs) bring together different generation, storage, and consumption technologies in a single system, improving the overall benefits compared to a system that depends on a single source. Originally designed as a combination of conventional, nonrenewable generation (e.g., diesel generators) with battery energy storage systems (BESSs), their definition has now expanded to include systems that are 100% based on renewable energy [e.g., solar photovoltaics (PV) and wind], or combine different energy storage systems (e.g., BESSs, fuel cells, and supercapacitors). HESs have also grown in capacity from small, off-grid systems of a few kilowatt, typically designed for low voltage DC and AC to larger megawatt systems expanding to medium voltage grid-connected systems. The specific use cases for HESs, availability of resources, power and voltage levels, and grid connection are among the main reasons that define the requirements for the power electronics converters used in HESs. This chapter will explore the broad range of power electronics converters (Fig. 6.1) suitable for HESs including standard configurations and topologies designed to integrate two or more energy resources in a single converter.

6.2

Classification

Several different power electronics converter topologies and configurations can be used for the large variety of energy sources and loads that are part of a modern HES. Fig. 6.2 provides a high-level overview and a classification of the different converter topologies and configurations for HES. As we will see later in the chapter, these power electronics converters range from standard off-the-shelf systems all the way to specially designed configurations with a specific application and combination of energy resources in mind. Based on the previous discussion, categories would include: 1. Passive connection: A direct or “passive” connection of different energy resources with no power electronics converters is the simplest form of a DC HES. However, drawbacks such as the lack of any form of control over the HES, limited operation range for each generation unit, and suboptimal system utilization make this an unpractical solution, which will not be explored in this chapter. Hybrid Renewable Energy Systems and Microgrids. DOI: https://doi.org/10.1016/B978-0-12-821724-5.00008-8 © 2021 Elsevier Inc. All rights reserved.

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Figure 6.1 An example hybrid energy system and the multiple roles of power electronics in it.

Figure 6.2 An AC-bus connected hybrid energy system. 2. AC-bus connected HES: All generators and loads are connected to a common AC-bus, effectively forming an AC microgrid. The operation of AC microgrids is detailed in other chapters of this book, whereas some standard converter topologies are introduced in Section 6.1.3. 3. DC-bus connected HES: Forming a DC microgrid based on multiple standard DC DC converters such as those introduced in Section 6.1.4. 4. DC-side integration: Multiple DC resources can be connected to a single DC AC converter through either a (1) cascaded, (2) series or (3) parallel configuration. These will be analyzed in Section 6.1.5. 5. Three-port converters: These special configurations of DC DC converters provide connection for two resources in a single converter and will be analyzed in Section 6.1.6. 6. DC DC converter based: DC DC converters that can integrate different elements of generation and energy storage that do not fit in strict categories are summarized in Section 6.1.7.

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7. High-frequency links: Multiple separate energy resources can be connected through a common high-frequency link. Details are provided in Section 6.1.8. 8. Neutral-point-clamped multilevel converters with multiple energy sources: Multilevel converters in the family of neutral point converters provide split DC-links that can be used in HES applications as explored in Section 6.1.9. 9. Cascaded and modular converters: The multiple DC-links of cascaded and modular converters can be used for connection and independent control of different sources as seen in Section 6.1.10. 10. Solid-state transformers (SSTs): Integration of DC and AC links at different voltage levels can be achieved through SSTs enabling HES at different voltage and power levels. These configurations will be analyzed in Section 6.1.11.

6.3

AC bus connected HES

Connection of different generation, storage, and loads through a common AC-bus, effectively leading to the creation of an AC microgrid, represents the most common option for HESs. The reasons from a power electronics point of view are more than obvious. Configuring each source with a dedicated converter to a common AC-bus allows the use of well-established inverter topologies many of which have been optimized for a specific application and are commercially available. Such approach reduces the overall cost of implementation, simplifies the design procedure as well as the operation and maintenance requirements of a given HES. An example of an HES based on an AC-bus is provided in Fig. 6.2. Considering the level of maturity both in terms of topologies and control, the choice of a power electronics converter in an AC microgrid is more closely linked to manufacturer requirements, stock availability, competitive pricing, etc. rather than the unique benefits of an individual power electronics converter topology. Using the connection of solar PV systems as an example, Fig. 6.3 shows six different inverter topologies that can be used for this given application. All these converters are currently commercially available from different manufacturers. Given the abundance of literature in the topic of typical power electronics inverters, interested readers can consider extended reviews of converters for solar [1 4], wind [5 7], energy storage systems [8,9] as well as related articles on control of AC microgrids [10 12].

6.4

DC-bus connected HES

A less common but growing implementation of HES is that based on a common DC-bus, effectively forming a DC microgrid (Fig. 6.4). A major advantage of an architecture based on a common DC-bus is its compatibility with many forms of generation and storage such as solar PV, BESS, and fuel cells as the simpler DC DC power electronics converters are used in such applications [13]. Other advantages of this configuration include easier integration of modern DC loads, elimination of the “frequency” element of an AC microgrid and avoiding synchronization between AC sources.

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Figure 6.3 Single-phase DC AC converters for grid-connection of solar PV systems.

When power flow is only required in one direction, as is the case with many renewable energy generators, simple unidirectional DC DC converters based on standard DC DC topologies [14] (e.g., buck converter, boost converter, buck-boost converter, and flyback converter—see Fig. 6.5) can be used. A typical requirement for all these converters is that of high conversion efficiency, whereas the simple structure and control offered by these low-cost topologies is a major advantage in DC-bus connected HES. However, standard DC DC converters are not well-suited for all applications related to hybrid energy storage systems (HESSs), especially when the difference between the two DC voltage levels is substantial. This is due to the high-voltage stress across the devices, high current ripple, and relatively high loss in a standard boost DC DC converter. In that case, high-gain DC DC converters such as the switched capacitor converter of Fig. 6.6(A) and the multilevel cell DC DC converter of Fig. 6.6(B) are used to meet the requirements of the applications. Other high-gain DC DC topologies include interleaved converters, interleaved converters with coupled inductors and active clamps [15].

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Figure 6.4 A Hybrid energy storage system as part of a DC-bus connected HES. HES, Hybrid energy system.

Figure 6.5 A subset of the family of unidirectional DC DC converters.

In the case of energy storage systems, it is apparent that DC DC converters should be bidirectional to enable power to flow to and from the energy storage system. These converters [16] can include the bidirectional equivalent DC DC converters of the unidirectional converters, such as the bidirectional boost converter (Fig. 6.7A), bidirectional buck-boost converter (Fig. 6.7B), and the bidirectional SEPIC converter (Fig. 6.7C). These converters provide a simple structure with the basic bidirectional functionality required for BESS.

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Figure 6.6 High-gain DC DC converters.

Figure 6.7 A subset of the family of bidirectional DC DC converters.

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Figure 6.8 A Vienna rectifier as a unidirectional AC DC converter in an HES. HES, Hybrid energy system.

Bidirectional DC DC converters can also include additional circuits to facilitate zero voltage switching (ZVS) or zero current switching, interleaved DC DC converters, and high-gain bidirectional converters. In the case where galvanic isolation is required, this can be provided through dual-active bridge-based converters such as the ones presented in Refs. [17,18]. One exception to the previous case is the connection of distributed generation units that generate AC output voltage such as wind turbines and microturbines or in the case energy is stored in the form or a flywheel. In most of these cases, typical two-level and three-level converters are used to interface between the AC output and the DC-bus of a microgrid. In applications where full four-quadrant flow might not be necessary, topologies such as the Vienna rectifier [19] (Fig. 6.8) can be used in DC-bus connected HES.

6.5

DC-side integration of HES

This section overviews HESs that are formed by connecting DC resources to a single DC AC converter through either a (1) cascaded, (2) series or (3) parallel configuration.

6.5.1 Cascaded DC-connection In a cascaded HES with two DC resources either one or two DC DC converters can be used to connect them to a DC AC converter, as illustrated in Fig. 6.9A and B. In Fig. 6.9A, one DC resource is connected to the DC DC converter, whereas the other DC resource is directly connected to the DC AC converter. In this configuration, the DC DC converter connected DC resource can by fully controlled, whereas the other DC source acts as a DC link for the DC AC converter. In Fig. 6.9B, both DC resources are connected in cascade via individual DC DC converters. Individual DC DC converters allow full control of both DC resources. However, the overall efficiency is reduced as the output from the first DC DC converter feeds the second converter and the second converter (connected to the DC link) must handle the total load power.

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Figure 6.9 Configurations of DC DC converters for DC-side integration of HES. (A) Single cascaded connection, (B) double cascaded connection, (C) series connection, (D) parallel connection.

6.5.2 Series DC connection In a series HES, individual DC DC converters are used to interface the DC resources with the outputs connected in series and then in parallel with the DC link of the DC AC converter, as shown in Fig. 6.9C. This allows full control of each individual DC resource. One of the disadvantages, if implemented with conventional boost DC DC converters, is low fault tolerance due to inability to bypass a faulty converter. However, this is not the case if H-bridge-based DC DC converters that allow to bypass a faulty converter are used.

6.5.3 Parallel DC connection In a parallel HES, individual DC DC converters are used to interface the DC resources and the outputs are connected in parallel to the DC link of the DC AC converter, as shown in Fig. 6.9D. This configuration allows full individual control of the DC resources and each interfacing DC DC converter can be rated to handle only the

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power requirements of its DC resource. The structure provides a degree of fault tolerance since the system is operational if at least one of DC DC converters is not faulty.

6.5.4 DC-side integrated hybrid energy storage systems This section outlines implementations of HESSs using described configurations. HESSs combine complementary benefits of two different energy storage chemistries, most frequently batteries and supercapacitors. Batteries have high specific energy but limited power capability. For example, lithium batteries have specific energy of 50 500 W h/kg, specific power of 10 500 W/kg and typical charge/discharge efficiency of 75% 90% [20]. To achieve required power rating many batteries must be connected in parallel, which increases cost. Furthermore, the batteries’ lifetime is significantly affected by high current rates, current fluctuations, and continuous current ripples. On the other hand, supercapacitors have high power capability, long cycle life but low specific energy. Typical supercapacitors have specific energy of 1 10 W h/ kg, specific power of 1000 5000 W/kg and charge/discharge efficiency of 85% 95% [20]. Hence, by combining a battery and a supercapacitor into an HESS, a single ESS with high power and high energy capability, longer lifetime, higher charge/discharge efficiency, and lower overall cost can be created. In a one-converter based cascaded HESS, either a battery or a supercapacitor is connected to a DC DC converter. If the battery is connected to the DC DC converter then full battery control is possible and a low voltage battery can be used in case of a boost DC DC converter [21 24]. However, the supercapacitor voltage must be maintained at the required DC link voltage which does not allow optimal utilization of the supercapacitor’s stored energy. Also, since a typical maximum charge voltage of a supercapacitor cell lies between 2.5 and 2.7 V, many supercapacitor cells must be connected in series to build up the DC link voltage. If the supercapacitor is connected to the DC DC converter then the supercapacitor’s energy can be optimally utilized since its voltage can be controlled via the DC DC converter [25 27]. In case a boost DC DC converter is used then fewer supercapacitor cells must be connected in series. On the other hand, a high-voltage battery is required at the DC link. In a two-converter cascaded HESS, the battery and supercapacitor are connected in cascade via individual DC DC converters and their positions can be swapped. Individual DC DC converters allow full control of both the battery and the supercapacitor, which allows using lower voltage modules [28,29]. In a parallel HESS, two boost DC DC converters can be used to interface the battery and the supercapacitor to the DC link of the DC AC converter. Both the battery and the supercapacitor can have lower voltages than the DC link voltage. The battery and the supercapacitor currents can be controlled independently based on the power requirements [30].

6.6

Three-port converters

The concept of three-port (and consequently multiport) DC DC converters stems from the combination and optimization of standard DC DC converters to enable connection

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Figure 6.10 Three-port converters for direct integration of two sources.

to multiple dc sources. Owing to their ability to interface with different energy sources and storage (Fig. 6.10), they are a well-suited family of power electronics converters in HESs. Other applications of three-port converters include HESSs, combining batteries of different chemistry, or batteries with supercapacitors and fuel cells, and single BESS where the third port can be used as a ripple port that helps optimize the operation of the BESS, eliminating low and high-frequency ripples. Although three-port and multi-port DC DC converters lack the “plug and play” capabilities of typical single input singly output topologies, their advantages include increased conversion efficiency, higher power density, and improved converter utilization. As three-port converters can be derived by combinations and/or redesign of typical DC DC converters, the possibilities and resultant converter configurations are endless. One way of classifying these topologies is based on the degree of isolation between the different ports of the converter [31] into: 1. Nonisolated converters, where there is no galvanic isolation between any of the ports of the system, 2. Partially isolated converters, where the output port of the converter is isolated from the input ports, and 3. Fully isolated converters, where all converter ports have complete and independent galvanic isolation from each other.

Nonisolated converters are typically smaller in size and weight, lower in cost, and higher in efficiency due to the lack of transformers and reduced component count. On the other hand, partially and fully isolated three-port converters can provide higher voltage gain as the transformer ratio can be used as a variable in the design of the converter and are the only solution in applications where galvanic isolation is required. Fig. 6.11 shows an example of a nonisolated (Fig. 6.11A), a partially isolated (Fig. 6.11B) and a fully isolated three-port converter (Fig. 6.11C). Those readers interested in an in-depth review of three-port DC DC converters can refer to Refs. [31,32] and the associated references.

6.7

DC DC converter based

The flexibility in designing DC DC converters based on the requirements of a specific project or application has led to several topologies that do not strictly fit the previous categories. This is especially the case for DC DC converters in off-grid

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Figure 6.11 Configurations of three-port converters.

Figure 6.12 DC DC converter-based PV/battery systems in standalone applications.

or standalone applications where the requirements for voltage levels or inputs in an HES can vary substantially. Some example converters that fit this section are shown in Fig. 6.12. Fig. 6.12A [33] shows a power electronics converter that partially processes the power out of the solar PV system with the addition of an energy storage element in the output. However, as there is no direct control over the battery, it does not strictly fit the structure of a threeport converter, such as the ones presented in Section 6.1.6. A similar concept, with a battery integrated solar PV system, is shown in Fig. 6.12B [34]. The proposed converter can achieve distributed maximum power point tracking (MPPT) operation, whereas the inclusion of a battery increases the system yield for a standalone HES.

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Other topologies, like the ones introduced through the two previous power electronics converters, can be found in the literature; however, due to the limited scope and area of applications, further examples are omitted.

6.8

High-frequency link

High-frequency link power electronics converters represent a specific family of multiport DC DC with galvanic isolation where multiple different sources are connected through a common high-frequency DC-link. The independent control of each input in the multiport converter and the “plug and play” options of high-frequency link converters make them a competitive alternative in HES applications. One key advantage of such converter configurations is the reduction in the number of conversion stages required to interface different energy resources with energy storage. Power electronics converters with high-frequency links can be further classified into two distinct categories. The first one includes converters with individual transformers where each of the input port windings is linked to a separate transformer winding in the output port as shown in Fig. 6.13A [35]. The main advantage of this configuration is the true isolation between all inputs and outputs of the converter. The second category includes converters where all input windings are connected to a single winding in the output side of the converter. Examples of this configuration are provided in Fig. 6.13B D, where the simplification of the circuit compared to the ones with individual transformers can be appreciated [36 38].

Figure 6.13 High-frequency link integration for HES.

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As high-frequency link topologies require primary and secondary windings, most of these converters are based on the dual-active bridge converter. The specific requirements will further define whether the converters are designed as unidirectional or bidirectional, whether half-bridge or full-bridge modules are used, etc.

6.9

Neutral-point-clamped multilevel converters with multiple energy sources

The neutral-point-clamped (NPC) converter and other multilevel converters generate the multilevel voltage waveform through a split DC-link, which provides opportunities for connection of multiple sources and storage through a single converter in an HES. The key benefit of such an approach is the improved converter utilization. However, special care needs to be taken with both DC-side balancing and AC voltage generation. Fig. 6.14 shows three possible configurations of the NPC converter in an HES [39]. In the case of Fig. 6.14A, the solar PV system is connected across the DC-link voltage and MPPT is achieved by regulating the voltage Vc1 1 Vc2. Control of the battery is achieved by controlling the voltage across the lower NPC capacitor Vc2. This configuration requires appropriate sizing of the PV and energy storage system to regulate the two voltages without substantial voltage deviations. Operation of the system solely as an ESS is also not possible. A second configuration which also limits the operation of the system without the PV is shown in Fig. 6.14B. Here the PV and the energy storage system are connected independently to each of the two DC-link capacitors of the NPC with MPPT achieved by regulating the upper capacitor voltage Vc1 and control of the battery state of charge by regulation of the lower capacitor voltage Vc2. A way to address the limitations of the two previous configurations is through a parallel ESS connection to the total DC-link (Fig. 6.14C). The topology introduced in [39] can be considered similar to the parallel connection of the PV system and the ESS with the inclusion of two relays that allow some further control on which battery is being used as well as allowing operation as a grid-connected converter with a single resource (either PV or storage) when the other resource is not available.

6.10

Cascaded and modular multilevel converters

The family of cascaded and modular multilevel converters (MMCs), such as the cascaded H-bridge (CHB) converter and the MMC, provides multiple opportunities for the connection of multiple resources in an HES. The multiple DC-links that each topology provides allow, with some additional hardware and controllers, full control, and utilization of power flow within the converter. For the CHB converter shown in Fig. 6.15, each of the DC-links in the topology can be associated with a separate generation (e.g., solar PV, fuel cell) or storage element (e.g., batteries) providing a single converter solution for an HES. Cross-connected structures between bridges of different phases in a three-phase structure alleviate the

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Figure 6.14 Configurations of the NPC converter for HES. NPC, Neutral-point-clamped; HES, hybrid energy system.

problems of power generation unbalance at the cost of more complex circuitry and voltage isolation between the bridges. Compared to the CHB, the MMC provides an additional high-voltage DC-link shared between the three phases, which can be used as an additional port in an HES. Some example configurations include (1) HESSs with batteries in the main DC-link and supercapacitors in each MMC sub-module (SM), (2) PV-storage integrated

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Figure 6.15 Modular and cascaded multilevel converters for HES. HES, Hybrid energy system.

Figure 6.16 Cascaded boost converter.

converter with a main PV DC-link and storage in the individual SMs, or (3) a hybrid PV-storage system with all components integrated in the MMC SMs [8]. The structure of cascaded converters with multiple DC-links can be extended further to fully utilize their advantages in HES. One such topology is the cascaded boost converter shown in Fig. 6.16. This topology can be used as a HESS with different battery chemistries or supercapacitors in each of its modules or for integration of sources and storage. The main advantages of the cascaded boost converter compared to a CHB include lower number of modules for equivalent output AC

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voltage, lower second harmonic, and switching ripple at the batteries, together with a high-quality sinusoidal output voltage [40]. Another example of modular topologies suitable for HES is the hybrid MMC of [41] shown in Fig. 6.17, which combines the common three-phase DC-link for integration of renewable energy resources with an energy storage system that is partially integrated in one SM per arm and shared between the three phases of the converter. This configuration allows for multiple power flows between the DC and AC sides of the converter and the energy storage system while eliminating low-frequency harmonics from the batteries.

Figure 6.17 Hybrid modular multilevel converter.

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Figure 6.18 A three-stage solid-state transformer.

6.11

Solid-state transformers

SSTs or power electronics transformers [42] are modern network devices that combine multiple power electronics converter stages with a medium frequency (B2 10 kHz) or high frequency (.20 kHz) transformer. Extending the typical functionalities of a regular transformer, advantages of SSTs include enhanced power flow control, decoupling between the high-voltage and low voltage networks, enhanced voltage regulation, harmonic isolation, and power quality regulation [43]. The most common and flexible configuration of an SST is based on the threestage configuration (shown in Fig. 6.18) with three distinct converter stages: (1) an interface between the HVAC and HVDC side, (2) an interface between the LVDC and LVAC side, and (3) a DC DC conversion stage, which incorporates the medium frequency/high frequency transformer. Owing to the three-stage configuration, four input terminals (two AC and two DC) are creating a flexible multiport device. The four different ports can serve as four independent AC and DC buses, allowing the different elements of an HES to be connected while providing regulation of the power transfer between the different generators and loads [44]. Alternatively, they can be used for direct integration of single HES elements such as wind turbines and AC loads on the AC-side or energy storage on the DC-side while maintaining full controllability of the overall system. Additional benefits of the three-stage SST in an HES are the independent design based on power requirements at each terminal and the use of standard topologies for each conversion stage. These may include (1) modular converters such as the CHB and MMC for the HVAC side of the SST, (ii) unidirectional or bidirectional topologies for the DC DC stage including dual-active bridge and LLC converters, and (3) interleaved two- and three-level converters for the LVAC side

6.12

Summary

The nature of HESs, with sources and loads on AC and DC buses, means that power electronics are fundamental components of any HES. As explored in this chapter, power electronics converters in HES can range from the well-established DC DC converters and DC AC inverters to specialized topologies for specific applications including high-gain converters, hybrid topologies, multilevel converters, etc. By appropriately selecting and designing the HES, power electronics can contribute to its optimal, reliable, robust, and resilient operation.

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Acknowledgment The authors would like to acknowledge Mr. Pingyang Sun for helping with the preparation of the figures in this chapter.

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Photovoltaic power plant planning and modeling

7

Julius Susanto School of Engineering and Information Technology, Murdoch University, Perth, WA, Australia

7.1

Introduction

Solar photovoltaic power plants (referred to as “PV plants” hereafter) are characterized by several unique features: (1) subject to fairly predictable diurnal and seasonal variations in output power, (2) intermittent in nature and subject to less predictable weatherinduced output power fluctuations (e.g., due to cloud cover), and (3) inherently inertia less [1,2]. As a result of these features, PV plants pose a few challenges for their integration into small standalone hybrid energy systems and microgrids (referred to as “hybrid microgrids” hereafter). For example, the hybrid microgrid must have enough flexibility to respond to the PV plant’s active power variations over short-term intervals. Despite these weaknesses, PV plants have a number of advantages that make them attractive in hybrid microgrid applications, for example, competitive capital costs, no special resource requirements except for land and/or roof space, largely modular design that facilitates rapid construction, lower environmental impacts compared to other forms of generation, no ongoing fuel costs, and relatively low operations and maintenance requirements [3,4]. In many cases, PV plants represent one of the lowest cost, if not the least cost generation option [5]. This is particularly the case in remote and isolated areas, where the introduction of PV plants can lower average energy generation costs [6]. Therefore there is often an economic incentive to maximize the penetration of PV wherever possible. In the literature the planning of PV plants for hybrid microgrid applications has predominantly focused on modeling energy balances and the techno-economic optimization of the microgrid using software-based tools such as HOMER [7,8], RETSCREEN [9], and Sandia’s Microgrid Design Toolkit [10]. These long-term techno-economic planning models are designed to search for the least cost capacity investment mix of energy resources (including PV) for a self-sufficient microgrid operating under normal conditions [11]. However, long-term techno-economic models do not tend to consider the real-time technical aspects of integrating PV into hybrid microgrids, such as managing intermittency, excess PV output, frequency stability, and system strength. These integration and operational challenges are covered in more focused technical studies [12]. A central microgrid controller can be used to manage energy resources and loads in real time [13]. The control strategy of the microgrid can also be designed to regulate frequency and voltage with specific consideration to high penetration of intermittent energy Hybrid Renewable Energy Systems and Microgrids. DOI: https://doi.org/10.1016/B978-0-12-821724-5.00011-8 © 2021 Elsevier Inc. All rights reserved.

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sources such as PV [14]. Other methods proposed for facilitating greater PV penetration into a microgrid include more accurate short-term forecasting of the solar resource [15], demand response using productive dump loads [16], and the use of inertial support devices such as flywheels [17]. In this chapter an overview of the planning and modeling of PV plants for hybrid microgrid applications is discussed, with an emphasis given to the technical issues related to PV plant integration. This chapter is organized as follows: Section 7.2 introduces key metrics for planning PV plants for hybrid microgrids. Section 7.3 discusses how the design of the hybrid microgrid influences PV plant planning. Section 7.4 describes specific technical issues that PV plants pose to hybrid microgrids and how these issues can be managed. Section 7.5 offers conclusions and a summary of the chapter. Finally, the appendix presents a brief overview of the standard planning considerations applicable to all PV plants.

7.2

Photovoltaic plant planning for hybrid microgrids

Despite there being an economic incentive to maximize the penetration of solar PV, there are techno-economic constraints on the amount of PV capacity that can be practically integrated into a hybrid microgrid. This is because hybrid microgrids are small in scale (i.e., relative to a conventional utility grid) and unlimited PV plant output cannot simply be absorbed by the system in an uncontrolled fashion. As a result, the planning of PV plants for hybrid microgrid applications must consider the nature of the solar resource itself (e.g., diurnal and intermittent) and how it compares with the microgrid demand profile. In this section, three planning metrics specific to PV plants for hybrid microgrids are introduced to help guide the PV planning process: 1. load matching index (LMI), which quantifies the proportion of the load that is supplied directly by the PV plant; 2. PV utilization index (PUI), which quantifies the proportion of the PV plant output that is consumed by the load; and 3. solar irradiance variability index (VI), which quantifies the level of intermittency of the solar resource relative to ideal clear sky conditions.

7.2.1 Load matching index Load matching refers to the degree of temporal alignment between the output of the PV plant and the load of the hybrid microgrid. A number of load matching indicators were reviewed and assessed by Salom et al. for net-zero energy buildings that are similar to hybrid microgrids, in that they have local generation, load, and storage [18]. For the planning of PV plants for hybrid microgrids the following daytime LMI adapted from Ref. [18] is proposed: ÐT max½Pload ðtÞ 2 PPV ðtÞ; 0dt (7.1) LMI 5 1 2 0 ÐT 0 Pload ðtÞdt

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where PPV ðtÞ is the PV plant output at time t, Pload ðtÞ is the system load at time t, and T is the evaluation period for the LMI calculation (e.g., evaluated over 1 day, 1 season, 1 year). In general, it is desirable to have the highest data resolution possible (e.g., 1-min samples), but for planning studies, only coarse time-sequential data may be available (e.g., 30- or 60-min averaged data from energy yield software simulation outputs). The LMI is a dimensionless metric that indicates how closely the PV plant output tracks the load (on average). An LMI of 1 represents a perfect alignment between the PV plant output and load, while an LMI of 0 represents no alignment whatsoever (e.g., all the load is at night). Because of the diurnal nature of solar PV, an LMI greater than 0.5 is considered high. Fig. 7.1 shows examples of LMI calculations for four different PV plant output and load profiles (evaluated over a 24-h period). Fig. 7.1A depicts a load that is completely unaligned with the PV plant output, that is, demand is low during the day but high at night. Fig. 7.1B shows a relatively flat and consistent load profile that is representative of an industrial or commercial load that operates 24 h a day. Fig. 7.1C is typical of a load profile in a tropical or hot climate where air conditioning and commercial activity lead to higher demand during the day. Finally, Fig. 7.1D depicts a largely daytime load such as a commercial hub during working days.

Figure 7.1 Examples for the LMI and PUI: (A) low LMI (0.11), low PUI (0.24); (B) low LMI (0.27), high PUI (0.92); (C) medium LMI (0.40), medium PUI (0.63); (D) high LMI (0.64), high PUI (0.81). LMI, Load matching index; PUI, PV utilization index.

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The LMI is important in PV plant planning as it determines an upper limit for the PV plant’s ability to supply the microgrid load, that is, there is a threshold at which increasing the PV plant capacity will not lead to corresponding increases in the LMI. Clearly, a high LMI is desirable, but this is largely contingent on the nature of the load. Systems with intrinsically low LMIs (like in Fig. 7.1A and B) suggest that small levels of PV penetration (as a share of the total generation mix) is most suitable, unless significant levels of energy storage are employed or there is high flexibility in the load (e.g., to accommodate load shifting). Conversely, systems with intrinsically high LMIs (like in Fig. 7.1D) lend themselves to high levels of PV penetration.

7.2.2 Photovoltaic utilization index A counterpart to the LMI is the PUI, which is an indicator of how much of the PV output is consumed by the load (and by implication, how much PV output needs to be curtailed, stored, or diverted): ÐT PUI 5 1 2

0

max½PPV ðtÞ 2 Pload ðtÞ; 0dt ÐT 0 PPV ðtÞdt

(7.2)

Like the LMI, the PUI is also dimensionless. A PUI of 1 means that all of the PV outputs are consumed by the load, while a PUI of 0 means none of the PV output is consumed by the load. PUI calculations are also shown in Fig. 7.1. In Fig. 7.1A, it can be seen that because the LMI is low and the PV plant is oversized for the load, the PUI is also correspondingly low. On the other hand, Fig. 7.1B depicts a case where the PV plant is sized to be highly utilized despite a low LMI (as is the case in Fig. 7.1D, except with a high LMI). Fig. 7.1C shows a moderate level of PV utilization, in which the excess unused PV output needs to either be curtailed, stored, or diverted. For the planning of PV plants, it is generally desirable to maximize the PUI, particularly when coupled with a high degree of load matching. However, it should be noted that a very high LMI and PUI (near 1) indicate that the PV plant is actually undersized and a higher capacity PV plant could be integrated (barring other technical factors). Finally, a low utilization does not necessarily mean the PV plant is oversized, as long as the excess PV output is being used productively (e.g., energy storage or a productive dump load). The management of excess PV output is covered in more detail later in Section 7.4.2.

7.2.3 Solar irradiance variability index Fluctuations in solar irradiance throughout a day can be quantified as a single value using the VI proposed by Stein et al. [19]: qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ½GHIðt11Þ2GHIðtÞ2 1 ΔT 2 t51 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi VI 5 P  2 T21 GHIclear-sky ðt11Þ2GHIclear-sky ðtÞ 1 ΔT 2 t51 PT21

(7.3)

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239

where GHI is the measured global horizontal irradiance (W/m2), GHIclear-sky is the clear-sky irradiance and ΔT is the averaging interval, while T is the number of consecutive measurements (e.g., given a minute-averaged time series, ΔT 5 1 and T 5 1440 for a 24-h period). The VI provides a useful measure for classifying different days based on their solar intermittency relative to the expected clear-sky irradiance profile. A VI of close to 1 represents the ideal clear-sky day (refer to Fig. 7.2A), while a high VI is more representative of a mixed-sky day (refer to Fig. 7.2B). Note that an overcast day may have a low VI despite having uniformly low irradiance throughout the day (as an example, see Fig. 7.2D). VIs for a single location can be calculated for each day over a period of time (typically over one full calendar year to cover seasonal variations) and combined to form empirical cumulative distribution functions (CDFs). The empirical CDFs for four weather stations in Australia over the 2017 calendar year are shown in Fig. 7.3. The CDFs provide insights into a site’s variability over time. For example, the Broome site (refer to Fig. 7.3A) shows VIs that are close to 1 for roughly 41% of

Figure 7.2 Solar irradiance and the corresponding VI for 4 sample days at a single location: (A) a clear sky day (VI 5 1.04), (B) a day with frequent cloud movements or mixed-sky day (VI 5 31.76), (C) a clear day with a few passing clouds throughout the day (VI 5 6.07), (D) an overcast day (VI 5 3.36). VI, Variability index.

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Figure 7.3 Empirical CDF for the VI at four weather stations in Australia over the full year 2017—(A) Alice Springs, Northern Territory: Broome, Western Australia, (B) Adelaide, South Australia: Wagga Wagga, New South Wales, (C) Rockhampton, Queensland: Darwin, Northern Territory, (D) Cape Grim, Tasmania. CDF, Cumulative distribution function; VI, variability index.

the year, indicating a site that has consistent clear sky days. On the other hand, Darwin (refer to Fig. 7.3C) exhibits a much higher spread in variability indices, suggesting a site with a higher prevalence of mixed-sky days. The VI data in the CDFs can also be represented as probability of nonexceedance (PONE) values, indicating the probability that a VI is not exceeded. For example, Table 7.1 shows PONE values for the four weather stations in Fig. 7.3. At Broome a P90 value of 10.8 means that the VI will not exceed 10.8 for 90% of the year. The implications of the VI on PV plant planning revolve around the flexibility that is required of the hybrid microgrid, that is, to accommodate fast changes in PV plant output. At sites with low average VIs (such as the Broome site above with VIs less than 5 over 60% of the time), less flexibility is required, and larger PV plants can be integrated into the system. On the other hand, sites with high average VIs (such as the Cape Grim site above) have more prevalent PV output fluctuations and thus require more flexibility from the power system. The management of PV intermittency and management of excess PV output is covered in more detail later in Sections 7.4.1 and 7.4.2, respectively.

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Table 7.1 Probability of nonexceedance (PONE) values for four weather stations in Australia over the calendar year 2017. PONE

P50 P75 P90 P95 P99 P100

VI Broome, WA

Wagga Wagga, NSW

Darwin, NT

Cape Grim, TAS

2.8 7.3 10.8 13.8 21.2 28.7

5.3 9.4 14.0 16.7 20.9 25.6

7.3 11.6 15.6 17.2 21.4 24.4

8.2 12.1 16.9 19.7 24.6 32.9

VI, Variability index.

7.3

Hybrid microgrid design and photovoltaic plant planning

The planning of PV plants for hybrid microgrid applications is significantly influenced by the design philosophy, topology, and control architecture of the microgrid itself. Key system design decisions can materially limit the maximum PV plant capacity that can be practically integrated into the microgrid. In this section the following four key design decisions for hybrid microgrids are introduced and discussed in terms of their impact on the planning of PV plants: 1. 2. 3. 4.

synchronous versus inverter-based grid forming centralized versus decentralized control centralized versus distributed generation AC versus DC coupling

7.3.1 Synchronous versus inverter-based grid forming Grid-forming generators can generate a reference voltage and can operate a standalone system by managing system frequency and voltage, for example, diesel/gas engine genset, biomass generator, battery inverter. [The terms “grid forming” and “grid following” have traditionally only referred to inverters, but here they are extended to all types of generators (without any loss of applicability).] Also called off-grid or standalone. Grid-following generators require an existing voltage reference to tie into and cannot operate independently of an existing grid, for example, solar PV inverters, wind turbine generators (all types). Also called on-grid, grid-tie, or grid-supporting.

A standalone hybrid microgrid needs to have at least one grid-forming generator or power source. Only dispatchable sources can be grid forming, such as conventional thermal generators, as well as energy storage inverters [20].

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There are typically two types of grid-forming generators: (1) synchronous (rotating) machines and (2) static inverters. The type of grid-forming generator is material for PV plant integration because of the inherent differences between each type of generator. Synchronous machines are electromechanically coupled to the grid, and the rotational speed of the prime mover (e.g., diesel engine, gas turbine, steam turbine) is directly proportional to the system frequency. Any instantaneous oversupply or shortfall of generation is balanced by the energy stored in the inertia of the rotating masses electromechanically coupled to the system (e.g., shafts, flywheels, turbines). Power supply and demand imbalances are naturally transmitted throughout the system via a changing frequency. As a result, frequency has natural significance in a synchronous power system and indicates mismatches between generation and load [21]. For PV plants connected to synchronous power systems, frequency stability, system strength, and the minimum loading of the synchronous machine prime movers become important considerations for planning (refer to Sections 7.4.27.4.4 for more detail). Grid-forming inverters are coupled to the grid via power-electronic interfaces and can generate voltages at any frequency desired. Therefore, unlike in synchronous systems, frequency is not intrinsically meaningful in inverter-formed grids. Instead, instantaneous supply and demand imbalances are manifested in voltage dynamics at the DC side of the grid-forming inverter. In other words, the DC voltage (and the energy storage devices that support it) plays an equivalent role as inertia in a synchronous system [22]. For PV plants connected to inverter-formed power systems, frequency stability is not seen as an issue because frequency is more or less an artificial quantity generated by the inverter itself (and not necessarily bound by physical laws like in synchronous systems) [23]. There is preliminary evidence to support the notion that issues related to low system strength in synchronous systems [e.g., phase-lock loop (PLL) stability and synchronization problems] are potentially limited in inverterformed power systems as the voltage waveform generated by the inverter is not significantly distorted under fault conditions [24]. However, for inverter-formed power systems, consideration should be given to sizing the PV plant such that the gridforming inverter operates within its stable operating range. This could include enabling remote control capabilities on the PV plant to prevent excessive oversupply imbalances that could trigger the grid-forming inverter protection. Note that there can be multiple grid-forming generators in a microgrid (see Fig. 7.4), but typically, only one can be in grid-forming mode at any one time. The multiple grid-forming generators could be of different types (i.e., an interchangeable grid-forming inverter and synchronous machine), so the microgrid could be synchronous or inverter-formed depending on which of the generators form the grid.

7.3.2 Centralized versus decentralized control Control of hybrid microgrid can either be centralized or distributed (decentralized): 1. Centralized control: is when a single master microgrid controller controls all generators and other system elements (e.g., dump loads, load shedding). The master microgrid controller may also determine and select which of the generators is grid forming (Fig. 7.5A) [13].

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Grid-forming generator (A)

243

Grid-following generator

(B)

Figure 7.4 (A) Single and (B) multiple grid-forming generator architectures.

Figure 7.5 (A) Centralized and (B) decentralized control architectures. 2. Decentralized control: is when individual generators and system elements have their own independent control systems (that may or may not be linked via communications interfaces) (Fig. 7.5B).

Centralized control has the advantage of facilitating precise and responsive control of all elements in the system from a single location. However, this requires communication links between the master microgrid controller and elements to be controlled. Moreover, the system may be more difficult to upgrade or extend as the master microgrid controller would need to be modified and there may be a risk of vendor lock-in. Finally, the master microgrid controller itself could be a single point of failure. Centralized control would be suitable for smaller systems and those where resource optimization is important. For PV plant integration into centrally controlled microgrids, consideration should be made to installing a communications interface to the master microgrid controller, as well as updating the configuration and logic of the control algorithm. Decentralized control allows for system elements to operate independently and thus offers higher redundancy (particularly in systems with multiple grid-forming

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generators). The system is also more flexible to upgrades or ad hoc extensions (e.g., connection of additional generation and load) as a master microgrid controller does not to be updated. However, there is a risk of unforeseen and adverse interactions between the independent control systems. Furthermore, the control is less precise and response and may not optimally manage energy resources in the system. Decentralized control is typically more suitable for larger geographically diverse systems with distributed generation. For PV plant integration into decentralized microgrids, considerations should be made to ensuring that connection of the PV plant does not adversely impact the other generators in the system, for example, causing thermal generators to operate below minimum load due to high PV penetration (refer to Section 7.4.2 for more detail).

7.3.3 Centralized versus distributed generation Generating units in a hybrid microgrid can be centralized or distributed [25]: 1. Centralized generation: is where all generators are physically situated in one location, for example, at a central power plant (Fig. 7.6A) and 2. Distributed generation: is where generators are distributed around the network, ideally close to loads to minimize distribution losses (Fig. 7.6B).

Centralized generation topologies have the advantage of being easier to maintain since all generators are physically installed at a single location. Communication links between generators and/or to a master microgrid controller are also cheaper to install and more reliable. However, for microgrids with more dispersed loads, there may be higher network losses and voltage regulation issues as the centralized generation can be located a long way from the loads. Centralized generation is generally suitable for small, compact systems or systems with low levels of load matching. In distributed generation topologies, generators can be installed close to loads to reduce network losses and support local bus voltages. This is particularly advantageous if the load is amenable to a distributed PV design with both high load matching and PV utilization indices. When both the LMI and PUI are high, power flows

Figure 7.6 (A) Centralized and (B) distributed generation topologies in hybrid microgrids.

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between PV plants and other generators/storage are minimized. However, when the LMI and PUI are low, excess PV output may potentially need to flow long distances to be consumed by a load or energy storage device. Distributed generation is typically suitable for geographically large, sparse, and low-density systems. For the integration of distributed PV into a microgrid, consideration should be given to aligning each PV plant capacity with the load in the vicinity of the PV plant to minimize network losses. For centrally controlled systems, consideration should also be made to optimize the distributed PV plant layout to minimize the communications cabling required to the master microgrid controller, for example, by daisy-chaining communications cables between successive PV plants.

7.3.4 AC versus DC coupling For hybrid microgrids with DC generation, energy storage, and/or loads, a key design decision is whether these DC elements are either AC or DC coupled [26]: 1. AC-coupled systems: are when the DC elements are connected together at the AC network level and all DC elements have their own DC/AC inverter/converters (Fig. 7.7A). 2. DC-coupled systems: are when the DC elements are connected at a separate DC bus and use one or more common AC/DC converters (which can be unidirectional or bidirectional). Either a common converter or a synchronous generator can form the AC grid. Note that DC/DC converters (e.g., PV charge controllers) may still be required for connection to the DC bus (Fig. 7.7B).

AC-coupled systems are generally more efficient if there are only AC loads in the system as the PV plant output can directly supply the load with only a single stage of conversion. AC-coupled systems are also more flexible in terms of system design and expandability since multiple generations and storage sources can be added in a parallel and distributed manner with fewer limitations than in DC-coupled systems (where the capacity of the common AC/DC converter may constrain expansion). The most

Synch hronous generator

Synchronou us generator PV plant PV plant AC

AC C AC loads DC AC loads Energgy storage

DC loads

(A)

Figure 7.7 (A) AC- and (B) DC-coupled systems.

(B)

Energy storage

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common applications for AC coupling include larger systems with distributed generation, systems without batteries or DC loads, systems with high load matching factor or low penetration of intermittent sources. For PV plant integration into AC-coupled systems, consideration should be given to optimizing the capacity of the PV plant so as to supply the loads and charge the energy storage while not causing the synchronous generator(s) to operate underloaded or to minimize spilled/curtailed energy. Communications to a microgrid controller may be required to facilitate PV curtailment. DC-coupled systems are more efficient for supplying DC loads or for heavy battery charging from intermittent generation (e.g., solar PV). However, the capacity of the common AC/DC converter(s) may limit expansion in both PV plant capacity and AC load demand. Moreover, there may be geographical limits for the spatial location of elements connected to the DC bus as it is usually low voltage and elements connected at long distances can be subject to excessive network losses. The most common applications for DC coupling include small systems with centralized generation, systems with DC loads, systems with high levels of battery charging, and/or high penetration of intermittent generation. For PV plant integration into DC-coupled systems, consideration should be given to optimizing the capacity and distance of the PV plant to prevent large DC-side losses, as well as operating within the capacity of the common AC/DC converter(s). There may also need to be communications and control coordination between parallel solar charge controllers and/or the common AC/DC converter(s) (i.e., if bidirectional and controlling DC-side voltage) to ensure that the DC bus voltage is stably and accurately regulated. Finally, there is also the possibility of a hybrid AC/DC-coupled system with different generation and storage sources coupled at both the AC and DC buses.

7.4

Special technical considerations for hybrid microgrids

The nature of the solar PV resource (i.e., intermittent and nondispatchable) and PV plant technology (i.e., inertia less and inverter interfaced with low fault contribution) pose special technical issues for integrating PV plants into hybrid microgrids. The intermittency of the PV output requires flexibility from the microgrid, while its nondispatchability means that any excess PV energy needs to be managed so as not to disrupt the system (ideally without wasting the energy generated). On the other hand, the inertia-less nature of PV plants can impact frequency stability, while the inverters may have problems remaining synchronized in weak synchronous systems. In this section, these special technical issues for integrating PV plants into hybrid microgrids are addressed: 1. 2. 3. 4.

management of PV intermittency management of excess PV output frequency stability system strength

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7.4.1 Management of photovoltaic intermittency The inherent uncertainty in a solar PV plant’s power output (e.g., from clouds) means that the microgrid must provide short-term operational flexibility (particularly in active power response). Flexibility is defined as the ability of a power system to deploy its resources to respond to changes in the load net of variable generation [27]. As indicated in Section 7.2.3, the amount of flexibility required depends on the expected VI of the solar resource. There are essentially two elements to providing operational flexibility in a hybrid microgrid: 1. Operational reserves: the aggregate headroom and footroom available from all dispatchable resources (in kW or MW) and 2. Aggregate ramp rate: the maximum ramp rate (in aggregate) of all dispatchable resources (in kW/min or MW/min).

In the context of PV plant intermittency the two elements of flexibility basically determine whether the system has sufficient quantity and speed of reserves to balance out short-term PV output fluctuations. Fig. 7.8 shows the flexibility limits of a power system graphically. With the dispatchable resources in the system initially operating at point OP at time t0, the shaded area represents the feasible active power trajectory of the system in response to PV output fluctuations. The PV plant should be sized such that the expected fluctuations stay within the “flexibility boundary.” Previous studies indicate that the output fluctuations from utility-scale PV plants can reach extremes of up to 50% loss in plant output in 1 min [28]. For smaller fractional megawatt PV plants, this can potentially be even higher. The speed of such fluctuations may serve to put a limit on the maximum capacity of the PV plant as these fluctuations may be beyond the aggregate ramping capability of the other generators (and load responses). In synchronous microgrids, large frequency deviations may occur under such circumstances. To mitigate shortfalls in operational reserves and ramping capability that cannot be alleviated through operational measures (e.g., generator redispatch), PV plants can be designed with smoothing (or firming) capabilities. This can be achieved via the PV

Figure 7.8 Power system flexibility boundary.

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plant control system, for example, having the PV plant curtailed and dispatched below its capacity with a fixed headroom to accommodate short-term fluctuations [29]. Another option is to adjust the DC/AC capacity ratio of the PV plant so that the PV inverter capacity (in kWac or MWac) is lower than the nominal DC capacity of the PV array (in kWp or MWp). For example, a DC/AC capacity ratio of 1.25 means that there is 25% more DC capacity, which essentially serves as variable headroom when the PV plant is near full AC output. Alternatively, an energy storage system can be installed to provide smoothing or firming of the PV plant output [30,31]. In Ref. [32] an empirical relationship between the VI and minimum size of a smoothing battery (using a 10-min moving average smoothing algorithm) was proposed: Ebat 5 ð0:0046 VI 1 0:0567ÞPn

(7.4)

where Ebat is the minimum capacity of the smoothing battery (in kW h) and Pn is the nominal power of the PV plant (in kWp). For example, if the VI is 10 (at P95) and the PV plant capacity is 100 kWp, then the minimum smoothing battery capacity is 10.3 kW h. Finally, the rapid and accurate control of a dump load (such as a dynamic resistor) can be used to smooth out PV plant output fluctuations [16].

7.4.2 Management of excess photovoltaic output When the PV plant output exceeds the instantaneous load demand, there are four general options for dealing with the excess PV output: 1. Other generation in the microgrid can be redispatched or automatically turned down to accommodate the excess PV output. 2. The excess PV output can be curtailed. 3. The excess PV output can be used to charge the energy storage system, for example, batteries. 4. The excess PV output can be diverted to other auxiliary/discretionary loads.

The default behavior of the system when there is excess PV output depends on the hybrid microgrid design: G

G

G

In DC-coupled systems the solar charge controller will naturally prevent overvoltages on the DC bus and curtail the PV output by disconnecting the PV array from the DC bus or by adjusting the operating point on the VI curve to a low current value. In AC-coupled inverter-formed systems the grid-forming inverter will typically have features to curtail excess PV output, either by direct control of the PV inverters or through a decentralized control strategy. For example, the SMA Sunny Island inverter will increase the microgrid frequency and the PV inverters will automatically curtail their output based on an overfrequency ramp-down characteristic [33]. In AC-coupled synchronous systems, there is no inherent curtailment of excess PV output, but the grid-forming generator (typically in isochronous or droop-control mode) will balance the system by decreasing its output to accommodate the excess PV.

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For thermal synchronous generators (such as diesel or gas engine generators), decreasing output to accommodate excess PV may cause units to operate below the minimum generation levels recommended to maintain high fuel efficiency and/or prevent long-term damage (e.g., for diesel generators, loading should be .40% to prevent damage and .70% for high fuel efficiency) [17]. Specific microgrid control functionality can be installed to curtail PV output to prevent underloading of thermal generators. For hybrid microgrids with energy storage the excess PV output can be used to charge the energy storage system. As solar PV is arguably one of the most economic methods to charge energy storage systems, this option for dealing with excess PV output should take precedence (if it is available). However, the problem persists in cases where the energy storage system is fully charged and there is still an excess of PV output. The final option is to divert the excess PV output to another load. In the ideal scenario, there is enough flexibility in the underlying microgrid loads to switch on discretionary loads that are otherwise productive. This could include load shifting, where flexible loads that would have otherwise been switched on at another time are rescheduled (e.g., washing machines, hot water heaters, and water pumping), as well as productive but discretionary loads such as refrigerative cooling (e.g., for cold storage), hydrogen gas electrolysis, and water desalination [34]. Alternatively, the excess PV output can be diverted to an unproductive dump load (typically a resistor bank). Using an unproductive dump load essentially wastes the excess PV output and is more or less equivalent to curtailment. The exception is when diversion to a dump load provides additional ancillary services such as frequency control, intermittency smoothing, and/or contingency reserves. For example, a dynamic resistor is a finely controlled dump load that can rapidly adjust its active power consumption to balance the system. Dynamic resistors have been installed in several hybrid microgrids including the Coober Pedy and Rottnest Island microgrids in Australia [16,35]. A recommended order of precedence for dealing with excess PV is as follows: 1. Charge energy storage system. 2. If energy storage system is fully charged, then divert excess energy to discretionary, but productive loads. 3. Divert excess energy to unproductive loads that can provide ancillary services. 4. Redispatch the other generation within normal operating limits. 5. Curtail PV output or divert to an unproductive dump load.

7.4.3 Frequency stability For hybrid microgrids with synchronous grid forming, frequency stability can put an upper limit on the capacity of the PV plant that can be practically integrated. This is because hybrid microgrids are typically low-inertia systems, and as a result, even moderate imbalances between generation and load (e.g., from a PV plant contingency or extreme output fluctuation event) can lead to material system frequency deviations. For example, consider the curves in Fig. 7.9, where in each subfigure, the system frequency response is shown for a PV plant contingency/trip event at

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Figure 7.9 System frequency responses for a loss of PV plant contingency at different PV plant nominal capacities (in percentage relative to load). PV plant capacity relative to load: (A) 5%; (B) 10%; (C) 15%; (D) 25%. PV, Photovoltaic.

different PV penetration levels (expressed as PV plant capacity relative to system load). When the PV penetration is small (such as in Fig. 7.9A with 5% PV penetration), the frequency deviation is also small, and the system is stable. But as the PV penetration is increased, the frequency deviation also increases (nonlinearly) until there is frequency collapse (see Fig. 7.9D with 25% PV penetration). Frequency stability studies for small systems such as hybrid microgrids can be performed using low-order single-frequency models [36]. Such models estimate the frequency response of a system after a disturbance based on a solution to the firstorder differential equation known as the swing equation: df fn 5 ΔP dt 2KE

(7.5)

where fn is the nominal system frequency (in Hz), ΔP is the instantaneous imbalance between generation and load (in kW or MW) and KE is the synchronous system inertia (in kW s or MW s).

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A coarse estimate of the maximum PV plant capacity to maintain stable frequencies was proposed where a metric called the stiffness ratio (SR) is calculated [37]: SR 5

β Pn

(7.6)

where Pn is the nominal capacity of the PV plant connected to the system (in kWac or MWac) and β is the power system stiffness or composite frequency response characteristic (in kW/Hz or MW/Hz). β typically includes the aggregate influence of all synchronous inertia in the system, primary frequency response from units that have been enabled to respond to frequency deviations (e.g., speed-droop governors) and slower secondary frequency control loops. The power system stiffness β can be estimated through practical observations and/or recordings from historical contingency events or via power system softwarebased dynamic simulations in which the network’s frequency deviation is measured following a loss of generation event of fixed capacity. The SR measures the inertia and primary frequency response characteristics of a synchronous power system relative to the PV plant to be connected. A system is considered weak if the SR , 5. The threshold was selected to prevent the system frequency from falling outside the typical normal operating band following the trip of the PV plant [38]. The maximum capacity for the PV plant is calculated as the capacity when the SR 5 5: Pmax 5

β 5

(7.7)

where Pmax is the maximum capacity of the PV plant (in kWac or MWac). There are a number of options to improve frequency stability and increase the maximum capacity of the PV plant: 1. Decrease the single contingency size by distributing the PV plant around the system (see Section 7.3.3). 2. Carry more operational reserves, that is, aggregate headroom from all dispatchable resources. 3. Increase the speed of response of the primary frequency response, for example, configure energy storage systems to provide fast frequency response. 4. Install more synchronous inertia to the system, for example, synchronous condensers or flywheel storage [17].

7.4.4 System strength System strength is a localized characteristic of the network, measuring its ability to maintain a stable voltage (at a specific location) in response to system fluctuations and power flows. It is typically measured by the available fault level at a specified location, where a higher fault level represents higher system strength. This is pertinent for PV plants in a hybrid microgrid as the fault levels in such systems are

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typically low and can ultimately limit the size of the PV plant that can be practically connected to the hybrid microgrid. The system strength metric most commonly used for planning PV plants is the short-circuit ratio (SCR) as defined in IEEE Standard Std-1204 [39]: SCR 5

SSC Pn

(7.8)

where SSC is the three-phase fault level at the connection bus (in MVA) and Pn is the nominal power of the PV plant connected to the same bus (in MWac). The fault level at the connection bus is usually determined via power system software-based short-circuit simulations according to IEC or ANSI standards under minimum three-phase fault conditions. In synchronous systems, inverter-based generating systems such as PV plants generally require a minimum system strength at the connection bus to maintain stable operation, that is, to maintain PLL stability and remain in synchronism to the system during (and after) disturbances. As per the IEEE Std-1204, a system is considered weak if it has an SCR # 3, and the size of the PV plant may need to be capped so that the SCR does not fall below this level. There are a few options to mitigate system strength issues and increase the maximum capacity of the PV plant: 1. Increase fault levels at the connection point, for example, more generators dispatched or installation of synchronous condensers. 2. Relocate PV plant connection point so that it is closer to the other generation. 3. Consult with the PV inverter vendor to ensure that the inverter(s) can operate stably at low system strength levels.

7.5

Conclusion

One of the key conclusions from this chapter is that the planning and design of PV plants for hybrid microgrids cannot be performed without careful consideration of the load profile, microgrid design philosophy, and technical issues such as managing intermittency, handling excess PV energy, frequency stability, and system strength. To this end, it is recommended that the planning of the PV plant is conducted in conjunction with the overall hybrid microgrid planning process as several iterations may be required to reach the optimal resource mix that is both technically robust and economically efficient.

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Appendix: Standard photovoltaic plant planning considerations In the PV plant planning process the ultimate objective is to arrive at a PV plant design that generates electricity efficiently, reliably, and at the lowest possible cost. In this appendix, the standard planning considerations that are applicable to all PV plants (and are not specific to hybrid microgrids) are briefly covered. Note that these standard planning considerations are addressed in more detail in other references (such as Refs. [40,41]) and are only lightly touched upon in this appendix.

Site selection For both ground-mounted and rooftop PV plants, site selection has a material impact on the energy yield of the plant, as well as influencing design and constructability of the plant. Factors for assessment include: G

G

G

G

G

G

G

solar resource potential, based on irradiance measurements or satellite-based solar resource maps (such as SolarGIS or Meteonorm); land availability and zoning, to ensure that the land (or roof space) can be procured and utilized for a PV plant; climatic conditions, such as ambient temperature and humidity directly affect the energy yield (hotter climates have lower yields), while wind speeds influence mounting system design; topographical characteristics (for ground-mounted systems), where flat sites are desirable to minimize cut and fill operations or zones that cannot be utilized (e.g., due to changes in elevation that are outside of the mounting system alignment tolerances); geotechnical and soil characteristics (for ground-mounted systems), which will affect the type of foundations that can be used for the mounting system; air pollution, where high levels of particulate air pollution will adversely affect the soiling loss on the modules and lead to lower energy yields; and water supply sources, the availability and quality of which will determine whether water for module cleaning can be sourced locally or must be brought in from external sources.

Photovoltaic plant layout The design of the PV plant layout can affect not only the potential energy yield, but an inefficient layout can also incur additional costs during construction and operations. Factors for assessment include: G

Module orientation/azimuth, to face the direction that yields maximum energy production, for example, south-facing for northern hemisphere installations and north-facing for southern hemisphere installations.

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G

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Layout of PV strings, combiner boxes and inverters, should optimized to minimize cable lengths and electrical distribution losses. Potential obstructions, that may cause full or partial shading of the PV plant at specific times of the day should be avoided where possible. Interrow spacing (for ground-mounted systems), should be specified to minimize interrow shading. Sufficient personnel and vehicle access, to allow for regular maintenance activities as well as replacement of major equipment (e.g., inverters) without having to shut down the entire plant.

Electrical system design The electrical system design includes the distribution system connecting the PV strings, inverters, combiner boxes, and switchgear through to the microgrid connection point. Factors for assessment include: G

G

G

G

voltage levels of DC and AC systems, selected to align with equipment ratings while minimizing distribution losses; DC/AC capacity ratio/oversupply factor, where a higher ratio between the DC capacity (PV modules) and AC capacity (inverters) can provide a flatter PV output profile and smooth out short-term fluctuations; redundancy, where a redundant system design (such as redundant PV main cable feeders) is inherently more reliable but increases cost; and expandability, by specifying key equipment (such as switchgear, main cables) to facilitate future expansions of the PV plant.

Mounting system design The PV mounting systems are the structures onto which the PV modules are mounted. Factors for the selection and design of the mounting system include (primarily for ground-mounted systems):

Figure 7.A1 Ramming posts and structure assembly.

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G

G

G

257

Type of mounting system, which includes fixed tilt, adjustable (seasonal) tilt, and singleaxis tracking. Tracking systems deliver higher energy yields but require more land. Structure foundations, such as piled/rammed posts (Fig. 7.A1) and concrete footings, depend on the geotechnical assessment of the site, as well as expected wind loading. Alignment tolerances, which include tolerances for both horizontal and vertical misalignments.

Photovoltaic module and inverter selection The PV module and inverter are the major equipment items with the most material impact on both cost and energy yield. Factors for assessment include: G

G

G

G

manufacturer quality, where Tier 1 status, track record, and length of product warranties should be considered; supply chain availability and local technical support, to avoid where possible selecting products with no local support and long lead times, for example, for replacement parts; photovoltaic module selection, including parameters such as maximum voltage, temperature coefficient of power and power output tolerances; and PV inverter selection, including the choice between string inverters and central inverters, and parameters such as the number of maximum power point inputs and DC input voltage range.

Energy yield simulations Based on the selected site, PV plant design and equipment selection, the annual energy yield can be predicted using industry-standard simulation software such as PVSyst or Helioscope. Standard metrics such as the performance ratio (PR) and capacity utilization factor (CUF) can also be calculated. Consideration should be made to iteratively adjust the PV plant design and equipment selection to optimize the PR and CUF.

Grid integration modeling Power system studies need to be performed to ensure that the operation of the PV plant does not adversely the hybrid microgrid. Standard assessments include: G

G

G

power flow studies, to ensure that voltages are well regulated and thermal limits are not violated under all operating conditions; short-circuit and protection studies, to ensure that equipment such as switchgear is sufficiently specified, and protection devices can be adequately coordinated; and power quality studies, to ensure that power quality factors such as harmonics and voltage flicker comply with the relevant technical standards.

Environmental and social impacts Since PV plants can be cover a relatively large land area, environmental and social impacts need to be considered to gain community acceptance, as well as planning approvals. Factors for assessment include: G

voluntary land acquisition, where forced resettlements or coercion to leave the site should be avoided;

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cultural heritage, such as archeological sites or culturally important sites to the indigenous people are not disturbed by the PV plant; biodiversity loss, to ensure that the clearing of land for the PV plant does not disturb ecologically sensitive habitats, particularly protected areas such as natural forests and wetlands; and visual amenity impacts, should be avoided by consultation with the local community and ensuring that and adverse visual or landscape impacts are mitigated.

Wind power plant planning and modeling

8

Leidy Tatiana Contreras Montoya1, Mohamed Yasser Hayyani1, Mohamad Issa2, Adrian Ilinca3, Hussein Ibrahim4 and Miloud Rezkallah4 1 University of Quebec at Rimouski, Rimouski, QC, Canada, 2Department of Applied Sciences, Quebec Maritime Institute, Rimouski, QC, Canada, 3Department of Mathematics, Computer Science and Engineering, University of Quebec at Rimouski (UQAR), Rimouski, QC, Canada, 4Technological Institute for Industrial Maintenance (ITMI), College of Sept-Iˆles, Sept-Iˆles, QC, Canada

8.1

Chapter overview

The lecture of this chapter allows the reader to answer questions like what is the required information to characterize wind energy potential, what are the aerodynamic mechanisms to capture flow energy, how the control of the wind turbines is done, and what are the environmental impacts and economic parameters to be considered when developing a project? The kinetic energy in wind is a significant source of renewable energy in many parts of the world, with considerable potential for further development. The energy produced by the turbines is heavily dependent on the average local wind speed. Regions that usually have the most important wind potential are located near coasts, inland areas with open fields, or at the edge of water bodies. There is also a strong potential in mountainous regions. Despite these geographical constraints, there is enough land in most parts of the world to implement wind energy projects to supply a large portion of the local electricity needs. The worldwide market for wind turbines has rapidly increased during the last 20 years. Only in 2001 the wind energy industry installed approximately 5500 MW of new generating power. It is estimated that more than 650 GW of wind power is presently installed worldwide. Most of this development has been driven by the requirement to use “cleaner energy” for electric power. In addition to these largescale projects, there are a range of other wind turbine applications, such as medium-scale hybrid systems in remote grids and off-grid areas, for pumping water and supplying smaller quantities of energy for battery charging in stand-alone systems. As the theoretical power output is proportional to the cube of the wind speed, wind energy projects are much more profitable in “windy” areas. However, practically, a wind turbine’s power output variation is closer to the square of the average wind speed. The aerodynamic, mechanical, and electrical conversion characteristics Hybrid Renewable Energy Systems and Microgrids. DOI: https://doi.org/10.1016/B978-0-12-821724-5.00012-X © 2021 Elsevier Inc. All rights reserved.

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and wind turbine efficiencies account for the disparity. This means that with every 10% increase in wind speed, the energy that a wind turbine will generate will rise from 20% to 30%. The accurate evaluation of the wind resource and proper siting of the wind turbines is crucial to a financially viable project. While the human perception of wind is typically focused on short-term observations of climatic conditions such as windstorms and wind chill experiences, the proper evaluation of wind resources is a practice and an important component for any wind energy project development. In this chapter, we provide an overview of the resource assessment, technical, environmental, and economic aspects of a wind project cycle. We introduce the most important elements needed to be considered for the development of a wind power plant.

8.2

Wind resource

Humans have used wind energy for hundreds of years, first to move the sailboats, then to operate the windmills, and most recently to produce electricity by using wind turbines. Wind turbines convert the wind kinetic energy to produce electricity or mechanical energy, generally using blades. The blades’ rotor shaft is connected to a generator to produce electricity or used as mechanical energy to perform tasks such as pumping water or grinding grain. Four main phenomena drive the atmospheric airflow. These are the pressure gradient, Coriolis force resulting from the rotation of the Earth, inertia forces due to large-scale circular motion, and friction forces on the Earth’s surface. The pressure gradient force results from the force difference acting on the surface of an air volume and is the result of a pressure gradient. This force vector acts on an air volume from a high-pressure area toward a low-pressure area. The Coriolis force is the result of Earth’s rotation on weather patterns and ocean currents. The Coriolis effect makes storms swirl clockwise in the southern hemisphere and counterclockwise in the northern hemisphere. The development of weather patterns, such as cyclones and trade winds, are examples of the impact of the Coriolis effect. The pressure force and Coriolis force generate the geostrophic wind, shown in Fig. 8.1. We can identify two broad types of wind: the geostrophic and the surface wind. The difference between them lies in the forces acting on the masses of air. The frictional force occurs mainly at the contact of the airflow with the Earth’s surface. Normally, it has a slowing down effect on the wind. This effect can be significant or not depends on the type of contact surface and it decreases with height. The wind speed and direction are fluctuating and can change at different time scales from seconds to annual periods. Even their average values change from year to year. The short-term variations can change from a few seconds to diurnal variations. The nature of these variations originates from local differential heating and the characteristics of the terrain, an illustration is in Fig. 8.2. The long-term variations correspond to and annual or interannual time span. They relate to the seasonal variations at a given location as seen in Fig. 8.3.

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Figure 8.1 Geostrophic wind, Fc, and Fp, Fc, Coriolis force; Fp, pressure force on the air. From: J.F. Manwell, J.G. McGowan, A.L. Rogers, Wind Energy Explained: Theory, Design and Application, second ed., John Wiley & Sons, 2009 [1].

Figure 8.2 Wind speed variation for a short period.

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Figure 8.3 Wind speed variation for a long period.

8.2.1 Impact of the height At the Earth surface, the wind speed is zero due to friction and behaves as a boundary layer flow as we move away from the surface. Surface roughness determines how this vertical variation of wind speed is affected by the friction that the nature of Earth’s surface. On smooth surfaces, such as a calm sea, the variation of speed with elevation is only modest. The impact of the roughness of the surface on the wind speed can be written by power law profile:



α V Z 5 V0 Z0

(8.1)

where V is the wind speed at height Z, V0 is the wind speed at the reference height Z0, and α is the friction coefficient (or power law exponent). Table 8.1 shows friction coefficient for some typical terrains. Another relationship, called the log law, is used to characterize the wind speed vertical variation from a reference height Zr , to another level using the following equation: VðzÞ lnðZ=Z0 Þ 5 VðZr Þ Zr =Z0

(8.2)

Table 4.5 available in Section 4.3.1.1 from Chapter 4, Renewable Energy Sources, gives some surface roughness lengths for some types of terrain.

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Table 8.1 Friction coefficient. Terrain characteristics

Friction coefficient α

Smooth hard ground, calm water Smooth hard ground, calm water High crops, hedges, and shrubs Wooded countryside, many trees Small town with trees and shrubs Large city with tall buildings

0.10 0.15 0.20 0.25 0.30 0.40

From: J.F. Manwell, J.G. McGowan, A.L. Rogers, Wind Energy Explained: Theory, Design and Application, second ed., John Wiley & Sons, 2009.

Table 8.2 Density of dry air at a pressure of 1 atm. Temperature ( C)

Air density (kg/m3)

Density ratio KT

215 210 25 0 5 10 15 20 25 30 35 40

1.368 1.342 1.317 1.293 1.269 1.247 1.225 1.204 1.184 1.165 1.146 1.127

1.12 1.10 1.07 1.05 1.04 1.02 1.00 0.98 0.97 0.95 0.94 0.92

From: G.M. Masters, Renewable and Efficient Electric Power Systems, second ed., John Wiley & Sons, 2013.

8.2.2 Temperature and altitude correction for air density Normally, when wind power data are presented, it is often assumed that the air density is 1.225 kg/m3; corresponding to an air temperature of 15 C and pressure of 1 atm. By using the ideal gas law, we can determine the air density under different conditions. p p 5 r T and 5ρ ρ rT

(8.3)

where p is the absolute pressure in Pa (N/m2), ρ is the air density in kg/m3, r is the ideal gas constant 5 0.287 kJ/kg K. Table 8.2 shows air density for a range of temperatures. Air density, and hence power in the wind, depends on atmospheric pressure as well as temperature. Since air pressure is a function of altitude, it is useful to have

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Table 8.3 Air pressure at 15 C as a function of altitude. Altitude (m)

Pressure (atm)

Pressure ratio (Kp )

0 200 400 600 800 1000 1200 1400 1600 1800 2000 2200

1 0.977 0.954 0.931 0.910 0.888 0.868 0.847 0.827 0.808 0.789 0.771

1 0.977 0.954 0.931 0.910 0.888 0.868 0.847 0.827 0.808 0.789 0.771

From: G.M. Masters, Renewable and Efficient Electric Power Systems, second ed., John Wiley & Sons, 2013.

a correction factor to help estimate wind power at sites above sea level. We can write the incremental pressure dp for an incremental change in elevation, dz as: dp 5 2 ρg dz

(8.4)

p dp 52 g dz rT

(8.5)

The variation of atmospheric pressure with the elevation can be written as: 24

p 5 p0 e21:185:10

H

24

5 1ðatmÞ  e21:185:10

H

(8.6)

where p0 is the reference pressure of 1 atm and H is in meters. We can combine the temperature and pressure corrections for density as follows: ρ 5 1:225 KT Kp

(8.7)

where KT correction factor for the temperature, Kp correction factor for the pressure. Table 8.3 shows some pressure correction factors [2].

8.3

Types of wind turbines

Wind energy is a renewable energy that does not directly produce greenhouse gases (GHGs) during operation. Wind energy is an indirect form of solar energy as solar radiation absorbed in the atmosphere causes differences in temperature and pressure. Thus

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the air masses move and accumulate kinetic energy. It can be processed and used for several purposes. We can distinguish two modes of wind energy exploitation: G

G

onshore wind turbines, installed on land, Fig. 8.4A offshore wind turbines, installed at sea, Fig. 8.4B

There are currently two main types of wind turbines: horizontal axis wind turbines (HAWTs) or vertical axis wind turbines (VAWTs). This distinction was made according to the rotation axis; as their name suggests, HAWT devices rotate in the horizontal plane (see Fig. 8.5), while VAWT rotates in vertical plane (see Fig. 8.6) [4,5]. Some advantages that VAWTs has over HAWT are that VAWTs do not need a yaw control (rotation of the unit about the tower axis [6] to keep them facing the wind, they are available to work with the wind from any direction [6]; VAWT works better in turbulent environments (e.g., urbans) and they are more esthetic. But, VAWTs are less efficient in comparison with HAWTs [4,5].

8.3.1 Horizontal axis wind turbines HAWTs have the main rotor shaft and electrical generator at the top of a tower, and the rotor is oriented perpendicular to the wind. Most of them are equipped with a gearbox, which transforms the slow rotation of the rotor into a faster rotation, suitable to drive an electrical generator. Illustrated in Fig. 8.7, the most common commercial configurations of HAWT are the two- and three-bladed turbines. Twoand three-bladed turbines can operate either upwind or downwind [4]. The “upwind” configuration is more common because this way of operation produces less noise and reduces the rotor fatigue (e.g., in blades, tower, nacelle) [1].

Figure 8.4 (A) Onshore and (B) offshore wind turbines. From: R. Probert, Don’t let barriers block wind farms and windfalls, 2012. https://www. raconteur.net/sustainability/dont-let-barriers-block-wind-farms-and-windfalls [3].

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Figure 8.5 Horizontal axis wind turbine (HAWT). From: B.K. Hodge, Alternative Energy Systems and Applications, first ed., John Wiley & Sons, 2010.

8.3.2 Vertical axis wind turbines The rotor of the VAWT rotates around a vertical axis. The main advantage is that it can receive wind from any direction, so when the wind changes, the wind turbine of this kind has no need to initiate the steering device to deviate the rotor to face the wind. Because there is no need of the steering device, the structure of the vertical wind turbine is simplified. Another merit of this type of wind turbine is that the gearbox and the generator can be installed on the ground, which facilitates maintenance.

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Figure 8.6 Vertical axis wind turbine (VAWT). From: B.K. Hodge, Alternative Energy Systems and Applications, first ed., John Wiley & Sons, 2010.

For the VAWT presented in Fig. 8.6 the most used configurations are the Savonius and the Darrieus, but they have not been extensively commercialized [4]. These turbines are also classified based on their operating principle as drag or lift devices. Savonius turbine is an example of drag-type turbine that uses the drag of the wind on the “cups” to generate the torque on the axis. On the other hand,

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Figure 8.7 HAWT schematic. HAWT, Horizontal axis wind turbine.

Darrieus turbine (Fig. 8.8) is a lift-type device because the torque comes from the lift on the blades [4].

8.3.3 System elements Wind turbines are equipped with several components such as rotor, gearbox, generator, yaw mechanism, tower, and foundations. Fig. 8.7 shows the schematic of a typical HAWT and Fig. 8.8 shows the schematic of an H-Darrieus type VAWT. In both configurations the tower sits on a base or foundation. The nacelle in HAWT is located on top of the tower and contains the gearbox, controls, and generator subsystems. The main, low speed, rotor shaft is attached to the gearbox and the secondary, high-speed shaft, is attached to the generator. In VAWT the power train (gearbox and generator) is located on the ground, which facilitates maintenance.

8.3.3.1 The rotor It consists of one or several blades and the hub. This component is responsible for transforming the kinetic energy in the wind into mechanical energy at the shaft. The blades extract part of the kinetic energy from the moving air masses using the

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Figure 8.8 VAWT schematic type H-Darrieus. VAWT, Vertical axis wind turbine.

lift and drag forces. After numerous experiences and studies the optimum number of blades for an HAWT is three, as this configuration has a favorable mass distribution, presents less vibrationdynamic problems, less noise associated to tip speed ratios (TSRs) between 6 and 10 and its dynamic control is easier [7]. The blades are usually made of composites and sometimes also of steel or wood. In general, fiber-reinforced materialcontaining glass or carbon fibers are used. The predominant criterion for material selection is fatigue strength but also the specific weight, admissible stress, modulus of elasticity, and breaking strength. However, some factors such as the development, materials, and manufacturing costs are also important to consider, especially in small wind energy projects [7]. The hub connects the blades to the main shaft. The materials used for the hub construction include welded steel sheets, cast-steel bodies, and forged pieces. There are different types of hubs, but the most used in three-blade rotors is the “rigid or hingeless hub.” This hub has the advantage of low manufacturing and maintenance costs as well as low wear; however, due to the rigid junction, the blades and other turbine components suffer relatively high stress, which affect the lifetime of the turbine [7].

8.3.3.2 The gearbox The gearbox is part of the power train, which joins the wind turbine shaft and generator shaft. It divides the drive system into the “slow” and the “fast” generator

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shaft. It is located inside the nacelle of the wind turbine and often serves as the main rotor bearing [7]. The efficiency of this component is around 98% per gear level, and the energy losses are associated to the gearwheel friction that causes heat transmission and sound emissions. The latter may constitute a limiting factor about wind energy exploitation in view of the acceptance by the population and must be avoided to prevent system components from acting as resonance bodies [7].

8.3.3.3 The generator The generator converts the rotational mechanic energy of the power train into electrical energy. The most common types are synchronous and asynchronous generators; nonetheless, synchronous generators are characterized by slightly higher efficiencies. The synchronous generators are equipped with a fixed stator on the outside and a rotor on the inside located on top of a pivoting shaft. The excitation process is carried out by the direct current transmitted to the rotor by slip ring, which creates a magnetic field inside the rotor winding. When this type of generator is connected to a stable grid which operates at a frequency of 50 or 60 Hz, it can only be operated at the number of revolutions predetermined by the grid. The above generates in the wind turbine operation a high strain inside the power train, especially during gusty wind [7]. The asynchronous generators are also equipped with a fixed stator and a pivoting rotor, but the “excitation” process (creation of a magnetic field) is performed differently. The rotors are provided with windings that have direct or shunt short circuits. As this winding is short-circuited, there is heavy current flow, so that a magnetic field is generated inside the rotor. As a result, the rotor magnetic field tends to follow the stator magnetic field, so the rotor is accelerated. The specific difference between both numbers of revolutions of the rotor and the rotating field in relation to the rotating field is referred to as “slip.” More slip is associated with more induced voltage, more current, and a stronger magnetic field. Due to these excitation conditions, voltage and current are not in phase, so that reactive power is required. Therefore the installation of condensers is necessary, which implies higher costs in isolated systems [7].

8.3.3.4 The yaw mechanism (horizontal axis wind turbine) This component serves to adjust the nacelle direction, and thus the rotor, as exactly as possible perpendicular to the respective wind direction. The adjustment is performed by a gearwheel mounted on top of the tower and operated by mechanical, hydraulic, or electromechanical mechanisms. In small wind turbines the yaw is usually done with mechanical mechanisms driven by wind vanes, servomotors, or small size windmill. Larger wind turbines are usually provided with hydraulic, electromotive, or electromechanical servo drives. Finally, the entire wind direction yaw mechanism is controlled by a special control system that receives data from a wind direction measuring device mounted at the nacelle [7].

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8.3.3.5 The tower The main function of the tower of a HAWT is to enable wind energy capture at enough height above ground, to absorb and securely discharge static and dynamic stress exerted on the rotor, the power train, and the nacelle into the ground. It is also important to consider the natural vibration of the towernacellerotor overall system to prevent dangerous resonance, particularly during rotor start-up [7]. For transport requirements, factors such as dimension, weight, and available roads are important to consider. At the installation site, factors such as erection methods, cranes, and accessibility of the nacelle are important. Finally, for the tower construction it is important that the material used has weathering resistance and high resistance to fatigue; most towers are made of steel and/or concrete. The towers can be lattice, anchored, or self-supporting tubular, commonly of conic design; the latter being the most common tower type nowadays [7]. The minimum height is dictated by the rotor radius; however, the common tower heights vary considerably depending on site conditions [7].

8.3.3.6 The foundations The type of foundation used to anchor towers, and thus wind turbine, into the ground depends on the plant size, meteorological and operational stress, and local soil conditions. The support structures are subdivided into shallow and deep foundations; however, the optimum foundations design is determined by appropriate soil investigation. Finally, what is sought during the installation process is the lowest possible cost in both manufacturing (mass production and material selection) and assembly (logistics and rapid installation) and long service-life (considering factors such as corrosion and fatigue) [7]. For the offshore wind turbine, additional factors must be taken account.

8.4

Wind energy production estimate

8.4.1 Power in the wind Wind turbine power production consists in the extraction by the turbine rotor of a fraction of the kinetic energy available in the wind. The wind turbine cannot capture all the energy available in the airflow. The design optimization of the wind turbines is mainly oriented toward increasing this conversion efficiency. The instantaneous wind speed is the sum of the mean and turbulent fluctuations. The kinetic energy available in the wind is: 1 KE 5 m  V 2 2

(8.8)

As the power is the energy per time unit, the power available in a mass of air moving at velocity V through area A is: P5

1 ðmÞ 1 _  V2 A  V 2 5 mA 2 ðtÞ 2

(8.9)

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With the air mass flow m_ 5 ρAV the power available in the wind is: Pw 5

1 ρAV 3 2

(8.10)

where Pw is the power in the wind (W), ρ the air density (kg/m3), A the cross-area through which the wind passes (m2), and V the wind speed (m/s). The wind turbines are designed to transform a fraction of this kinetic energy in the wind into useful energy. The extracted energy from the fluid by the rotating blades is converted into mechanical power at the rotor shaft. Mechanical power in the shaft is then transferred to a machine, such as a generator or a pump [7]. As can be apprehended, the power extracted by a wind turbine is a fraction, named power coefficient CP, of the power available in the wind (see Eq. 8.11): Pturbine 5 Pw  CP 5

1 ρAV 3  CP 2

(8.11)

Eq. (8.10) reveals that the wind turbine power is proportional to the rotor swept area and the cube of the incoming wind speed. The former demonstrates the reason why wind turbines are located in areas with high wind speeds [1,4,7].

8.4.2 Betz limit As seen, not all the available power can be extracted from the wind. A simple model attributed to Betz can be used to determine the power extracted with an ideal turbine rotor, the thrust of the wind on the ideal rotor, and the effect of the rotor operation on the local wind field [1,4,7]. The analysis uses the “actuator disk theory,” which consists in representing the turbine by a uniform disk that creates a pressure discontinuity in the stream tube of air flowing through it as it is shown in Fig. 8.9. The subscript 1 indicates upstream, subscript 4

Figure 8.9 Representation of the actuator disk theory.

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indicates downstream and, 2 and 3 indicate just before the turbine and just after the turbine, respectively [1,4]. To simplify the calculation the following assumptions are made: G

G

G

G

G

The fluid flow is homogenous, incompressible, and steady. No frictional drag. An infinite number of blades. Uniform thrust over the disk or rotor area. A nonrotating wake.

By applying the linear momentum conservation theory for a one-dimensional, incompressible, steady flow, the thrust is equal and opposite to the rate of change of momentum of the air stream. By writing the change of momentum upstream and downstream of actuator disk, we obtain: T 5 V1 ðρAVÞ1 2 V4 ðρAVÞ4

(8.12)

where ρ is the air density, A is the cross-sectional area, V the wind speed. For a steady flow the mass flow rate is equal to: m_ 5 ðρAVÞ1 5 ðρAVÞ4

(8.13)

We can write: _ 1 2 V4 Þ T 5 mðV

(8.14)

By applying the Bernoulli theorem on the two sides of the actuator disk: P1 1

1 2 1 ρV1 5 P2 1 ρV22 2 2

(8.15)

P3 1

1 2 1 ρV 5 P4 1 ρV42 2 3 2

(8.16)

where P1 5 P4 5 Pa and V2 5 V3

(8.17)

The thrust on the actuator disk can also be written relatively to the pressure on both sides as: T 5 A2 ð P 2 2 P3 Þ

(8.18)

Replacing pressure values from Eqs. (8.15) to (8.17) in Eq. (8.18), we can write: T5

  1 ρA2 V12 2 V42 2

(8.19)

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We suppose the average wind speed: V2 5

V1 1 V4 2

(8.20)

And we define a, the axial velocity induction factor: a5

V1 2 V 2 V1

(8.21)

Therefore: V2 5 V1 ð1 2 aÞ

(8.22)

V4 5 V1 ð1 2 2aÞ

(8.23)

The power extracted P is equal to the thrust multiply by the velocity at the disk: P5

  1 1 ρA2 V12 2 V42 V2 5 ρA2 V2 ðV1 1 V4 ÞðV1 2 V4 Þ 2 2

(8.24)

P5

1 ρAV 3 4að12aÞ2 2

(8.25)

or

Using Eq. (8.11), we can write the power coefficient as: CP 5

P Power at the rotor 5 ð1=2ÞρV 3 A Power contained in the wind

(8.26)

And, in dimensionless form, as a function of the axial induction factor a as: Cp 5 4að12aÞ2

(8.27)

The graphical representation of the relation (8.27) is in Fig. 8.10. To find the maximum possible rotor efficiency, we simply take the derivative of (8.27) with respect to a and set it equal to zero. Thus the maximum value of the power coefficient CP 5 16/27 corresponds to a value of the induction factor a 5 1/3. This value of the power coefficient, CP 5 16/27 5 59.3% is called the Betz limit.

8.4.3 Airfoil fundamental concepts The cross sections of wind turbine blades have the shape of airfoils (see Fig. 8.11). The airfoils are structures with specific geometric shapes that are used to generate

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Figure 8.10 The blade efficiency reaches a maximum when the wind is slowed to one-third of its upstream value. From: M. Kaltschmitt, W. Streicher, A. Wiese, Renewable Energy: Technology, Economics and Environment, Springer Science & Business Media, 2007.

mechanical forces due to the relative motion of the airfoil and a surrounding fluid [1]. The shape of the airfoil greatly influences the aerodynamic performance of wind turbines and therefore their efficiency. Taking into account the above, some key concepts are discussed in general. In Fig. 8.11 the chord line is the straight line connecting the leading and trailing edges and is designated as c. The thickness is the distance between the upper and lower surfaces and is perpendicular to the chord line. The camber is the halfway between top and bottom surfaces. The angle of attack (α) is defined as the angle between the relative wind (Urel ) and the chord line. The span is the length of the airfoil perpendicular to its cross section. The geometric parameters that have an effect on the aerodynamic performance of an airfoil include the leading edge radius, mean camber line, thickness distribution of the profile, and the trailing edge angle [1]. These parameters are subject to constant study to obtain maximum aerodynamic performance in wind turbines. The airflow over an airfoil produces forces over the airfoil surface, that is, lift, drag, and thrust forces. The airflow velocity is lower on the “pressure” side of the airfoil and accelerates, resulting in lower average pressure, on the “suction” side of the airfoil. Meanwhile, viscous friction between the air and the airfoil surface slows the airflow to some extent next to the surface [1,8]. The forces are defined as follows: G

G

G

Lift force (F L ): defined perpendicularly to the direction of the oncoming airflow. It is a consequence of the unequal pressure on the upper (suction) and lower (pressure) airfoil surfaces. Drag force (F D ): defined parallel to the direction of the incoming airflow. This force is due to viscous friction forces at the airfoil surface and to unequal pressure on the airfoil surfaces facing toward and away from the incoming flow. Pitching moment: defined to be about an axis perpendicular to the airfoil cross section.

All of these forces are related to dimensionless parameters or coefficients.

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Figure 8.11 Forces and moments on an airfoil section, α angle of attack, and U wind velocity. The direction of forces and moments is indicated by the direction of the arrows.

8.4.3.1 Lift coefficient The lift is calculated by evaluating the resulting force generated by the pressure distributions in the upper surface (suction zone) and in the lower surface (pressure zone) of the airfoil. The lift coefficient Cl is defined as: Cl 5

FL ð1=2Þρa U 2 A

(8.28)

The angle of attack α has a fundamental influence as the lift coefficient increases with it, reaching a maximum value, from which there is a decrease that is known as stall [8]; typical behavior can be seen in Fig. 8.12.

8.4.3.2 Drag coefficient Drag coefficient is due to friction, pressure, and induced drag. The friction component is associated to the boundary layer development, its magnitude depends on the characteristics of the fluid (i.e., viscosity), and the Reynolds number; hence, a body immersed in a nonviscous fluid experiences no drag because it has no friction. The pressure component is due to the pressure difference between the leading and trailing edge of the profile. The induced drag is produced by the eddies that are generated at the tip of the blade. The drag coefficient is calculated as: Cd 5

FD ð1=2Þρa U 2 A

(8.29)

In the same way of the lift coefficient the drag coefficient also varies with the angle of attack (see Fig. 8.13); usually, it is much lower at low angles of attack and increases rapidly at higher angles [1,8].

8.4.3.3 Drag versus lift wind turbines There are two different physical principles to extract power from wind by rotating wind energy converters, which can also be combined under certain

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Figure 8.12 Lift coefficient curve. From: S.N. Shaha, M.S.A. Pachapuri, NACA 2415—finding lift coefficient using CFD, theoretical and javafoil, IJRET: Int. J. Res. Eng. Technol. 04 (07) (2015) 444449. https:// doi.org/10.15623/ijret.2015.0407070 [9].

Figure 8.13 Drag coefficient curve. From: T. Burton, D. Sharpe, N. Jenkins, E. Bossanyi, Wind Energy Handbook, John Wiley & Sons, Ltd, 2001.

conditions [7]; these principles have divided the turbine into two different types of devices: lift and drag. In drag devices the wind pushes on the blades’ surfaces, forcing the rotor to turn on its axis. Some examples are cup anemometers, vanes, and paddles, which are shielded from the wind or move parallel to the wind on half of the rotor cycle [6].

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Generally, drag devices have not been evolved into commercial wind turbines for producing electricity, since they are inefficient and require a lot of material for blades [6,7]. However, for conversion of the wind energy using the drag principle, the power received by the wind-blown surface (S) is calculated by means of the drag force (FD ) and the velocity (vS ), at which the wind-blown surface moves as can be observed in the following equation [6,7]: Pdrag device 5 FD vS

(8.30)

The relative speed between wind velocity and the speed of the wind-blown surface (U 2 vS ) has a deciding effect on the air drag. By means of the air drag coefficient (Cd ), drag force is determined by Eq. (8.31). Thus drag force largely depends on the square of the effective approach/rotor tip velocity (U 2 vS ). FD 5

1 ρ Cd ðU2vS Þ2 S 2 a

(8.31)

Replacing Eq. (8.31) in (8.30) the power extracted is calculated as: Pdrag device 5

1 ρ Cd ðU2vS Þ2 SvS 2 a

(8.32)

And the power coefficient will be expressed as: CP; drag 5

Cd ðU2vS Þ2 vS U3

(8.33)

Differentiating and zeroing Eq. (8.33) with respect to vS enable to determine the maximum wind power extracted using the drag principle. The mathematical operations reveal that the maximum power coefficient is achieved if the wind-blown surface is moved with one-third of the wind velocity (vs 5 ð1=3ÞU), and, if the rotor is moving at the wind velocity, the power is zero. For this speed relation a maximum power coefficient is determined by [1,6,7]: CP; drag max 5

4 Cd 5 0:1481Cd 27

(8.34)

For instance, an infinitely large plate has a drag coefficient of 2.01; under these circumstances the maximum power coefficient would amount to about 0.3. For the rotor blades of drag-type wind turbines, maximum drag coefficients of 1.3 are achieved, whereas the corresponding maximum power coefficient amounts to maximum 20% [7]. In the lift devices the blades can move faster than the wind, have a more efficient power conversion due to aerodynamics, and use less material for manufacturing the blades [1,6]. The lift devices use airfoils similar to propellers or airplane wings. Due to their higher efficiency, lift devices are commonly used to produce electricity [6,7].

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Figure 8.14 Power coefficients of wind rotors of different designs. From: E. Hau, H.V. Renouard, Wind Turbines: Fundamentals, Technologies, Applications, Economics, second ed., Springer, Berlin, Heidelberg, 2006.

Fig. 8.14 presents the expected range of power coefficients for well-designed wind turbines of different configurations. The TSR or λ showed in the x-axis of the figure is determined by the tangential velocity of the rotor blades in relation to the undisturbed axial airflow, the wind velocity [10]. In conclusion, TSR is the speed of the blade tip divided by the wind speed [6] and is expressed as: λ5

ωR U

(8.35)

where ω is the rotor rotational speed, R the rotor radius, and U is the wind speed. Example 8.1: A 40-m diameter, three-bladed wind turbine produces 600 kW at a wind speed of 14 m/s. Air density is the standard 1.225 kg/m3. 1. At what rpm does the rotor turn when it operates with a TSR of 4.0? 2. What is the tip speed of the rotor? 3. If the generator needs to turn at 1800 rpm, what gear ratio is needed to match the rotor speed to the generator speed? 4. What is the efficiency of the complete wind turbine (blades, gearbox, generator) under these conditions?

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Solution: 1. Using the TSR definition (Eq. 8.35) rpm 5

TSR 3 60 U 4 3 60 s=min 3 14 m=s 5 5 26:7 rpm πD 40π m=rev

2. Tip speed of the rotor Tip speed 5

26:7 rev=min 3 π40 m=rev 5 55:9 m=s 60 s=min

3. If the generator needs to spin at 1800 rpm, then the gearbox in the nacelle must increase the rotor shaft speed by a factor of: Gear ration 5

Generator rpm 1800 5 5 67:4 Rotor rpm 26:7

4. The power in the wind is:

Pw 5

1 π  1:225   402  142 5 2112 kW 2 4

And the overall efficiency of the wind turbine: Efficiency 5 CP 5

600 kW 5 0:284 5 28:4 % 2112 kW

8.4.4 Wind speed distribution (Weibull and Rayleigh) A statistical distribution characterizes the time variation of the wind speed, which parameters need to be known to determine the wind energy production (EP) during a given period. Noted f(v), the wind speed probability density function (PDFs) is used to determine the probability that the wind speed v has a value between v1 and v2: Probability ðv1 # v # v2 Þ 5

ð v2

f ðvÞdv

(8.36)

v1

The wind speed PDF should satisfy the following physical condition that means that the wind speed should be a positive value: Probability ð0 # v # NÞ 5

ðN

f ðvÞdv 5 1

(8.37)

0

If the period covered by the wind speed probability distribution is 1 year, we can determine the number of hours per year during which the wind speed

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lies between two given values by multiplying (8.36) by the number of hours per year (8760 h): Number of hours per year ðv1 # v # v2 Þ 5 8760

ð v2

f ðvÞdv

(8.38)

v1

As for any density of probability, we compute the average wind speed using the following formula: vavg 5

ðN

v  f ðvÞdv

(8.39)

0

While the average value of the cube of wind speed is:  3 v avg 5

ðN

v3  f ðvÞdv

(8.40)

0

Weibull and Rayleigh distributions are mostly used to represent wind speed variation. Both give poor estimation for low mean wind speed situations but are much more accurate at higher speeds [6]. The Weibull distribution represents better the wind speed variation in regions with steady winds. As it uses two parameters, it can better represent a wider variety of wind regimes. The two parameters are a shape factor (k) and a scale factor (c). Both parameters are functions of mean wind speed and standard deviation [1,6]. The Weibull probability density is defined as:    k vk21 v k exp 2 f ðvÞ 5 c c c

(8.41)

where k is the shape factor and c the scale parameter (Fig. 8.15). The particular case when k 5 2 (Fig. 8.16) is called the Rayleigh distribution: f ðvÞ 5

   2v v 2 exp 2 2 c c

(8.42)

Rayleigh distribution can also be expressed as a function of the mean wind speed, vavg [1,6]:
 "
 # π v π v 2 f ðvÞ 5 exp 2 2 vavg 4 vavg

(8.43)

Rayleigh distribution is a realistic representation of wind speed variation for a majority of sites and is commonly used to determine wind EP at a site when

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Figure 8.15 Weibull probability density function for a scale factor c 5 5 and three values of the shape factor k.

detailed data are not available. From Eqs. (8.42) and (8.43), we determine the relationship between the scaling parameter c and the average wind speed vavg : vavg 5

ðN

v  f ðdvÞdv 5

ðN

0

0

   pffiffiffi π 2v2 v 2 cD0:886 c exp 2 5 2 c c 2

(8.44)

or: 2 c 5 pffiffiffi vavg D1:128 vavg π

(8.45)

The average value of the cube of wind speed with a Rayleigh PDF becomes:  3 v avg 5

ðN 0

v3  f ðvÞdv 5

ðN 0

v3 

   2v v 2 3 pffiffiffi exp 2 dv 5 c3 π 2 c c 4

(8.46)

Therefore the relation between the average of the cube of wind speed and the average wind speed is:
  3 3 pffiffiffi 2 vavg 3 6 3 v avg 5 5 vavg 5 1:91v3avg π pffiffiffi π 4 π

(8.47)

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Figure 8.16 Histogram of the number of hours per year and MW h per year at each wind speed for NEG Micon 1000/60 in a site with 7 m/s average wind speed using Rayleigh distribution.

By assuming Rayleigh statistics, we can rewrite the average power in the wind as: Pavg 5

61 ρ Av3avg π2

(8.48)

Example 8.2: Estimate the average power in the wind at a height of 50 m when the wind speed at 10 m averages 6 m/s. Assume Rayleigh statistics, a standard friction coefficient α 5 1/7, and standard air density ρ 5 1.225 kg/m3.
1=7 Z50 v50 5 v10 5 7:55 m=s Z10 The average wind power density is: P50 5

6 1 3 6 1  ρv 5   1:225  ð7:55Þ3 5 504 W=m2 π 2 π 2

We can find the average power at 10 m using another way: 6 1 6 1  ρ v3 5   1:225  ð6Þ3 5 252:67 W=m2 π 2 π 2
3x1=7
3α Z50 50 P50 5 P10 Z 5 252:67 10 5 504 W=m2 P10 5

10

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8.4.5 Wind turbine energy production estimates The estimation of the EP is the most important factor in the development of a wind power plant as it has the highest impact on increasing revenues (if the project consists in selling energy at a given price per kW h) or reduces energy cost (if the produced energy is for self-use). Of course, the larger the EP, the better is the project, and this is the reason, when developing a wind power plant, to choose the windiest locations and most efficient turbines. The factors affecting the EP are the wind speed distribution and the turbine power curve. As seen in the previous sections, Weibull (8.41) or Rayleigh (8.42) PDFs characterize the wind speed annual distribution. The aerodynamic and control of the wind turbines will determine its power curve P(v), a function that determines the power P generated by the wind turbine at a given wind speed v. Each manufacturer will provide the power curve of the wind turbine for standard atmospheric conditions (pressure and temperature). The power curves for some wind turbines are illustrated in Table 8.4 and allow the user to determine what power the wind turbine will produce at any given speed. The EP of a wind turbine over a given time period T is: ðT EP 5 PðvÞdt (8.49) 0

To evaluate the integral (8.50), we need to determine the duration of a given wind speed over the period and integrate the PDF in Eq. (8.50). In general, we are considering the annual EP (AEP) as a reference for project analysis and it can be determined by combining Eqs. (8.50) and (8.38): ðN vX cutout PðvÞf ðvÞdvD8760 Pðvi Þf ðvi Þ (8.50) AEP 5 8760 0

i5vcutin

Additional hypothesis has been integrated in Eq. (8.50) that the turbine will produce P(vi) for wind speeds v (vi 2 0:5 # v # vi 1 0:5) and ð vi 10:5 Probability ðvi 2 0:5 # v # vi 1 0:5Þ 5 f ðvÞdvDf ðvi Þ ðEulerÞ (8.51) vi 20:5

In Example 8.3 is explained how it is possible to use a spreadsheet to determine AEP from Eq. (8.50). Example 8.3: Annual wind turbine energy production estimation using a spreadsheet (Table 8.5). Suppose that a NEG Micon 60 m diameter wind turbine (Table 8.4) having a rated power of 1000 kW is installed at a site having Rayleigh wind statistics with an average wind speed of 7 m/s at the hub height [2]. 1. Find the annual energy generated. 2. From the result, find the overall average efficiency of this turbine in these winds. 3. Find the productivity in terms of kW h/year delivered per m2 of swept area.

Table 8.4 Examples of wind turbine power specifications. Manufacturer

Venturicon 3. ZERO

Hummer H8.1610KW

Aria Libellula 60i

NPS Northern Power NPS 100C-24

Nordex N29

Enercon E-40/5.40

Enercon E-58/10.58

Bonus B76/2000

Multibrid M5000

Rated power (kW)

3.17

10

60

100

250

500

1000

2000

5000

Diameter (m)

1.3

8.2

19

24.4

29.7

40.3

58.6

76

116

0 0 0 0 17.6 25.7 35.8 57.5 95.4 127.7 158.1 189.5 210 224.6 234.5 240.8 250 250

0.00 0.00 1.14 4.37 18.87 40.39 69.36 112.19 165.38 236.89 328.00 396.64 465.15 495.95 500.00 500.00 500.00 500.00

0 0 0 2.6 25.6 75.8 140.3 232.8 366.6 516.2 680.8 854.8 952.4 1000 1000 1000 1000 1000

0 0 0 0 46.91 133 237 401 623 886 1190 1502 1740 1891 1962 1988 1996 1999

0 0 0 0 10 330 560 1000 1470 1980 2800 4000 4800 5000 5000 5000 5000 5000

Avg. wind speed v (m/s)

v (mph)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

0.0 2.2 4.5 6.7 8.9 11.2 13.4 15.7 17.9 20.1 22.4 24.6 26.8 29.1 31.3 33.6 35.8 38.0

0.00 0.00 0.00 0.00 0.04 0.08 0.13 0.22 0.32 0.46 0.64 0.86 1.13 1.41 1.74 2.16 2.62 3.17

0 0 0 0 0 1 2 4 6 8 9 10 11 11 11 11 11 11

0 0 0 0 4 8 15 25 35 48 56 58 59 59 59 59 59 59

0 0 0 1.2 7.2 14.5 24.7 37.9 58.7 74.8 85.1 90.2 94.7 95.3 95.1 94.2 92.9 91.1

(Continued)

Table 8.4 (Continued) Manufacturer

Venturicon 3. ZERO

Hummer H8.1610KW

Aria Libellula 60i

NPS Northern Power NPS 100C-24

Nordex N29

Enercon E-40/5.40

Enercon E-58/10.58

Bonus B76/2000

Multibrid M5000

Rated power (kW)

3.17

10

60

100

250

500

1000

2000

5000

Diameter (m)

1.3

8.2

19

24.4

29.7

40.3

58.6

76

116

250 250 250 250 250 250 250 250

500.00 500.00 500.00 500.00 500.00 500.00 500.00 500.00

1000 1000 1000 1000 1000 1000 1000 1000

2000 2000 2000 2000 2000 2000 2000 2000

5000 5000 5000 5000 5000 5000 5000 5000

Avg. wind speed v (m/s)

v (mph)

18 19 20 21 22 23 24 25

40.3 42.5 44.7 47.0 49.2 51.4 53.7 55.9

3.17 0.00 0.00 0.00 0.00 0.00 0.00 0.00

11 11 11 11 11 11 11 11

59 59 59 59 59 59 59 59

88.9 87.1 84.1 81.3 78.6 76.1 74.3 71.7

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Table 8.5 Annual energy production calculation using a spreadsheet. Wind speed (m/s)

Power (kW)

Probability [f(v)]

h/year at v ½8760 3 f ðvÞ

Energy (kW h/year)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

0 0 0 0 33 86 150 248 385 535 670 780 864 924 964 989 1000 998 987 968 944 917 889 863 840 822 0

0 0.032 0.06 0.083 0.099 0.107 0.108 0.102 0.092 0.079 0.065 0.051 0.038 0.028 0.019 0.013 0.008 0.005 0.003 0.002 0.001 0.001 0.000 0.000 0.000 0.000 0.000

0 280 526 727 867 937 946 894 806 692 569 447 333 245 166 114 70 44 26 18 9 9 0 0 0 0 0

0 0 0 0 28,619 80,610 141,912 221,593 310,279 370,241 381,498 348,473 287,608 226,639 160,448 112,627 70,080 43,712 25,938 16,959 8269 8033 0 0 0 0 0

Total

2,843,540

For better understanding, we detail the calculation of the term v 5 6 m/s in Eq. (8.45). From Table 8.4, at 6 m/s the NEG Micon 1000/60 generates 150 kW. The Rayleigh PDF at 6 m/s in a regime with 7 m/s average wind speed is: 2 3 2 3
2
2 πv π π  6 π v 6 55 5 5 0:10801 f ðvÞ 5 2 exp4 2 exp4 2 4 v 4 7 2v 2  72 Hours @6 m=s 5 8760 h=year 3 0:10801 5 946 h=year Energy ð@6 m=sÞ 5 150 kW 3 946 h=year 5 141; 929 kW h=year

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The total energy produced is 2.85 3 106 kW h/year. 1. The average efficiency is the fraction of the wind’s energy that is actually converted into electrical energy. By assuming Rayleigh statistics, we can write: P5

6 1 6 1 π  ρAv3 5   ð1:225Þ  ð60Þ2  73 π 2 π 2 4

P 5 1:134 3 106 W 5 1134 kW Energy in wind 5 8760 h=year 3 1134 kW 5 9:938 3 106 kW h: The average efficiency of this machine in these winds is: Eff 5

2:85 3 106 kW h=year 5 0:29 5 29% 9:938 3 106 kW h=year

2. The productivity (annual energy per swept area) of this machine is:

Productivity 5

2:85 3 106 kW h=year 5 1008 ðkW h yearÞ=m2 : ðπ=4Þ  602 m2

A histogram of the number of hours per year and MW h per year at each wind speed for the above example is presented in Fig. 8.16.

8.5

Wind turbine control and hybrid systems

8.5.1 Wind turbine control systems 8.5.1.1 Standard control configurations Wind turbine control is a complex problem and involves several research areas including materials, aerodynamics, and power systems. As the turbine structures get larger, their components become more expensive. Manufacturers of wind turbines can try to offset cost increases by using lighter weight components that can be more versatile. Such big, costly, and flexible components may be more susceptible to fatigue failure and extreme loads arising from the wind’s turbulent nature. Optimization of the control system to avoid extreme loads and to reduce fatigue load cycles is necessary to prevent failure of the components. The control of wind turbines is usually divided into four primary operating regions of the wind turbine, as shown in Fig. 8.17. Region 1 covers service from start-up to “cut-in” wind speed where the generator is turned on and starts generating electricity. The turbine is said to be in Region 2 when wind speeds are above “cut-in” but still too low to generate the full output. The objective in this region is to maximize aerodynamic efficiency to capture as much energy from the wind stream as possible. In Region 3, wind speeds are sufficiently high to drive the generator at its rated power output; in this case the aim is to safely regulate speed and power at rated levels. Region 4 occurs when the turbine is shut down due to the high wind speeds to prevent turbine damage.

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Figure 8.17 Wind power, turbine power, and operating regions for a 1-MW turbine example.

Figure 8.18 Control loops for a standard wind turbine.

In all these regions the turbine speed and power are controlled by varying the generator load torque and the blade pitch angles based on the generator shaft speed measurement. The generator torque is induced on the load side of the drivetrain by power electronics, and the actuation is swift enough that it is assumed to occur with negligible delay compared to the rotor dynamics and structural loads. Usually, the generator shaft speed is determined using an encoder, and the signal is also fed through a low-pass filter to prevent high-frequency actuation. During turbine operation, yaw control is often used to keep the rotor perpendicular to the primary wind direction, usually based on 10-s average wind direction measurements, with a 0.5 /s yaw rate [10]. Many of the existing wind turbines use proportionalintegral (PI), a collective blade pitch controller, and a torque controller to achieve the two main objectives of optimum power extraction and control of induced power. As illustrated in Fig. 8.18, the rotor speed is used as the only calculated variable to either produce the necessary collective blade pitch angle β com or the necessary rotor torque τ g depending on

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the operational goals to be achieved. The ultimate purpose of a wind turbine control strategy is to regulate the power output of the turbine as a function of wind speed and direction. In addition, the control protocol must ensure safe operation over all wind conditions [4].

8.5.1.2 Advanced control methods Today, the focus of wind EP has shifted to lowering production costs, improving the quality of electricity produced, safety and reliability. However, researchers have suggested numerous novel control approaches to overcome the deficits of traditional control methods to resolve these challenges. Most of these advanced control algorithms are aimed to optimize and improve the efficiency to produce electricity by controlling speed/power and/or reducing structural load in wind turbines. As stated in Ref. [11], asymmetric loads around rotor disks cause blades to be excited by a dominant once per revolution (1p) in addition to other higher order harmonic flap-wise load range, that is, 2p, 3p, 4p. Nonrotating portions of the turbine, on the other hand, are governed by 3p harmonic load components and their higher order integral multiples. Because wind turbines are essentially multi-input multi-output systems with measurements taken from both the fixed and rotating structural components, it is necessary to design a controller with all state variables expressed relatively to a fixed coordinate system, rather than in a mixed coordinate system. This can be achieved by transforming all system model dynamics expressed in respect of the rotating rotor into a fixed coordinate system. As illustrated in Fig. 8.19, the variables represented in the xi yi zi rotating coordinate system are mapped into the xyz fixed coordinate system. The defined coordinate scheme is generally referred to as collective, cosinecyclical, and sinecyclical coordinates.

Figure 8.19 Rotating and fixed reference coordinates.

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The majority of the recorded work on the individual pitch controller focuses on the harmonic rotor blade load 1p [1113]. Coleman transformation is normally used to express bending moments of the root blade with respect to fixed orthogonal axes of direct and quadrature. Then there are two separate PI-control loops designed to suppress loads on both these axes. Finally, the reverse Coleman transformation is performed to produce the corresponding required individual pitch angle. To reduce 1p harmonic loads on the rotor blades and 3p harmonic loads on the fixed structure of the wind turbine, an individual pitch controller is added to a double fed induction generator wind power system [11]. To achieve this task the bending moments on the rotating rotor blades were transformed into moments of fixed dq coordinate axes (yaw and tilt bending moments) using Coleman transformation. Two standard PI controllers are used to regulate moments in d and q. Since the primary source of fatigue loads on the rotor blades is 1p harmonic load, a lowpass filter was used to reduce the transmission of higher order harmonics to the fixed turbine structure. The tests showed a load reduction boost as opposed to a collective pitch controller (CPC). A research aimed at reconciling two competing goals to minimize tower deflection and speed control in a two-bladed wind turbine is discussed in Ref. [14]. It is done by integrating IPC (individual pitch controller) and CPC control schemes. It was found that CPC affected aerodynamic blade torque and could cause interference with the rotor speed control target when used to damp tower oscillation. On the contrary, IPC can minimize tower vibrations by allowing for just a brief moment a higher aerodynamic rotor load. Thus a trade-off between these two competing goals was achieved by merging these two control strategies. The findings reported indicate an improved trade-off between tower deflection and rotor speed regulation when a combination of collective and individual pitch controller is used as opposed to independently CPC or IPC. Individual pitch control can also be used to minimize torque variations in wind turbines. This is done by rising edgewise bending moments of the blade that minimizes the variance of the rotor torque. The adjustment of individual pitch angles is based on multistage dynamic weight distributions, which are measured according to rotor azimuth location, tower shadow effect, and vertical wind shear. The efficiency of this control method was measured against a single proportional integral derivative controller using artificial neurons network. This control method not only minimizes torque variance but also smooths the fluctuation in the flap-wise bending moments, the yaw bending moments, and the tilt bending moments. Higher harmonic loads can also significantly contribute to the fatigue and need to be mitigated, especially in large-scale wind turbines. A number of higher harmonic controllers (HHCs) have been investigated in literature, with applications to wind turbines. Normally, individual parallel-connected HHC loops are designed to reduce higher harmonic loads in the rotor, resulting in a reduction of 3p harmonic and integral multiple structural loads on the fixed structure. Most of the structural load reducing controllers used in wind turbines depend on blade load measurements. To avoid direct measurements, control methods focused on the calculation of aerodynamic structural loads using inflow conditions were explored. Pitot tubes

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are usually used to determine the inflow conditions. In Refs. [14,15], local measurements of the inflow on each blade are used to quantify blade’s bending moments and build an individual pitch controller. All blades are assumed to be physically and aerodynamically identical because the properties of a single blade are used to determine loading for all the other blades. In Table 8.6, various control methods for the high wind speed region are developed to regulate speed/power and mitigate structural loads. Most of these approaches are based on the principle of linear control, meaning they are effective only around a given point of operation. Steps must also be taken into account for the uncertainty arising from the shift in the operation point due to variability in incoming wind speeds. Although the PI gain scheduling controller is easier to build and more reliable because it is a single-inputsingle-output controller, it has drawbacks in handling multiobjective problems. Linear quadratic methods can provide an optimal solution by reducing a given cost function by trading-off opposing requirements. In most cases, complete system state information is needed to design this type of controller but not all states are available for measurement. This has prompted the necessity of developing observers to recreate unknown states. Observers can be conceived using linear quadratic methods, including HN , pole positioning. Linear quadratic gaussian (LQG) has been used in literature to control generator speed and to minimize structural loads when wind turbine is believed to be affected by disturbances

Table 8.6 Comparison of control methods in area with high wind speed. Control method

Manipulated variable

Description

PSF

Torque

TSR

Torque

OTC

Torque

Perturbed and observation Extremum seeking

Torque

Requires an understanding of the optimal power curve for the wind turbine. A measurement of the wind speed is not required. Established optimum point of service is assumed. A measurement of the wind speed is needed. Needs turbine knowledge of optimum curve characteristics. The torque of reference is always proportional to the speed of the rotor square. Suitable for wind turbine systems with low inertia.

Sliding mode

Torque/Pitch

Adaptive controller

Torque

Torque/Pitch

Take time to converge to the best possible solution. Conduces performance degradation when wind speeds change rapidly. Does not guarantee that the tracking errors converge in finite time. Leads to loss of efficiency if wind velocity changes rapidly. Adapts the benefit of power (gain) depending on the wind speed coming in.

OTC, Optimal torque control; PSF, power signal feedback; TSR, tip speed ratio.

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and noise with stochastic properties. LQG controllers are limited in terms of applications because of these stringent criteria. Another concern associated with the LQG controller is low gain margins, which are improved with the recuperation of the loop transfer. The regular linear quadratic regulation controller is usually adjusted to add an integral action to reduce the steady-state error. Another control system that has drawn considerable attention in wind turbine applications is the disturbance accommodating controller. This is due to its ability to predict device and destructive states as well as to account for uncertain disruptions. In the operation of wind turbines the deviation of incoming wind from its nominal value is regarded as disturbance. Majority of recorded research in wind turbine application compensate for unknown disturbances using static disturbance rejection method that assumes that disturbances affect the turbine through the same channel as the manipulated variables, resulting in poor performance when violating this condition. On the contrary, dynamic disturbance rejection method can offer an effective means of compensating for unknown disturbances, nonlinearities, and uncertainties due to un-modeled dynamics. Since most linear controllers are designed for a specific point of operation, any substantial deviation from this point may result in deterioration of performances. It is therefore necessary to consider variable gain controllers such as gain scheduling and linear parameters which vary in the application of wind turbines. To achieve the goal of enhancing the efficiency of produced power and ensuring reliability in mega-scale wind turbines, multivariable control schemes along with evolving technologies such as LIDAR sensors, hybrid drives, and active aerodynamic control devices such as trailing edge flaps and micro-tabs are inevitable. It will be necessary to combine more than one control strategy to reduce structural loads in different subsystems rather than separately consider each subsystem as is the case for most of the controllers proposed.

8.5.1.3 Power control Power controls prevent mechanical deterioration of the rotor at higher wind speeds. Currently, two different control methods are applied for wind turbines to limit extractable wind power. They are referred to as stall and pitch controls. Both methods are suitable to limit the power absorbed by the rotor [7]. G

G

Stall control: it consists in inducing the stall phenomenon (intentional separation of the flow) on the blades. For the above, the turbine must operate at a constant number of revolutions regardless of wind speed. Under these conditions, the turbine will behave differently depending on the wind speed. For example, at wind speeds above the cut-in and below the nominal, the angle of attack increases gradually. When the nominal speed range is reached, the angle of attack becomes so large that the airflow cannot follow the surface contour (flow separation). Due to the airflow stall, the lift in the rotor is diminished; therefore the power extracted from the wind can be maintained at a constant level. The stall phenomenon also produces eddy currents, which, in turn, cause the rotor to slow down or maintain an effective torque at a constant level [7]. Pitch control: this power control method consists to turn the rotor blades and, thus, change the angle of attack of blade sections. Flow conditions and the air forces incident on the rotor blade are modified such as to keep the wind power absorption by the rotor at speeds above the nominal wind speed almost at a constant level. In this case, it is

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necessary that the rotor blades have an “adjustment mechanism,” which is responsible for continuously adjusting the angle of attack of the rotor blade to the airflow, avoiding the stall effect. The adjustment area covers the range from 90 to 100 degrees to reach the socalled feathered pitch from the operating angle (almost 0 degree). In feathered pitch (position without any tangential force) the blade resembles a wind vane and produces no or very little rotor rotation. However, if the number of revolutions of the rotor increases, the blade angle needs to be adjusted to maintain a positive, lifting angle of attack [7].

Compared to stall control, the pitch control allows the wind turbine to be shutdown intentionally and relatively smooth when the wind speed exceeds the cut-out speed. Also, pitch control avoids abrupt transition from the installed capacity to zero and thus prevents the resulting high mechanical strain exerted on the turbine [7].

8.5.2 Hybrid power systems One of the characteristics of wind power systems [as well as photovoltaic (PV) power systems] is their unpredictable nature associated to the dependence on weather and climatic changes that make them not completely reliable. As a result, there is frequently a difference between the energy produced and the load demand. Hybrid power systems (also called hybrid renewable energy systems—HRESs) emerged to compensate such lack of reliability. In general, HRES can be defined as an energy system, consisting of two or more renewable sources including or not fossil fuel generator and storage capacity, such that the electricity produced is more stable and cost-effective. An HRES can be connected to grid or work in isolation [16,17]. As can be seen in Fig. 8.20, a PV/wind hybrid system consists of a wind energy system, solar energy system, controllers, battery, and an inverter for either

Figure 8.20 Photovoltaic/wind hybrid system representation with battery storage.

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connecting to the load or integrating with a utility grid. Here, the main energy sources are wind and PV, the battery charges when the produced power is in excess and discharges when the power demand is higher than the actual production [17]. The HRES compensates renewable energy sources, that is, the availability of solar radiations is relatively greater in summer, while winds are more accessible in the evening times of winters [16]. The HRES is classified as small, medium, or large, depending on the kW power generated and the load. An HRES is small when its capacity is less than 5 kW; this system is generally used to serve loads in remotely located homes or telecommunication relay systems. When the capacity is from 5 to 100 kW, the HRES is medium and is typically used to power a remotely located community that contains several homes and other required amenities. For most cases, medium systems work in isolated mode and sometimes may be connected to grid. Finally, if the capacity exceeds 100 kW, the system is large and is capable to power a region, these systems are generally connected to grid, to enable the power exchange between the grid and the system in case of surplus or deficiency [17].

8.5.2.1 Sizing of photovoltaic/wind hybrid renewable energy system In addition to different equipment characteristics, knowing the local renewable energy resources and the load variation is essential in the optimal sizing of the hybrid system. A feasibility study determines the optimal size of the system, the choice of the production, storage, and conversion equipment according to specific renewable energy resources and load variation [17] such as the following power equilibrium equation is always respected: Pgenerated 5 Pload 1 Pstorage

(8.52)

In the design process of the HRES three situations occur: generation meet up demand (Pstorage 5 0), overgeneration (Pstorage . 0), and overdemand (Pstorage , 0) [16]. The relevant parameters for such a feasibility study are solar radiation, ambient temperature, and wind speed, which should be available for relatively long periods (from 1 to 30 years). For most sites, these data are not available and should be reconstructed using high-level meteorological information and statistical reconstruction methods. As seen in Eq. (8.52), the local load profiles are also required to find whether the generated energy is matching the load demand. Here, the hourly average load demand is necessary; this information is generally available at local load dispatch centers. In case it is not available, it should be reconstructed based on historical, previous experience, charge decomposition, or literature available data. Statistical methods such as Gaussian distribution, linear, nonlinear, and multiple linear regression analysis have been widely used for the prediction of the residential energy consumption [17].

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Table 8.7 summarizes some models available to size the different components of the HRES. Batteries are described as the most vulnerable source in the entire HRES due to lifetime and cost issues [17]. Generally, the batteries must be replaced at least 34 times throughout the plant life cycle, and they require very frequent maintenance. To reduce this vulnerability, different storage systems such as superconducting magnetic energy storage, compressed air energy storage, ultra-capacitors, flywheel storage, pumped hydro storage, and hydrogen storage have been investigated. However, these systems are still in an incipient state, some have lower efficiencies, shorter durations, and shorter life cycle or are more expensive to implement. Table 8.7 Models used to size each of the components of hybrid renewable energy systems. Component

Model

Photovoltaic system

Simulation model Simple model of a solar cell

Relevant parameters

IV curve of a solar cell. Tilt angle at the time of calculation of solar radiation itself.

PV output Solar model

Wind system

Open circuit voltage, short circuit current. Diffused, scattered, reflected, and incident radiation on an inclined surface.

Solar generator efficiency Perez model

Overall heat loss coefficient.

Power curve Model based on Weibull distribution Wind turbine model Zamani and Riahy model

Less complexity, still accurate.

Sky clearness index.

HDKR model

TMY

Characteristics

Estimate the power output of PV panels.

Offers the reduced brightness coefficients to estimate the radiation on the surface. It is an observational technique picking particular months from different years using the FleckensteinSchafer accurate system. Consideration of power curve to be linear, quadratic, cubic form, or a piece-wise linear function with few nodes. Wind speed, shape factor, and scale factor.

Rotor swept area. Instantaneous wind speed variations.

Introduce two factors: EPF and turbine controllability (Ca) for modifying the power curve to track instantaneous changes in wind speed variations.

(Continued)

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Table 8.7 (Continued) Component

Model

Relevant parameters

Battery storage

Equivalent circuit model Kinetic model (KiBaM)

Include components as the resistances of internal components of the battery. Gives the amount of energy stored/released at each time step by the battery. The battery is modeled as a two-tank system, one with readily available energy that can be converted to DC output and the other bound energy that cannot be converted instantaneously. The battery can be modeled in terms of instantaneous SOC, which is a function of SOC at the previous instant, selfdischarge rate, and efficiency of battery while charging and discharging. This model gives the accuracy up to 2% in case of standalone systems. Represents load demand during odd conditions and it is taken to be two or three in general.

Empirical approach —SOC

Semiempirical Shepherd model Days of autonomy Converters

Individual converts for both PV and wind systems Multiinput converter

Characteristics

It can result in a bulky and less cost-effective because the additional converter also increases the system size and often requires extra control mechanism which will increase the cost. The MPPT is used for both solar and wind systems and the power can be delivered to grid independently or simultaneously.

EPF, Energy pattern factor; HKDR, Hay, Davies, Klucher and Reindl; KiBaM, Kinetic Battery Model; MPPT, Maximum Power Point Tracking; PV, photovoltaic; SOC, state of charge; TMY, typical meteorological year.

Another important component is associated to system control. As said earlier, the intermittent nature of renewable resources may cause problems of power quality and stability. Thus the control should ensure the maximum power extraction [maximum power point tracking (MPPT) operation], the improvement of power quality by reducing the effect of harmonics, and voltage recovery post fault conditions [17]. Different control strategies have been used; some are mentioned as follows: G

G

G

G

G

G

G

intelligent energy management scheme using fuzzy logic; supervisory predictive control; energy dispatch model based on the model predictive; dynamic mathematical model in synchronous reference frame; passivity mode control; sliding mode control; and artificial neural network used for the MPPT for both wind and PV side.

While sizing the hybrid system components, it is a common practice to consider the worst month scenario to make the system more reliable. Nonetheless, this practice has been found to be less cost-effective and considerable oversizing has been done [17].

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8.5.2.2 Optimization of photovoltaic/wind hybrid renewable energy system For any optimization study the limits will always be a key part of determining the optimal solution, as they will define the boundaries of the search space. Each constraint included in the problem specifies a parameter and several limitations to optimize the size of HRES by different research strategies [17]. Table 8.8 summarizes some of these limitations. The optimization process provides a balance between the overall system cost and the reliability. Numerous methods have been adopted to determine the optimal configuration of the systems such as iterative techniques, graphical methods, stochastic approaches, and artificial intelligence techniques. The artificial intelligence techniques are widely used in optimization process. The genetic algorithm is the preferred algorithm, as its execution technique does not rely on the error, which makes it more suitable for the multicriteria optimizations [16,17]. The hybrid system is considered optimal to the extent that it provides constant and sufficient energy at the lowest cost [16]. Regardless of the type of technique used, the optimization analysis tried to achieve any of the objectives listed as follows: G

G

G

G

G

G

Deficiency of power supply probability (DPSP) and levelized cost of energy for reliability and cost analysis. Wind turbine size and number of PV modules required to minimize the difference between power generated and power demanded. Optimize the system for a given load pattern. If a line extension from an available utility grid or a new hybrid system is cost-effective. Operating reserve capacity. Scheduling of battery discharge/charge rate and cycles during peak load periods.

8.5.2.3 Reliability analysis It is necessary to ensure the reliability of the supply while keeping the cost of the system low. There are various methods for carrying out the reliability analysis, some of them are listed as follows:

Table 8.8 Limitations for hybrid renewable energy system optimization. PV

Wind

Battery

Environmental factors

Grid integration

Array capacity; number of PV panels; panel tilt angle; area

Wind capacity; area; installation height

SOC; DOD; charge/ discharge power and rate; number of cycles

Pollutant emissions; social acceptability

Fluctuations of power injected; cost of line extension

DOD, Depth of discharge; PV, photovoltaic; SOC, state of charge.

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299

Loss of power supply probability (LPSP): it can be defined as the probability that loss of power supply occurs, which means the combined hybrid system is unable to supply the power to the load. The mathematical form is given by:

P Tn

LPSP 5

n51

time ðif Psupplied , Pdemand Þ Tn

(8.53)

This technique is widely utilized where probability of inadequate energy supply to load demand is considered for planning and designing of the hybrid system [16]. G

G

G

G

G

Loss of load probability: it can be defined as the probability that the system load demand has exceeded the system capacity. It is also defined as the fraction of number of hours of load insufficiency probability to the number of hours of load satisfaction [16]. Loss of load risk: probability of failure to satisfy the daily demand of electrical energy due to deficiency in generation of energy by renewable sources. DPSP: is the probable condition of lack in supply of energy in comparison to the load demand. It is also a specialized criterion for evaluation and sizing of a solar PV and wind hybrid system for energy generation. Mathematically, it is represented as the ratio of power supply deficiency and cumulative load demand on annual basis [16]. Expected energy not supplied: mean predicted value of energy that has not been supplied. It occurs when the available electric generation capacity cannot meet the hourly load demand. Loss of load hours: it is summation of hourly expected loss in load for a specified duration of 1 year, which cannot be able to meet load demand due to lacking in power generation at the time of interest.

8.5.2.4 Performance assessment The HRES needs assessment of its real performance [17]. The assessment should include varying wind speeds, turbine’s wind power curve, ratings of the generator, efficiencies of various components in the system, payback period of the initial capital cost, life cycle savings, different capacities of PV panels, different storage capacities, and variation of tilt angle of the PV cell at different periods of the day, among others. There is no standard performance assessment of the HRES. However, this can be done by modeling and simulating the system using a Simulink type platform for the accessible insulation, speed of wind, electrical load, and various components [16]. The simulation is the most common practice, saves time and cost for the analysis and the assessment of hybrid systems. Various software tools are available for this purpose such as HOMER, AEOLIUS, BALMOREL, COMPOSE, EMCAS, RETScreen, RAMSES, WASP, EMPS, simREM, and iHOGA [17].

8.5.3 Particular operating conditions for the wind power plant 8.5.3.1 Operation in extreme climatic conditions Severe climates include sites with high extreme winds, high moisture and humidity, cold weather, high temperatures, and lightning. Table 8.9 shows the problems and actual solutions related to extreme climates.

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Table 8.9 Description of mainly issues associated to severe climates. Climate High temperatures and moisture in warm climates

Issues G

G

G

G

Cold temperatures

G

G

G

G

G

G

G

G

G

Lightning

G

Thin lubricants Degrade operation of electronics Affect motion in mechanical systems that expand with the heat Metal corrosion Turbine icing Degradation of turbine performance No or misleading information from anemometers and wind vanes than have been frozen Rotor imbalance Malfunctioning aerodynamic brakes Downed power lines Danger to personnel (falling ice) Material properties (reduced flexibility of rubber seals, increases lubricating oil viscosity) Delayed and expensive maintenance Damages in blades, and in mechanical and electrical components

Actual solutions G

G

G

G

G

G

G

G

G

G

Use of desiccants, dehumidifiers, and improved sealing systems Site-specific design details

Special blade coatings to reduce ice build-up Heating systems Electrical or pneumatic devices to dislodge accumulated ice Heaters Special materials for cold weather or to ensure adequate fatigue life

Provide very low impedance electrical paths to ground that bypass important turbine components Voltage surge protectors Design a low impedance grounding system

From: J.F. Manwell, J.G. McGowan, A.L. Rogers, Wind Energy Explained: Theory, Design and Application, second ed., John Wiley & Sons, 2009.

8.5.3.2 Special purpose applications The special applications include water pumping, water desalination, space or water heating, and ice making. On the other hand, the heating is usually undertaken with some type of thermal storage, so the heating system serves a load management role, that is, when there is excess of energy from the wind turbine, some of it is used to heat up the thermal storage which could be water or a ceramic material [1].

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8.5.3.2.1 Water pumping Wind has been used for water pumping for many hundreds of years and still has a role to play for that purpose today, especially in places where water is available in wells or aquifers. There are four main types of applications for wind water pumping: (1) domestic use, (2) irrigation, (3) livestock watering, and (4) drainage [1]. The principal components in wind water pump are the wind rotor, tower, a mechanical (or electrical, depending on the situation) pump, mechanical linkage, a well or other water source, and a piping system. Sometimes, a system for water storage is used, depending on the application it can be tanks, ponds, or reservoirs [1]. Fig. 8.21 shows the diagram of a mechanical water pumping windmill. It should be noted that the pump shown in Fig. 8.21 is of piston type, which has been historically used due to its simplicity [1]. For the implementation of this water pumping system, some considerations are important. Regarding the wind turbine rotor, it is generally designed to have its maximum power coefficient at low TSRs which is consistent with the requirements of high torque and low pump operating speeds [1]. As said earlier, water pumping has many applications, and depending on the application, it will also be the flow that the pump will extract, this parameter allows to estimate the power required to pump water as: Ppump 5

ρw gzV_ ηpump

Figure 8.21 Mechanical water pumping windmill.

(8.54)

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where ρw is the water density in kg/m3, g is the acceleration of gravity, z is the total pumping head in m, V_ is the volume flow rate of water in m3/s, and ηpump is the pump mechanical efficiency. The power of the rotor must match the pumping power; therefore Eqs. (8.10) and (8.54) can be combined. In this case the water volume rate V_ is a function of the stroke volume (stroke length multiplied by the pump area) and the pump speed [stroke length multiplied by the pump crank angular speed (Ω)], multiplied by the volumetric efficiency of the pump: π Ω V_ 5 ηvol spump D2pump 4 2πgr

(8.55)

where ηvol is the volumetric efficiency of the pump, spump is the stroke of the pump, Dpump is the pump diameter, gr is the ratio between the speed of the rotor and that of the pump crank. Thus expressing the final expression in terms of TSR, the design wind speed may then be found using: Ud 5

ηvol spump D2pump λd ρw gz 4CP;max ηpump ρa πR3 gr

(8.56)

Finally, for a piston pump, an important parameter to take into account is the starting torque that varies with the force on the piston and the length and position of the crank associated with raising and lowering of the piston rod; hence, the maximum torque occurs when the crank is perpendicular to the pump rod, so that: QP;max 5

D2pump spump ρw gπ z 2 4

(8.57)

As mentioned in the previous sections, the direction control and high wind protection are also important in this type of application. In most of the cases the yaw control and protection is realized by using a tail vane (see Fig. 8.21). The vane is mounted perpendicular to the plane of the rotor. The water pumping can be integrated in a hybrid system. In this case the water pumping is carried out using excess energy. The tank that needs to be refilled acts as an energy storage device. As the pumping, if sufficient water reserve is available, is a shedding load, only excess energy could be used to pump water. If there was no excess energy in a day, energy from battery storage or the diesel generators would need to be used to ensure that at the end of the day, the water tank will be sufficiently filled.

8.5.3.2.2 Wind-powered desalination In 2019 a report by the World Health Organization (WHO) and United Nations Children’s Fund (UNICEF) found that 2.2 billion people live far below contemporary standards for safe water and sanitation [18]. However, many of these locations

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are close to saltwater that can be transformed into drinking water by desalination. Desalination is an energy-intensive process what reduces its availability. The use of wind energy, alone or in a hybrid system, is an interesting solution especially for remote and low-income communities [1]. Desalination can be made by a thermal process or a membrane process. The most common methods are reverse osmosis. Reverse osmosis is a filtration process in which a semipermeable membrane is used to allow water to pass through while rejecting salts. The power required is a function of the pressure required to force water through the membrane and the desired flow rate of the permeate [1]. Hence, in this application, the wind turbine could be used to power the pump used in the reverse osmosis process. The wind turbine can be connected directly, in a hybrid system or in a large central electric grid.

8.6

Environmental impacts of wind energy projects

Wind power plants, while contributing to reduce GHG emissions in EP, have some negative environmental impacts that are discussed in this section. Before any decision to implement a wind power plant is taken, the potential negative impacts should be determined and predicted such as to minimize damages to environment. The most significant negative impacts of a wind power plant are the visual impact, the noise, and the effect on the wildlife. Some other impacts include the disruption of radar or television reception due to magnetic forces generated by the wind turbine and the increased possibility of being struck by lightning. Wind turbines require a lot of land, compared to other energy sources, the impact in the land use of wind power systems is relatively low and it is due mainly to the foundations, access roads, and administration buildings, which represent only 5% of the land being used. The remaining 95% related to the distances required between wind turbines is used for agriculture [7]. It is important to mention that early in the siting process, the developer of the wind energy project should conduct a critical analysis of the environmental issues: required permits, licenses, regulatory approvals, threatened or endangered species or habitat, avian and bat species, identification of wetlands and other protected areas, and location of known archeological and historical resources [6].

8.6.1 Visual impact of wind turbines The most significant visual impact associated to the wind turbines is the rotor’s size (number of blades), turbine color, and the tower type (solid, lattice, etc.), this applies far and foremost to plains and exposed sites based in low mountain ranges, as wind turbines are still visible from far distances. For example, population more easily accepts turbines with three blades and solid towers. Such optical evaluations are not subject to any objectively defined criteria but primarily rely on the associations and the respective spectator’s personal taste. In most cases, looking at wind

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turbines to date is not considered disturbing [7]; people are in favor of renewable energy, but “not in my backyard.” In the category of visual impacts, we include the disco effect and the shadow impact. Both are related to the solar radiation. The first one is produced in times of high direct solar radiation that generates a luminous reflectance at rotor blades. The reflectance occurs accidentally and is only perceived temporarily at certain sun positions. The effects can be reduced by low reflecting rotor blade design [7]. Regarding the shadow impact, the term refers to the moving shadow created at time of sunlight at by the rotor blades. It depends on weather conditions, solar altitude, rotor size, and the form of operation. Nevertheless, shadows may affect the people living in the neighborhood of wind energy turbines or wind farms and are measured by the duration (exposure time to the shadows). According to Ref. [7], some studies have been conducted to determine whether periodic shadow impacts with a duration of 30 min and single occurrence may cause stress. While none of the individuals studied encountered substantial pressure, the documented increased pressure on psychological and physical resources is suggestive that accumulated long-term effects will meet the criterion of being an additional stress factor. The reduction of these visual impacts is associated to an adequate planning process and an appropriated micro-siting.

8.6.2 Wind turbine noise The sound emissions represent an essential parameter for the design of wind turbines. It is important to know the noise source that can be associated to aerodynamic noise or sound radiation. Knowing the noise source helps to determine mitigation measures. For instance, aerodynamic sound emissions are due to the airflow around the rotor blades and the rotor blade coming through the tower shadow [7], these noises usually occur at medium- and high-blade tip speeds. An optimized shape of rotor blade and blade tip has reduced the aerodynamic sound. Gearbox and generator cause the sound radiation. Converter casings and gearless converters contribute to reduce this type of noise [7]. To obtain a building permit for a wind energy plant the observation of sound limits must be in accordance with the local legislation related to sound emissions at residential, industrial, and mixed areas. In all the cases the limits must not be exceeded, not only to avoid legal problems but also to avoid possible rejection of neighbors near the project.

8.6.3 Bird and bat interaction with wind turbines Wind turbines may have a negative interaction with birds; hitting impacts on flying/migrating birds and interferences with feeding and resting birds have been reported. For instance, some species of resting birds tend to avoid and keep a distance of several hundreds meters from wind turbines. About birds hitting, this problem has been observed occasionally and it has been associated to rotor sound and

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that the wind farm has been built in the middle of a bird migration corridor [7]. However, the collision rates per turbine per year vary from 0.01 to 0.23, and overall, less than 1% of the ducks and geese flew close enough to the turbines to be at any risk of collision. About bat fatality, rates are estimated between 1.5 and 46 per turbine per year in the eastern United States [6]. Finally, adverse effects of the use of wind power on bird and bat life are minimized if determined areas are entirely excluded from wind power utilization, for example, natural reserves, protected areas, and migration corridors; also the use of fewer but larger turbines and bury electrical lines helps to reduce the impacts on birds.

8.6.4 Other impact considerations Some other impacts should be considered depending on the location of the wind power plant. For instance, in cold climates, the meteorological conditions could generate blade icing that may result in ice throw. However, the mortal risk of ice throw at approximately 200 m is comparatively low and almost equal to that of a lightning stroke [7]. In offshore wind farms, impacts such as noise, infrasonic sound, luminous reflectance, and shadow impact are less important. However, potential effects should be discussed, for example, sound radiation of wind turbines, also into the water, and thus possible noise irritation of animals; effects on seabed symbioses and fish fauna; and risk of vessel crashes and consequent effect on environment. Besides the impacts associated to the operation of wind turbines, the end-of-life impact should be considered. The correct final disposition of the turbine blades is an issue that has not yet been completely resolved due to the material they are made of (glass reinforced plastic); however, recycling or reuse techniques can be a good solution.

8.7

Economic and financing aspects of wind energy projects

Wind energy projects have many economic benefits in wind-rich areas, including direct and indirect jobs, land lease payments, local tax revenues, and lower electricity prices. Although project-specific impacts depend on factors such as location, size, and ownership, the aggregate economic effect of the production of utility-scale wind power is easily defined. The calculation and understanding of project costs and benefits is an integral element of planning any wind EP project. Wind project financing is typically composed of three primary sources of capital: equity—including supporting equity and tax equity—and debt. For a given project the combination and proportion of each of these sources of capital are referred to as the capital structure or capital stack. At a basic level, most wind project capital structures including a sponsor equity partner (commonly a developer), a debt

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Figure 8.22 Evaluation process and financial management of wind energy projects.

provider, and other projects will use a third-party tax equity investor in return for the project’s tax benefits. The ventures involving renewable energy can be of varying sizes and can stretch over various time horizons but often require technological, financial, and human resources that have to be combined to produce the anticipated outcome. The renewable energy projects share the characteristics typical of all other projects. The project needs a particular organization that unites all parties, regardless of other (existing permanent) organizational links or relationships, as shown in Fig. 8.22. This section focuses on the financial and economic assessment for a wind energy projects.

8.7.1 Revenues and financing of wind energy projects 8.7.1.1 Average wind energy cost The average wind energy cost depends on the size of the turbine, the project size, and the location. Usually, average wind energy costs fall into two categories: turbine costs and installed project costs. Researchers from the Energy Department say that wind turbine rates dropped from their 2008 highs to $700$900 per kilowatt in 2018. In 2018 the national average purchase price for wind power agreements plummeted to below 2b/kW h. This means that new wind energy projects—and federal incentives such as the Production Tax Credit—offered some of the cheapest forms of new power generation available at the time. Utility-scale wind power can also be sold for long periods at fixed rates (e.g., 20 years), providing a shield for fluctuations in commodity fuels such as natural gas.

8.7.1.2 Offsetting energy use and costs Another way in which wind energy projects will make a meaningful economic impact is to offset the energy costs. Distributed wind turbines may be used to directly counteract power use by customers. This idea, called net metering, enables

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the installation of a wind turbine near a load and the direct supply of energy to that load, thereby minimizing the electricity consumed by the user at retail rates. Any electricity not used is attributed to the user, because it falls back into the energy network. Producing several MWh of electrical energy annually, such a wind project is suitable for homes or farms, schools or other civic facilities, and large business or industrial establishments. Generating your own electricity can also provide a buffer against the future increase in energy prices for retail companies.

8.7.1.3 Wind energy financial incentives Financial incentives affect the overall economics of the project and are an important part of the effective implementation of distributed and utility-scale wind power. Like for other energy projects, financial incentives are also available for federal, state, municipal, and utility wind projects. The US federal government uses grants and opportunities to promote development for all renewable technologies. Those grants can include: 1. federal incentives offering direct cash return to energy producers or customers; 2. tax reduction for corporations or individuals taking particular steps impacting energy output, distribution, transmission, use, or conservation; 3. R&D programs aimed at increasing the US energy supply or enhancing the development of end-use technologies; 4. support for federal and rural utilities; and 5. loans and loan guarantees that offer financial support to energy technologies by promising repayment of loans obtained on the private debt market or by directly lending capital to investors in the energy sector.

8.7.1.4 Production tax credits and investment tax credits The production tax credit (PTC) provides a tax credit of 12b/kW h for the first 10 years of electricity generation for utility-scale wind. The alternative, investment tax credit (ITC) offers a loan at the outset of the project for 12%30% of construction costs. ITC is particularly interesting for offshore or distributed wind power plants as these projects are more capital intensive and benefit from the upfront tax benefits. In December 2019 Congress approved PTC and ITC extensions. As a result, the existing PTC for wind power is extended until 2020. If developers start construction before the end of the allocated time, wind projects should qualify (Table 8.10).

8.7.1.5 Equity capital and tax equity Equity typically refers to a share of ownership of an asset that may take the form of a security (e.g., equity or bond) or a direct investment in a business. Usually, equity investors are likely to lose some or all of their investment depending on whether the company or project is successful. By comparison, equity capital often meets

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Table 8.10 Production tax credit for wind power farms in the United States. If construction begins

The estimated allowable tax credit is

After Dec. 31, 2016 By Dec. 31, 2017 By Dec. 31, 2018 By Dec. 31, 2019 By Dec. 31, 2020

1.9b/kW h 1.8b/kW h 1.4b/kW h 1b/kW h 1.5b/kW h

original expectations if the company or product meets projections or if the project is sold to another group. To allow the most effective use of the tax benefits—the PTC or ITC in accordance with the Accelerated Cost Recovery System (MACRS)—a taxable entity will apply them to taxable income (depreciation) and tax liability (credits) in the year when the benefits were produced. However, many sponsors or developers in the wind industry do not have the tax capacity to do so and would otherwise have to carry forward the benefits (thus reducing their present value due to the time value of money) if it were not for the willingness of outside investors to “monetize” them. These investors, known as tax equity investors, will commit capital to a project in exchange for access to the PTC or ITC and accelerated depreciation, thus providing the project with a significant portion of its capital needs (typically 30% 50% of the total). Because this type of investment requires significant capital and tax liabilities, tax equity investors are often large financial entities such as banks and insurance funds. There are also many multinationals participating in the tax equity sector. To receive the tax benefits, creditors must show ownership for tax purposes of the project properties (a decision made by the IRS). In wind projects, this ownership usually takes the form of a partnership with the developer (unless the project is owned by a single entity that can take full advantage of the tax incentives). Two critiques of making use of investments outside of tax equity are often mentioned. First, in any given year there are relatively few successful tax equity partners on the market. Since demand for this form of capital frequently exceeds the supply available, tax equity investors may expect a higher return than a comparable debt product, usually varying from 7% to 10% depending on investment specifics and overall market tax equity supply [19]. The complicated financial structuring is the second criticism of tax equity financing. Establishing an arrangement includes high transaction costs—for example, legal services fees, tax advice, advisors, financial structuring, and other services [20]. These transactional costs minimize the nominal value of tax benefits and push for a larger project size (to keep the fixed transaction costs smaller compared to the size of the project). This can have the effect of limiting competitiveness in the market place for wind development, as smaller developers may not be able to access funding as readily as bigger players do.

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8.7.1.6 Equity sponsor In a project the equity sponsor (sponsor) closely resembles a conventional equity investor and may be the project’s initial owner. The sponsor’s equity is usually the project’s main financial backstop, and therefore the last party to earn payment in the project’s revenue distribution. As the sponsor usually faces the highest risk in the partnership, he also has the highest return requirements. However, because the sponsor’s equity is usually either back-levered or is just a small portion of the capital stack, this highest cost equity will only have a minimal effect on the weighted average capital cost (WACC) of the project that accounts for the total capital cost from all the sources in the capital stack of the project [21]. If the sponsor is also the owner, it is responsible for bringing the project from the initial design to the building and commercial operations through the comprehensive development process. In certain cases the sponsor will eventually manage the project’s long-term operation, provide operations & maintenance services, fulfill the PPA obligations (if any) or handle the electricity dispatch to wholesale markets. The sponsor may also be a relatively passive or nonactive owner in the project in some cases, and contract the project’s day-to-day operations & maintenance. The sponsor may also earn some of the profit distributions of the project as well as a “production fee” that it receives upon the project’s commercial activity [22].

8.7.2 Economic evaluation of wind energy projects In the broadest context an “investment” is an occasion where financial resources (capital) are used for beneficial purposes. The money can be invested in the production of new goods, the acquisition of a competitor, or the construction of a new power plant. An investment is, in a narrower sense, restricted to cases where financial resources are used to acquire or create tangible capital assets (capital cost). The investments have important implications for the investor, since a significant amount of capital is required that will not be available for other purposes or opportunities, equally attractive. The effects of a wrong investment decision can be severe and may threaten the investor. It is natural that investment decisions request a long and thorough review of the investment’s potential attractiveness. Appropriate settings for the investment costs are applied in project economic assessment. The most common is the WACC value. This is determined using the formula: rWACC 5 ð1 2 WD Þre 1 WD rD ð1 2 tÞ

(8.58)

where rWACC is the weight average cost of capital; WD the capital structure (ratio between the debt and total capital of the project, i.e., the sum between debt and equity); re the equity cost; rD the debt cost before tax; and t the taxes. Debt and equity serve to finance the project’s assets. The WACC measures the weighted average of the funding sources’ costs. The interest rate for working capital loans is easy to determine, as it is known to borrowers from the interest payments. The interest rate applied to equity is less

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evident. In finance theory, alternative approaches for estimating the cost of equity are proposed; the most common are the approaches of opportunity costing, methods based on discounted cash flow (DCF) and methods based on capital asset pricing model. Both strategies have a drawback, because they are applicable in open capital markets (share transactions through stock exchanges). In these situations, when the investor considers alternative investment opportunities with equity and/or oblivious to the anticipated return on investment as “cost of capital” for the proposed project, the opportunity cost approach has to be taken. Some financial indicators that are used for the decision making in the development of wind energy projects are presented in the following sections.

8.7.2.1 Simple payback The simple payback (SPB) is defined as the time (number of periods) taken to refinance the initial investment with the cash flow of the project. In other words, the SPB is expected to recover the initial investment through positive project cash flows. The project has recovered all the initial investment before that time or at least half of the invested capital is still at risk (if the project fails). The SPB measures the project risk: the higher the return time, the greater the risk to investors, because (in part), it takes longer, and it is more difficult to recover the invested money. In a traditional project the negative cash flow early in the project (initial investment) is followed in subsequent periods by positive cash flows (return). Mathematically speaking, SPB can be expressed as the smallest t satisfying the condition: ðCi2CoÞ1 1 ðCi2CoÞ2 1 ? 1 ðCi2CoÞt 5

X

ðCi2CoÞt $ Co0

(8.59)

where Ci is the cash inflows; Co the cash outflows; Co0 the initial investment; and t the number of periods. For investment projects in renewable energy, as the wind energy power plants, it is possible to measure SPB using the following equation: SPB 5

ICC AAR

(8.60)

where ICC is the initial capital cost and AAR the average annual revenue based on hourly production.

8.7.2.2 Net present value The net present value (NPV) is another method of projects’ economic assessment. The NPV considers the money value over time. The value variation of capital in time accounts for the fact that, in the present, a given amount of money is worth more than in the future. The NPV is the direct implementation of the present value equation and

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represents the difference in the current value of cash inflows and outflows. The NPV is the sum of all the associated DCFs and should be positive for a viable project. The general equation is: NPV 5 ðCi0 2 Co0 Þ 1

ðCi1 2 Co1 Þ ðCi2 2 Co2 Þ ðCit 2 Cot Þ X ðCit 2 Cot Þ 1 1?1 5 2 ð1 1 iÞ ð11iÞt ð11iÞt ð11iÞ

(8.61) where Ci is the cash inflows; Co the cash outflows; Co0 the initial investment; i the discount rate; and t the number of periods. The production and financing of wind projects remain a complex and costly process that can affect the economic competitiveness of wind energy relative to other, less capital-intensive source of generation. Wide changes to the financial sector such as the possibility of significant corporate tax reform, the currently planned phase out of the PTC and ITC for wind, and, in particular, a shift in the position of tax equity could radically reshape the prevailing mechanism for investing in wind energy. Funding will continue to have at least a moderate effect on the overall economic competitiveness of a project and attempts to open up for more capital sources would be one of a number of levers to improve the economic competitiveness of wind power.

8.8

Conclusion

Potential consumers, decision-makers, developers, project financiers, and equipment suppliers must be able to determine rapidly if a renewable energy project is viable when compared with alternatives. This preliminary evaluation should require a minimal time and effort and provide a go/no go decision to warrant further studies. In this chapter, a comprehensive analysis of the wind power plant planning and modeling is provided. It discusses the wind resources, wind turbine technology and hybrid power systems, and the environmental and economic aspects of wind energy projects to be considered.

References [1] J.F. Manwell, J.G. McGowan, A.L. Rogers, Wind Energy Explained: Theory, Design and Application, second ed., John Wiley & Sons, 2009. [2] G.M. Masters, Renewable and Efficient Electric Power Systems, second ed., John Wiley & Sons, 2013. [3] R. Probert, Don’t let barriers block wind farms and windfalls, 2012. https://www.raconteur.net/sustainability/dont-let-barriers-block-wind-farms-and-windfalls. [4] B.K. Hodge, Alternative Energy Systems and Applications, first ed., John Wiley & Sons, 2010. [5] RetScreen International, Clean Energy Project Analysis, third ed., Minister of Natural Resources Canada, 2005.

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[6] V. Nelson, Wind Energy Renewable Energy and the Environment, CRC Press Taylor & Francis Group, Boca Raton, FL, 2009. [7] M. Kaltschmitt, W. Streicher, A. Wiese, Renewable Energy: Technology, Economics and Environment, Springer Science & Business Media, 2007. [8] T. Burton, D. Sharpe, N. Jenkins, E. Bossanyi, Wind Energy Handbook, John Wiley & Sons, Ltd, 2001. [9] S.N. Shaha, M.S.A. Pachapuri, NACA 2415—finding lift coefficient using CFD, theoretical and javafoil, IJRET: Int. J. Res. Eng. Technol. 04 (07) (2015) 444449. Available from: https://doi.org/10.15623/ijret.2015.0407070. [10] E. Hau, H.V. Renouard, Wind Turbines: Fundamentals, Technologies, Applications, Economics, second ed., Springer, Berlin, Heidelberg, 2006. [11] Y. Zhang, Z. Chen, M. Cheng, J. Zhang, Mitigation of fatigue loads using individual pitch control of wind turbines based on FAST, in: Paper Presented at the 2011 46th International Universities’ Power Engineering Conference (UPEC), 2011. [12] E.A. Bossanyi, Individual blade pitch control for load reduction, Wind Energy Int. J. Prog. Appl. Wind Power Convers. Technol. 6 (2) (2003) 119128. [13] F. Gao, Individual pitch control of large-scale wind turbine based on load calculation, in: Paper Presented at the Proceedings of the 10th World Congress on Intelligent Control and Automation, 68 July 2012, 2012. [14] M. Jelavi´c, V. Petrovi´c, N. Peri´c, Estimation based individual pitch control of wind turbine, Automatika 51 (2) (2010) 181192. Available from: 10.1080/ 00051144.2010.11828370. [15] S.C. Thomsen, H. Niemann, N.K. Poulsen, Individual pitch control of wind turbines using local inflow measurements, IFAC Proc. Vol. 41 (2) (2008) 55875592. Available from: https://doi.org/10.3182/20080706-5-KR-1001.00942. [16] F.A. Khan, N. Pal, S.H. Saeed, Review of solar photovoltaic and wind hybrid energy systems for sizing strategies optimization techniques and cost analysis methodologies, Renew. Sustain. Energy Rev. 92 (2018) 937947. Available from: https://doi.org/ 10.1016/j.rser.2018.04.107. [17] A. Mahesh, K.S. Sandhu, Hybrid wind/photovoltaic energy system developments: critical review and findings, Renew. Sustain. Energy Rev. 52 (2015) 11351147. Available from: https://doi.org/10.1016/j.rser.2015.08.008. [18] K. Ritter, 2.2 billion people still don’t have access to clean drinking water, World Economic Forum, 2019. Retrieved from https://www.weforum.org/agenda/2019/06/hotspots-h2o-new-un-report-details-global-progress-and-problems-with-access-to-safewater-and-sanitation/. [19] W. Staff, World wind energy report 2010, in: Paper Presented at the 10th World Wind Energy Conference & Renewable Energy Exhibition—Greening Energy: Converting Deserts into Powerhouses (WWEC2011), 2010. [20] A.D. Hansen, Evaluation of Power Control With Different Electrical and Control Concept of Wind Farm: Part 2—Large Systems, Retrieved from Technical University of Denmark, 2010. [21] J. Aho, A. Buckspan, J. Laks, P. Fleming, Y. Jeong, F. Dunne, et al., Tutorial of wind turbine control for supporting grid frequency through active power control, in: Paper Presented at the 2012 American Control Conference (ACC), Montreal, QC, 2012. https://www.osti.gov/biblio/1037922. [22] L.Y. Pao, K.E. Johnson, A tutorial on the dynamics and control of wind turbines and wind farms, in: Paper Presented at the 2009 American Control Conference, 1012 June 2009, 2009.

Fuel cell and hydrogen power plants

9

Himadry Shekhar Das1, Md. Fahim F. Chowdhury2, Shuhui Li1 and Chee Wei Tan3 1 Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL, United States, 2Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States, 3School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia

9.1

Chapter overview

Power generation using alternative energy systems has grown attraction since the past decade. Among alternative energy sources, renewable energy-based systems such as solar photovoltaics (PVs), wind turbines, biomass energy systems, hydro power plants are popular. Fuel cell (FC) is also a promising alternative solution to fossil fuel-based energy systems. Although the renewable energy-based systems (PV, wind) are vastly dependent upon the weather (solar irradiance, wind speed), FC systems are independent of those resources as the source of FC power is hydrogen. FC produces electricity through electrochemical reaction of hydrogen and oxygen [1]. FC systems have multiple advantages over fossil fuel-based systems, such as cleanliness, modularity, high efficiency, high reliability, low maintenance, long life, fuel compatibility, environment independence, and applicable as combined heat and power (CHP) system [2]. In this chapter, a technological overview of FC and its application in power plants is presented. This chapter is organized in the following manner: Section 9.2 presents an overview of principle of operation of FC and different FC types. Section 9.2 elaborates the application of FC for power generation. In Section 9.3, FC-based hybrid system modeling and simulation steps are presented as a case study.

9.2

Fuel cells

FCs are open thermodynamic systems that operate based on electrochemical reactions, and the reactants are supplied from external sources [3]. Based on the FC types, the supplied reactants vary from direct hydrogen to a product of hydrogen, for example, methanol, ethanol, or other type of hydrocarbon [4]. In this section, basic principle of operation of FC and its classification are presented.

Hybrid Renewable Energy Systems and Microgrids. DOI: https://doi.org/10.1016/B978-0-12-821724-5.00009-X © 2021 Elsevier Inc. All rights reserved.

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9.2.1 Principle of operation The electricity generation in FCs occurs via electrochemical reaction between hydrogen and oxygen. The product of the reaction is heat and water. Although different type of FCs have different electrolytes, the basic chemical reaction that takes place in all FCs is the same [5]. Eq. (9.1) shows the chemical reaction, where energy is produced as a form of heat and electricity [6]. Fig. 9.1 shows the basic operating principle of an FC [7]. 2H2 ðgÞ 1 O2 ðgÞ ! 2H2 O 1 ðElectricity 1 HeatÞ

(9.1)

From Fig. 9.1, it is clear that an FC is constructed with four essential parts: an anode, a cathode, a liquid electrolyte to submerge the anode and cathode, and the external circuitry. The job of the anode is to oxidize the hydrogen coming from fuel and generate protons (H1) and electrons (e2). The electrons travel through the external circuitry, which results in an electricity flow, and the protons get into the liquid electrolyte. Based on the electrolyte type, either protons travel from the anode to cathode or the oxide ions travel from the cathode to anode through the electrolyte. The electrolyte is an electron preventive ion conductor, where only protons or oxide ions can travel. Finally, at the cathode, the oxygen is reduced to oxide ions by the electrons and then merged with the proton to produce water. Heat is also produced at this stage as a byproduct of the reaction. The anode and cathode general reactions can be presented as follows: Anode: 2H2 ! 4H1 1 4e2

(9.2)

–

2e

Load

Fuel in

Oxidant in 1/2O2

H2 Positive ion

Negative ion H2O

Electrolyte membrane

H2O

Depleted oxidant and product gases out

Depleted oxidant and product gases out Anode

Figure 9.1 Operating principle of a fuel cell.

Cathode

Fuel cell and hydrogen power plants

315

Cathode: O2 1 4e2 1 4H1 ! 2H2 O

(9.3)

The property of electrolyte differs based on the type of FC. As a result, the chemical reaction to produce the electricity also varies. Basically, there are two type of electrolytes that can distinguish the hydrogen and oxygen reaction: acidic and nonacidic. In acidic electrolyte, the protons (H1) travel from the anode to cathode, and in the cathode they reunite with the electron and oxygen supplied from the air. However, in nonacidic electrolyte, oxide ions travel from the cathode to anode and the electricity generation reaction is occurred in the anode. In FC, the distance between the electrodes is small. The contact area between electrodes and the electrolyte is also miniscule. As a result, the current generated in a single FC is very small. To improve the electricity generation efficiency and to increase the surface area, flat and porous electrodes are used, and the electrolyte is set as a thin layer. The efficiency of FC vastly depends on the operating condition (how much current is drawn from the unit). The typical relation between the current and voltage of a FC unit is shown in Fig. 9.2 [8]. From the figure, it can be seen that there are three operating regions of an FC: active polarization (nonlinear), ohmic (linear), and concentration polarization (nonlinear). The linear region is the best operating region, because in this region only ohmic loss is present and other losses are minimum. In the nonlinear operating regions, either reaction loss or gas transportation loss dominates, which may result in poor efficiency, high maintenance, as well as low life cycle of the FC.

9.2.2 Construction of fuel cell stack Practically, a FC unit has cell voltages of 0.60.8 V, and power capacity is also low. To make the power produced by FCs usable, multiple cells are combined together into Polarizaon curve of PEMFC

Cell voltage (V)

1.2

Acve polarizaon 1 region (reacon rate loss) 0.8

Total loss Concentraon polarizaon region (gas transport Ohmic polarizaon loss) region (resistance loss)

0.6 0.4 0.2

0.2

0.4 0.6 0.8 1 Current density (mA/Cm2)

Figure 9.2 Typical fuel cell IV curve.

1.2

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Hybrid Renewable Energy Systems and Microgrids

a stack in such a way that the stack voltage as well as the supplied power increases. To construct an FC stack, the following information should be gathered: G

G

Electrical configuration Physical configuration

9.2.2.1 Electrical configuration Determining the electrical configuration includes defining the electrical requirements, such as maximum power, stack voltage, stack current, number of cells, and efficiency. The equation to determine the stack output power is given as follows: PFC ðWÞ 5 Vstack 3 IStack

(9.4)

where the current of a FC stack is dependent upon the total active area of the stack and the current density of the FC. The stack current can be determined by using the following equation: IStack ðAÞ 5 Acell 3 i

(9.5)

The current density varies based on the type of FC. The FC stack voltage is determined by multiplying the cell numbers with the cell voltage. The cell voltage is calculated from the FC polarization curve (IV curve) as shown in Fig. 9.2. Both the cell voltage and stack voltage equations are given as follows: VCell 5 f ðiÞ

(9.6)

VStack 5 VCell 3 NCell

(9.7)

Determining the number of cell is crucial for FC stack design. Based on the number, the stack voltage as well as maximum power of the FC stack varies. Generally, the cells in a stack are connected in series; however, parallel assembly can be performed based on the current requirement from the stack. Eq. (9.7) is determined based on series connection of cells. Another crucial parameter of FC stack is the operating efficiency, which depends on the size of the cell active area, operating point on the IV curve, temperature, humidity, water flow in the membranes, MEA (membrane electrode assembly) materials, etc. While designing the stack, the active area should not be optimum, and having bigger or smaller active area can cause higher resistive losses. Also, larger active area can cause trouble for temperature management, humidity control, as well as water flow management conditions. Higher efficiency can be achieved by operating the FC at higher cell voltage. However, if the FC operating condition gets into the activation polarization region, it will cause another type of loss called activation loss, which will degrade the system performance as well as FC stack life. The efficiency of the FC stack can be approximated by Eq. (9.8), assuming it is operating in the ohmic region. ηStack 5 VCell =1:482

(9.8)

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317

9.2.2.2 Physical configuration For designing the common bipolar stack, multiple cells are stacked together in series configuration, meaning the anode of one cell is linked with the cathode of the next cell. A single cell FC stack has the following components assembled in sequence from left to right: the end plate, insulation plate, current collector plate, flow field plate, gasket, and MEA. Then the assembly order is reversed from gasket to end plate to finish the structure. For multiple cell stack assembly, each MEA is surrounded by gaskets and flow field plate. The flow field plate mainly facilitates the fuel, oxidant, water, and airflow in the MEA. All these components are tied together properly with bolts, rods, or other any other mounting tools to avoid leakages. A common FC stack assembly is shown in Fig. 9.3 [9]. An efficient FC design should consider the following facts [10]: G

G

G

G

Uniform temperature should be maintained throughout the whole stack. Fuel and oxidant supply should be uniform for all the cells and also across the whole surface area. For polymer electrolyte membrane type of FC, proper humidity of the membrane must be ensured. The membrane should not be let dry or be flooded with water. Stack must be sealed properly to avoid any gas leakage.

9.2.3 Classification of fuel cell FCs differ based on the fuel types, operating temperature, power generation capacity, cost, efficiency, and application areas. They can be segregated into following six key groups [11,12]: G

G

G

G

G

G

Solid oxide fuel cell (SOFC) Molten carbonate fuel cell (MCFC) Phosphoric acid fuel cell (PAFC) Alkaline fuel cell (AFC) Proton exchange membrane fuel cell (PEMFC) Direct methanol fuel cell (DMFC) Bipolar plate Gasket Membrane electrode assembly Gasket Pressure sensitive film

Collector plate Insulation plate End plate

Figure 9.3 Physical configuration of an Fuel cell stack [9].

Air cooling channel

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Hybrid Renewable Energy Systems and Microgrids

SOFC

MCFC

PAFC

AFC

PEMFC

DMFC

2000

10000

Operating temperature (˚C)

1000 900 800 700 600 300 200 100 0 1

100

200

300

400

Output power (kW) Figure 9.4 Operating temperature and power output of different type of fuel cells [7].

In Fig. 9.4, the FC types are presented according to their operating temperature and output power capacity of typical FC stack [7]. It can be seen that SOFC operates at high temperature, and power delivery capacity can vary from low to high. MCFC can be determined as high temperature and high power FC. PAFC is a medium temperature and medium power FC, whereas AFC and PEMFC are classified as low temperature and medium to low power capacity. DMFC is the only FC that operates in the lowest temperature and produces very low power. Based on these two major properties, FC application is determined. For example, high power and medium to high temperature FCs are suitable for power plant application. The heat generated from these type of FCs can be utilized by a CHP system to increase the efficiency. The medium power FCs are suitable for portable power supplies (electric vehicles, uninterruptible power supplys, etc.). Low power and low temperature FCs are more suitable for microscale power supplies (e.g., electronic devices). The operating principle and the reactions occur in the anode and cathode for each type of FCs are elaborated in the following subsections.

9.2.3.1 Solid oxide fuel cell SOFC, named after its electrolyte, is made of a mixture of zirconia (ZrO2) and yttria (Y2O3). It is a metal oxide having high chemical stability as well as thermal stability. The conductivity in this electrolyte is also pure ionic. The anode electrode is made of a porous cermet of nickel and yttria-stabilized zirconia (Ni/YSZ), and the air is used as a cathode electrode. The input of SOFCs is hydrocarbon-based fuels. The FC reforms the fuel into hydrogen and carbon monoxide internally using air as the oxidant. The operating temperature of SOFC is B1000 C. At this high

Fuel cell and hydrogen power plants

319

2e–

Fuel input

H2O

H2O and fuel express output

2e–

–-

+

2e-– 2e

2e–

O2– Air input O2–

+ + 2H+ 2H + O2O2– Anode

O2– O2– Electrolyte (solid oxide)

2e+

O2– O2Express air output

Cathode

Figure 9.5 Solid oxide fuel cell.

temperature, oxidation reaction of fuel occurs at the anode, and reduction reaction of oxygen occurs at the cathode (Fig. 9.5) [12]. The reactions are given in Eqs. (9.9) and (9.10). As the anode electrode is porous, it can handle the fuel and oxidation products effectively so that they are isolated from the electrolyte and electrode surfaces. Anode: ð1=2ÞO2 ðgÞ 1 2e2 ! Oð22Þ ðaqÞ

(9.9)

Cathode: Oð22Þ ðaqÞ 1 H2 ðgÞ ! H2 OðlÞ 1 2e2

(9.10)

The power capacity of SOFCs is large, typically in megawatts and the byproduct heat can be used in gas turbines to generate more power. As a result, SOFC-based power plants in CHP structure provide better efficiency (in the range of 70% and 80%). Also, the emission of harmful gases such as NOx and SOx is low for SOFCs. However, as hydrocarbon-based fuels are used as input, it can emit COx, and the system is still dependent on fossil fuel.

9.2.3.2 Molten carbonate fuel cell MCFCs are constructed by a mixture of molten carbonate salts (lithium, sodium, potassium, etc.) as electrolyte and non-noble metal as electrodes. In the anode nickel and in the cathode nickel oxide is generally used. MCFCs are high-temperature devices, typically operate at 600 C. At this high temperature, the carbonate salts melt and the carbonate ions ðCO22 3 Þ become free to move between anode and cathode. Hydrocarbonbased fuel is used as an input in MCFCs, because in the electrolyte carbonate ions are used as charge carrier. In the anode, first hydrocarbon-based fuel in gaseous form (such as methane CH4) reacts with water to generate hydrogen and carbon gases (CO and CO2). Then through two simultaneous reactions (Eqs. (9.11) and (9.12)) with carbonate

320

Hybrid Renewable Energy Systems and Microgrids

2e–

2e–

–

+

Fuel in H2

2e–

2e–

O2 Air input O2

CO3– CO2 Water and heat

CO2 CO2

H2O Anode

Electrolyte

Cathode

CO2

Figure 9.6 Molten carbonate fuel cell [13].

ions from the electrolyte, electrons are formed. Water and carbon dioxide are also generated in one of these reactions. In the cathode, the oxygen and carbon dioxide react to generate carbonate ions (Eq. (9.13)). These ions then travel to the anode through the electrolyte. Anode: H2 ðgÞ 1 COð322Þ ðaqÞ ! H2 OðlÞ 1 CO2 ðgÞ 1 2e2

(9.11)

COðgÞ1COð322Þ ðaqÞ ! 2CO2 ðgÞ 1 2e2

(9.12)

Cathode: ð1=2ÞO2 ðgÞ 1 CO2 ðgÞ 1 2e2 ! COð322Þ ðaqÞ

(9.13)

MCFCs have high power and high efficiency if they are operated in CHP structure. The heat generated in the reactions can be used to generate more power that can increase the efficiency up to 20%. Also, the carbon dioxide gas supply required at the cathode to generate the carbonate ions can be arranged by collecting the CO2 from the anode (Fig. 9.6) [13]. That means, with structural modification, major limitation of MCFC systems can be overcome. However, the power density and power capacity of MCFC are subjacent to its competitor SOFC.

9.2.3.3 Phosphoric acid fuel cell PAFCs use highly concentrated (.95%) liquid phosphoric acid (H3PO4) as electrolyte and porous carbon-based electrodes for anode and cathode. The electrodes comprise platinum catalyst and the electrolyte is contained in a silicon carbide matrix for regulating the chemical reaction. PAFC is required to operate at 150 C220 C to increase the ionic conductivity in the electrolyte. Typically hydrocarbons from fossil fuel or alcohols (methanol or ethanol) are used as input, and hydrogen is generated at the anode. The hydrogen is then oxidized to separate the

Fuel cell and hydrogen power plants

321

2e–

2e– –-

+

Fuel in 2H+ 2H+ 2H+

2e– H2

2e–

O2 Air input O2 H2O H2O

Excess fuel Anode

Electrolyte

Cathode

Figure 9.7 Phosphoric acid fuel cell [14].

electron and proton (H1) at the anode. Proton is the charge carrier through the electrolyte in this type of FCs. Protons travel from anode to cathode through the electrolyte and then reunite with the electron and oxygen at the cathode to produce water. The electrons travel through the external circuitry, which results in current flow. The anode and cathode reactions are given in Eqs. (9.14) and (9.15), and the diagram is shown in Fig. 9.7 [12]. Anode: H2 ðgÞ ! 2H1 1 2e2

(9.14)

Cathode: ð1=2ÞO2 ðgÞ 1 2H1 1 2e2 ! H2 OðlÞ

(9.15)

PAFC is a medium power, medium temperature FC. It has the high efficiency (up to 80%) if used in CHP applications, otherwise the efficiency is around 40% 50%. As the electrolyte is pure phosphoric acid, the internal parts of PAFC must be able to withstand the high corrosive environment. The benefit of phosphoric acid electrolyte is that it can tolerate high amount of CO and CO2, thus different fuel types can be used as input.

9.2.3.4 Alkaline fuel cell AFCs, named after the type of electrolyte, use a 70% water and 30% potassium hydroxide (KOH) solution as electrolyte, and platinum is used as electrodes for anode and cathode. Pure hydrogen fuel is supplied to the anode. The charge carrier is hydroxyl ion (OH2) of the electrolyte, which travels from cathode to anode. In anode, hydrogen is oxidized by hydroxyl ion and generates water and electron. The electron travels through the external circuitry and in the cathode oxygen, water, and electrons through a reduction reaction generate hydroxyl ions (Fig. 9.8) [12]. The anode and cathode reactions are given as follows: Anode: 2H2 ðgÞ 1 4OH2 ðaqÞ ! 4H2 OðlÞ 1 4e2

(9.16)

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Hybrid Renewable Energy Systems and Microgrids

2e–

2e–

–

Input H2

2e–

2e– H2

OH–

+

OH–

2OH–

+

H2O and heat output

+

OH–

O2–

+ H2O H2O

2K+ Anode

O2 Air input

Electrolyte (KOH)

Cathode

Figure 9.8 Alkaline fuel cell [12].

Cathode: O2 ðgÞ 1 2H2 OðlÞ 1 4e2 ! 4OH2 ðaqÞ

(9.17)

It can be noticed that the water generated at the anode can be used for the cathode reaction by using a feedback system. AFC generally operates at 60 C90 C; however, recent technological development has made it possible to operate it at lower temperatures (23 C70 C) also. AFC has several advantages, such as fast startup and response time, low-cost catalysts and electrolyte, high efficiency, and low-temperature operation. However, the electrode material is expensive, the power generation capacity is limited to 20 kW, and the input supply is pure hydrogen and it is highly sensitive to carbon dioxide (requires total elimination of CO2 from the air used in the cathode). These limitations put AFCs in debatable position for the most popular medium-scale power generation option.

9.2.3.5 Proton exchange membrane fuel cell PEMFC uses two porous electrodes and a solid polymer electrolyte for MEA. The MEA is made of dispersed catalyst layer, carbon cloth or gas diffusion layer, and a membrane. The catalyst layer converts the hydrogen into proton and electron, the gas diffusion layer ensures the fuel supply uniformity, and the membrane works as a medium to exchange protons between two electrodes and prevents electron flow through the electrolyte. Usually, platinum (Pt) catalyst is used in PEMFCs due to its better catalytic properties, operating environment stability, and high corrosion resistance. At anode, hydrogen is decomposed into protons and electrons, whereas at cathode, oxygen is reduced to oxide ions and then reacted with protons to form water (Fig. 9.9) [12]. The anode and cathode chemical reactions are given as follows: Anode: H2 ðgÞ ! 2H1 1 2e2

(9.18)

Fuel cell and hydrogen power plants

323

2e–

2e– –-

Input H2

2e– + 2H+

+

2H+ 2H+ 2H+

Excess H2 Anode

Electrolyte

2e– + 2H+ + ½O2

O2 air input

H2O output

Cathode

Figure 9.9 Proton exchange membrane fuel cell.

Cathode: ð1=2ÞO2 ðgÞ 1 2H1 1 2e2 ! H2 OðlÞ

(9.19)

PEMFC is a low temperature (60 C100 C) device with low-to-medium power supply capability. The advantage of PEMFC is its fast startup, flexibility in input fuel, compact design, lightweight, low cost, and solidity of electrolyte. It can take pure hydrogen, methanol, as well as formic acid as input. It is ideal for portable and motile as well as stationary applications. The efficiency of PEMFC increases with the increase of the temperature due to increased reaction rate. However, it can suffer from membrane dehydration if the temperature rises more than 100 C. Generally, the electrical efficiency of PEMFC is 40%50% and the power capacity at output is up to 250 kW. Due to the benefits, PEMFC is the most suitable candidate for electric vehicle and portable power supply applications.

9.2.3.6 Direct methanol fuel cell DMFC is named after its input fuel type, which is liquid methanol or alcohol. There are two types of DMFCs: acid-based and alkaline-based. In terms of structure, both type of FCs are the derivatives of PEMFC. That means the physical structure of DMFC is similar to PEMFC. DMFC uses an MEA where platinum rubidium (PtRu) catalyst is used as the electrodes and polymer electrolyte membrane (either acidic or alkaline based) is used as electrolyte. At the anode of acidicbased DMFC, the supplied methanol mixed with water is oxidized to generate carbon dioxide. At cathode, oxygen reacts with electron and proton and produce water. In this case, the protons travel from anode to cathode through the PEM membrane. The anode and cathode reactions are given in Eqs. (9.20) and (9.21). On the other hand, in the anode of alkaline-based DMFC, methanol gets oxidized by hydroxyl ion to produce carbon dioxide and water. At cathode, oxygen reacts with water to generate the hydroxyl ion that travels to the anode through the electrolyte (Fig. 9.10) [15]. The anode and cathode reactions are given as follows:

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Hybrid Renewable Energy Systems and Microgrids

6e–

6e–

–-

+

Fuel in 3H2

6e–

–

6e

6e–

3/2 O2 O2 Air input

6e–

–-

+

6e–

6e–

3/2 O2 + H2O

Fuel in 3H2

6H+

6OH– H2O

H2O 3H2O

CO2

H2O

Anode Cathode Proton exchange membrane (PEM)

Anode Cathode Alkaline-anion exchange membrane li (AAEM)

(A)

(B)

Figure 9.10 Direct methanol fuel cell (A) PEM, (B) AAEM [15].

In acidic DMFC: Anode: CH3 OHðgÞ 1 H2 OðlÞ ! CO2 ðgÞ 1 6H1 1 6e2

(9.20)

Cathode: ð3=2ÞO2 ðgÞ 1 6H1 1 6e2 ! 3H2 OðlÞ

(9.21)

In alkaline DMFC: Anode: CH3 OHðgÞ 1 6OH2 ðaqÞ ! CO2 ðgÞ 1 5H2 OðlÞ 1 6e2

(9.22)

Cathode: ð3=2ÞO2 ðgÞ 1 3H2 OðlÞ 1 6e2 ! 6OH2 ðaqÞ

(9.23)

DMFC operates at low temperature (60 C120 C) and the efficiency is around 35%. The advantage of DMFC is its long life, low design complexity, high specific power density, quick refueling option, cheap fuel availability, and methanol storage convenience. However, the output power of DMFC is lower than PEMFC, typically between 25 W and 5 kW. In addition, it has several operating limitations, such as methanol crossover, deterioration due to osmotic swelling, advanced reformer requirement for efficient operation, and so on. Due to the limitation, DMFCs are not ideal for portable power supply applications.

9.3

Hydrogen-based power plants

Based on the electrical and chemical properties discussed in the previous section, FCs can be used for different applications, such as small-to-large scale stationary power

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generation and portable power supply and in powering the electric vehicles. In this section, the application of FCs for stationary power generation is presented. In hydrogen-based power plants, hydrogen supply is a key factor. With the technological advancement, a massive improvement in the hydrogen generation process has been taken place recently, which has improved the power plant efficiency in a greater scale. The modern methods of generating hydrogen are discussed in the next subsection. The hydrogen-based stationary power plants are either operated as grid-connected (dispersed) power generation for utility use or standalone power generation for onsite power supply. For both applications, the power plant size and the operating configuration play an important role [16]. Hydrogen-based power plants are categorized into large scale stationary power plants, hybrid distributed generation systems, and CHP systems, according to its operating structure, which are discussed later in this section [17].

9.3.1 Hydrogen generation processes Hydrogen is an effective medium for energy storage, the use of which provides many advantages in FCs for stationary, transportation, and portable power applications. Hydrogen can be obtained through various processes from a number of sources, both renewable and nonrenewable. Global hydrogen production has so far been dominated by fossil fuels, with steam reforming of hydrocarbons (e.g., natural gas) being the most important new technologies. Electrolysis of water, an energydemanding process, also produces pure hydrogen. Another process of hydrogen production is from biomass. Fig. 9.11 provides a summary of the methods used for the production of hydrogen along with their primary energy and material sources.

9.3.1.1 Hydrogen generation from fossil fuel Conventionally large scale hydrogen production in industries is performed from fossil fuels. Natural gas reforming, coal gasification, and partial oxidation (POX) of hydrocarbons (natural gas or heavy oil) are several industrial processes to generate hydrogen from fossil fuel. These three processes are briefly explained later.

9.3.1.1.1 Hydrogen from natural gas methane-steam reforming Natural gas reforming is the most prominent method of hydrogen production all over the world. The technology is currently mature and industrially adopted in a Hydrogen production

Source material

Process

Fossil fuels

Natural gas reforming Hydrocarbon partial oxidization Coal gasification

Water

Electrolysis Photo electrochemical method

Biomass

Biomass gasification Bio-oil reforming

Figure 9.11 Overview of hydrogen production methods.

Biological

Microbial method Photobiological method

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Steam

CH

Desulfurization

Reformer

Sulfur

Fuel

Heat recovery

Shift conversion

CO recovery

Methanaon

H

CO

Figure 9.12 Simplified flow diagram of methane-steam reforming for hydrogen production.

mass scale, especially for power plant applications. The main component of natural gas is methane (CH4), using thermal processes like steam reformation, hydrogen can be produced from it [18]. Fig. 9.12 shows a simplified diagram of hydrogen production process from methane-steam reforming [19]. In methane-steam reforming process, there are three major steps: (1) syngas generation, (2) watergas shift, and (3) hydrogen purification. Apart from these steps, there are several additional steps such as desulfurization and heat recovery, which are necessary to maintain purity, temperature, and pressure of the gas. First, the methane from natural gas is desulfurized and then fed to the reformer. In the syngas generation process, high temperature (700 C1000 C) steam reacts with the methane under 325 bar (43.5362.5 psig) of pressure. The output of the reaction is hydrogen and carbon monoxide (CO). This reaction is an endothermic reaction, which implies that to proceed the reaction to generate hydrogen heat must be supplied. The CO generated from the reforming reaction is then involved in another reaction called “water gas shift reaction,” where CO reacts with steam and generate carbon dioxide (CO2) and additional hydrogen. Small amount of heat is generated in this stage of the reaction. The final stage of the process is named as “pressure swing adsorption.” In this stage, the impurities and byproduct gases are eliminated from the gas stream and pure hydrogen is collected at the end. This process is also applicable for hydrogen production from ethanol, propanol, or even gasoline. The chemical reactions that take place in different stages are given as follows: Steam-methane reforming reaction: CH4 1 H2 Oð 1heatÞ ! CO 1 3H2

(9.24)

Watergas shift reaction: CO 1 H2 O ! CO2 1 H2 ð 1small amount of heatÞ

(9.25)

9.3.1.1.2 Hydrogen from hydrocarbon partial oxidation POX is another well-known method to produce hydrogen from fossil fuel. Hydrocarbon-based fossil fuels such as natural gas, coal, and heavy oil can be converted in to hydrogen from this method. There are two types of POX: catalytic that occurs at around 590 C and noncatalytic that occurs at around 150 C1315 C. Noncatalytic POX is ideal for hydrogen generation from heavy hydrocarbons that cannot react over catalyst instantly. This process is also called as gasification when

Fuel cell and hydrogen power plants Steam

Ash

R Raw gass Coal

327

K–T gasificaon

Desulfurizaon

a Syngas

Compression

Shi conversion

CO2 recovery

Methanation or PSA

H2

O2 Sulfur Air

Air separaon

N2

CO2

Figure 9.13 Simplified flow diagram of partial oxidation of hydrocarbon.

coal is the source. A simplified diagram of POX process to generate hydrogen is shown in Fig. 9.13 [19]. The fuel used in the diagram is heavy oil. Thus several additional steps are added to manage the byproducts. There are three principle steps of the process: (1) syngas generation, (2) watergas shift reaction, and (3) hydrogen purification. The basic idea of syngas generation step is to oxidize the hydrocarbon with limited supply of oxygen, so that the hydrocarbon does not get fully oxidized. The product of this reaction is hydrogen and carbon monoxide (CO). If the oxidization is performed with air instead of pure oxygen, as byproduct nitrogen, small amount of carbon dioxide, and other components can be generated. In the next step of the process named as “water gas shift”, the CO reacts with water and produces more hydrogen and carbon dioxide. The total process is exothermic, which means heat is generated as a byproduct of the reactions. In the final step, hydrogen gas is purified from CO2 and other components before collection. The chemical reactions are given as follows: POX reaction: CH4 1 1=2O2 ! CO 1 2H2 ð 1heatÞ

(9.26)

Watergas shift reaction: CO 1 H2 O ! CO2 1 H2 ð 1small amount of heatÞ

(9.27)

9.3.1.1.3 Hydrogen from coal gasification Coal gasification is another way of hydrogen production using fossil fuel. The conventional technology is environmentally harmful; however, with carbon capture, storage, and utilization technology coal gasification process can be improved and vastly used for hydrogen production [20]. Coal has a very complex chemical property and it can be converted into various type of fuels. It can be converted into a range of liquid or gaseous fuels by using coal gasification. A simplified diagram of industrially accepted process of coal gasification is shown in Fig. 9.14 [19]. The process is adopted by KopperTotzek, thus named as KopperTotzek (KT) gasifier. In this process, the crushed coal is partially oxidized by steam and oxygen at atmospheric pressure in the KT gasifier. The oxygen is extracted by the air separation process. The raw gas produced in the gasifier is then cooled and quenched with water to remove the ash particles from the raw gas. The purified gas also

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Hybrid Renewable Energy Systems and Microgrids Steam

Ash

R gass Raw Coal

K–T gasificaon

Desulfurizaon

a Syngas

Compression

Shi conversion

CO2 recovery

Methanation or PSA

H2

O2 Sulfur Air

Air separaon

N2

CO2

Figure 9.14 Simplified flow diagram of KopperTotzek coal gasification for hydrogen generation.

called as syngas is then passed through the compression chamber, shift conversion chamber, and purification chamber. At the output hydrogen is collected at about 2.8 MPa (400 psig) pressure and of purity greater than 97.5%. The chemical reaction associated with the process is shown as follows: Coal gasification reaction: CH4 1 O2 1 H2 O ! CO 1 CO2 1 H2 1 other species

(9.28)

The pressure of the produced hydrogen is not enough for general use. Thus it needs to compress further to store in hydrogen tanks. Furthermore, coal gasification process involves handling of rock-solid coals and removal of large amount of coal ash. These processes have a significant economic impact on the production cost. All these limitations make coal gasification less favorable than the other liquid or gaseous hydrocarbon-based hydrogen generation processes.

9.3.1.2 Hydrogen generation from water The idea of producing hydrogen from water has dissuaded the fossil fuel-based hydrogen production. Most popular idea is using electrolysis to split water into hydrogen and oxygen. Another process is called photoelectrochemical water splitting, where water is dissociated using sunlight. Photoelectrochemical materials, a specialized semiconductor material, play a vital role in this process. This process is still in the research and development stage but has a great potential in renewable hydrogen production pathway. In this section, only the water electrolysis process is described.

9.3.1.2.1 Water electrolysis Electrolysis is a promising technology to produce hydrogen using renewable resources [21]. It is a process where water is broken into hydrogen and oxygen using electricity. The device that handles the chemical reaction is called electrolyzer. The electrolyzer is constructed by an anode, a cathode, and an electrolyte where the anode and the cathode are submerged. The construction is similar to FCs. The energy input of the electrolyzer is electricity, which can be generated from any renewable energy source or from grid. Using renewable energy source to supply the electrolyzer load makes the process zero emission and independent on grid [22]. However, the hydrogen production system combined with renewable energy sources is now in the research and development (R&D) stage. Before making it commercially

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available, technical and economic feasibility must be performed. Currently, for mass scale hydrogen production, alkaline electrolyzers are commercially available. Proton exchange membrane electrolyzers are also available and they are suitable for small scale hydrogen production. Another promising electrolyzer type in the R&D stage is the solid oxide electrolyzers. It uses steam to produce hydrogen, and electricity consumption is lower than the other two types. General working principle of electrolysis is presented later [23]. Water electrolysis is a process where direct current is circulated through water to split it into hydrogen and oxygen molecules. The current flows from one electrode to the other through the electrolyte so that ionic conductivity of the electrolyte is increased. The general chemical reaction in the electrolyzer is shown as follows: Water electrolysis reaction: H2 OðlÞ ! H2 ðgÞ 1 1=2O2 ðgÞ

(9.29)

There should be a diaphragm between anode and cathode to separate the hydrogen and oxygen produced in the reaction. The diaphragm has electrical resistance that prevents the electrodes from getting short circuited. Furthermore, it should have high ionic conductivity and high physical and chemical stability. Fig. 9.15 shows an electrolyzer cell diagram. Cathode is negatively charged, means electrons come to the cathode from outer circuitry, and at the cathode hydrogen ion (H1) gets reduced and generates hydrogen. At anode, oxygen ion (O22) gets oxidized by releasing the electron and generates oxygen. The electrons leave the anode through external circuitry, thus the electrode remains positively charged. Due to the diaphragm, the generated oxygen and hydrogen do not mix and react with each other and hydrogen can be collected from cathode. As a byproduct, pure oxygen can also be collected and used for other applications.

9.3.1.3 Hydrogen generation from biomass Biomass gasification is an established method of generating hydrogen and other hydrocarbon-based gases. The biomass is an organic source that includes agro-residues

Cathode –

Anode +

H2 Diaphragm

O2

bubbles H2 bubb Electrolytic tr soluon

Figure 9.15 Electrolyzer cell diagram.

O2 bubbles Electrolytic soluon

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Hybrid Renewable Energy Systems and Microgrids

CO2, N2

Air

Air separaon unit

Drying

Biomass

Oxygen

Steam

Gasificaon/ sificaon/ pyrolysis

Dryerr Gasifier Pure hydrogen Waste gas

Membrane separaon / Pressure ssu swing absorpon  Hydrogen separator Hy ar r

Gass treatment  filtering, (parcle lf iz desulfurization, tar wash cracking)) Reformer

Syngas cr Increase en hydrogen co content in syngas (gas treatment) Water–gas shi reactor

Figure 9.16 Simplified flow diagram of hydrogen production from biomass gasification.

(e.g., corn stover, wheat straw), household kitchen residues, forest residues (trees, bushes), municipal organic waste, animal waste, and so on. By using a gasifier, which is the chamber where this biomass is gathered and prepared for chemical reaction, hydrogen and other gases can be produced. A simplified process diagram to generate hydrogen from biomass is shown in Fig. 9.16 [24]. Hydrogen production process from biomass can be arranged in three major steps: (1) drying the biomass to 15%30% of its moisture, (2) gasification of the dried biomass to produce syngas, and (3) treatment, purification of the syngas, and separation of hydrogen from it. For the gasification process in step 2, limited amount of pure oxygen and high amount of steam are required. Gasification converts the carbonaceous materials of biomass into hydrogen, carbon monoxide, and carbon dioxide at a high temperature ( . 700 C) without any combustion. Simultaneously, pyrolysis process can occur in the gasifier, where the biomass is gasified without any oxygen and as output hydrogen, carbon monoxide, and carbon dioxide is produced. However, pyrolysis process cannot gasify the biomass easily. Thus the product gas can contain other hydrocarbons, and to reform these hydrocarbons another intermediate step is required. The chemical reaction that occurs in the gasification process is given as follows [25]: Biomass gasification reaction: Biomass 1 O2 1 H2 O ! CO1CO2 1H2 ð 1othersÞ

(9.30)

Pyrolysis reaction: Biomass ! H2 O1H2 1CO1CO2 1 tar 1 char 1 hydrocarbon volatile

(9.31)

In the next step, the syngas goes through the gas treatment process, where the carbon monoxide of the syngas reacts with water to produce more hydrogen. This

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331

process is called “watergas shift reaction”. The chemical reaction that occurs in this step is shown as follows: Watergas shift reaction: CO1H2 O ! CO2 1H2 1 small amount of heat

(9.32)

The syngas, rich with hydrogen, goes through different purification processes, for example, particle filtering to remove the ash from the gas, desulfurization to remove the sulfur, and tar wash cracking to remove the tar from the gas. Then hydrogen separation is performed using either membrane separation or pressure swing absorption process. The collected hydrogen at the end of the system has high purity (typically .95%), and the pressure is moderate (30 bar or 435 psig). However, based on application or storage process, it may require to compress further to get high-pressured hydrogen. With the technological advancement, several types of gasifier, reformer, and separator have been invented. Due to these inventions, the efficiency is increased and the cost, size, and operational complexity is decreased. For example, Lin et al. proposed an advanced method of hydrocarbon gasification called “hydrogen production by reaction integrated novel gasification (HyPr-RING),” where a single reactor can handle the total process [26]. The main reaction of this gasifier is given in Eq. (9.33). The reaction occurs at comparatively low temperature and high pressure. This gasifier has helped to reduce the size of the system to a large extent. HyPr-RING reaction: C12H2 O 1 CaO ! CaCO3 12H2

ΔH 5 288 kJ=mol

(9.33)

9.3.1.4 Hydrogen generation from biological process Another way of possible hydrogen generation is using microbes, for example, microalgae and bacteria to perform biological reactions using solar radiation and organic substances. There are two possible ways to generate hydrogen: microbial and photobiological processes. All these processes are still in R&D. However, the laboratory demonstrations show that they can be promising method toward sustainable hydrogen production.

9.3.1.4.1 Microbial hydrogen production This process is one type of biomass conversion process, where microorganisms consume and digest biomass and release hydrogen. As it is a natural process and does not require any electricity, if proper regulation can be implemented, this process can be viable for commercial scale hydrogen generation. Microbial process can be fermentation based only or it can be combined with microbial electrolysis cells (MECs) to increase the hydrogen production. In fermentation process, organic matter of biomass is broken down into hydrogen by bacteria. This process does not require any solar radiation. Thus it is also called dark fermentation. Generally, fermentation process is slow, and hydrogen production rate is also low. Researchers are working on to make the process faster as well as increasing the amount of

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Hybrid Renewable Energy Systems and Microgrids

Added Ad d voltage vo e– CO2 e– H2 e– CO2 Organic maer (e.g., acecc acid)

Anode

e–

e–

H+

H2

H+ Cathode th

Figure 9.17 Microbial electrolysis cell.

hydrogen production from the same amount of biomass. One of the major improvement is the addition of MECs with the fermentation process. MECs can harness the generated hydrogen as well as the protons produced by the microbes from the biomass. Then using a small electric current, it can convert the protons into hydrogen. A schematic diagram of a MEC is shown in Fig. 9.17 [27]. From the figure, the microbes are shown as brown ovals that are attached to the anode. They consume the organic substances from the biomass and generate proton and electron. The anode can carry the electrons to the outer circuitry. The protons travel to the cathode and with the application of small voltage, hydrogen can be generated by combining the proton with electron. This technology is in R&D stage currently. Finding appropriate materials for anode and cathode as well as identifying proper microbes are the subject to study till date.

9.3.1.4.2 Photobiological hydrogen production Opposed to microbial process, photobiological process uses sunlight with microorganisms to convert water or organic matters into hydrogen. Microbes, such as microalgae or cyanobacteria, can break down water and generate proton and oxygen ion. These protons can be collected and reduced to hydrogen by any direct or indirect method. The challenge in this method is the limited rate of hydrogen production and the process of separating hydrogen and oxygen in the natural environment. On the other hand, photosynthetic microbes use solar radiation to break down organic matter and produce hydrogen. This process is called photo fermentative process of hydrogen production. The limitation of this method is also low hydrogen production rate. In addition, the efficiency of hydrogen production from solar radiation is low. The limitations make both the photobiological methods infeasible for commercial application at this moment. Researchers are working on to choose better microbes and better energy harvesting methods to enable these processes compatible to the other developed methods.

9.3.2 Large scale stationary power plants Power generation in large scale (megawatt) using FC is becoming popular with the latest technological development. For large scale power generation, three high

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power and high temperature FCs are very popular: PAFC, MCFC, and SOFC. Apart from these, several PEMFC-based power plants are also developed. However, the power capacity of PEMFC-based power plants may not be as high as the other type of FCs [28]. The latest development in FC-based large scale power generation is the multistage power plants, where the multiple FC modules are connected in series, and the fuel is utilized in multiple stages. In this case, the temperature of the stages gradually increases, and the gas combustion occurs in different rate. At the final stage, the exhaust gas has the minimum fuel portion available. This not only maximizes fuel utilization but also eliminates the requirement of heat exchanger to cool down the emitted gas of each stage. Generally, for large scale power generation natural gas or coal are used as the source of hydrogen, and fuel reformers are used to generate the hydrogen. In Table 9.1, examples of different large scale power plants and their attributes are presented. Apart from these, several new large scale power plants have been proposed worldwide. South Korea East West Power Company started to develop a 50 MW PAFCbased secondary FC power plant. The company is planning to expand the power generation from FC up to 1 GW by the year 2030. Another Korean company Korea Southern Power Company (KOSPO) has already built a 20 MW FC-based power plant in Incheon, South Korea. Several FC manufacturing companies, such as, Bloom Energy, Convion, Mitsubishi, FuelCell Energy, Hydrogenic, Doosan Fuel Cell America, Fuji Electric, and Toshiba, are commercializing FC-based power plant solution in large scale [33]. The power capacity of the FC systems supplied by these companies’ ranges from 100 kW to 10 MW. In recent years, PEMFC is gaining popularity, whereas PAFC is becoming less popular. Most popular options are either SOFC or MCFC.

9.3.3 Hybrid distributed generation systems With the modernization of the power grid, FCs have become popular sources of distributed generation alongside other renewable energy sources like solar PV and wind farms. Most popular and efficient structure of distributed generation is the hybrid system formation, where multiple energy sources and energy storage devices are combined together. Hybrid systems can operate either in standalone or in grid-connected conditions [34,35]. The operation strategy ensures efficient operation and better health of the each elements of the hybrid system. PEMFC is the ideal candidate for this type of energy systems, because of its low temperature operation and low-to-medium range of power supply capability. Examples of FC-based hybrid distributed generations (DGs) where FC operates as main or auxiliary power source are presented in this subsection.

9.3.3.1 FC as main power source in DG FCs can act as main power source in a hybrid energy system when energy storage systems are incorporated with it. Generally, battery and ultracapacitor are the storage systems that are used in the hybrid configuration. The necessity of adding an energy storage device is to mitigate the limitation of FC. FC is unable to supply high instantaneous power, and cold startup is not possible instantly. In these moments, storage

Table 9.1 Examples of large scale FC-based power plants. Developer

FC type

Fuel type

Plant capacity (electrical), MW

Plant efficiency

Operating temperature,  C

Hydrogenics [29] Tokyo Electric Power Company [30]

PEMFC PAFC

Pure hydrogen Natural gas

1 11

70 207

MC Power [31] Energy Research Corporation [31] Siemens Westinghouse [31]

MCFC MCFC SOFC (pressurized)

Natural gas (externally reformed) Natural gas (internally reformed) Natural gas

1 3 4.5

Siemens Westinghouse [32]

SOFC (pressurized)

Coal

500

.50% 41.8% (HHV) 48.4 (LHV) 54% (HHV) 58% (LHV) 66.6% (LHV) 60.1% (HHV) 59.7% (LHV) 62.6% (HHV)

650 650 1000 1000

HHV, Higher heating value; LHV, lower heating value; FC, fuel cell; PEMFC, proton exchange membrane fuel cell; PAFC, phosphoric acid fuel cell; MCFC, molten carbonate fuel cell; SOFC, solid oxide fuel cell.

Fuel cell and hydrogen power plants

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H2 tank

Electrolyzer Fuel cell

DC/DC

AC grid Battery

DC/DC

~

DC/AC DC load AC load

Ultracapacitor

DC/DC

Figure 9.18 Fuel cell as main power source.

devices supply the load until the FC is ready to take over. Different literature provides feasible operation and efficient control mechanism for such hybrid systems. For example, FCbatterysupercapacitor system is presented in Ref. [36], FCbattery is shown in Ref. [37], and FCsupercapacitor system is presented in Ref. [38]. Fig. 9.18 shows a generalized structure of a modular hybrid system that can be integrated to grid or be operated as standalone system. The system can be used for both DC and AC power supply. The most important issue of these hybrid systems is the control and operating mechanism. Based on the mechanism, the hybrid structure can vary. For example, battery or ultracapacitor can be interfaced with the DC bus with or without any converter. Adding DCDC converters to interface the storage devices increases the size, cost, and control complexity. However, it also increases the operating efficiency as well as helps to maintain better health of the FC and storage devices. This type of hybrid systems are usually used for emergency or backup power source for buildings.

9.3.3.2 FC as auxiliary power source in DG To operate FC as auxiliary power source, it is incorporated with the intermittent renewable energy sources like PV and wind. Based on available renewable energy resources, several configuration of hybrid systems can be developed. For example, PVFC [39], PVwindFC [40], windFC [41], windFCbattery [42], windFCbattery supercapacitor [43] are already proposed for DG-based power generation. In these configurations, when renewable energy-based sources cannot supply power, FC supplies the load. If the load nature is dynamic, battery and/or ultracapacitor energy storage are added in the system to meet the instantons high power demand. These type of hybrid systems are very popular for standalone systems in remote locations where grid is not present, and in microgrids where distributed generation is encouraged (Fig. 9.19). For FC-based hybrid systems, the fuel type is determined based on the size and location of the system setup. For example, if the system size is small and hydrogen

336

Hybrid Renewable Energy Systems and Microgrids

~ AC/DC

Wind turbine DC load

Photovoltaic

Battery

DC/DC with MPPT AC grid

DC/DC

~

DC/AC

DC/DC

Ultracapacitor AC load Electrolyzer

DC/DC

Fuel cell H2 tank

Figure 9.19 Fuel cell as auxiliary power source.

transportation is available, it is not feasible to set up a fuel reformer or electrolyzer to generate hydrogen. Thus for systems of small capacity or setup in urban areas, pure hydrogen is supplied. On the other hand, if the size is large or the setup is in rural areas, onsite fuel reformer or water electrolyzer set up is feasible to generate hydrogen. Even in certain cases, fossil fuel supply may not also be feasible. In such cases water electrolysis is the only way to generate hydrogen.

9.3.4 Combined heat and power systems CHP systems, also called as cogeneration systems, are becoming more popular for power generation now a days. In conventional power plants, heat is generated as a byproduct of electricity and it is released to environment. CHP systems have the ability to recover the waste thermal energy and use it for further electricity generation or for other heating purposes. CHP systems have on average efficiency of 85%, which is a lot higher than conventional power plants [44]. CHP systems are in

Fuel cell and hydrogen power plants

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the center of interest for industrial and commercial on site power generation systems as well as rural distributed generation systems. CHP systems can provide enough thermal energy to mitigate the industrial and commercial demand for air conditioning systems in buildings while supplying the electrical load. In rural areas the extracted heat can be utilized for hot water supply in the houses. Two forms of energy achieved from the single process can provide significant energy savings compared to individual electricity and thermal power generation process. The benefits of CHP systems are as follows: G

G

G

G

G

Reduced pollutant emission in the air (SO2, NOx, and Hg). On-site electricity generation reduces the dependency on grid support. Critical electrical and thermal loads can be empowered during grid power outages. Load congestion constraints can be overcome for an existing infrastructure. Enhanced reliability, reduced cost of energy, reduced initial setup cost, and increased resiliency against power disruption.

FCs are ideal element for CHP systems, both in large stationary power plants and distributed generators. In FC-based large stationary power plants, CHP systems are already deployed, because high power FCs operate at a very high temperature (800 C1000 C) and without CHP the efficiency would drop immensely. On the contrary, distributed generators use low temperature FCs (60 C100  C), and CHP systems are being deployed in DGs quite recently. Several literature proposed FCbased CHP systems and showed that use of CHP system helped to increase the efficiency to a large extent [4547]. Fig. 9.20 shows the block diagram of a FC-based

AC power to the load

AC grid

~ ~

AC load

~

AC/DC Wind turbine

DC/AC

DC/AC Water DC/DC

DC/DC with MPPT

Photovoltaic

Thermal load

Electrolyzer

H2 H2

CH4 Natural N t all gas

Fuell ref Fuel reformer former

H2

H2 tank

Hot water

Air

Natural gas burning for heating

Heat recovery system

Fuel cell Heat

Figure 9.20 Block diagram of a fuel cell-based cogeneration system [48].

Heat

338

Hybrid Renewable Energy Systems and Microgrids

CHP system applied for building heating and power backup [48]. From the figure, it can be seen that the electricity generated from the FC is converted and combined with the grid power to support the electrical load of the commercial building. On the other hand, the heat generated by FC is recovered and combined with the natural gas-based heating system for the building. It increases the fuel efficiency for the heating system of the building. The FC system here can also function as a backup power system that can operate during any heating or electricity outage. Either way it will improve the performance of the system.

9.4

FC energy system modeling

In this section, an example of FC-based hybrid system modeling in MATLAB/ SIMULINK is presented. The system is modeled in the article [38] where proton exchange membrane FC is used as main energy source. The energy system has three major subsystems in addition to the controllers: the PEMFC, DCDC converter, and the load. Step-by-step modeling of each subsystem is explained in the subsequent subsection. The circuit diagram of the proposed system is given in Fig. 9.21.

9.4.1 Fuel cell PEMFC is a low temperature (60 C100 C) device, which is light weight, compact in size, and has high power density with rapid startup capability. Moreover, it has long lifetime and low manufacturing cost. The operating principle of PEMFC is that it uses hydrogen and oxygen as reactant, and through a chemical reaction it produces electricity and releases water as product. The voltage produced by a PEMFC unit is 1.23 V theoretically. However, practically the amount is only around 0.60.7 V at rated current due to factors like activation, ohmic, and mass transportation loss. The PEMFC model used in the system is a 5 kW Ballard-Mark-V electrochemical model, which incorporates the static, dynamic, and thermodynamic behaviors [49]. The steady-state stack voltage ðVstack Þ equation of PEMFC model can be written as follows: Vstack 5 Enernst 2 Vact 2 Vohm 2 Vcon La

Rl Cl

(9.34)

R La

D RS

a

Sa

Rh

Da

FC Figure 9.21 Circuit diagram of the fuel cell energy system.

Ch

L o a d

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where Enernst is the reversible potential the Nernst potential, Vact is the activation voltage loss, Vohm is the ohmic voltage loss, and Vcon is the concentration voltage loss. Enernst can be represented by the following equation: Enernst 5 1:229 1 0:85 3 1023 ðT 2 298:15Þ 1 4:3085 3 1025  T  ½lnðPH2 Þ 1 1=2 lnðPO2 Þ

(9.35)

where PH2 and PO2 are the partial pressure of hydrogen and oxygen in the two electrodes of the PEMFC. The equation of voltage drop due to activation is shown as follows [49,50]: Vact 5 2½ξ1 1 ½½286 1 20 lnðAÞ 1 4:3 lnðCH2 Þ 3 1025   T 1 ξ 3  T  lnðCo2 Þ 1 ξ4  T  lnðiÞ

(9.36)

where ξ 1 , ξ3 , and ξ 4 are the parametric coefficients and are constants for steadystate operation. CH2 and Co2 are hydrogen and oxygen concentration. The ohmic voltage drop equation is given as follows [49]:
 ρ ‘ Vohm 5 2i Rc 1 M A

(9.37)

h    T 2  i 2:5 i 181:6 1 1 0:03  Ai 1 0:062  303 A      ρM 5  λ 2 0:634 2 3  Ai  exp 4:18  T 2T303

(9.38)

where i is the FC current in ampere (A), Rc is the membrane resistance in ohm (Ω) for the proton flow, ρM is the specific resistivity of the membrane for the electron flow (Ω cm), ‘ is the membrane thickness in centimeter (cm), and A is the active area of the cell (cm2). The parameter (λ) defines the saturation status of the membrane and ranges from 14 to 23. Practically, the membrane is oversaturated and the λ value is considered equal to 22 and 23 [50]. The concentration voltage drop can be described as:
 J Vcon 5 2B  ln 1 2 Jmax

(9.39)

where B is generally 0.016 but depends on the cell and its operation state, Jmax is the maximum current density of FC. The dynamic behavior in FC performance is caused by the double-layer charging effect. The dynamic relationship can be expressed as follows [51]: d ðVact 1 Vconc Þ i ðVact 1 Vconc Þ i 5 2h ðVact 1 Vcon Þ dt Cdl Cdl

(9.40)

i

where Cdl is the double-layer charge and i is the fuel cell current (A). The thermodynamic model provides the transient temperature of the FC, which is used to calculate the reactant gas diffusion, Nernst potential and all the voltage

340

Hybrid Renewable Energy Systems and Microgrids

drop equations. The differential equation of transient temperature is given as follows [49,51]: mC

dT 5 iðEnernst 2 Vcell Þ 2 hAðT 2 T0 Þ dt

(9.41)

where mC is the product of mass and the average specific heat of the cell body (J/K), i is the FC current (A), E is the reversible voltage (V), Vcell is the output voltage for a single cell (V), hA is the total heat transfer coefficient for the surface of FC (W/K), T is the reference temperature or the operating temperature (K), and T0 is the standard temperature (296.5 K). These equations are used to construct the PEMFC model and the parameters used in the model are given in Table 9.2. From Fig. 9.22, the maximum power of the PEMFC model is 5750 W at a stack voltage of 28 V while supplying 200 A current. The voltage level decreases with the increasing amount of current drawing. An operating voltage range can be determined from the characteristics curve. In this case, to ensure maximum power supply the operating voltage range can be 2550 V. In this range the FC power range would be 25005750 W.

9.4.2 DCDC converter There is a DCDC boost converter in the system that boosts the output voltage level of FC at the DC load terminal voltage level. A unidirectional type boost DCDC converter is used in the system. The converter power specification is taken as 6 kW. The DCDC converter switching circuit incorporates inductor (La), input and output capacitors (Cl, Ch), controlled switch (Sa), inductor internal resistances Table 9.2 Parameters of Ballard-Mark-V fuel cell [49]. Parameters

Symbol

Values

Operating temperature Room temperature Anode partial pressure in atm Cathode partial pressure in atm Concentration loss coefficient Resistance to transfer of protons through the membrane Parametric coefficients for fuel cell

T0 (K) Tr (K) Pa (atm) Pc (atm) B Rc (Ω) E1 E3 E4 Y Jmax (A/cm2) Jn (A/cm2) A (cm2) L (cm) n C (F)

323.5 298.5 2.3816 2.3816 0.016 0.0003 20.948 7.6e 2 5 21.93e 2 4 23 1.5 0.1 232 178e 2 4 35 8.12

Adjustable parameter range between 14 and 23 Maximum current density Initial current density Effective area of fuel cell Thickness of fuel cell Number of cell Equivalent electrical capacitance

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Figure 9.22 Characteristics curve of proton exchange membrane fuel cell model. Table 9.3 Design parameter and values for DCDC converters. Parameter

Boost converter

Vin Vbus D Lused (La) Cused (Cl, Ch) IL_max Ih_max Rl, Rh RLa

2560 V 80 V 0.250.68 3.55 mH 2.2 mF 240 A 75 A 0.09 Ω 0.03 Ω

(RLa), and capacitor internal resistances (Rl, Rh). For design purpose, input voltage is denoted as Vin, duty cycle D, and output voltage is denoted by Vbus. Eqs. (9.42) (9.44) show the boost converter average equations. L

diL 5 vin 2 Dvbus 2 ½RL 1 Rsb2 2 ðRsb2 2 Rsb1 ÞDiL dt

(9.42)

Ch

dvbus 1 1 5 2DiL 2 vbus 1 vh R R dt

(9.43)

Cl

dvuc 1 1 5 2iL 2 vUC 1 vl Rl Rl dt

(9.44)

The design parameter for the DCDC converter is given in Table 9.3.

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9.4.3 Controller design For a stable DC bus voltage operation a proportional integral (PI) controller is designed. The step-by-step design of the controller is elaborated in this subsection. To boost up the low DC voltage of FC, an efficient controller is required. This will ensure required dynamic response from the system. The proposed PI controller uses a dual loop PI controller for the FC boost converter. The dual loop PI controller has both output voltage control and input current control. As FC output voltage is dependent on the current drawn from it, a regulation for the input voltage of the boost converter is mandatory. The duty cycle to current response of the converter called the current loop transfer function is given as follows: 1 ðRh IL 1 Vbus 2 Rh Ih Þs 1 LC1 h IL ð1 2 dÞ i^L ðsÞ 5 L 1 S2 2 L ðRh d 2 Ri 2 Rh 2 RL Þs 1 LC1 h ð12dÞ2 d^ðsÞ

(9.45)

By using the parameters from Table 9.2 in Eq. (9.45), the continuous transfer function can be obtained. The current to voltage control loop is derived using the power conservation theorem. The theorem explains the balance between power input and power output. The current to voltage transfer function is given in Eq. (9.46). v^h ðsÞ ðRh Ch Vbus 2 LIL Þs 1 Vl 2 2IL ðRi 1 RL 1 Rh Þ   5 i^L ðsÞ 1 1 Rh s 1 2I 1 1 Rh C V h bus

L

Rbus

(9.46)

Rbus

In Fig. 9.23, the architecture of a general power converter controller is shown [52]. The controller consists of two control loops for controlling the output voltage as well as input inductor current. The voltage control loop ensures that the output voltage follows the reference voltage. If there is any mismatch between the two voltages, an error signal is generated that is used as reference current signal for the current loop controller. The reference current is then compared with the feedback current to generate the error signal for the current loop controller. The current loop

+

PI

Vref _

iL_FC

PWM Current controller modulator

+

iL_ref +

_

PI _

K

Gi

Current Feedback Current feedback gain

Hc (s) Voltage feedback gain

Hv (s)

Figure 9.23 Control architecture of a double loop control system.

I L

Gv vh

Vbus

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controller generates the duty cycle, which is used to generate the control pulses of the converter switches. By using the transfer functions mentioned in Eqs. (9.45) and (9.46), the optimum values of the PI controllers are obtained. The PI compensator values ðkpi ; kii Þ for input current control loop are 0.68 and 185. For the output voltage control loop kpv ; kiv are 0.5 and 13, respectively.

9.4.4 Simulation results In this part, the MATLAB/SIMULINK simulation result of the proposed FC energy system is presented. The simulation results include the current and voltage response for constant and variable load resistances. Fig. 9.24 shows the Simulink model of the fuel cell hybrid system.

9.4.4.1 Results with constant resistive load After interfacing the PEMFC model with the boost converter and resistive DC load, the output voltage and current are observed. It can be seen from Fig. 9.25 that at certain resistive load of 3 Ω, the output voltage is constant at 80 V DC. The system output depicts that the output current is 30 A at 80 V output voltage, producing output power of 2500 W. However, the amount of supplied power can be increased to the maximum power point by adjusting the load requirements. The FC supply voltage is 50 V while supplying 50 A of current. The load voltage and current waveforms are shown in Fig. 9.25. The FC supply voltage and current wave shapes are also shown in Fig. 9.26.

Figure 9.24 MATLAB/SIMULINK diagram of the fuel cell hybrid energy system.

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Figure 9.25 Output voltage and current response for constant resistive load with proportional integral controller.

Figure 9.26 Fuel cell supply voltage and current waveform with constant resistive load.

9.4.4.2 Results with variable resistive load A dynamic resistive load for the PEMFC system is modeled to observe the effect of load change. The test load is designed to verify the functionality of closed loop control of the system. The required current pattern at constant output voltage of 80 V is shown in Fig. 9.27. Fig. 9.28 (A and B) shows the load voltage, output current, FC output voltage, and FC supply current. It can be observed that, with the sudden change in load current, there is overshoot and undershoot in the output voltage, and also when the current is zero, the output voltage shifts up to 120 V, then gradually decreases. This phenomenon occurs due to the capacitive effect of the electrodes of PEMFC. The PEMFC output current has some ripples, although major part of the ripples has been reduced using a capacitor at the input side of the boost converter.

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Figure 9.27 Required load current at constant voltage 80 V.

Figure 9.28 Voltage and current waveform with dynamic load (A) system output, (B) fuel cell supply.

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This section presents the step-by-step modeling and simulation process of a PEMFC energy system using MATLAB/SIMULINK. The performance of the FC system with DC load controlled by the PI controller was evaluated. The results of simulation revealed that the proposed controller can handle both transient as well as steady-state load.

9.5

Conclusion

In this chapter, the basics of hydrogen-based power generation have been presented. The principle of operation of the device that produces electricity from hydrogen, named fuel cell, has been elaborated. Different types of FC and their working principle, the operating environment, power generation capacity, and other attributes have been discussed. The detail of different type of FC-based power plants and their operating methods, and the generation of hydrogen for all these power plant applications are presented. At last, FC-based hybrid system modeling and control procedure using MATLAB/SIMULINK are explained. Simulation results are also presented as a verification of the control method. FC is an emerging technology, and with the development of this technology the cost associated with it is reducing. Thus adoption of FC for different application, especially for power generation, is increasing nowadays. With the modernization of electric grid, the popularity of FC will increase, because it is a potential source for distributed generation application for smart grid or microgrids.

9.6

Nomenclature

AC AFC CHP DC DG DMFC FC HHV IEEE KT LHV MEA MEC MCFC NREL PAFC PEMFC PI PID POX PWM SOC

Alternative current Alkaline fuel cell Combined heat and power Direct current Distributed generation Direct methanol fuel cell Fuel cell Higher heating value Institute of Electrical and Electronics Engineers KopperTotzek Lower heating value Membrane electrode assembly Microbial electrolysis cell Molten carbonate fuel cell National renewable energy laboratory Phosphoric acid fuel cell Proton exchange membrane fuel cell Proportional integral Proportional integral derivative Partial oxidation Pulse width modulation State of the charge

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SOFC UPS

347

Solid oxide fuel cell Uninterrupted power supply

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[22] Kelly, N., Hydrogen production by water electrolysis, in: Advances in Hydrogen Production, Storage and Distribution, Elsevier, 2014, pp. 159185. [23] A. Ursua, L.M. Gandia, P. Sanchis, Hydrogen production from water electrolysis: current status and future trends, Proc. IEEE 100 (2) (2011) 410426. [24] Z. Fang, R.L. Smith, X. Qi, Production of Hydrogen from Renewable Resources, 5, Springer, 2015. [25] K. Akubo, M.A. Nahil, P.T. Williams, Pyrolysis-catalytic steam reforming of agricultural biomass wastes and biomass components for production of hydrogen/syngas, J. Energy Inst. 92 (6) (2019) 19871996. [26] S.-Y. Lin, et al., Developing an innovative method, HyPr-RING, to produce hydrogen from hydrocarbons, Energy Convers. Manag. 43 (912) (2002) 12831290. [27] Department of Energy, U.S. Hydrogen production: microbial biomass conversion. Available from: ,https://www.energy.gov/eere/fuelcells/hydrogen-production-microbial-biomass-conversion., 2020 (cited 2020 27th May). [28] Das, H.S., et al. Modeling and simulation of stand-alone fuel cell system for distributed generation application, in: 2016 3rd International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), 2016. IEEE. [29] Hydrogenics. Fuel cell megawatt power generation platform. Available from: ,https://www. hydrogenics.com/hydrogen-products-solutions/fuel-cell-power-systems/stationary-stand-bypower/fuel-cell-megawatt-power-generation-platform/., 2020 (cited 2020 24th May). [30] Stauffer, D., J. Hirschenhofer, and J. White. Carbon dioxide capture in fuel cell power systems, in: Intersociety Energy Conversion Engineering Conference, 1994. [31] J.H. Hirschenhofer, et al., Fuel Cell Handbook, U.S Department of Energy, 1998. [32] EG&G Technical Services, I., Fuel Cell Handbook. 7th Ed. 2004: US Department of Energy. [33] Hydrogeit. Fuel cell power plants compared. Available from: ,https://www.h2-international.com/2018/06/05/fuel-cell-power-plants-compared/., 2018 (cited 2020 24th May). [34] E. Eriksson, G.E. MacA, Optimization and integration of hybrid renewable energy hydrogen fuel cell energy systemsa critical review, Appl. Energy 202 (2017) 348364. [35] V. Das, et al., Recent advances and challenges of fuel cell based power system architectures and controla review, Renew. Sustain. Energy Rev. 73 (2017) 1018. [36] H. Rezk, et al., Comparison among various energy management strategies for reducing hydrogen consumption in a hybrid fuel cell/supercapacitor/battery system, Int. J. Hydrog. Energy (2019) (In press). [37] H. Radmanesh, M. Samkan, Quasi-Z-Source DC-DC converter for fuel cell-battery power generation system, Electr. Electron. Technol. Open. Access. J. 2 (4) (2018). [38] H.S. Das, et al., Fuel cell and ultracapacitor energy system control using linear quadratic regulator proportional integral controller, Electr. Eng. 101 (2) (2019) 559573. [39] M. Tiar, et al., Optimal energy control of a PV-fuel cell hybrid system, Int. J. Hydrog. Energy 42 (2) (2017) 14561465. [40] H. Fathabadi, Novel standalone hybrid solar/wind/fuel cell power generation system for remote areas, Sol. Energy 146 (2017) 3043. [41] F. Alvarez-Mendoza, et al., Stochastic model of wind-fuel cell for a semi-dispatchable power generation, Appl. Energy 193 (2017) 139148. [42] Kamal, T., et al. Energy management and control of grid-connected wind/fuel cell/battery Hybrid Renewable Energy System, in: Proceedings of the 2016 International Conference on Intelligent Systems Engineering (ICISE), 2016. IEEE. [43] S.A. N’guessan, et al., Optimal sizing of a wind, fuel cell, electrolyzer, battery and supercapacitor system for off-grid applications, Int. J. Hydrog. Energy 45 (8) (2020) 55125525.

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[44] N.M. Isa, C.W. Tan, A. Yatim, A comprehensive review of cogeneration system in a microgrid: a perspective from architecture and operating system, Renew. Sustain. Energy Rev. 81 (2017) 22362263. [45] S. Pedrazzi, G. Zini, P. Tartarini, Modelling and simulation of a wind-hydrogen CHP system with metal hydride storage, Renew. Energy 46 (2012) 1422. [46] K. Roy, K.K. Mandal, A.C. Mandal, Modeling and managing of micro grid connected system using improved artificial bee colony algorithm, Int. J. Electr. Power Energy Syst. 75 (2016) 5058. [47] Sy Obara, J.R.M. Rios, M. Okada, Control of cyclic fluctuations in solid oxide fuel cell cogeneration accompanied by photovoltaics, Energy 91 (2015) 9941008. [48] N.M. Isa, et al., A techno-economic assessment of a combined heat and power photovoltaic/fuel cell/battery energy system in Malaysia hospital, Energy 112 (2016) 7590. [49] C.-H. Lee, J.-T. Yang, Modeling of the Ballard-Mark-V proton exchange membrane fuel cell with power converters for applications in autonomous underwater vehicles, J. Power Sources 196 (8) (2011) 38103823. [50] Chen, H.-C., S.-y. Tzeng, and P.-h. Chen. Optimization design of PID controllers for PEMFC with reformer using genetic algorithm, in: 2010 International Conference on Machine Learning and Cybernetics (ICMLC), 2010. IEEE. [51] Xiao, Y. and K. Agbossou. Interface design and software development for PEM fuel cell modeling based on Matlab/Simulink environment, in: WRI World Congress on Software Engineering (WCSE’09), 2009. IEEE. [52] O. Arrieta, R. Vilanova, P. Balaguer, Procedure for cascade control systems design: choice of suitable PID tunings, Int. J. Comput. Commun. Control. 3 (3) (2008) 235248.

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10

Hussein Ibrahim1, Miloud Rezkallah1, Adrian Ilinca2 and Mazen Ghandour3 1 Technological Institute for Industrial Maintenance (ITMI), College of Sept-Iˆles, Sept-Iˆles, QC,Canada, 2Department of Mathematics, Computer Science and Engineering, University of Quebec at Rimouski (UQAR), Rimouski, QC, Canada, 3Faculty of Engineering, Lebanese University, Hadat, Beirut, Lebanon

10.1

Chapter overview

The use of modern renewable energy sources (RESs), such as wind and solar energy, produces less pollution than burning fossil fuels, especially concerning net emissions of greenhouse gases, which helps to prevent the impact of climate change. Unfortunately, the increasing penetration of energy generated from RESs in the grid will have a significant impact on performance and reliability. This is basically because of the intermittency of RESs and the lack of a large-scale economical storage capability. If considering the efficiency of generation from RESs, distribution network, and their associated losses, decentralized generation gives the advantages of generating and storing power where it is needed. The microgrid (MG), as a small-scale grid, is a promising solution to achieve these tasks and ensure a clean and uninterruptible power supply to the local loads. A dedicated energy storage system (ESS) could contribute to better integration of RES into the MG by smoothing the RESs intermittency, improving the quality of the injected power into the grid, and enabling additional services like voltage and frequency regulation [1]. Generally, MG that consists of energy sources (ESs), ESS, power electronics devices, and loads can operate either in the grid-connected mode or in the standalone mode, which allows us to keep supplying the locally connected loads even if there is an outage on the main grid. With the help of appropriate storage technology and advanced control, MG can also ensure system stability by compensating the intermittency of RESs and providing a peak shaving to the grid [2]. There are many types of storage technology as detailed in Fig. 10.1 [3]. Each technology is characterized by their (1) power density and (2) energy density as demonstrated in Fig. 10.2 [4]. Well, batteries such as lead
acid and fuel cells possess high energy density but low power density. For applications that require a fast-dynamic response, these storage technologies are not suggested. Flywheel, which uses electric energy input to store in the form of kinetic energy, can provide high power density, but it is not suggested for applications that require high energy density. However, there are no storage technologies that possess a high power and energy density at the same time. To get both the advantages and extend the life of the Hybrid Renewable Energy Systems and Microgrids. DOI: https://doi.org/10.1016/B978-0-12-821724-5.00004-0 © 2021 Elsevier Inc. All rights reserved.

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Figure 10.1 Storage technology classification.

Figure 10.2 Power and energy density for different storage technology.

batteries, as well as achieve the self-discharge rate, and high-efficiency, a hybrid configuration is proposed as a solution in Refs. [5,6]. Combining two or more storage technologies to form a hybrid energy storage system (HESS) is also suggested for standalone operations for MG applications in Ref. [3]. Supercapacitor (SC) cells and lithium batteries as HESS are proposed to compensate for the intermittency of the generated power from the solar photovoltaic (SPV) system [7]. Although the SC will ensure a longer lifetime in terms of charge cycles and has a large range of

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operating temperatures, it has the drawback of having low energy density and high costs. In this regard, the lithium battery has a higher energy density but requires an accurate charge profile to increase its lifetime. Combining these two storage elements for this application helps to achieve a good compromise in terms of energy density. A detailed study of various methods of storage that combine two different storage technologies has been shown in Refs. [8,9]. Fig. 10.3 demonstrates short- and long-term HESS methods. The selection of the appropriate technology is based on the RESs available on the site, type of loads, and the objectives to achieve dynamic response during the transition and long- and short-term energy availability. ESS is consists of two elements: (1) energy storage element (ESE) and (2) power converters. Generally, the power electronics devices are required to connect the ESE to DC or the AC bus. Also, these power electronics devices are controlled using appropriate control strategies to convert the electric power from one form to another (DC or AC) and to inject a clean and stable power into the grid when MG operates in grid-connected mode and to ensure uninterruptible power supply with fixed voltage and frequency to the connected loads when the MG operates in standalone mode. Therefore, based on the appropriate topology and control system design, the ESS can simultaneously perform both instantaneous active and reactive power flow control, as required in MG. In Fig. 10.4, simple solutions for MG based on SPV systems and storage element are demonstrated. In Fig. 10.4A, battery energy storage (BES) and SPV are connected directly to the common DC bus. Using this configuration, one cannot achieve high performance and compensate much faster the intermittency of SPV [7]. To achieve high performance and fastdynamic response by controlling the output voltage and current of SPV and to reduce its output power intermittency, a controlled DC
DC power converter is added as shown in Fig. 10.4B. This solution is perfect for short-term compensation and it should reinforce to ensure uninterruptible power supply to the connected loads for long-term compensation, especially during standalone operations of MG. All disadvantages of the solutions shown in Fig. 10.4A
C are solved in the solution

Figure 10.3 Short and long-term HESS. HESS, Hybrid energy storage system.

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Figure 10.4 Simple solutions for MG based on solar photovoltaic system. MG, microgrid.

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given in Fig. 10.4D. Each element in MG is connected to its controlled power converter where the performance of the SPV is achieved using appropriate maximum power point tracking (MPPT) technique, and BES and SCs are controlled to obtain short- and long-term compensation. Generally, this application power management strategy as reported in Refs. [7
9] is suggested. Operating many power converters which are connected at the same point with different switching frequency [10
12] can affect the BES aging and reduce its performance. In the same context of the effective and safe integration of ESS into MG for medium and high voltages, multilevel inverters are suggested in Refs. [13,14]. Based on the obtained simulation results, mitigation of voltage and frequency instability problems arising from the integration of renewable resources with intermittent patterns is solved. Regarding the inverter topologies, many topologies have been evaluated based on the required capacity, cost, and harmonic distortion. The cascade topology-based multilevel inverter is selected as an efficient and reliable power converter with less power loss and stress on switches. A comparison study of different storage technologies is shown in Refs. [15,16]. Based on the characteristics of each storage technology, none of them can provide high power and high density at the same time. Each technology possesses advantages and faces some limitations. Based on discharge duration, the storage technology that is characterized by short-term (seconds to minutes) and medium-term (daily) discharge duration is preferable for grid service, day
night load unbalance, peak shaving, and load applications. Regarding the long-term discharge duration (weekly to monthly), the technology is preferable for bridge periods of solar and wind duration as well as for seasonal storage [17]. So, combinations of electrochemical batteries and ultracapacitors can help to achieve the desired performance from the point of view of power and energy density, time response, efficiency, span life, cost, weight, as well as the size of the installation. This combination is mostly suggested as modern storage technology for MG as well as for electric vehicle (EV) applications [15,18
21].

10.2

Hybrid energy storage system configuration classification

Depending on the application and the desired objectives, the appropriate storage technology is selected. Based on the selected storage technology, the optimal configuration and control design will be selected [8,15,21]. Fig. 10.5 shows the classification of configurations based on HESS, such as batteries and SCs. It consists of three categories: (1) passive HESS, (2) semiactive HESS, and (3) full active HESS.

10.2.1 Passive configuration Fig. 10.6 demonstrates the passive configuration for HESS based on BES and SCs. Both the storage technologies are connected directly to the common DC bus

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Figure 10.5 Classification of configurations based on HESS. HESS, Hybrid energy storage system.

Figure 10.6 Passive configuration for HESS based on BES and SCs. HESS, Hybrid energy storage system.

without using DC
DC power converters. For this configuration, the voltage of the BES and SCs should be equal to the DC link voltage. Seeing that the output voltages of the BES and SCs are not controlled, the exchange of power between ESs and storage elements is determined by the internal resistance and the (V
I) characteristics.

10.2.2 Semiactive configuration In Fig. 10.7A and B, the semiactive configurations of HESS based on BES and SCs are demonstrated. Compared to the passive configurations shown in Fig. 10.7, the

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Figure 10.7 Semiactive configurations, (A) SCs semiactive HESS, and (B) BES semiactive HESS. HESS, hybrid energy storage system, SCs, supercapacitors.

DC
DC power converter is integrated between the SCs and the DC bus in Fig. 10.7A and between the BES and DC bus in Fig. 10.7B. Using the DC
DC converter allows control of the power follow between the DC bus and the storage element. Compared to the passive configuration, the semiactive configuration is an effective but more complex view of the point hardware implementation.

10.2.3 Series-active configuration The series-active configuration HESS based on BESs and CSs is demonstrated in Fig. 10.8. Both the storage elements are connected in cascades through power converters to the common DC bus. Compared to the passive and semiactive configurations, the series-active configuration is more effective because the power follows between the DC bus and the ESSs is managed according to the load power demand,

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Figure 10.8 Series-active configuration-based HESS. HESS, hybrid energy storage system.

generated power from the RESs, and the state of charge ESSs. This configuration is a more complex view of the point hardware implementation. Besides, the use of two power converters can affect the lifespan of the ESSs due to the high ripple switching. However, the coupling inductors should design to achieve the minimum ripples of the inductor current.

10.2.4 Parallel-active configuration In Fig. 10.9, the parallel-active configuration for HESS based on BESs and SCs is demonstrated. For this configuration, each storage element is connected to the common DC bus through a controlled DC
DC power converter. With this configuration, one gets more flexibility in control of the power flows between the ESSs and the DC bus. To achieve high performance with less stress on switches of the DC
DC power converters, the size rating of the coupling inductors should be smaller. For this objective, the output ESSs voltages should be slightly less than the DC link voltage.

10.3

Control strategies for hybrid energy storage system configurations

Fig. 10.10 shows the general structure of control for HESS. As demonstrated in Fig. 10.10 [8], the control of HESS is subdivided into two parts: (1) energy management control and (2) underlying control. The reference power or current is generated by the energy management control using the appropriate control (classical or intelligent-based control). The obtained reference signal is compared to the sensed signal and the error is fed to the underlying control unit using the appropriated controller, such as a fuzzy controller or PI controller, the obtained error is the control

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Figure 10.9 Parallel-active configuration-based HESS. HESS, hybrid energy storage system.

Figure 10.10 The general structure of control for HESS configurations. HESS, hybrid energy storage system.

signal, which is compared with the triangular signal to control the switches of the power converter.

10.4

Control of microgrid configuration based on solar photovoltaic
wind turbine, and hybrid energy storage system

In this section, the developed control strategies for MG based on SPV and wind turbine (WT) and HESS are shown in Fig. 10.11. The MG configuration consists of WT-driven variable speed permanent magnetic brushless DC generator (PMBLDCG) connected

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Figure 10.11 MG based on HES and HESS. MG, microgrid, HESS, hybrid energy storage system.

to the common DC bus through a three-phase diode bridge and a controlled DC
DC boost converter. The SPV is connected to the common DC bus through a controlled DC
DC boost converter. For the MG configuration, two storage technologies are used. For short-term compensation, SCs are employed and for long-term compensation, Ni
Cd batteries are used. The active parallel configuration is selected to connect SCs and Ni
Cd batteries to common DC bus. RESs and HESS are connected to the AC bus through controlled interfacing inverter. DC loads and AC loads are supplied from the DC bus and the AC bus respectively. The MG configuration can operate in grid-connected and standalone modes. The general structure of MG control is demonstrated in Fig. 10.12. Each power converter is controlled to achieve tasks such as MPPT, control of DC and AC voltages, control of the battery and SC currents, maintain the system frequency constant, and improve the power quality at the AC bus.

10.4.1 Control of wind turbine As detailed in Fig. 10.11, the WT is coupled with PMBDCG and is connected to the common DC bus through the DC
DC boost converter, which is controlled to achieve MPPT. Perturbation and observation technique (P&O) to estimate the maximum current (imWT). The sensed output current of the WT is compared with the estimated current. The error is fed to the proportional
integral (PI) controller to get the signal control (d1) as: ð d1 5 kp ðimWT 2 iWT Þ 1 ki ðimWT 2 iWT Þdt

(10.1)

where kp and ki are the proportional and integral controller gains. The obtained signal control is compared with a triangular signal to get the switching signal for switch S1 of the DC
DC boost converter of WT.

10.4.2 Control of solar photovoltaic The SPV as shown in Fig. 10.11 is connected to the common DC bus through a controlled DC
DC boost converter to achieve MPPT. P&O technique is employed

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Figure 10.12 Generalized control strategies for RES and HESS. RES, renewable energy source, HESS, hybrid energy storage system.

to estimate the maximum SPV current (imPV) using the sensed output PV current (iPV) and voltage (vPV). The sensed SPV current is compared with the estimated maximum current and the error is fed to the PI controller to get the signal control (d2) as:

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ð d2 5 kp ðimPV 2 iPV Þ 1 ki ðimPV 2 iPV Þdt

(10.2)

where kp and ki are the proportional and integral controller gains. The obtained signal control is compared with a triangular signal to get the switching signal for switch S2 of the DC
DC boost converter of the SPV.

10.4.3 Control of Ni
Cd batteries The Ni
Cd batteries are connected to the common DC bus through the DC
DC buck
boost converter. The sensed DC link voltage (Vdc) is compared with DC-link reference (Vdc ) and the error is fed to the PI controller to get the battery current reference (ibat ) as: ð       ibat 5 kp Vdc 2 Vdc 1 ki Vdc 2 Vdc dt

(10.3)

where kp and ki are the proportional and integral gains for the outer control loop. The battery reference current is compared with the sensed battery current (ibat), and the error is fed to the PI controller to obtain the control signal (ds3, s4) as: ð     ds3;s4 5 kp ibat 2 ibat 1 ki ibat 2 ibat dt

(10.4)

where kp and ki are the proportional and integral gains for the inner control loop. The obtained signal control is compared with a triangular signal to get the switching signals for switch S3, and S4 of the DC
DC buck
boost converter of the Ni
Cd batteries.

10.4.4 Control of SCs The parallel-active configuration is selected to control the SCs and Ni
Cd batteries. The SCs connected to the common DC bus through the DC
DC buck
boost converter, which is controlled to charge and discharge the SCs. The sensed SCs current (iSC) is compared with its reference current (iSC ) and the error is fed to the PI controller to obtain the control signal as: ð     ds5;s6 5 kp iSC 2 iSC 1 ki iSC 2 iSC dt

(10.5)

where kp and ki are the proportional and integral gains for the inner control loop. The obtained signal control is compared with a triangular signal to get the switching signals for switch S3, and S4 of the DC
DC buck
boost converter of the Ni
Cd batteries.

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As presented in Fig. 10.12, the input and the output currents of the low pass filter used in the outer loop control for DC link voltage control are compared and the obtained error is added with the obtained error by comparing the sensed and estimated battery currents. The obtained signal is multiplied with the voltage ratio (Vbat/VSC) and the result represents the estimated SCs current (iSC).

10.4.5 Control of the interfacing inverter As demonstrates in Fig. 10.11, all DC elements are connected to the AC bus through AC interfacing inverter. This power converter as shown in Fig. 10.12 is controlled to achieve many tasks, such as control of the AC voltage and frequency, improve the power quality, and ensure the power follows between both the buses. As given in Fig. 10.12, two-level control is employed for the standalone and gridconnected modes. Depending on the grid current (iG) and the conditions of synchronization between the grid and AC bus, the selector will select the right control for the operation mode.

10.4.5.1 Control for standalone operation mode For this operation of MG, the control should regulate constant voltage and frequency at the AC bus and inject power from the DC bus to the connected load. The developed control strategies detailed in Ref. [22] are employed. To achieve the desired tasks, the measured load voltage (VLabc) in (abc) reference frame is transferred to direct (vd), quadrature (vq) rotating reference frame. The references of the AC voltage in d
q axis (vd , vq ) are compared with their measured loads’ voltage in d
q axis and the errors are fed to the PI controllers to get the signals control in d
q axis as: (

  Ð dd 5 kp vd 2 vd 1 ki Ð dq 5 kp vq 2 vq 1 ki

   vd 2 vd dt vq 2 vq dt

(10.6)

where kp and ki are the proportional and integral gains for the outer control loops. With the help of the inverse Park transformation when obtaining the desired control signals in the natural (abc) reference frame (dabc1), which are compared with the triangular signal to get the signals switching for the switches (S7
S12) of the interfacing inverter.

10.4.5.2 Control for grid-connected mode For this operation mode, the control should compensate the deviations of the AC voltage and frequency, improve the power quality, and control the power follows between AC and DC buses. To achieve the desired tasks, modified synchronous reference frame control, shown in Ref. [23], is employed. The references grid currents in the d
q axis (iGd and iGq) are obtained using the measured load current, AC voltage and current as:

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iGd 5 iLd 1 if iGq 5 2 iLq 1 iv

(10.7)

where if and iv represent the output of the PI controllers for the frequency and AC voltage regulations, respectively. With the help of the inverse Park transformation, one obtains the reference grid currents in the natural reference frame (iGa , iGb , and iGc ). The measured grid currents (iGa, iGb, and iGc) are compared with the reference grid currents and the errors are fed to PI controllers to get the signal controls (dabc2) as: 8   Ð < da2 5 kp iGa 2 iGa  1 ki Ð d 5 kp iGb 2 iGb  1 ki Ð : b2 dc2 5 kp iGc 2 iGc 1 ki

  2 iGa dt iGa  iGb 2 iGb dt iGc 2 iGc dt

(10.8)

where kp and ki are the proportional and integral gains for the inner control loops. The obtained desired signal controls (dabc2) are compared with a triangular signal to get the switching signals for S7
S12 of the interfacing inverter.

10.5

Results and discussion

To test the performance of the proposed active configuration of HESS based on SCs and Ni
Cd batteries, many scenarios are tested such as sudden variation of the weather conditions, DC and AC loads, and transitions between standalone and gridconnected mode. The HESS should balance the intermittency of the RESs and maintain constant the voltage and frequency at the AC bus using short- and longterm compensation, as well as ensure continuous power supply to the connected load during the transition. All these tests are performed using MATLAB/Simulink.

10.5.1 Performance at the DC bus As shown in Fig. 10.11, MG configuration possesses two types of loads; AC loads are connected to the AC bus and DC loads, as well as WT, SPV, and HESS are connected to the DC bus. To test the performance of the developed control strategies detailed in Fig. 10.12, the system is subjected to DC load and weather conditions change. In Fig. 10.13, the dynamic performance of solar irradiation (G), output SPV voltage (vpv) and current (iPV), and the generated power (PPV) are presented. This test system is subjected to an increase and decrease of solar irradiation change at t 5 0.2 s, t 5 0.4 s, and t 5 0.7 s. One sees clearly that the output SPV voltage varies slightly with solar irradiation change but the output SPV current increase at t 5 0.2 s and increase more at t 5 0.4 s and decrease when solar irradiation decreases at t 5 0.7 s. The SPV current follows its estimated reference using the

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Figure 10.13 Performance of the SPV under solar irradiation change. SPV, solar photovoltaic.

P&O technique. The generated power from SPV also varies with the variation of the solar irradiation. This confirms that the P&O performs well during solar irradiation change. In Fig. 10.14, the dynamic performance of developed mechanical torque (Tm), the PMBLDCG stator voltage (vsa) and current (isa)of phase “a,” DC voltage at the output of three-phase diode bridge (vWT), the DC output current (iWT), and the generated power (PWT). This test is performed under wind speed change at t 5 0.1 s, t 5 0.3 s, t 5 0.5 s, and t 5 0.7 s. One observes that stator voltage varies slightly but the current varies with the variation of the wind speed. It increases at t 5 0.1 s and increases more with increasing of mechanical torque. One observes that WT behaves as SPV, which helps to use the same P&O technique to achieve MPPT without using speed sensors. The generated power from WT varies with the variation of wind speed, which confirms that P&O performs. The performance of Ni
Cd batteries voltage (Vb), battery current (ib), and its reference (iba ), the state of charge (SOC%), and the batteries power (Pb) are shown

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Figure 10.14 Performance of the WT under wind speed change.

in Fig. 10.15. These tests are performed under weather and DC load conditions change. One observes that the battery current follows its reference and increases and decreases based on the load and weather conditions change. One observes that Ni
Cd batteries help to balance the power in the system by discharging (positive sign) and charging (negative sign). This confirms that the developed control strategy for the buck
boost converter performs well without any saturation issue. The performance of SCs voltage (vsc), SCs current (isc) and its reference, state of the charge, and SCs power are shown in Fig. 10.16. This test is performed under weather and DC loads change. One observes that the SCs current follows its reference and SCs provide short-term compensation during sudden increasing or decreasing of the load, as well as a sudden variation of wind and solar irradiation. This confirms that SCs and their control perform well during the transition. The performance of the DC link voltage (Vdc) and its reference (vdc ), consumed powers by load 1 (pL1) and load 2 (PL2), generated power from SPV (Ppv), and WT

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Figure 10.15 Performance of Ni
Cd batteries under load and weather conditions change.

(PWT), BES power (Pbt) and SCs power (Psc), are presented in Fig. 10.17. One observes that DC link voltage is regulated constant. One observes that SCs provide short compensation to maintain the supply of DC loads at t 5 0.2 s, t 5 0.4 s, and t 5 0.7 s during sudden variation of the solar irradiation, as well as at t 5 0.1 s, t 5 0.3 s, t 5 0.5 s, and at t 5 0.7 s during sudden variation of the wind speed. For long period power compensation Ni
Cd batteries perform well. One observes that between t 5 0.5 s and t 5 0.7 s, BES and SCs powers are equal to zero. This is because the generated power from the WT and SPV is equal to consumed power by loads1 and 2.

10.5.2 Performance at the AC bus The performance of load voltages (vL), grid currents (iG), AC load currents (iL), inverter current (iinv), system frequency (fs), DC link voltage (vdc) and its reference (vdc ), batteries current (ibt) and its reference, and the SCs current (iSC) and its reference (iSC ). This test is performed in the presence of nonlinear load and during the

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Figure 10.16 Performance of SCs under load and weather conditions change. SCs, Supercapacitors.

transition between the standalone and grid-connected modes. One observes clearly that SCs helps to maintain the operation stable during the transition at t 5 0.2 s during the transition between standalone and grid-connected modes and at t 5 0.3 s during a sudden increase of nonlinear load. The DC link voltage is kept constant during all transitions. One observes that between t 5 0 s and t 5 0.2 s, the load voltage and frequency are regulated constant and the nonlinear load is supplied without interruption. This confirms the robustness of the first level of control for interfacing inverter. One observes that between t 5 0.2 s and t 5 0.7 s, MG is connected to the grid and the interfacing inverter behaves as an active filter, it compensates harmonics created by nonlinear load and balance the grid currents. This confirms that the second level of the control strategy of the interfacing inverter performs well when MG operates is grid-connected mode in the presence of nonlinear loads. The transition between both operation modes is achieved without any issue and loads are supplied without interruption. This confirms the robustness of the proposed control

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Figure 10.17 Generated and consumed power in MG. MG, microgrid.

strategies for interfacing inverter and both the buck
boost converters for Ni
Cd and SCs (Fig. 10.18).

10.6

Conclusion

In this chapter, the configurations for a HESS are discussed in detail. Also, the proposed control strategies for semiactive and active configurations are given. The performance of the parallel-active configuration of HESS based on Ni
Cd batteries and SCs is tested under severe conditions when it is connected at the DC bus for real MG applications. The obtained results at the DC bus during transitions show satisfactory performance where the AC and DC loads are supplied without interruption. It has been demonstrated that SCs compensate for the short-term demand during sudden variation of solar irradiation, wind speed, and sudden transition between

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Figure 10.18 Dynamic performance during the transition between standalone and grid-connected modes in the presence of nonlinear loads.

the grid-connected and standalone modes, and Ni
Cd batteries perform well for long-term compensation. Furthermore, it has been demonstrated that the active parallel configuration for HESS based on SCs and Ni
Cd performs well for MG application-based on RES.

References [1] Q. Tabart, I. Vechiu, A. Etxeberria, S. Bacha, Hybrid energy storage system microgrids integration for power quality improvement using four-leg three-level NPC inverter and second-order sliding mode control, IEEE Trans. Ind. Electron. 65 (1) (2017) 424
435.

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[2] F.B. Wu, B. Yang, J.L. Ye (Eds.), Grid-Scale Energy Storage Systems and Applications, Academic Press, 2019. [3] L.W. Chong, Y.W. Wong, R.K. Rajkumar, R.K. Rajkumar, D. Isa, Hybrid energy storage systems and control strategies for stand-alone renewable energy power systems, Renew. Sustain. Energy Rev. 66 (2016) 174
189. [4] K.T. Møller, T.R. Jensen, E. Akiba, H.-W. Li, Hydrogen—a sustainable energy carrier, Prog. Nat. Science: Mater. Int. 27 (1) (2017) 34
40. [5] V. Bolborici, P.D. Francis, K.K. Lian, Hybrid energy storage systems: connecting batteries in parallel with ultracapacitors for higher power density, IEEE Ind. Appl. Mag. 20 (4) (2014) 31
40. [6] T. Bocklisch, Hybrid energy storage systems for renewable energy applications, Energy Procedia 73 (2015) (2015) 103
111. [7] F. Ongaro, S. Saggini, P. Mattavelli, Li-ion battery-supercapacitor hybrid storage system for a long lifetime, photovoltaic-based wireless sensor network, IEEE Trans. Power Electron. 27 (9) (2012) 3944
3952. [8] S. Hajiaghasi, A. Salemnia, M. Hamzeh, Hybrid energy storage system for microgrids applications: a review, J. Energy Storage 21 (2019) 543
570. [9] E. Chemali, M. Preindl, P. Malysz, A. Emadi, Electrochemical and electrostatic energy storage and management systems for electric drive vehicles: state-of-the-art review and future trends, IEEE J. Emerg. Sel. Top. Power Electron. 4 (3) (2016) 1117
1134. [10] W. Li, Z. Jiao, Q. Xiao, J. Meng, Y. Mu, H. Jia, et al., A study on performance characterization considering six-degree-of-freedom vibration stress and aging stress for electric vehicle battery under driving conditions, IEEE Access. 7 (2019) 112180
112190. [11] P.K.P. Ferraz, J. Kowal, A comparative study on the influence of DC/DC-converter induced high frequency current ripple on lithium-ion batteries, Sustainability 11 (21) (2019) 6050. [12] M. Rezkallah, A. Chandra, B. Singh, S. Singh, Microgrid: configurations, control and applications, IEEE Trans. Smart Grid 10 (2) (2017) 1290
1302. [13] Delavari, A., I. Kamwa, and A. Zabihinejad. A comparative study of different multilevel converter topologies for battery energy storage application, in: 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 1
5. IEEE, 2017. [14] Trintis, I., S. Munk-Nielsen, and R. Teodorescu. A new modular multilevel converter with integrated energy storage, in: IECON 2011-37th Annual Conference of the IEEE Industrial Electronics Society, pp. 1075
1080. IEEE, 2011. [15] W. Jing, C.H. Lai, S.H.W. Wong, M.L.D. Wong, Battery-supercapacitor hybrid energy storage system in standalone DC microgrids: a review, IET Renew. Power Gener. 11 (4) (2016) 461
469. [16] Energy storage Monitor latest trends in energy storage. ,https://www.worldenergy. org., 2019. [17] A. Serpi, M. Porru, A. Damiano, A novel highly integrated hybrid energy storage system for electric propulsion and smart grid applications, in: X. Chen, W. Cao (Eds.), Advancements Energy Storage Technologies, 2018, p. 79. [18] Nielson, G., and A. Emadi. Hybrid energy storage systems for high-performance hybrid electric vehicles, in: 2011 IEEE Vehicle Power and Propulsion Conference, pp. 1
6. IEEE, 2011. [19] Lukic, S.M., Sanjaka G.W., F. Rodriguez, J. Cao, and A. Emadi. Power management of an ultracapacitor/battery hybrid energy storage system in an HEV, in: 2006 IEEE Vehicle Power and Propulsion Conference, pp. 1
6. IEEE, 2006.

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[20] Glavin, M.E., and W.G. Hurley. Ultracapacitor/battery hybrid for solar energy storage, in: 2007 42nd International Universities Power Engineering Conference, pp. 791
795. IEEE, 2007. [21] S. Hu, Z. Liang, X. He, Ultracapacitor-battery hybrid energy storage system based on the asymmetric bidirectional Z-source topology for EV, IEEE Trans. Power Electron. 31 (11) (2015) 7489
7498. [22] Zheng, X., H. Zaman, X. Wu, H. Ali, and S. Khan. Direct fuzzy logic controller for voltage control of standalone three phase inverter, in: 2017 International Electrical Engineering Congress (iEECON), pp. 1
4. IEEE, 2017. [23] B. Singh, A. Chandra, K. Al-Haddad, Power Quality: Problems and Mitigation Techniques, John Wiley & Sons, 2014.

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Miloud Rezkallah1,2, Ambrish Chandra2, Hussein Ibrahim1, Zoe Feger1 and Mohamad Aissa3 1 Technological Institute for Industrial Maintenance (ITMI), College of Sept-Iˆles, Sept-Iˆles, 2 QC, Canada, Department of Electrical Engineering, E´cole de Technologie Superieure (ETS), Montre´al, QC, Canada, 3Department of Pure and Applied Sciences, Maritime Institute, Rimouski, QC, Canada

11.1

Chapter overview

The microgrids (MGs) are small power systems consist of inertial and non-inertial energy sources (ESs). Generally, the inertial ESs, such as wind turbines (WTs), micro-hydropower (MHP), diesel generators, flywheels, and non-inertial ESs, such as solar photovoltaic (PV) systems, and batteries, as well as controlled loads are connected to the common AC or DC bus through interfacing power converter devices, which are located at different levels of the voltage as shown in Fig. 11.1. Depending on the operation mode of the MG (grid-connected mode or islanding mode), the interfacing power converter devices are classified as grid-forming, grid-supporting, or grid-feeding converter [1
3]. Generally, MGs are classified into simple MG, corporate MG, feeder area MG, substation area MG, and independent or standalone MG [4]; and the coordinated control of hybrid energy system (HES) is realized using various control techniques, ranging from a basically centralized to a fully decentralized control approach. Either centralized or decentralized controls are developed to ensure stable operation, achieve high performances, and ensure a soft and secure synchronization between different levels of voltage in AC or DC MG application. The hierarchical control for MG is structured in the international standard IEC 62264 and ISA-95 for enterprise-control system integration [5,6]. This control is

Figure 11.1 Solid-state transformer for MG application. MG, Microgrid. Hybrid Renewable Energy Systems and Microgrids. DOI: https://doi.org/10.1016/B978-0-12-821724-5.00003-9 © 2021 Elsevier Inc. All rights reserved.

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subdivided into three levels (primary, secondary, and tertiary control) as demonstrated in Fig. 11.2. Regarding the primary control, the local measurements are used as input data for the inner and outer control loops to adjust the system frequency and the magnitude of AC voltage. In addition, the primary control is responsible for controlling and balancing the power between ESs [7,8]. Due to the lack of inertia in the MG, this level of control should act in milliseconds. For reliability reason, the communication at this level of control is often not used [9]; however, lesscommunication control techniques, such as droop control technique, which is based on the behavior of the synchronous generator, is suggested for parallel inverters in MG [10,11]. By drooping the frequency, against the active power and the output voltage against the reactive power as expressed in Eq. (11.1), loads sharing between ESs can be achieved without communication.


V 5 V  2 KpV ðQ 2 QÞ f 5 f  2 Kpf ðP 2 PÞ

(11.1)

where f, V, P, Q, f , V, P, Q; KpV , and Kpf denote the frequency, voltage, active and reactive power and their references, and the proportional gains of the voltage and frequency, respectively. Generally, the dynamic characteristic of the conventional droop method for the primary control applied to MG is poor because the inertia of inverters-based ESs is extremely low and the transmission line is short. However, to provide inertia support to the system, enhanced droop control techniques based on virtual synchronous generators and synchro converters are suggested in Refs. [12,13]. To restore the

Figure 11.2 Hierarchical control and function for MG application. MG, Microgrid.

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system frequency and the amplitude of the voltage deviation in MG caused by the primary control, the secondary control is employed. View of point dynamic response, this control is slower than the primary control, which allows the decoupled dynamic between primary and the secondary control loops. The frequency and amplitude of the AC voltage deviations are estimated as follows [14]:


ð Ð δf 5 Kpω ðf  2 f Þ 1 KIω ðf  2 f Þdt 1 Δfs δV 5 KpV ðV  2 V Þ 1 KIV ðV  2 V Þdt

(11.2) where Kpω , KpV , KIω , and KpV denote the proportional and integral gains of the controllers. The term Δfs in the frequency controller is considered to facilitate the synchronization of MG with the grid. For standalone operation mode, this term is equal to zero [14,15]. In addition, the secondary control can be implemented with decentralized, centralized, and distributed policy [16]. The decentralized control is simple compared to centralized control, which requires advanced communication between different levels of control. Compared to the decentralized control in the distributed control interaction between units is considered [17
19]. The third level of control, which is called tertiary control, is responsible for coordinating the operation of multiple MGs interacting with one another in the system and communicating. This control level typically operates in the order of several minutes, providing signals to secondary level controls at MGs and other subsystems that form the full grid [20]. Regarding the communication configuration in MG, it depends on the control objectives, implementation, and maintenance costs [21,22] and is divided into three categories: (1) tightly coupled, (2) loosely coupled, and (3) broadcast/multicast communication [21]. Need of the availability of the network for the tightly coupled configuration is required, but for the second and third configurations, they can operate independently. A detailed study of communication configuration, protocols, and tools for MG are shown in Ref. [21]. Based on the compared study shown in Ref. [23], a considered delay or failures in communication may affect the system reliability. In the same context and to better understand this complex technology based on HESs, in this chapter new MG configurations, operation and control concepts, and validation using MATLAB/Simulink are detailed in the following sections.

11.2

Configuration of HES-based MG

MG consists of small-scale ESs, such as WT, solar PV system, MHP, and loads; energy storage system (ESS); and power converters. MG is classified into three categories: AC-MG, DC-MG, and hybrid AC/DC-MG as demonstrated in Fig. 11.3. Generally, for the DC-MG as shown in Fig. 11.3A, inertial and no inertial ESs are connected to the DC bus through controlled power converters and to the grid

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Figure 11.3 Classification of MG configurations; (A) DC-MG, (B) AC-MG, and (C) hybrid AC/DC-MG. MG, Microgrid.

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through interfacing DC/AC power converter. Compared to the AC-MG, the DCMG configuration is reliable and stable because of no need to synchronization and no frequency aspect, as well as, no reactive power reduction in losses [24]. In addition, the DC-MG configuration uses less power converters to connect and to control the ESs, which helps to reduce the hardware and control complexity. The hierarchical control can be applied to the DC-MG configuration [25], where droop control technique is mostly used for power charring in the primary control. Centralized, decentralized, as well as distributed control configuration are also suggested for DC-MG configuration [23]. Most of the inertial ESs and loads are AC in nature, which facilitates their integrations in AC-MG configurations. Generally, for this MG configurations, synchronization and reactive power sharing between different units and AC bus, adding the power quality are big issues. However, additional power converters and advanced control as shown in Fig. 11.3B are required to solve these issues and increase the system performance [26,27]. Hierarchical control shown in Fig. 11.2 can be used where the droop control technique is preferred for the primary control. Compared to DC-MG configurations, all standardization used for the conventional AC grid is applied to the AC-MG configurations. The AC/DC hybrid-MG configuration illustrated in Fig. 11.3C brings together the advantages of the AC and DC-MG configurations.

11.2.1 AC/DC hybrid-MG configurations based on two ESs The AC/DC hybrid-MG configurations based on two ESs are demonstrated in Fig. 11.4. The proposed configurations can operate in grid-connected or islanding modes. In Fig. 11.4A, the AC/DC hybrid-MG configuration consists of solar PV systems, variable speed diesel generators (VSDG), and storage energy systems (SES). This MG configuration is suggested to the areas that possess a good solar irradiation. To achieve high performance without synchronization issues, all ESs of this configuration are connected to the DC bus through controlled power converters and to the AC bus through DC/AC interfacing inverter. The DC and AC loads are connected to DC and AC bus, respectively. The AC/DC hybrid-MG configuration shown in Fig. 11.4B is dedicated to the areas that possess a good wind speed; it consists of variable speed WT, VSDG, and SES. Regarding, the third configuration is suggested for the areas, which are located near the rivers. It consists of variable speed MHP, VSDG, and SES. The proposed AC/DC hybrid configurations shown in Fig. 11.4 are cost-effective, easy to use, and more flexible because they possess less power converters, which help to easily implement the hierarchical control.

11.2.2 AC/DC hybrid-MG configurations based on three ESs In Fig. 11.5, the cost-effective AC/DC hybrid-MG configurations based on three ESs with less power converters are presented. In all configurations, the VSDGs are used as backup ES. Configuration shown in Fig. 11.5A is suggested to the areas that possess a good solar irradiation and located near the rivers, and the second MG configuration shown in Fig. 11.5B is dedicated to the areas that possess a good

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Figure 11.4 Hybrid AC/DC MG configurations based on two ESs. MG, Microgrid; ES, energy sources.

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Figure 11.5 AC/DC hybrid-MG configurations based on three ESs. MG, Microgrid; ES, energy sources.

379

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wind speed and solar irradiation. In addition, the third MG configuration presented in Fig. 11.5C is suggested to the locations that possess a good wind speed and are located near the rivers. To achieve high performance from ESs and provide a clean and uninterruptible power to the connected loads, as well as to achieve a perfect and secure synchronization between different units and grid, all ESs are connected to the DC bus through a controlled power converter and to the AC bus through DC/ AC interfacing inverter. The proposed AC/DC hybrid-MG configurations shown in Fig. 11.5 can operate in grid-connected or islanding mode. As detailed in Fig. 11.2, optimization of the performance of ESs and the balance of power are achieved in the primary control. In addition, the synchronization between the AC bus and grid, as well as regulation of active and reactive power are achieved by the secondary control. In addition, the import/export of energy form grid to the AC/DC hybridMGs is achieved by the tertiary control.

11.2.3 AC/DC hybrid-MG configuration based on four ESs In Fig. 11.6, the cost-effective AC/DC hybrid-MG configuration based on four ESs is illustrated. This configuration is dedicated to the locations that possess a good wind speed and solar irradiation and are located near the rivers. To solve the synchronization issue and to reduce the cost of installation as well as the hardware complexity, all ESs are connected to the DC bus through controlled power converters and to the AC bus through controlled DC/AC interfacing inverter. The hierarchical control can be applied to this configuration, where the maximum power point tracking (MPPT), local stability, and DC-link voltage regulation are achieved by the primary control, and the secondary control should take care on synchronization, AC voltage, and frequency deviation and improve the power quality at the AC

Figure 11.6 Hybrid AC/DC-MG configuration based on four ESs. MG, Microgrid; ES, energy sources.

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bus. The optimization and power management should be considered in the third level of control.

11.3

AC/DC hybrid-MG configuration under study

Fig. 11.7 shows the detailed scheme of the AC/DC hybrid-MG configuration already demonstrated in Fig. 11.5B. It consists of three ESs, WT-driven variable speed permanent magnetic brushless DC generator (PMBLDCG) (WT), PV solar system, diesel engine-driven variable speed permanent magnetic synchronous generator (VSDG), battery energy storage system (BES), DC and AC loads. The proposed MG configuration possesses less power converters and can operate in grid-connected and islanding mode. VSDG is employed as backup ES, it operates only if AC/DC hybrid-MG operates in islanding mode and the generated power from WT and PV is less than the load power demand. If AC/DC hybrid-MG operates in a connected-grid mode, WT and PV provide power to the connected DC and AC load, the rest of power is injected into the grid. BES is employed to balance the power in the system during load and weather conditions change.

Figure 11.7 Detailed scheme of hybrid AC/DC-MG configuration based on three ESs. MG, Microgrid; ES, energy sources.

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Table 11.1 Operation mode of AC/DC hybrid MG. Mode

Conditions

Energy sources

Grid-connected/islanding mode

Mode 1 Mode 2 Mode 3 Mode 4

PPV 1 PWT $ PL, SOC $ 50% PPV 1 PWT # PL, SOC ,50% PPV 1 PWT $ PL, SOC $ 50% PPV 1 PWT , PL, SOC # 50%

PV, WT

Grid connected

PV, WT

Grid connected

PV, WT

Islanding mode

PV, WT, DG

Islanding mode

MG, Microgrid; WT, wind turbine; PV, photovoltaic; SOC, state of the charge of the battery; DG, diesel generator.

11.3.1 Operation modes of selected AC/DC hybrid-MG configuration The possible operation modes of the selected AC/DC hybrid-MG configuration shown in Fig. 11.7 are detailed in Table 11.1. Modes 1 and 2 are reserved to gridconnected mode and modes 3 and 4 are reserved to the islanding mode of operation. However, conditions that allow the system to switch from mode 1 to mode 2 are (1) the generated power from WT (PWT) and PV (PPV) should be less than the load power demand (PL), and (2) the state of the charge of the battery (SOC) should become less than 50%. In addition, the islanding mode operation, conditions that allow the system to switch from mode 3 to mode 4 are (1) the sum of PPV 1 PWT should be less than PL, and (2) the SOC should be less than 50%. Regarding the operation, mode 4 is selected only if the provided power from ESs is less than PL and SOC is less than 50%. In this mode of operation, the VSDG is turned on to charge the BES and provide power to the connected loads, simultaneously.

11.4

Control for AC/DC hybrid-MG configuration

Fig. 11.8 shows the detailed control of the selected AC/DC hybrid-MG configuration, the available ESs in this location; WT and VSDG, PV, and BES are connected to the AC bus through DC/AC controlled interfacing inverter to achieve high performance, to balance the power in the system, to import and export power from the grid, as well as to improve the power quality at the AC bus. As demonstrated in Fig. 11.8, hierarchical control is applied to achieve these tasks. However, the input side of the power converters is controlled to achieve MPPT by controlling the speed of generators and the output current of the PVs. The interfacing inverter when operates as grid-forming power converter (modes 3 and 4) is controlled to regulate constant the voltage and frequency as well as to improve the power quality at the AC bus. If AC/DC hybrid-MG configuration operates in modes 1 and 2, the interfacing inverter behaves as grid-following power converter, it controls the injected active

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Figure 11.8 Detailed control of selected AC/DC hybrid-MG configuration. MG, Microgrid.

and reactive power into the grid, ensures the system stability by adjusting the deviation in voltage and frequency, improves the power quality, and ensures the synchronization between ESs and grid. In addition, the BES is controlled to balance the power in the system and to compensate the intermittency of the WT and PV.

11.4.1 Hierarchical control As already discussed, and detailed in Fig. 11.2, that the hierarchical control for MG configuration is divided in three parts: (1) primary control, (2) secondary control,

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and (3) tertiary control. Generally, the control of ESs such as WT, PV, and VSDG as detailed in Fig. 11.8 is part of the primary control. The secondary and the tertiary controls are achieved by controlling the DC/AC interfacing inverter.

11.4.1.1 Primary control The developed primary control for the proposed AC/DC hybrid-MG configuration shown in Fig. 11.7 is responsible for the reliability and improving the system performance and stability for the local voltage. Generally, this level of control act in milliseconds [28]. The control ESs, such as controlling of the rotor speeds of the generator and achieving the MPPT form PV, are part of the primary control. To avoid the synchronization issue, all ESs of the AC/DC hybrid-MG are connected to the DC bus.

11.4.1.1.1 Control of the variable speed wind turbine The WT-driven variable speed PMBLDCG is connected to the DC bus through three-phase diode bridge and to the DC
DC boost converters. This electrical generator possesses a better speed versus torque characteristics, highly dynamic response, and high efficiency. By connecting the stator terminals of the PMBLDCG, its stator current varies with variation of the wind speed, but its stator voltage varies slightly, which helps to achieve high performance from the WT without using any mechanical sensors. Furthermore, the conventional MPPTs technique such as perturbation and observation (P&O) technique can be used to estimate the maximum current as detailed in Fig. 11.8. So, the sensed output WT current (iWT) and voltage (vWT) are used as input for P&O technique to estimate the maximum output currents (imWT), which is compared after with sensed output WT current to get the error of current. The obtained error is fed to PI controller as: ð uiWT 5 kpWT ðimWT 2 iWT Þ 1 kiWT ðimWT 2 iWT Þdt

(11.3)

where uiWT , kpWT , and kiWT denote the output of the PI controller and their gains. Using the dynamic model of the DC
DC boost converter one can get the control laws (ds1 ) as: ds1 5 1 1

uiWT2VWT VOUtWT

(11.4)

where ds1 , VWT , and VoutWT denote the control law, the input and the output voltages of the DC
DC boost converter of the WT, respectively. The control law is sent to pulse width modulation (PWM) to obtain the switching signal to control the switch SW1 of the DC
DC boost converter of WT.

11.4.1.1.2 Control of the solar photovoltaic system The PV is connected to the DC bus through controlled DC
DC boost converter as detailed in Fig. 11.8. To achieve high performance from PV, P&O technique is

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employed. The obtained maximum PV current is compared with sensed output SPV current and the error is fed to PI controller as: ð uiPV 5 kpPV ðimPV 2 iPV Þ 1 kiPV ðimPV 2 iPV Þdt

(11.5)

where uiPV , kpPV , and kiPV denote the output of the PI controller and their gains. Based on the dynamic model of the DC
DC boost converter one can get the control laws (ds2 ) as: ds1 5 1 1

uiWT2VWT VOUtWT

(11.6)

where ds2 , VPV , and VoutPV denote the control law, the input and the output voltages of the DC
DC boost converter of the PV, respectively. The control law is sent to PWM to obtain the switching signal to control the switch SW2 of the DC
DC boost converter of PV.

11.4.1.1.3 Control of the variable speed diesel generator The proposed VSDG is connected to the DC bus through three-phase diode bridge and DC
DC boost converters. The VSDG is employed as backup ES, it operates in mode 4 as detailed in Table 11.1. To achieve high performance and satisfy the load power demand, as well as charges the BES when system operates in islanding mode, the reference rotor speed (ωr ) of the permanent magnet synchronous generator (PMSG), which is used to estimate the optimal electromechanical torque (Te .) is obtained with help of the characteristic PDG 5 f ðωr Þ given in Ref. [29]. Comparing the measured rotor speed ωr with its reference ωr and the obtained error is fed to PI controller to estimate Te as: ð Te 5 kpωr ðωr 2 ωr Þ 1 kωr ðωr 2 ωr Þdt

(11.7)

where kpωr and kiωr denote the PI controller gains. And the quadrature-axis component of the reference rotor current (id ) is calculated as follows: id 5

4Te 3Pλm

(11.8)

P is the number of poles and λm is the flux linkage. The reference rotor current (id  ) is compared to the measured current (iDG ), the error is fed to PI controller and the obtained signal represents the control law (ds5 ), which is fed to PWM to get the switching signal to control the switch of the DC
DC boost converter of VSDG.

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11.4.1.1.4 Control battery storage system The BES as shown in Figs. 11.7 and 11.8 is connected to the DC bus through controlled DC
DC buck/boost converter. The proposed control of the BES is demonstrated in Fig. 11.8. Based on the control of the common DC-bus voltage (Vdc ), one can easily estimate the battery current reference (ibat ) as: ð   ibat 5 kpvdc ðVdc 2 Vdc Þ 1 kiVdc ðVdc 2 Vdc Þdt

(11.9)

 where Vdc , kpvdc , and kiVdc are the DC-link voltage reference and the PI controller gains. The obtained ibat is compared with the sensed battery current (ibat ) and the error is fed to PI controller to obtain the control law (ds3;4 ) as:

ð ds3;4 5 kpibat ðibat 2 ibat Þ 1 kiibat ðibat 2 ibat Þdt

(11.10)

And the obtained signal is compared with the triangular waveform to get the switching signal for the switches of the DC
DC buck/boost converter of BES.

11.4.1.1.5 Control of the AC/DC interfacing inverter The control of the AC/DC interfacing inverter detailed in Fig. 11.8 is designed to achieve many tasks of the secondary and tertiary control, such as regulation of the AC voltage and system frequency and to inject power into grid and improve the power quality at the AC bus and power management. Regarding the frequency regulation, the interfacing inverter must operate at desired frequency, that is, 50/60 Hz. The proportional resonant [Anti-windup proportional resonant controller (AWPRC)] and the proportional integral [Anti-windup proportional integral controller (AWPI)] controllers with anti-windup are suggested for the inner and outer control loops to achieve high performance without saturation during the transition. As presented in Fig. 11.8, two levels coordinated control algorithm is employed. A selector switch is used for correct control options (d1(abc)) if AC/ DC hybrid-MG configuration operates in modes 3 and 4 or d2(abc) if operates in modes 1 and 2. For modes 1 and 2, the interfacing inverter operates as grid-following converter and for the modes 3 and 4 the interfacing inverter operates as grid-forming converter. The AC bus phase reference voltages are expressed as follows: 8
> i 5 ðV P  1 Vβ QÞ > < sα Vα2 1 Vβ2 α 1 >  > > : isβ 5 V 2 1 V 2 ðVβ P  2 Vα QÞ α β

(11.20)

With the help of inverse Clark’s transformation, the estimated source currents reference (isα , isβ ) are transformed to three-phase currents (isa , isb , and isc ) and compared with the sensed currents of the grid. The errors of currents are fed to AWPI and the output signals are used to obtain the gating pulses for interfacing inverter.

11.4.1.2 Secondary control As already discussed and demonstrated in Fig. 11.2, the secondary control undertakes the restoration of the system frequency and AC voltage deviation, and synchronization between AC/DC hybrid-MG and grid and ensures the transition between grid-connected mode and islanding mode. As demonstrate in Fig. 11.9, the system frequency deviation (Δf )is obtained as follows: Δfs 5 fs 2 fs where Δf ; and fs denote the reference of the system frequency.

Figure 11.9 Secondary and tertiary control.

(11.21)

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The amplitude of the AC voltage deviation (ΔVLp ) is calculated as follows:  ΔVLp 5 VLp 2 VLp

(11.22)

 where ΔVLP denotes the reference of the amplitude of the AC-bus voltage. Synchronization between AC/DC hybrid-MG and grid is ensured by the secondary control. As is detailed in Fig. 11.8, the amplitude and phase shift of AC-bus voltage are required to switch on/off the static transfer switch (STS). These two critical parameters are obtained using phase locked loop. To switch on STS, the amplitude and phase variation should be equal to zero (ΔVp 5 0; Δθ 5 0).

11.4.1.3 Tertiary control The AC/DC hybrid-MG import/export of the power from the grid is ensured by the tertiary control as is demonstrated in Fig. 11.9. The required active and reactive power to import or to export is integrated with secondary control as is expressed in Eqs. (11.16) and (11.19).

11.5

Results and discussion

The performance of the suggested AC/DC hybrid-MG configuration is shown in Fig. 11.7, and it developed hierarchical control, as detailed in Fig. 11.8, which is tested using MATLAB/Simulink. In both operations modes, grid-connected and islanding mode, system is subjected to sudden load and weather conditions change. Fig. 11.10 demonstrates the waveforms of the DC-link voltage (Vdc ) and its refer ence (Vdc ), BES current (ib ) and its reference (ib ), the rotor speed of the PMSG-coupled diesel engine (ωrDG ) and its reference (ωrDG ), DC output current of VSDG (iDGdc ), rotor speed of PMBLDCG-coupled WT (ωWT ), stator voltage of phase “a” (va ) and current (ia ), DC current (iWTdc ), and PV current (ipv ) and its reference. For this test, system is subjected to sudden wind speed variation at t 5 0.3 s and to solar irradiation increasing at t 5 0.4 s and t 5 0.6 s, and to sudden increase in AC load at t 5 0.4 s. One observes that the DC-link voltage is kept constant during these variations, and the BES current balance the power in the system by charging the generated power from WT and PV. Seeing that the SOC of BES is greater than 50% and system operates in grid-connected mode, VSDG is turned off that is why its speed and current are equal to zero. One observes that the output currents of the WT and PV vary with variation of the wind speed and solar irradiation, it increases at t 5 0.3 s, t 5 0.4 s, and t 5 0.6 s, which confirms the well operation of the control strategies for MPPT. One observes that the system operation remains stable instead of perturbations. In Fig. 11.11A the waveforms of the grid voltages (vs ) and currents (is ), load currents (iL ), interfacing inverter current (iinv ), frequency (deltaf ), and amplitude of the AC-bus voltage deviation (deltav ) are demonstrated and their zoomed waveforms

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Hybrid Renewable Energy Systems and Microgrids

Figure 11.10 Dynamic performance at DC bus when MG operates in grid-connected mode. MG, Microgrid.

between t 5 0.3 s and t 5 0.5 s are presented in Fig. 11.11B. As already indicated that in grid-connected mode, the interfacing inverter operates as grid-following converter and the tasks of the secondary and tertiary controls are achieved by its developed control. To test the performance of the developed coordinated two-level control for the interfacing inverter of the AC/DC hybrid-MG detailed in Fig. 11.8, system is subjected to sudden increase in nonlinear load at t 5 0.4 s. it is observed that the AC-bus voltage and the system frequency are kept constant during this transition and the amplitude of the AC-bus voltage and frequency deviations are equal to zero. One observes that the output interfacing inverter currents are increased at t 5 0.4 s to balance the source current and mitigate harmonics. One observes clearly in Fig. 11.11B that the grid-following converter behaves as active filter, it balances the source currents and improve the power quality at the AC bus. Seeing that the

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391

(A)

(B) Figure 11.11 (A) Dynamic performance at AC bus when MG operates in grid-connected mode, and (B) the zoomed waveforms of (a) between t 5 0.3 s and t 5 0.5 s. MG, Microgrid.

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Hybrid Renewable Energy Systems and Microgrids

SOC% is not completely charged, grid help AC/DC hybrid-MG to provide power to the connected AC loads, and the ESs charge the BES and provide powered the connected DC loads. One sees clearly that the developed control strategies (second level of the coordinated control) based on AWPI controller operate well without saturation issues. Fig. 11.12 demonstrates the waveforms of the DC-link voltage (Vdc ) and its ref erence (Vdc ), BES current (ib ) and its reference (ib ), the rotor speed of the PMSGcoupled diesel engine (ωrDG ) and its reference (ωrDG ), DC output current of VSDG (iDGdc ), rotor speed of PMBLDCG-coupled WT (ωWT ), stator voltage of phase “a” (va ) and current (ia ), DC current (iWTdc ), and PV current (ipv ) and its reference. For this test, system is subjected to sudden wind speed variation at t 5 0.3 s and to solar irradiation increasing at t 5 0.4 s and t 5 0.6 s, as well as to sudden increase in AC

Figure 11.12 Dynamic performance at DC bus when MG operates in islanding mode. MG, Microgrid.

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393

load at t 5 0.4 s. Based on the obtained results at the DC bus when AC/DC hybridMG operates in islanding mode, the developed control strategies based on dynamic model of the DC
DC boost converters for the WT and PV operate well under wind speed and solar irradiation change. One observes that the tasks of the primary control such as achieving MPPT from WT and PV and local stability are realized. One observes in this operation mode that the BES helps the ESs to balance the power in the system (positive sign of BES current). Between t 5 0.6 s and t 5 0.8 s, the BES current becomes zero, this is because the generated power from WT and PV is equal to the load power demand. Seeing that the SOC of the BES is greater than 50%, the VSDG stay off in this operation mode as detailed in Table 11.1. In Fig. 11.13A, the waveforms of the grid voltages (vs ) and currents (is ), load currents (iL ), interfacing inverter current (iinv ), and the system frequency (fs ) are demonstrated, and their zoomed waveforms between t 5 0.3 s and t 5 0.5 s are presented in Fig. 11.13B. As already indicated, the interfacing inverter in islanding mode operates as a grid-forming converter. It is responsible for regulating AC voltage and system frequency and injecting power into AC bus. One observes clearly in Fig. 11.13A that the AC-bus voltage and system frequency are kept constant and regulated at their rated values in the presence of all conditions. One can see that the output interfacing current increases with increase in the load current, because the AC/DC hybrid-MG provides the required power to the connected AC and DC loads. Based on the waveforms shown in Fig. 11.13B, the AC-bus voltage waveforms are sinusoidal instead of the presence of balanced dynamic nonlinear load. The system frequency is kept constant during the transition, which confirms the robustness of the first level (control for islanding mode operation) of coordinate control based on AWPRC controllers. Based the obtained results, one observes that the transitions are achieved without any saturation issue, which confirms the robustness of the AWPRC controllers employed for the outer and inner control loops. In Fig. 11.14A, the waveforms of the grid voltages (vs ) and currents (is ), load currents (iL ), interfacing inverter current (iinv ), system frequency (fs ), and DC-link  voltage (Vdc ) and its reference (Vdc ) are demonstrated and their zoomed waveforms between t 5 0.35 s and t 5 0.55 s are presented in Fig. 11.14B. The objective of this test is to show the performance of the system during the transition between islanding mode and grid-connected mode. As already indicated that the secondary control is also responsible for the synchronization. One observes clearly that at t 5 0.4 s, the system switches from the islanding mode to grid-connected mode. One observes that the transition was soft without diverge of the control. The AC voltage kept constant and sinusoidal during the transition at t 5 0.4 s and during sudden increase of nonlinear load at t 5 0.5 s, one observes that during all simulations the system frequency remains constant, which confirms the robustness of the proposed control for voltage and frequency regulation in islanding and grid-connected modes. One observes that between t 5 0 s and t 5 0.4 s, the grid-following converter acts as active filter; it balances the source currents and improves the power quality at the AC bus. The DC-link voltage is also kept constant during the transition between the both modes, which confirms the local stability.

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(A)

(B) Figure 11.13 (A) Dynamic performance at AC bus when MG operates in islanding mode, and (B) the zoomed waveforms of (a) between t 5 0.3 s and t 5 0.5 s. MG, Microgrid.

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(A)

(B) Figure 11.14 (A) Dynamic performance during the transition between islanding and grid-connected modes, and (B) the zoomed waveforms of (a) between t 5 0.35 s and t 5 0.55 s. MG, Microgrid.

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Hybrid Renewable Energy Systems and Microgrids

Conclusion

In this chapter, the control of AC/DC hybrid-MG configurations based on HES is discussed in detail. New AC/DC hybrid-MG configurations with less power converters based on two three and four ESs are proposed for different locations. In all suggested MG configurations, VSDG is employed as backup ESs. To achieve higher flexibility and reliability from ESs, simple and robust hierarchical control for the selected MG configuration is presented where in the primary control, reliability and the optimal performance of ESs, synchronization issue, as well as the local stability are taken in consideration. AWPI and AWPRC controllers are employed in the inner and outer control loops to achieve high performance without saturation issues. The amplitude of the AC voltage and the system frequency deviation, as well as the power quality improvement at the AC bus, are considered in the secondary level. For the proposed hierarchical control, the tertiary control, which ensures the import/ export of energy from the AC/DC hybrid-MG, is combined by the secondary control. The proposed coordinates two-level control allows the interfacing inverter to operate easily as grid-forming and grid-following converter. The performance of the suggested AC/DC hybrid-MG configuration and it developed hierarchical control has been validated using MATLAB/Simulink and the obtained results under severe conditions show satisfactory performance.

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Farhad Shahnia Discipline of Engineering and Energy, Murdoch University, Perth, WA, Australia

12.1

Introduction

Sustainable development is the core principle of maintaining our finite but vital resources for future generations. In the context of electrical systems, sustainable development is correlated with the application and control of renewable energy resources for electricity generation instead of fossil fuels such as coal, oil, or gas. In addition, it is highly preferred to generate electricity near the consumer centers to minimize the adverse impacts on the environment when building transmission and distribution lines. The rising electricity demand and the inevitability of reliability improvement and cost reduction are motivating the application of distributed energy resources (DERs) within distribution networks instead of expanding them. The term microgrid refers to a small-scale electricity generation and distribution system in which a cluster of loads is supplied locally by a few DERs and/or battery energy storage systems (BESs) [1]. Fig. 12.1 shows schematically a typical ac microgrid. The key feature of the microgrids is that they can operate in gridconnected mode or islanded mode (standalone) (when the grid does not exist or is unavailable provisionally). The microgrid is a promising approach to increase local renewable energy-based DERs near load centers. The microgrid can integrate various renewable-based DERs with BESs and can coordinate them to supply the demand while connected to the grid or standalone. Hence, employing standalone microgrids is thought of as a substantial step toward supplying the electrification of the edge-of-grid and remote areas that have limited or no access to the grid. On the other hand, grid-connected microgrids can enhance the grid’s resiliency and reliability. To deliver power with high quality and reliability, the microgrid must perform as a controlled system that responds to demand variations in a timely manner. Realizing a 100% renewable penetration is the ultimate goal for a microgrid; however, in the current transitional stage, it is inevitable to use some small-scale diesel or gas-driven synchronous generators as some of the DERs [2
6]. This is because most of the renewable-based DERs rely primarily on varying ambient conditions [7
9] and, thereby, are uncontrollable except when accompanied by a power smoothing BES [10,11]. Also, their accurate forecasting is very hard in real-time. The uncertainties of the output power of renewable-based DERs, as well as demand Hybrid Renewable Energy Systems and Microgrids. DOI: https://doi.org/10.1016/B978-0-12-821724-5.00007-6 © 2021 Elsevier Inc. All rights reserved.

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Load DER Load

BES BES

DER

Figure 12.1 Schematic presentation of a microgrid.

consumption, and these along with the high cost of BESs, complicate the costeffective operation of a standalone microgrid, within the desirable range of the voltage and frequency. The uncontrolled DERs, also referred to as nondispatchable DERs (NDDERs), operate in grid-following mode and provide the maximum available or the nominal (rated) power. NDDERs always operate based on the maximum power point tracking and their outputs will dynamically vary depending on the prevailing weather conditions. On the other hand, the controllable DERs (such as diesel or gas-driven synchronous generators or those renewable-based DERs that are coupled with an appropriate power smoothing BES) are referred to as dispatchable DERs (DDERs). In grid-connected mode, the grid dictates voltage and frequency of the network. As such, the DDERs of a grid-connected microgrid will operate at their rated capacities. However, in the standalone mode, the DDERs operate in grid-forming mode and are responsible for controlling the microgrid’s voltage and frequency. In this condition, when multiple DDERs exist in a microgrid, they can share the total load using several techniques such as the droop control [12]. Droop control can help the DDERs to share the active and reactive power of the loads without any communication among them. In addition to the basic droop control, other techniques have also been proposed in the literature, which are either a modification of a droop control (such as adaptive, intelligent, cost-based, etc.) or an optimization-based or potential function type [13
15]. An appropriate power-sharing among DDERs is crucial to satisfy the stability of the microgrid. An upper-level control can then be used to supervise and monitor the entire microgrid and respond to the voltage and frequency deviations. This controller must have a slower dynamic compare [16]. As an example, this upper-level control can determine the set points for droop control of DDERs to maintain the voltage and frequency of the microgrid within acceptable limits. Furthermore, the control of the charging and discharging of BESs, as well as demand response, load-shedding, and renewable curtailment can be coordinated with the operation of DERs to resolve the voltage and frequency problem in microgrids. On the other hand, the voltage and frequency problem can be eliminated by provisionally coupling a microgrid to an external entity in either form of a neighboring microgrid or a utility feeder. This chapter presents an overview of the various control aspects of microgrids that are necessary for the microgrid’s appropriate operation.

Microgrids and their control

12.2

401

Primary controllers of DDERs and BESs

A DDER’s local controller consists of outer and inner-loop controls [17
19]. The inner-loop control is in charge of appropriately turning on/off the switches within the converter of a converter-interfaced DDER or adjusting the set points of the governor and exciter of a rotary DDER to facilitate an appropriate tracking of the preferred reference at the DDER’s output. The local voltage and current measurements at the DDER’s output, as well as the references defined by the outer-loop control, are used in this controller. On the other hand, the outer-loop control, which is usually referred to as the primary controller of a DDER, is the lowest level of control in the microgrid’s hierarchical control scheme. The references defined by the primary controllers facilitate the suitable level of power that the DDERs inject into the microgrid. This controller produces suitable references for the inner-loop control, which changes depending on the microgrid’s grid-connected and standalone modes of operation. As was mentioned earlier, the ultimate goal of a microgrid is realizing 100% penetration of renewables for electricity generation. As such, this chapter focuses only on those renewable-based DDERs that are connected to the microgrid via power electronics-based converters, often referred to as converter-interfaced DDERs, and does not discuss dc microgrids [20
22], hybrid ac
dc microgrids [23
25], or single-phase DDERs within three-phase microgrids [26,27].

12.2.1 DDER’s primary controller Suppose that DDERs have a voltage source converter and an output inductor-capacitorinductor (LCL)-type passive filter, with a structure as shown in Fig. 12.2. The active

vTa

vTb iout

+

vcf

Cf S1

S1'

S1'

S1

1:a

+

_

vTn

Lcoup

_

Lf Rf Vdc

vTc

Figure 12.2 Assumed three-phase converter and passive output filter system for the DDERs and BESs. DDERs, Dispatchable distributed energy resources; BESs, battery energy storage systems.

402

Hybrid Renewable Energy Systems and Microgrids

and reactive powers (denoted respectively by p and q) supplied by the DDER to its point of common coupling can be given by p5

VT Vcf sin ðδcf 2 δT Þ ωLcoup

q5

VT Vcf cos ðδcf 2 δT Þ 2 VT 2 ωLcoup

(12.1)

in which VT is the voltage at the coupling point; Vcf denotes the voltage across capacitor Cf and V+δ denotes v(t)’s phasor notation, whereas ωLcoup denotes the inductive coupling impedance. The average injected active and reactive powers (denoted by P and Q) by a DDER can be determined from p and q and with the help of a low pass filter with a small cut-off frequency [28]. A voltage-controlled or current-controlled strategy can then be employed such that the preferred active and reactive powers are injected by the DDER. For example, assuming a voltage-controlled strategy, a suitable reference has to be determined for V cf and applied to the converter’s inner-loop control to be tracked, by producing the suitable on/off signals for the converter’ switches (see Fig. 12.3 that shows a closed-loop block diagram of this example) [29
31]. Now, consider the microgrid of Fig. 12.4 with two DDERs, supplying a load. Assume that DER-1 and DER-2 are coupled to the load via feeders with impedances of Zline,1 and Zline,2. Assume a highly inductive feeder (i.e., Zline  jωLline). Denoting the voltage at the load’s coupling point by Vload, the active and reactive powers that the load receives from a DDER can be defined from p5

Vload Vcf sin ðδcf 2 δload Þ ωLcoup 1 ωLline

q5

Vload Vcf cos ðδcf 2 δload Þ 2 Vload 2 ωLcoup 1 ωLline

c

b

(12.2)

a

vcf vcf vcf

p Power calculation c

b

q

Low pass filter

P Q

Vcf Droop control

d cf

Reference generation

a vref b vref c vref

Inner-loop 6 control

Converter and LCL filter

a

iout iout iout

Outer-loop control

Figure 12.3 Closed-loop controller of a converter-interfaced DDER. DDERs, Dispatchable distributed energy resources.

Microgrids and their control

403

Zline,1 Lcoup,1 DER DER-1

Zline,2

Lcoup,2 Load DER-2

Figure 12.4 A sample microgrid with two converter-interfaced DDERs and one load. DDERs, Dispatchable distributed energy resources.

The active power and reactive power are decoupled since the feeder is highly inductive. Furthermore, the dissimilarity of the voltage angle between Vcf and Vload is minuscule. Therefore a dc load flow analysis technique can be employed, from which the average active power that the load receives from a DDER becomes P5

Vload Vcf ðδcf 2 δload Þ ωLcoup 1 ωLline

(12.3)

Thus δcf, the angle of the voltage across DDER’s output capacitor, becomes δcf 5 P ðΓcoup 1 Γline Þ 1 δload

(12.4)

where Γ 5 ωL=Vcf Vload . To realize a decentralized power-sharing among DDERs, the DDERs output’s voltage magnitude and angle should be modified under the droop control as [32,33] mδ ðXline ðP 2 Prated Þ 2 Rline ðQ 2 Qrated ÞÞ Zline n ðRline ðP 2 Prated Þ 1 Xline ðQ 2 Qrated ÞÞ Vcf 5 Vrated 2 Zline

δcf 5 δrated 2

(12.5)

where δrated and Vrated are the DDER’s rated voltage angle and magnitude when it injects rated active and reactive powers (denoted by Prated and Qrated), whereas mδ and n are respectively the active power
angle and reactive power
voltage droop coefficients in rad/W and V/VAr. Eq. (12.5) can be updated as δcf 5 δrated 2 mδ ðP 2 Prated Þ Vcf 5 Vrated 2 n ðQ 2 Qrated Þ

(12.6)

if the microgrid’s lines are highly inductive. These two quantities are the outputs of the primary controller of the DDER and form the desired reference for the DDER’s

404

Hybrid Renewable Energy Systems and Microgrids

output voltage (across Cf). Thus the primary controller defines the reference of vcf (t) for a DDER as varef ðtÞ 5

pffiffiffi 2Vcf sin ð2π frated t 1 δcf Þ

(12.7)

as illustrated in the closed-loop block diagram of Fig. 12.3. This voltage will then be employed for the converter’s other two phases with a 6120 degrees phase shift. Alternatively, a frequency droop can be employed instead of an angle droop. In such a case, the desired references, expressed by primary controller, will be f 5 frated 2 m ðP 2 Prated Þ Vcf 5 Vrated 2 n ðQ 2 Qrated Þ

(12.8)

f 5 f max 2 mP Vcf 5 Vrated 2 nQ

(12.9)

or

assuming Qrated 5 0, to define the references as varef ðtÞ 5

pffiffiffi 2 Vcf sin ð2πf tÞ

(12.10)

where frated and f max are respectively the microgrid’s rated and maximum frequency, V max is the microgrid’s maximum voltage, and m (in Hz/W) is the droop coefficient. Fig. 12.5 shows these droop curves schematically. The voltage and frequency of the microgrid are preferred to be retained within the safe zone of Vrated 6 0:5ΔV max and frated 6 0:5Δf max , as seen from this figure, in which 0:5ΔV max and 0:5Δf max respectively denote the maximum allowed (desired) voltage and frequency variation in the microgrid from their rated values. Again, this voltage will be employed for the converter’s other two phases with a 6120 degrees phase shift.

f rated

f max

m2

m1

f rated

P1

P 1

min

V Δf

max

rated

P2

max

rated

max

P2

n2

n1

max

Q 2

Q1

max

Q 1

max

Vrated ΔV max V min

rated

= Q2

max

Q 1

Q max 2

Figure 12.5 Active power
frequency and reactive power
voltage droop curves for two DDERs.

Microgrids and their control

405

Assume that the microgrid’s frequency decreases by Δf max when a DDER’s output active power rises from zero to the rated level. Therefore the DDER’s active power
angle droop coefficient can be given by [17] mδ 5

2π Δf max Pmax

(12.11)

Assuming Δf max the same and fixed for all DDERs with dissimilar ratings yields Pmax mδ;i j 5 max mδ;j Pi

(12.12)

Likewise, presume that the microgrid’s voltage decreases by ΔV max when a DDER’s output reactive power rises from zero to the rated level. As such, the DDER’s reactive power
voltage droop coefficient can be given by [17] n5

ΔV max 2Qmax

(12.13)

Assuming ΔV max the same and fixed for all DDERs with dissimilar ratings yields Qmax ni j 5 max nj Qi

(12.14)

Now, suppose that DDERs i and j within a steady-state microgrid have an equal δrated. Therefore from Eqs. (12.6) and (12.12), one gets δcf;i 2 δcf;j 5 mδ;j Pj 2 mδ;i Pi

(12.15)

Replacing δcf,i and δcf,j from Eqs. (12.4) in (12.15) yields Pi ðΓcoup;i 1 Γline;i Þ 2 Pj ðΓcoup;j 1 Γline;j Þ 5 mδ;j Pj 2 mδ;i Pi

(12.16)

Therefore the relationship of the active power injected by two DDERs becomes Γcoup;i 1 Γline;i 1 mδ;i Pj 5 Pi Γcoup;j 1 Γline;j 1 mδ;j

(12.17)

Eq. (12.17) confirms that a DDER’s output active power is reciprocal to total Γ at observed at its output. Γline and Γcoup depend on the inductances between the load and the DDER. Because Γ includes the square of the voltage magnitude in the denominator, we have Γline ,, Γcoup ,, mδ

(12.18)

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Hybrid Renewable Energy Systems and Microgrids

Thus Eq. (12.17) can be updated as Pj mδ;i  Pi mδ;j

(12.19)

Likewise, the relationship of the reactive powers injected by two DDERs becomes Q j ni  Q i nj

(12.20)

According to the above notion, from Eqs. (12.12), (12.19), (12.14), and (12.20), the relationship of the output active and reactive power of two DDERs becomes equal to the proportion of their power ratings. On the other hand, it is preferred to have a small voltage angle difference across the coupling inductances (i.e., δcf
δT) to lie at the linear part P
δ characteristic of Eq. (12.1). Likewise, it is preferred to observe a small voltage magnitude difference across the coupling inductances (i.e., Vcf
VT). To realize this, the coupling inductances can be selected to be reciprocal to the DDERs’ power relationship as Pmax Qmax Lcoup;i j j 5 max 5 max Lcoup;j Pi Qi

(12.21)

Therefore a suitable power-sharing between two DDERs within a microgrid requires [17] Pmax Lcoup;i Pj mδ;i j  5 5 max Pi mδ;j Lcoup;j Pi Qmax Lcoup;i Qj ni j  5 5 max Qi nj Lcoup;j Qi

(12.22)

Example 12.1: Consider the microgrid of Fig. 12.4 with DER-1 and DER-2 and the technical data of Table 12.1. It is preferred to realize the power ratios of DER-1:DER-2 Table 12.1 Technical data of the network parameter. Network

400 Vrms line
line, 50 Hz, 1 pu 5 6 kVA

Line Load Converter and filter DER-1 DER-2

R 5 0.2 Ω, L 5 10 mH Constant-impedance type Rf 5 0.1 Ω, Lf 5 0.36 mH, Cf 5 50 μF, Vdc 5 350 V, a 5 2 Lcoup 5 1.36 mH, mδ 5 1.5708 rad/kW, n 5 9 V/kVAr Lcoup 5 2.72 mH, mδ 5 3.1416 rad/kW, n 5 18 V/kVAr

DER, Distributed energy resources; pu, per-unit.

Microgrids and their control

407

as 2:1. The microgrid is presumed at the steady-state condition and standalone with a total demand of 0.41 per-unit (pu). This load rises to 1 pu at t 5 0.5 s and then reduces to 0.53 pu at t 5 1 s. The microgrid’s load further decreases to 0.17 pu at t 5 1.5 s. The ratio of the output active powers between the DDERs is upheld as desired throughout all load changes, as seen in Fig. 12.6A. The total electrical demand of the microgrid is shown in Fig. 12.6B. As seen in Fig. 12.6C and D, the microgrid’s frequency and voltage stay within the permissible ranges throughout the study [17
19]. Since usually Lline ,, Lcoup, the active and reactive power that a DDER injects depends on its coupling inductance. Thus, the output active power of the DDERs in Fig. 12.4 can be expressed from Eq. (12.1) as p1 

Vload Vcf;1 sin δcf;1 ωLcoup;1

p2 

Vload Vcf;2 sin δcf;2 ωLcoup;2

(12.23)

The relationship of the average active and reactive powers between two DDERs can be expressed from Eq. (12.23) in the form of Lcoup;2 Vcf;1 sin δcf;1 P1 5 P2 Lcoup;1 Vcf;2 sin δcf;2 Lcoup;2 Vcf;1 cos δcf;1 2 Vload Q1 5 Q2 Lcoup;1 Vcf;2 cos δcf;2 2 Vload

(12.24)

Presume that the preferred ratio of active power between the DDERs is P1/P2 5 kp . 0. From Eq. (12.21), Lcoup,2/Lcoup,1 5 kp. As such, one gets [34
36] Vcf;1 sin δcf;1 5 Vcf;2 sin δcf;2

Figure 12.6 Results for the assumed microgrid.

(12.25)

408

Hybrid Renewable Energy Systems and Microgrids

Also, suppose that the ratio of their reactive power is Q1/Q2 5 kp. Since it is assumed that Lcoup,2/Lcoup,1 5 kp, one gets Vcf;1 cos δcf;1 5 Vcf;2 cos δcf;2

(12.26)

To be able to make the ratio of the output powers of the DDERs independent from that of their output inductances, two internal inductances can be applied to the DDERs by altering the controller of their converters to balance the equivalent inductance with the proportion of the preferred output active and reactive powers. This modification will cause the voltage appearing at each converter’s output to be slightly less than the original level. The primary objective is determining a suitable voltage drop across the internal balancing inductance (Lconv) in such a way that the preferred active and reactive power ratio is realized at the DDERs’ outputs, regardless of the proportion of the installed coupling inductances. To this end, it is preferred that Lcoup;2 1 Lconv;2 P1 mδ;2 5 5 P2 Lcoup;1 1 Lconv;1 mδ;1 Lcoup;2 1 Lconv;2 Q1 n2 5 5 Q2 Lcoup;1 1 Lconv;1 n1

(12.27)

Assuming P1 . P2 and hence, Lcoup,2 1 Lconv,2 . Lcoup,1 1 Lconv,1, one can allocate a very small Lconv,1 and then determine Lconv,2 to satisfy Eq. (12.27) without causing instability of the system. After imposing this balancing inductance value, the phase voltage at each DDER’s output (vref) is defined by [34,35] vref 5 vdroop 2 vconv

(12.28)

where vdroop is the initial voltage determined by the droop equation of Eq. (12.6), whereas vconv denotes the voltage drop across the internal balancing inductance. Expressing Eq. (12.28) in αβ coordinates in the form of α Vref 5 Vcf cos δcf 1 ωLconv jIjsin θ β 5 Vcf sin δcf 2 ωLconv jIjcos θ Vref

(12.29)

where ω 5 2πfrated, whereas |I|+θ is the phasor notation of iout(t), the current flowing through the coupling inductance, in phase-a. After transforming Eq. (12.29) into the polar coordinates, the voltage reference at the converter’s output can be expressed as pffiffiffi (12.30) varef ðtÞ 5 2 Vref sin ðωt 1 θref Þ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi β 21 α α 2 1 V β 2 and θ where Vref 5 Vref ref 5 tan ðVref =Vref Þ. This voltage will then be ref employed for the converter’s other two phases with a 6120 degrees phase shift.

Microgrids and their control

409

Table 12.2 Technical data of the network parameter. Network

400 Vrms line-line, 50 Hz, 1 pu 5 5.8 kVA

Line Load Converter and filter DER-1

R 5 0.2 Ω, L 5 6.4 mH Constant-impedance type Rf 5 0.1 Ω, Lf 5 0.36 mH, Cf 5 50 μF, Vdc 5 350 V, a 5 2 Lcoup 5 30 mH mδ 5 1.5708 rad/kW, n 5 9 V/kVAr Lcoup 5 60 mH (for Fig. 12.7), Lcoup 5 30 mH (for Fig. 12.8) mδ 5 3.1416 rad/kW, n 5 18 V/kVAr

DER-2

DER, Distributed energy resources; pu, per-unit.

Figure 12.7 Results for the assumed microgrid without internal balancing inductance.

Example 12.2: Consider the microgrid of Fig. 12.4 consisting of DER-1 and DER-2 and the technical parameters of Table 12.2. The microgrid is in the standalone mode and at the steady-state condition with a total demand of 0.88 pu. The microgrid’s load decreases to 0.77 pu at t 5 1.5 s, whereas it increases to 0.91 pu at t 5 3 s. Fig. 12.7A illustrates the microgrid’s active and reactive demand. It is preferred to maintain the proportion of the output active and reactive power between DER-1 and 2 as P1/P2 5 Q1/Q2 5 2. To this end, the proportion of their coupling inductances is Lcoup,1/Lcoup,2 5 0.5, whereas proportion of their droop coefficients is mδ,1/mδ,2 5 n1/n2 5 0.5. These assumptions satisfy Eq. (12.25). Fig. 12.7B and C shows that the output active and reactive power ratio between the DDERs is the same as the preferred level as a result of the setting of designed droop coefficients and coupling inductances. Now, assume that the internal balancing inductance is imposed on both DDERs. Assuming the coupling inductance of both DDERs as 30 mH, the internal balancing inductance of DER-1 (i.e., the DDER with a larger output power) is thought to be small (e.g., 0.5 mH) based on which the corresponding internal

410

Hybrid Renewable Energy Systems and Microgrids

Figure 12.8 Results for the assumed microgrid operating with the internal balancing inductance.

balancing inductance of DER-2 is defined to be 31.4 mH from Eq. (12.27). The impact of imposing the internal balancing inductances in realizing the preferred proportion of both active and reactive powers among the DDERs can be seen from Fig. 12.8A and B [36].

12.2.2 Battery energy storage systems’ primary controller Applying conventional droop control already discussed for the BESs has several limitations. With conventional droop control, the output power of BESs is assumed to be proportional to their power capacities, and their stored energy level is not considered. Therefore the period that a BES can supply a load is not controlled, and the BESs with a lower state of charge (SoC) will quickly run out of energy. When the microgrid consists of both BES and DDERs, a modified powersharing algorithm can be employed. Since the stored energy in the BESs decreases over time in discharging mode, the SoC of the BESs can be used instead of their ratings in the power-sharing Eq. (12.23). For this, the BES’s SoC [denoted by SoC(t)] is expressed as Ð int EBES 2 PBES ðtÞdt SoCðtÞ 5 max EBES

(12.31)

int max in which EBES and EBES are the initial stored energy and its maximum capacity, respectively, whereas PBES is the average output active power, measured at its converter’s output. It is preferred to control the BES’ output power as a function of its SoC. To achieve this, the droop control of the BES is dynamically adjusted and modified considering the SoC. Hence, the P
δ droop coefficient of each BES in Eq. (12.6) is updated based on its SoC variations. Therefore the BES with lower SoC supplies less active power. This prevents fast discharge of the BES with lower SoC. Fig. 12.9 shows this approach schematically from which it can be seen that as the BES’s SoC decreases, the operating point of the BES relocates from point A to B, resulting in an output active power reduction for the BES.

Microgrids and their control

411

δ max B

A

C

D

δ min new

PBES PBES

max max PDER PBES

new

PDER PDER

Figure 12.9 Dynamic P
δ droop coefficient change for a DDER and BES together. DDER, Dispatchable distributed energy resource; BES, battery energy storage system.

As the SoC of the BES changes dynamically, this value cannot be used as a droop coefficient. Hence, a discrete value of SoC (SoCd) oversteps of 5%
10% can be applied as [37,38] 8 0:9 > > < 0:8 d SoC ðtÞ 5 ^ > > : 0:2

0:9 # SoC # 1 0:8 # SoC , 0:9

(12.32)

0:2 # SoC , 0:3

The BES will stop discharging if the SoC drops to its minimum acceptable value. Assuming the maximum acceptable depth of discharge for a BES as 70%, the BES max continues to discharge till it reaches 30% of EBES . From Eq. (12.32), the SoC of each BES in the microgrid will be calculated over time. For each BES, the active power
angle droop coefficient is defined as PBES SoCd m0 5 5 P0 SoC0 mBES

(12.33)

in which m0 denotes a global droop coefficient designed for P0 and SoC0 5 1. From Eq. (12.33), for a BES, the droop coefficients can be modified over time as mBES ðtÞ 5

m0 SoC d ðtÞ

(12.34)

Therefore for a microgrid with two BESs, the power ratio equation can be expressed as PBES;1 SoC1 mBES;2 5 5 PBES;2 SoC2 mBES;1

(12.35)

The shortcoming of this approach is that a BES’s output power will change with respect to the reduction of the output power of another BES. To mitigate this

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Hybrid Renewable Energy Systems and Microgrids

Lcoup,3

BES-1

Zline,3 Zline,1

Zline,2

Lcoup,1 DER DER-1

Lcoup,2

Load

DER-2

Zline,4

Lcoup,4

BES-2

Figure 12.10 Assumed microgrid with two DDERs and two BESs. DDERs, Dispatchable distributed energy resources; BESs, battery energy storage systems. Table 12.3 Technical data of the network parameter. Network

400 Vrms line
line, 50 Hz

Line Converter and filter DER-1, DER-2, BES-1, BES-2

R 5 0.5 Ω, L 5 3.2 mH Rf 5 0.1 Ω, Lf 5 0.36 mH, Cf 5 50 μF, Vdc 5 350 V, a 5 2 Lcoup 5 30 mH, mo 5 0.8976 rad/kW, Po 5 3 kW

problem, the droop coefficients of the DDERs can be updated to adjust their output powers when a reduction occurs in the output power of a BES. Fig. 12.9 also illustrates this approach schematically. In this figure, as the SoC of one BES decreases and the operating point of that BES relocates from point A to B, the operating point of the DDER also relocates from point C to D. This results in the DDER picking up the output power reduction of the BES. Under this condition, a BES’s output power only relates to its own SoC and does not get affected by the SoC of other BESs. For this, the droop coefficients of each DDER are calculated from [37,38] N BES

mδ ðtÞ 5 m0 L SoCjd ðtÞ

(12.36)

j51

where NBES represents the number of the BESs within the microgrid, SoCjd accounts for the SoC of BES-j. Example 12.3: Consider the microgrid of Fig. 12.10 with two DDERs and two BESs and with the technical data of Table 12.3. The maximum power capacity of the DDERs (P1 and P4) is assumed to be P1/P4 5 2, whereas the maximum energy capacity of the BESs (P2 and P3) is assumed to be P2/P3 5 1. At the initial condition, it is

Microgrids and their control

413

Figure 12.11 Performance of the assumed microgrid when SoC-based droop is applied to the BESs. BES, Battery energy storage system; SoC, state of charge.

Figure 12.12 Performance of the assumed microgrid when SoC-based droop is applied to the BES and DDERs. DDER, Dispatchable distributed energy resource; BES, battery energy storage system; SoC, state of charge.

expected to have P1/P2 5 1. First, assume that the SoC-based droop technique is applied for the BESs, based on Eq. (12.35), while control of the DDERs relies on conventional droop control. Fig. 12.11A shows that the load demand is kept constant. From Fig. 12.11B, as the SoC of BESs decreases, they produce a lower amount of active power. As an example, at t 5 4.8 s, the SoC of BES-2 drops below 90%; hence, the P
δ droop coefficient of this BES increases, and as a result, the output active power of this BES (i.e., P3) decreases. This output active power reduction is picked up by the DDERs as well as BES-1. The SoC variations of the BESs are shown in Fig. 12.11C. This figure indicates that for the considered period, the SoC of the BES-1 decreases to 43% while the SoC of the BES-2 decreases to 61%.

414

Hybrid Renewable Energy Systems and Microgrids

Now, assume that the SoC-based droop technique is applied for the DDERs, based on Eq. (12.35). Fig. 12.12A shows that the load demand remains constant as before. Furthermore, as expected, when the SoC of BES decreases, they produce a lower amount of active power; however, the output active power reduction of a BES is only addressed by the DDERs, as seen from Fig. 12.12B. The SoC variations of the BESs are shown in Fig. 12.12C. This figure indicates that in the considered period, the SoC of the first BES drops to 52% while the SoC of the second BES drops to 75%, which is improved versus the above case [37,38]. DER, Distributed energy resource; BES, battery energy storage system. d From Eq. (12.35), it is evident that as SoCBES becomes smaller, the output active power of a BES will decrease. Thus a smaller share of the BES’s converter’s capacity (Smax BES ) is used for the flow of the active power. Thus the converter’s vacant capacity can be employed for reactive power exchanging. Therefore a reactive power exchange can also occur on top of active power exchanged between the BES and microgrid. This available capacity in BES’s converter (Qav BES ) is updated over time from Qav BES 5

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2 Smax BES 2 PBES

(12.37)

and later employed to determine the BES’s reactive power
voltage droop coefficient (nBES) in the form of nBES 5

ΔV max 2Qav BES

(12.38)

Note that the stored energy within the BES and its SoC do not get affected by exchanging reactive power between the converter and the microgrid.

12.3

Microgrid’s secondary controller

The secondary controller is the middle level of the microgrid’s hierarchical control scheme and acts as a standalone microgrid’s central controller [16,36]. The central controller interconnects with the network’s tertiary controller and the primary controllers of DDERs, BESs, as well as the local controllers of loads, NDDERs and switches. Its key role is defining the set points and references of the DDERs’ primary controller, which are used later to determine each DDER’s output active and reactive powers. This functionality will be in a slower frame versus the primary controller. As discussed earlier, each DDER not only has to supply a portion of the microgrid’s demand but should also regulate the frequency and voltage in the microgrid when operating in the standalone mode. In addition, the central controller oversees checking these parameters. Thus the central controller will determine new set points for the DDERs’ primary controllers and will transmit

Microgrids and their control

415

them this information if the microgrid’s voltage and frequency drop below or increase above the acceptable thresholds. This procedure must occur quicker than the predefined operation time of the microgrid’s under- or overvoltage and frequency protective systems. The microgrid’s central controller is composed of multiple modules, each with different functions and aims, as discussed below.

12.3.1 Dynamic power ratio adjustment The central controller determines the preferred ratio of the output power the DDERs within the microgrid when operating under both grid-connected and standalone modes. Therefore it can change this ratio dynamically and pass this data to the DDERs’ primary controllers. As an example, suppose the relationship of the output active powers of DER-1 and DER-2 of Fig. 12.4 is kp1 at t 5 t1. Therefore max Pmax 5 m1 =m2 5 kp1 according to Eq. (12.22). Thus from Eq. (12.9), one gets 2 =P1 f1t1 5 f max 2 m1 Pt11

(12.39)

If the central controller modifies this ratio to kp2 at t 5 t2, from Eq. (12.9), one gets f1t2 5 f max 2 m1 Pt12

(12.40)

Assuming the load demand is the same in t1 and t2, the DDERs’ total active power generation will remain unchanged at these two instants. Thus Pt11 1 Pt21 5 Pt12 1 Pt22

(12.41)

Eq. (12.41) can be simplified by replacing the power ratios as Pt11 1 kp1 Pt11 5 Pt12 1 kp2 Pt12 . Pt12 5 Pt11

1 1 kp1 1 1 kp2

(12.42)

From Eqs. (12.39), (12.40), and (12.42), the variation of the microgrid’s frequency because of this ratio change is  Δf 5 t

f1t2

2 f1t1

5

m1 ðPt11

2 Pt12 Þ 5 Pt11 m1

 1 1 kp1 kp2 2 kp1 12 m1 Pt11 5 1 1 kp2 1 1 kp2 (12.43)

This deviation is acceptable if the microgrid’s new frequency stays between fmax and fmin. Thereby, f min # f1t1 1 Δf t # f max . f min 2 f1t1 #

kp2 2 kp1 m1 Pt11 # f max 2 f1t1 1 1 kp2

(12.44)

416

Hybrid Renewable Energy Systems and Microgrids f min 2 f

t1

f max 2 f

t1

Assuming H1 5 m Pt11 and H2 5 m Pt1 1 , to avoid the observance of an 1 1 1 1 unacceptable frequency within the microgrid, kp2 must satisfy [36]   kp1 1 H1 kp1 1 H2 0 , kp2 # min ; (12.45) 1 2 H1 1 2 H2 where min(.) denotes the minimum function. Another analysis can be performed for the microgrid’s voltage magnitude. Suppose the relationship of the output reactive powers of DER-1 and DER-2 of max Fig. 12.4 is kq1 that at t 5 t1. Therefore Qmax 5 n1 =n2 5 kq1 according to 2 =Q1 Eq. (12.22). Under this condition, from Eq. (12.9), one gets V1t1 5 Vrated 2 n1 Qt11

(12.46)

If at t 5 t2, the central controller changes this ratio to kq2, from Eq. (12.9), one gets V1t1 5 Vrated 2 n1 Qt12

(12.47)

Assuming the load demand is equal at t1 and t2, the DDERs’ total reactive power generation remains unchanged at these instants. Thus Qt11 1 Qt21 5 Qt12 1 Qt22

(12.48)

Eq. (12.48) can be simplified by replacing the power ratios as Qt11 1 kq1 Qt11 5 Qt12 1 kq2 Qt12 .Qt12 5 Qt11

1 1 kq1 1 1 kq2

(12.49)

From Eqs. (12.46) to (12.48), the variation of the voltage because of this ratio change is 

ΔV1 5 V1 2 V1 t

t2

t1

5 n1 ðQt11

2 Qt12 Þ 5 Qt11 n1

 1 1 kq1 kq2 2 kq1 5 12 n1 Qt11 1 1 kq2 1 1 kq2

(12.50)

This deviation is acceptable if the microgrid’s new voltage magnitude stays within Vmin and Vmax. Thereby, V min # V1t1 1 ΔV t # V max . V min 2 V1t1 #

kq2 2 kq1 n1 Qt11 # V max 2 V1t1 1 1 kq2 (12.51)

V

t 2 V11 t1 n1 Q1

min

V

t 2 V11 t1 n1 Q 1

max

Assuming G1 5 and G2 5 , to avoid an unacceptable voltage magnitude within the microgrid, kq2 must satisfy [36]   kq1 1 G1 kq1 1 G2 0 , kq2 # min ; (12.52) 1 2 G1 1 2 G2

Microgrids and their control

f rated

m1

f

mnew 2

m2

rated

P2

max

max

P 1

P2

rated new

(P2 )

V

max

f min

rated

P1

417

max

Vrated

n new 2

n1

max

max

rated

Q1

V

max

Q 1

Q 1

Q 2

n2

rated

min

max

Q 2

rated new

= Q 2 = (Q 2

)

Figure 12.13 Schematic illustration of the adjustment of the droop curve to facilitate power ratio change.

Figure 12.14 Results for the assumed microgrid with variable ratios of power between two DDERs. DDERs, Dispatchable distributed energy resources.

DDERs’ f max and Vrated have to be updated accordingly to uphold the microgrid’s frequency and voltage magnitude within the acceptable ranges if kq2 is violated. The principle of the dynamic power ratio update is illustrated schematically in Fig. 12.13. Example 12.4: Consider the microgrid of Fig. 12.4 with two DDERs at the steady-state and standalone condition. Both DDERs are connected via a coupling inductance of 6.8 mH. As seen in Fig. 12.14A, the ratio of their output active power is preferred to be 2:1 initially. Thus as seen in Fig. 12.14B, the ratio of their droop coefficients is determined as m1/m2 5 1:2. Moreover, Prated =Prated 5 2. The central controller alters the ratio of their output active 1 2 powers to 1:1 and transmits this data to the microgrid’s central controller, which changes the ratio of their droop coefficients and rated active powers as m1/m2 5 1 and Prated =Prated 5 1 and then transmits this information to their pri1 2 mary controllers. Likewise, the central controller determines this ratio to be as 1:3 at t 5 1 s. Therefore the ratio of their droop coefficients and rated active powers is determined as m1/m2 5 3 and Prated =Prated 5 1=3 and transmitted to the 1 2 primary controllers. As a result, the preferred ratio of the active powers is observed for the DDERs as seen in Fig. 12.14A [36].

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Hybrid Renewable Energy Systems and Microgrids

12.3.2 Droop curve adjustment For a microgrid operating under the standalone mode, its central controller has to act to increase or reduce the rated levels of the droop curve if and when the microgrid’s frequency or the voltage magnitude drops below or rises above the permissible limits. It is expected that the microgrid’s voltage magnitude and frequency are adjusted to the preferred levels. To this end, it is necessary to determine the variation of the microgrid’s frequency from its preferred level, as well as a new maximum frequency as max fnew 5 f max 1 Δf 5 f max 1 ð fdesired 2 f Þ

(12.53)

Likewise, it is necessary to determine the variation of the microgrid’s voltage magnitude from its preferred level, as well as a new rated voltage as new Vrated 5 Vrated 1 ΔV 5 Vrated 1 ðVdesired 2 V Þ

(12.54)

as schematically illustrated in Fig. 12.15 [36]. Example 12.5: Consider the microgrid of Fig. 12.4 at the steady-state and standalone condition with a total demand of 0.15 pu. Fig. 12.16A shows that the ratio of the DDERs’ output active powers is kept as 2:1, as preferred by the microgrid’s central controller. As seen in Fig. 12.16B, the load increases to 0.4 pu at t 5 0.5 s and consequently, the microgrid’s voltage drops to 0.89 pu (below the permissible level of 0.9 pu), as seen in Fig. 12.16C. On the contrary, as seen in Fig. 12.21D, the microgrid’s frequency remains within the allowed limits of 49
51 Hz. As was discussed earlier, the microgrid’s central controller, which operates slower than the DDERs’ primary controllers, updates Vrated to 1.1 pu for both DDERs at t 5 1 s, as seen in Fig. 12.16E. As a result, the microgrid’s voltage increases to 1 pu. The load increases again at t 5 2 s, thereby the microgrid’s frequency reduces below the permissible level. Therefore, as seen in Fig. 12.16F, the central controller adjusts frated to 51.9 Hz at t 5 2.6 s which leads to the frequency to restore to 50 Hz t 5 2.6 s. As the loads were assumed as constant-impedance type, their active power consumption slightly increases after each increase of Vrated or frated [36]. max

f

m1

m2

fnew f

max

f

min

V n1

n

2

V max new Vrated Vrated V

min

Figure 12.15 Adjustment of the droop curve settings to avoid unacceptable violation in microgrid’s frequency and voltage magnitude.

Microgrids and their control

419

Figure 12.16 Results for the assumed microgrid after droop curve adjustments.

12.3.3 Selection of a suitable internal balancing inductance Assume a microgrid in which two DDERs are sharing the load based on their capacities. To this end, they function with droop coefficients of m 1 , m 2 , n 1 , and n 2 . Once the microgrid’s central controller decides on a new preferred relationship for the DDERs, it proceeds to determine a new set of ratios for their droop coefficients while satisfying Eq. (12.22). The variation of the droop coefficient for a DDER is same as the change of its virtual (assumed) maximum active and reactive power capacities; thereby, a DDER only injects a power lower than the minimum of its virtual and actual capacities. As discussed before, an internal balancing inductance can be employed by the controller of the DDERs’ converters to address this problem. For this, the microgrid’s central controller first defines an optimal internal balancing inductance for the DDER which has to generate the highest power, based on which the suitable internal balancing inductances for the other DDERs will then be calculated from Eq. (12.27). Fig. 12.17 shows the flowchart of the process of selecting the optimal internal balancing inductance for the DDER with the largest power generation [37,38]. To select the optimum Lconv, an objective function can be formulated as OF 5 ω1 εP 1 ω2 εQ

(12.55)

420

Hybrid Renewable Energy Systems and Microgrids

Read DERs, lines and loads data Select balancing inductances Lconv = Lconv-min : ∆Lconv : Lconv-max Define the operating point Define eigenvalues

System stable?

No

Yes Calculate objective function

No

Lconv ≥ Lconv-max Yes

Select the optimal Lconv

Figure 12.17 Flowchart to select the optimal internal balancing inductances.

to determine the difference of the power ratios for various values of Lconv in which εP 5 1 2

Pactual ratio Pdesired ratio

Qactual ratio εQ 5 1 2 desired Qratio

(12.56)

and ω1 and ω2 (e.g., ω1 5 ω2 5 0.5) are weighting coefficients. It is vital that the selected Lconvs does not cause instability for the microgrid. Thus a stability analysis has to be conducted as well, as shown in Fig. 12.17. Fig. 12.18 shows the closedloop control for the DDERs’ converters operating under this mechanism. Example 12.6: Consider the microgrid of Fig. 12.4 in which the power ratio of DER-1:DER-2 varies from 2 to 10. Fig. 12.19A illustrates εP and εQ for various Lconvs from which it can be seen that εQ decreases significantly as Lconv increases, whereas εP observes a negligible increase. The different optimum values for Lconv, determined via the flowchart of Fig. 12.17B, are shown in Fig. 12.19B and illustrate that a decrease of the optimal Lconv leads to an increase in the ratio of the power of two DDERs. Now, assume that the preferred ratio of the powers of DER-1:DER-2 is 3:1. Fig. 12.20A and B shows the output active and reactive powers of both DDERs from which εP 5 0.3% and εQ 5 0.002% are observed. This microgrid has also been

Microgrids and their control

v

v

v p

Power calculation

i

i

421

q i

P

Low pass filter

V

Q

Voltage drop calculation

Droop control d

V V

V Polar transform

d

P

V

m

n

Data from central controller

L

|I|

Reference generation

v v v

Inner-loop 6 control

Converter and LCL filter

θ

Component extraction

Outer-loop control

Figure 12.18 Closed-loop control block diagram of a converter-interfaced DDER with dynamic power-sharing ratio modification capability. DDERs, Dispatchable distributed energy resources.

Figure 12.19 (A) Error of the ratio of the active and reactive power; (B) optimal internal balancing inductance values.

Figure 12.20 The output active and reactive powers of the DERs in the assumed microgrid for various ratios of DER-1: DER-2. DERs, Distributed energy resources.

422

Hybrid Renewable Energy Systems and Microgrids

examined for ratios of 6:1 and 9:1 and their results are shown in Fig. 12.20C
F. Our studies show that εP and εQ are below 1% after determining the optimal internal balancing inductances and imposing them to the DDERs’ converter controllers [37,38].

12.3.4 Corrective controller In general, a microgrid operator has to predict the demand and NDDER’s generation in the day, month, season and year-ahead horizons, to be able to operate the microgrid optimally, often referred to as the long-horizon planning. In addition to this, the microgrid operator can further improve its network’s optimal performance by using 30 or 60-min short-horizon operational schemes. These are illustrated schematically in Fig. 12.21 in which ΔT1 and ΔT2 respectively denote the periods of long- and short-horizon planning. Consider a microgrid consisting of N DER DERs (including N DDER DDERs and NDDER N NDDERs), N BES BESs, and N load loads with demand response capability interconnected through N bus buses and N line lines among which N RL are reconfigurable. The microgrid operator prefers to operate it as standalone under normal operating conditions to maintain its autonomy. However, it allows the microgrid to provisionally couple to N EE external entities [39
41] (if available) to exchange power with under abnormal conditions, such as the overloading or over-generation emergencies, according to the command signal received from the microgrid central controller and considering the power exchange costs. The BESs are assumed to have primary controllers that adjust their charging and discharging power based on their SoC and the command signal received from the microgrid central controller. The demand response program is considered to be applied at the load points, which facilitates shedding nonessential loads or adding future-planned loads, based on the command signal received from the microgrid central controller. The microgrid has Start Fetch actual load and NDDG output

Fetch predicted load and NDDG output

No Time  ΔT3 Yes

No

Violation detected? Yes

Corrective controller

No

Violation predicted? Yes Preventive controller

Transmit data to local controllers

Short-horizon Long-horizon optimization optimization Wait ΔT2

Wait ΔT1

Figure 12.21 Microgrid optimization in long- and short-horizons, as well as very shorthorizon preventive controller and immediate corrective controller.

Microgrids and their control

423

the option of network reconfiguration with the reconfigurable lines (by turning on or off those lines according to the command signal received from the microgrid central controller). The output power of the NDDERs is assumed to be curtailable based on the command signal received from the microgrid central controller. As far as the voltage and frequency remaining within the safe zone of Fig. 12.22, no change is required, but to help the microgrid to minimize its operational costs, the corrective controller can run at discrete intervals in ΔT3 intervals (e.g., 10 min). To this end, it first finds the appropriate operating settings for the DDERs. The operation of DDERs is local, whereas the droop parameters of m, n, f max , and Vrated can change with the command signal from the microgrid central controller [42]. To regulate the frequency and voltage in a microgrid, the corresponding m and n of the DDERs can be modified. Note that, suitable internal level control mechanisms, such as the virtual impedance technique [43], should be used to guarantee proper reactive power-sharing among them. Due to the variability of the load and NDDERs, the microgrid may fall to the undes s s sired regions of Vrated 1 V1 # V # Vmax , Vmin # V # Vrated 2 V1 , frated 1 f1 # f # fmax , s s and fmin # f # frated 2 f1 of Fig. 12.22, in which superscript denotes the stability margin of the voltage and frequency [44
50]. As the voltage and frequency exceed the limits of the safe zone, the microgrid observes a small violation, denoted by a cautionary zone. The microgrid operation in the cautionary zone is not desired but is still permissible. When the voltage and frequency exceed the limits of the cautionary zone, the microgrid experiences an extreme violation beyond the maximum allowable limits for the voltage and frequency, denoted by the extreme zone. As soon as the voltage and frequency violation fall in either of the cautionary or extreme zones, the microgrid needs to be recovered immediately. To this end, the corrective controller analyses the voltage and frequency violation and acts to bring it back to the safe zone, as seen in Fig. 12.22. Thus this technique is eventtriggered for incidents that cause the voltage and frequency of the microgrid to fall

Before

After s X max

Extreme zone X max

Excessive generation or underloading

Xrated+x 1

Normal operation

Cautionary zone

Safe zone

X rated

Overloading or low generation

X rated – x1

Cautionary zone Extreme zone

X min s X min

Figure 12.22 Safe, cautionary, and extreme zones of the voltage and frequency (represented by X) with a sample illustration of the successful operation of the corrective controller.

424

Hybrid Renewable Energy Systems and Microgrids

t

Corrective Local controller controller Data Violation transmission detection

t

Time to operation ofprotective relay

Figure 12.23 Time sequence of actions to resolve the voltage and frequency problem in a microgrid.

Layer-1 Layer-2

Cost increase

m, f max , V rated and NSW P

ex

Layer-3

P BES Layer-4 DR

ΔP , ΔPRC Figure 12.24 Corrective controller’s various action layers.

outside of the safe zone (i.e., abrupt changes in load or generation of NDDERs), or time-triggered at ΔT3 intervals, when the voltage and frequency have stayed within the safe zone for a duration longer than ΔT3 , as seen in Fig. 12.21. This technique is an operation-stage algorithm as it collects the output power of the NDDERs and the consumed power by the loads, which are assumed to be measured at the relevant points by sensors with acceptable accuracies. It analyses the current state of the microgrid and transmits the optimal operating settings to the local controllers of each component to act accordingly. The reaction time of the total process to resolve the voltage and frequency problem must be smaller than the operation time of the under/over the voltage and frequency protective relays that operate following an extreme the voltage and frequency violation, as schematically depicted in Fig. 12.23. The corrective controller selects the best resort (i.e., sets of actions that retain the voltage and frequency within the safe zone). The actions are divided into four layers, based on their operational or hidden costs to the microgrid operator. The layers are schematically depicted in Fig. 12.24 and are [42] G

G

Layer-1: Adjusting the droop coefficient of m for each DDER and the voltage and frequency set points of Vrated and f max for all DDERs, as well as the on/off status of the switches of the reconfigurable lines (swRL ), Layer-2: Adjusting the power imported from or exported to external entities (Pex ),

Microgrids and their control

G

G

425

Layer-3: Adjusting the charge/discharge power of BESs (PBES ), and Layer-4: Adjusting the renewable curtailment of NDDERs (ΔPRC ) and the demand response level of loads (ΔPDR ).

Thus this technique is based on a multilayer scheme, but it does not operate based on the layer sequence. It considers all actions first and then aims to minimize the overall cost by selecting actions that have lower costs. This can be achieved through an optimization process, which can be solved by a metaheuristic approach. Thus if the less expensive layers are enough to resolve the voltage and frequency problems, then the corrective controller will not choose any of the actions in the costly upper layers. Such a mechanism guarantees the elimination of the voltage and frequency problem by those actions that are generally low cost, such as adjusting the droop parameters of DDERs when possible, rather than going to expensive options, like external power exchange with an external entity or more expensive ones such as control of BESs, renewable curtailment or demand response. Note that the cost of power exchange with an external entity is assumed cheaper than that of a BES because more frequent charging and discharging of a BES will reduce its life length and thus will increase its operational cost to the microgrid operator. Similarly, the hidden costs of renewable curtailment and demand response are assumed to be more expensive than power exchange with a BES. For the corrective controller to operate, a communication system is required to transfer data from sensors to the microgrid central controller and return the optimal control variables to the local controllers of DDERs, NDDERs, BESs, loads, as well as the switches of reconfigurable lines and the external entities. Fig. 12.25 schematically shows the communication links for the deployment of this technique. The communication system is preferred to be a point-to-multipoint wireless media [51
54]. An optimization problem is formulated as a mixed-integer nonlinear problem with an objective function in the form of [42] OF 5 ω1 OFtech 1 ω2 OFop 1 ω3 OFsus 1 ω4 OFadq

(12.57)

m ,f max,Vrated BES

DDER Central controller

Switch of reconfigurable line ISS

NDDER

P

BES

P

ex

ΔP

DR

ΔP

RC

sw CL load

Microgrid

Load External enty

P NDDG P Communicaon link

Figure 12.25 Data communication between the microgrid’s central controller and local controllers of various components.

426

Hybrid Renewable Energy Systems and Microgrids

where OFtech , OFop , OFsus , and OFadq are respectively the technical, Poperational,  sustainability, and dynamic adequacy costs. In Eq. (12.57), ω1 to ω4 ωi 5 1 are the corresponding weighting coefficients of each objective function and reflect the importance of each considered criterion. The microgrid operator needs to determine the weightings to satisfy its key performance indices such as minimizing the operational cost and improving technical factors or reliability. Since there is no mathematical approach to define them for complex systems like a distribution network, expert microgrid operators can suggest an acceptable method. A group of experts can contribute to the selection of the weights based on their experience. Eventually, the weighting of each criterion will be computed as the average of the weights proposed by the experts for that specific criterion [55
58]. Technical objective function: This objective function eliminates those sets of actions that violate any of the constraints. It also aims to define a set of actions that has the least voltage and frequency deviation when multiple sets of actions have equal operational costs. It is derived as OFtech 5 VDI 1 FDI 1 Penalty

(12.58)

where VDI and FDI are respectively the voltage and frequency deviation indices, and Penalty is a large number to eliminate those sets of actions that do not satisfy all considered constraints in Eq. (12.78) and are determined as VDI 5 maxðjVrated 2 Vi jÞ

’ iABUS

  FDI 5 frated 2 f   Penalty 5

106 0

(12.59a) (12.59b)

if any constriant is unsatisfied otherwise

(12.59c)

where max(.) is the maximum function while BUS is the set representing all buses. Operational objective function: This objective function presents the operational cost of the microgrid and is defined as OFop 5 Cgen 1 CBES 1 Cex 1 Csw 1 Closs 1 CDR

(12.60)

where Cgen is the power generation cost by DERs; CBES is the life loss cost of BESs; Cex is the energy trading cost with external entities; Csw is the cost incurred from switching reconfigurable lines; Closs is the cost corresponding to power losses in microgrid lines; and CDR is the cost of sacrificing customer comfort by controlling their loads under demand response. Cgen is calculated as Cgen 5

X  Pi fueli Cifuel 1 CiO&M 1 Cilife ΔT 1 Cistart ’ iADER

(12.61)

Microgrids and their control

427

in which DER is the set representing all DERs, whereas fuel and Cfuel are respectively the DERs’ fuel consumption (in L/kWh) and corresponding cost (in $/L) over the period of ΔT (in h); ), C O&M denotes its operation and maintenance cost (in $/hr), while Clife and respectively represents the life loss cost of the DERs and their start-up cost (if applicable), formulated as Cilife 5

Cicap ’ iADER Tiop

Cistart 5 CostiST xi

(12.62a) (12.62b)

in which, T op and Ccap are respectively the total operation lifetime of the DER (in h) and its capital cost (in $), whereas CostiST is the starting-up cost of a DER (in $) and x 5 1 if a DER starts up; otherwise, it is zero. CBES is considered as [59
61] CBES 5

X C cap Pi i

Eilife

ΔT ’ iABES

(12.63)

in which BES is the set representing all BESs while the charging or discharging power of a BES (in kW), its total cumulative throughput in its life cycle (in kWh) BES and its capital cost (in $/kWh) are respectively denoted by PBES , Elife , and Ccap . Cex is assumed as Cex 5

P

imp exp ðPex ðCostiex Þimp 2 ðPex ðCostjex Þexp ΔT ’ iAEEimp ; ’ jAEEexp i Þ j Þ

(12.64) where EEimp and EEexp are two sets, representing those external entities that the microgrid is respectively importing power from and exporting power to, while Costex is the power exchange costs (in $/kWh). Csw is derived from Csw 5 Nsw Costsw

(12.65)

where Nsw and Costsw are respectively the total number of switchings (i.e., connecting a line to or disconnecting it from the microgrid), and its corresponding cost (in $/switching). In this study, it is assumed that a connection or disconnection of a reconfigurable line involves closing or opening of two switches, at its two ends. Closs is defined as Closs 5 Ploss Costloss ΔT

(12.66)

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Hybrid Renewable Energy Systems and Microgrids

in which Ploss is the total power loss of the microgrid lines (in kW), calculated by the power flow analysis, and Costloss denotes the associated cost (in $/kWh). CDR is calculated as [62
66] CDR 5

P

ΔPDR i

shed 

CostiDR

shed

1

future P DR future ΔPj CostjDR ’ i; jADR (12.67)

where DR is the set representing those loads with demand response feature; ΔPDR is the amount of loads modified under the demand response (in kW) in which superscripts shed and future respectively denote those nonessential loads that have been shed or the additional future-planned loads that are turned on, whereas CostDR represents the corresponding costs (in $/kWh). Sustainability objective function: This objective function considers the cost associated with renewable curtailment, emissions from DERs, the contribution of renewable energy in the microgrid and microgrid’s dependency on external entities. It is formulated as OFsus 5 CRC 1 Cem 1 CEDI

(12.68)

in which CRC is the cost of curtailing renewable-based NDDERs; CEm is the emission cost of DERs; and CEDI is the cost of microgrid’s dependency on external entities. CRC is considered as CRC 5

X

RC ΔPRC i Costi ΔT ’ iANDDER

(12.69)

where NDDER is the set representing the NDDERs, and ΔPRC and CostRC are respectively the amount of renewable curtailment (in kW) and its associated cost (in $/kWh). CEm is assumed as Cem 5

X

Pi Emi Costem ΔT ’ iADER

(12.70)

in which Em and Costem are respectively the emission level of a DER for electricity generation (in kg/kWh) and corresponding cost (in $/kg). CEDI is calculated as CEDI 5 CostEDI EDI

(12.71)

in which EDI is an index representing the microgrid’s dependency on external entities, and is formulated as P ex jSi j EDI 5 P load ’ iAEE; ’ jALOAD Sj

(12.72)

Microgrids and their control

429

where Sex and Sload are respectively the apparent power that the microgrid exchanges with all EEimp and EEexp (denoted by the EE) and consumed by the loads (denoted by the LOAD), and CostEDI in Eq. (12.71) is its corresponding cost (in $). Dynamic adequacy objective function: This objective function considers the dynamic supply adequacy of the microgrid and reflects the probability of energy not supplied within the microgrid, as well as the microgrid’s capability to survive through sudden increases of its demand or decreases of the output power of its NDDERs or even the unexpected loss of any DERs, referred to as spinning reserve. It is considered as OFadq 5 CENS 1 CSR

(12.73)

to maximize the spinning reserve and minimize the energy not supplied to ensure maximum dynamic supply adequacy. In Eq. (12.73), CENS and CSR are respectively the cost of energy not supplied and low spinning reserve. CENS is assumed as CENS 5 ENSCostENS ΔT

(12.74)

where ENS shows the energy not supplied (in kW) and is derived from the given availability (Av) of DERs, BESs, and external entities from ENS 5

X

ð1 2 Avi ÞjPi j ’ iADER; BES; EE

(12.75)

where CostENS is its corresponding cost (in $/kWh). CSR is defined as CSR 5 SRICostSR ΔT

(12.76)

in which SRI represents the readily available spinning reserve within the microgrid, calculated from P Pi SRI 5 1 2 P max ’ iAROT-DDER Pi

(12.77)

where ROT-DDER is the set representing the rotating type DDERs and CostSR is the corresponding cost of spinning reserve (in $/hr). The OF in Eq. (12.57) has to be minimized subject to the technical constraints of VDI # ΔV max ’ iABUS

(12.78a)

FDI # Δf max

(12.78b)

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Hybrid Renewable Energy Systems and Microgrids

Ii # Iimax ’ iALINE

(12.78c)

SRI # SRI max

(12.78d)

RCI # RCI max

(12.78e)

EDI # EDI max

(12.78f)

Ploss # Pmax loss

(12.78g)

 DDER max  max Pi # PDDER # PDDER ’ iADDER αDDER i i i

(12.78h)



off , ðTDDER Þoff PDDER 5 0 if tDDER DDER on P . 0 if tDDER , ðTDDER Þon

( up max  DDER  if PDDER . PDDER t t2Δt DDER P  # RRDDER down 2 P t t2Δt max DDER RRDDER if Pt , PDDER t2Δt

(12.78i)

(12.78j)

in which Eqs. (12.78a) and (12.78b) show the maximum allowed deviations in voltage and frequency of the microgrid and Eq. (12.78c) represents the maximum thermal limits of all microgrid’s lines (denoted by LINE) when conducting a current. Eq. (12.78d) defines the maximum loading of the rotating active DDERs to satisfy its minimum spinning reserve, whereas Eq. (12.78e) represents the maximum allowable contribution from NDDERs, denoted by RCI and defined as P RCI 5

P Pi 1 Pj P ’ iANDDER; ’ jABES; ’ kALOAD Pk

(12.79)

On the other hand, Eq. (12.78f) defines the microgrid’s maximum acceptable dependency on the external entities, whereas the maximum allowable loss is set by Eq. (12.78g). The dispatching constraints of DDERs (i.e., their minimum loading, minimum downtime, minimum uptime, maximum ramp-up rate and maximum ramp down rate) are given in Eqs. (12.78h)
(12.78j) in which αDDER is the percentage of minimum loading of a DDER based on its efficiency constraints (defined in the manufacturer’s specifications); ðTDDER Þoff and ðTDDER Þon denote respectively the required time for a DDER that cannot turn on once it turns off and the required time that it has to operate after turning on; and RRmax DDER represents the maximum ramping-up/down rate of a DDER. It is to be noted that DDERs may include both rotating and converter-based DERs. Hence, the parameters of TDDER and RRmax DDER can be different from one DDER to another based on their type and operating principles.

Microgrids and their control

431

The boundaries of the assumed control variables (if applicable) are max max   # Pi # Pdischarge ’ iABES 2 Pcharge i i

(12.80a)

 max  exp max 2 Pimp # Pex ’ iAEE i i # Pi

(12.80b)

2

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2  ex 2ffi  ex 2  ex 2ffi ex Sex 2 P # Si 2 Pi # Q ’ iAEE i i i

(12.80c)

RC NDDER ’ iANDDER 0 # ΔPRC i # α Pi

(12.80d)

 shed  shed 0 # ΔPDR # αshed Pload ’ iADR i i

(12.80e)

 future  future 0 # ΔPDR # αfuture Pload ’ iADR i i

(12.80f)

in which the charging and discharging active power limit of BES is denoted by Eq. (12.80a), whereas the maximum allowed limit of exchanging active and reactive powers with the external entities is defined in Eqs. (12.80b) and (12.78c). The boundary of renewable curtailment is described by Eq. (12.80d) in which αRC is the allowable percentage of renewables that can be curtailed, whereas the allowed range of load-shedding and turning on additional loads under demand response are presented by Eqs. (12.80e) and (12.80f) denoted respectively by αshed and αfuture . Example 12.7: Consider the microgrid of Fig. 12.26 with two reconfigurable lines (i.e., line-1 and 2) and one connection to a neighboring microgrid. DDER-1 and DDER-2 are assumed respectively as biomass and diesel-driven synchronous generators, whereas DDER-3 is considered as a solar-driven, converter-based DER (with a power smoothing storage that makes it dispatchable). Thus DDER-1 and DDER-3 are the assumed renewable-based DDERs. NDDER-1 and NDDER-2 are

Figure 12.26 Assumed microgrid structure.

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Hybrid Renewable Energy Systems and Microgrids

thought as small-scale converter-based wind turbines operating under maximum power tracking scheme and thus are nondispatchable. The desired limits (safe zone) of the voltage and frequency are supposed as 1 6 0.05 pu and 50 6 0.5 Hz while s s s s Vmax 5 1.1 pu, Vmin 5 0.9 pu, fmax 5 51 Hz, and fmin 5 49 Hz. Furthermore, it is assumed that ω1 5 ω2 5 ω3 5 ω4 : Table 12.4 lists the assumed technical parameters and coefficients, as well as the costs of each action. Now, consider a 60-min period with the variations in its demand and NDDERs, as shown in Fig. 12.27A. Also, assume ΔT3 5 10 min. The variations of microgrid’s frequency in this period with and without the corrective controller in operation are depicted in Fig. 12.27B. As seen from this figure, the first time-triggered operation of the corrective controller occurs at t 5 11 min. There is an event of over frequency (f 5 50:62 Hz) at t 5 18 min as the microgrid demand decreases while the output of NDDERs increases. Thus the corrective controller acts promptly and brings down the frequency to 50.5 Hz (boundary of the safe zone). In the following 10 min, frequency remains within the safe zone; hence, another time-triggered action takes place at t 5 29 min to optimize the microgrid’s operation. At t 5 35 min, the microgrid’s demand increases while the output power of NDDERs decreases, which results in the frequency drop to 49.25 Hz. After sensing the frequency violation, the corrective controller takes an immediate action and brings the frequency up to 50 Hz. If corrective controller was not employed in the microgrid over this period, the microgrid’s frequency would have been lower than 49.5 Hz for a duration of 10 min (i.e., 30 , t , 40 min). As there are no significant events to violate the voltage and

Table 12.4 Assumed technical parameters, costs and coefficients. Parameters

DD ER-1

DD ER-2

DD ER-3

Costs

αDDER ½%  DDER max ½kW P ðTDDER Þoff ½min ðTDDER Þon ½min  max up RRDDER ½kW=s  max down ½kW=s RRDDER fuel ½L=kWh Cfuel ½$=L CO&M ½$=h Ccap ½$=kW T op ½h Em ½kg=kWh Cst ½$

10 7 5 15 10 10

10 7 5 15 10 10

0 7 0.5 0.5 60 60

ðCostex Þimp ðCostex Þexp

CostENS

0.3 $/kWh 0.3 $/kWh 0.04 $/kWh 25 $/kWh 0.037 $/kg 20 $/kWh

0.2 0.8 0.05 400 12,000 0 7

0.25 1 0.05 500 15,000 0.014 8

0 0 0.01 700 0 0 0

CostSR ðCostDR Þshed ðCostDR Þfuture BES Ccap Costsw CostEDI

30 $/hr 3 $/kWh 2 $/kWh 800 $/kWh 0.1 $/switching 20 $

Costloss CostRC Costem

AvNDDER 5 0:7; AvBES 5 0:9; AvEE 5 0:85; AvDDER-1 5 0:85; AvDDER-2 5 0:85; AvDDER-3 5 0:7; Elife 5 1; 950 kWh; RC I max 5 25 A; SRI min 5 10% of load; RCI max 5 50%; EDI max 5 20%; Pmax 5 50%; αfuture 5 30%; loss 5 10% of load; α αshed 5 50%; DDER, Dispatchable distributed energy resource.

Microgrids and their control

433

(A) 2

15

1.5

10

1

NDDG

Output power of NDDGs

[kW]

2.5

Demand

20

∑P

load ∑P [kW]

25

5

0.5

0

0 60 Time [min]

1

10

20

30

40

50

(B)

f [Hz]

51

With corrective controller

50.5

Without corrective controller

50

49.5 49 ΔT3

ΔT3 1

10

20

ΔT3 30

40

ΔT3 50

60 Time [min]

Figure 12.27 (A) Microgrid’s operation with and without the corrective controller.

frequency for t . 35 min, the corrective controller only takes time-triggered actions every 10 min for the remaining period of study [42].

12.3.5 Preventive controller Forecasting the demand is a vibrant research topic for distribution networks. Different techniques are proposed in the literature to estimate the demand of a network under a day, month, or season horizon. Load-forecasting in a microgrid is more difficult than larger distribution networks because a smaller group of loads observe higher randomness. Furthermore, due to the limited capacities on the user side, the load characteristics have fewer smoothing effects, leading to the higher load fluctuations. Therefore several approaches have been suggested in the literature to further improve the accuracy of load-forecasting within microgrids. On the other hand, along with the rapid growth of renewables in distribution networks, there has been increasing research for more accurate forecasting of the variability of renewables such as wind and solar. Various approaches are introduced to predict the output power of solar and wind systems based on the different scales of the forecast horizon. Current approaches for short-horizon prediction of uncertain elements of a microgrid (i.e., load consumption and renewable generations) can be used as a preventive controller that not only avoids the violation of the voltage and frequency within the microgrid, but also results in its optimal operation. Such a preventive controller needs to analyze the predicted state of the microgrid, using the short-horizon prediction data of demand and NDDERs’ generation, and then define the most optimal control variables (as illustrated schematically in Fig. 12.21) to operate the microgrid. Those optimal operating

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Hybrid Renewable Energy Systems and Microgrids

settings are then transmitted to the local controllers of each component to act accordingly using a communication link, as seen in Fig. 12.25. An optimization problem is formulated in the form of a mixed-integer nonlinear problem with an objective function in the form of X OF 5 πs OF s ’ sAS (12.81) where s s s s s OF s 5 ω1 OFtech 1 ω2 OFop 1 ω3 OFsup 1 ω4 OFrel 1 ω5 OFenv 1 Penalty

(12.82)

in which OFtech 5 VDI 1 FDI 1 LCL 1 SRI 1 RCI 1 EDI

(12.83a)

OFop 5 Cgen 1 Closs

(12.83b)

OFsup 5 Cex 1 CBES 1 Csw 1 CDR

(12.83c)

OFrel 5 CENS

(12.83d)

OFenv 5 Cem 1 CRC

(12.83e)

in which LLI represents the microgrid’s lines loading limit and is formulated as 8

< max Ii 2 1 max Ii LLI 5 : 0

' Ii . Iimax

’ iALINE

(12.84)

otherwise

and helps to temporarily allow the line currents exceed the maximum limit in the case of emergency (if required). Since the prediction data used may have some inaccuracies, the preventive controller assumes multiple scenarios in Eq. (12.81). Considering these scenarios allows the microgrid operator to consider the probable prediction error of the demand and generation of NDDERs. Table 12.5 lists the assumed scenarios. As seen from Eq. (12.81), each scenario is denoted by s and thought to have a probability of πs while S is the set representing all scenarios.

Table 12.5 Assumed scenarios. s P load P P iNDDER Pi

1

2

3

4

5

6

7

8

9

X Y

X1e Y1e

X2e Y2e

X1e Y2e

X2e Y1e

X1e Y

X2e Y

X Y1e

X Y2e

X 5 Predicted demand; Y 5 Predicted generation of NDDERs’; e 5 Prediction error.

Microgrids and their control

435

Example 12.8: Consider again the microgrid of Fig. 12.26 with the technical data of Table 12.4. It is assumed that all scenarios have the same probability (i.e., π1 5    5 π9 ) while the assumed prediction error is e 5 15%. Also, the assumed weightings in Eq. (12.82) are ω1 5 ω4 5 0:1, ω2 5 ω3 5 0:3, ω5 5 0:2: Consider the operation of the considered microgrid over a sample 60-min period, with the assumed variations in its demand and NDDERs, as shown in Fig. 12.28A. In this study, the forecast horizon is assumed as 5 min. As seen in Fig. 12.28B and C, at t 5 13 min, a frequency rise beyond the safe zone is forecasted for t 5 18 min.

[pu]

1.2 0.8

0.08

0.4

0.04 0

[Hz]

0 (B) Frequency

51 50.5 50 49.5 49

safe zone

With preventive controller

(C) VDI 0.07 [pu]

0.12 [pu]

(A) Demand and NDDGs’ generation ∑P NDDG ∑P load

Without preventive controller

0.05 0.03 0.01 (D) Droop coefficients

[pu]

0.14

m1 m2 m3

0.1 0.06 0.02

(E) Droop set points [pu]

1.05

f max max V

1.03

1.01 0.99

(F) Power transaction with external entity

[pu]

0.06 0.04 0.02 0

0

10

20

13 18

30

40

50

60 t [min]

28 33

Figure 12.28 Microgrid operating under the preventive controller.

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Hybrid Renewable Energy Systems and Microgrids

Therefore the preventive controller is activated promptly to prevent the prospective frequency deviation. The controller modifies the droop coefficients of the DDERs to a new optimal level (see Fig. 12.28D) to reduce the output power of DDER-2 and DDER-3 while the frequency droop set point of f max is also slightly reduced (see Fig. 12.28E) which results in retaining the voltage and frequency within the safe zone (see Fig. 12.5B and C). Likewise, at t 5 28 min, an increase in load and drop in NDDER’s power are forecasted for t 5 33 min, which would result in the voltage and frequency drop beyond the safe zone (see Fig. 12.28B and C). To prevent this, the preventive controller adjusts the power dispatch from the DDERs to new optimal levels by changing their corresponding droop coefficients while increasing the voltage and frequency droop set points (see Fig. 12.28D and E). However, these actions are not enough to prevent the undesired region operation. In addition, as depicted in Fig. 12.28F, a power of 0.015 pu is needed to be imported from the external entity. The preventive controller does not choose to request support from the expensive actions such as exchanging power with the BESs, demand response or renewable curtailment in these instances. This example illustrates the effectiveness of the preventive controller in retaining the voltage and frequency of the microgrid within the safe zone at the least cost.

12.4

Network’s tertiary controller

Tertiary control is the network’s highest level of control and interacts with the microgrids’ central controllers, as well as the circuit breakers and the protective relays within the system. Overall, this controller can use demand response, loadforecasting and electricity market data, to realize an optimal power flow amongst the microgrids and the grid. Moreover, this controller can employ the data transmitted from circuit breakers to determine the system’s overall status and configuration. Under such a condition, an agent coordinating the microgrids has to determine the necessity of the microgrids’ coupling and those circuit breakers that should open/ close to transmit the suitable activation command signals. Moreover, the isolation of the coupled microgrids from each other will be determined by this controller. Fig. 12.29 illustrates schematically this control system.

12.4.1 Self-healing capability Smart grid is a term referred to the improved condition of existing electric networks into a more reliable, efficient, sustainable, and customer interactive status by properly adding advanced metering, protection and communication infrastructures. Among its various features, self-healing is a key attribute, which is mainly driven by the requirement to improve system reliability. In a self-healing network, it is expected that the network can continuously detect, analyze, and respond to faults and restore feeders with minimum human intervention. Therefore in the case of a fault in the network, normal operation can be restored in different feeder sections by properly isolating only the faulted subsections such that the amount of the affected loads is minimized. Feasibility

Microgrids and their control

437

Tertiary controller Unit commitment

Electricity market

Load forecast

Central controller

Economic dispatch

Weather forecast

Microgrid Central controller

Grid

Central controller

Circuit breaker Primary DER 2 controller

Primary controller

Load

DDER

BES

Figure 12.29 Representation of the hierarchical controller for microgrids. CBM2

MG-2

CB M2

MG-3

MG-5 CB M1

CB M1

CB M1

CB M1

MG-7

CB M1

CBM3 CB M3

MG-1

MG-6 Zone-2

Grid CB G

CB M2

CB M2

CB M3

CB M3

MG-4 Zone-1

MG-8

MG-9

MG-10

Zone-3

Figure 12.30 A feeder with self-healing capability containing several microgrids.

studies conclude that integration of self-healing capabilities in future smart grids will bring high benefits to both utilities and customers by reducing the number of affected customers, as well as the amount of unsupplied energy. Assume a feeder with self-healing capability, as shown in Fig. 12.30. The assumed network contains 10 microgrids, which can be interconnected at certain times, to meet the power demand requirements of each other. Each microgrid is a combination of several DERs and loads. The network is divided into three zones by proper installation and coordination of circuit breakers CBG, CBM1, CBM2, and

438

Hybrid Renewable Energy Systems and Microgrids

CBM3 and deploying the required protection and communication infrastructure. Now, assume a fault occurs on the feeder. Based on the fault location, three separate cases can be identified as shown next [17
19]: G

G

G

Case-A (fault on the feeder within Zone-2): It is expected that CBM2 opens while all other circuit breakers are closed. In such a case, the DERs in MG-5, MG-6, and MG-7 have to independently supply their local load demand, and a load-shedding is required if their generation capacity is less than their load demand. Case-B (fault on the feeder within Zone-1): It is expected that CBG and CBM1 are open while all other circuit breakers are closed. In such a case, the DERs in MG-1 to MG-4 must independently supply their local load demand. However, as Zone-2 and Zone-3 microgrids are interconnected, they can share the load demand together, and this will prevent or reduce the load-shedding for the loads of these two zones. Case-C (fault on the upstream of Zone-1): It is expected that CBG opens while all other circuit breakers are closed. In such a case, the microgrids in all three zones are interconnected, and their DERs will share the load demand altogether. Hence, the load-shedding for the loads of these three zones will be prevented or reduced.

Example 12.9: Consider the feeder shown in Fig. 12.31 connected to two microgrids (i.e., MG-1 and MG-2) through distribution transformers. Assume that MG-1 has three DDERs (i.e., DER-1 to DER-3) and five loads, whereas MG-2 has two DDERs (i.e., DER-4 and DER-5) and several loads. In the standalone mode, CBM1 and CBM2 are open, and the DDERs of each microgrid will be sharing the loads of that microgrid separately. Now, if the load demand in a microgrid is higher than the power generation capacity of the DDERs in that microgrid, load-shedding must be applied to some of the nonessential loads in that microgrid. Now, assume a scenario in which the power Grid

CBG

Feeder PG , QG

CB M1

CB M2

PF2 QF2

PF1 QF1 MG-2

DER-5

DER-4

DER-1

DER-2

MG-1

Figure 12.31 Considered network and microgrid structure.

DER-3

Microgrids and their control

439

generation in MG-2 is less than its demand, whereas the power generation capacity in the DDERs of MG-1 is higher than its demand. Assuming the network has self-healing capability, CBM1 and CBM2 can be closed while CBG remains open. In this way, the two microgrids will be interconnected together. Hence, the DDERs in MG-1 can share some of the loads in MG-2 to prevent/reduce load-shedding. Consider the system of Fig. 12.31, at t 5 0, with circuit breakers CBG, CBM1 and CBM2 closed, and the network is in the steady-state condition. Now, assume that due to a fault in the grid, after the self-healing process, CBG is opened while CBM1 and CBM2 are closed at t 5 1 s. For simplicity, the operation of the protection devices and circuit breakers and resynchronization of interconnecting microgrids are not considered. At t 5 2 s a load increase of 25% and at t 5 3 s a load decrease of 25% are also applied in MG-1. The total active power supply from the grid in addition to the active power flow into each microgrid is shown in Fig. 12.32A. From this figure, MG-2 has a negative power flow and is delivering 20% of the load demand in MG-1. The active power output of all DDERs in MG-1 and MG-2 is shown in Fig. 12.32B and C. All DDERs in MG-1 and MG-2 are sharing the load demand of the system of interconnected microgrids proportional to their ratings.

Figure 12.32 Results for the system of coupled microgrids.

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Hybrid Renewable Energy Systems and Microgrids

Figure 12.33 Results for the system of coupled microgrids.

Now, assume a case in which the DDERs of MG-2 are running in their maximum capacity (i.e., 4 and 8 kW, respectively). Also assume at t 5 1 s, due to a fault in the grid, and after the self-healing process, CBG is opened while CBM1 and CBM2 are closed. A 25% load increase and decrease in the network are applied at t 5 2 s and 3 s, respectively. For this case, the total active power supply from the grid in addition to the active power flow into each microgrid is shown in Fig. 12.33A). From this figure, it can be seen that MG-1 has a negative power flow and is delivering, approximately, 30% of the load demand in MG-2. The active power output of all the DDERs in MG-1 and MG-2 is shown in Fig. 12.33B and C. It can be seen that all DDERs in MG-2 are running in their maximum rating at all times while the DDERs in MG-1 are sharing the rest of the load proportional to their ratings [17
19].

12.4.2 Coupling of two microgrids Coupling of microgrids allows the proliferation of the DDERs penetration in distribution networks. Assuming such a scheme, each overloaded microgrid may be externally supported by one or a group of its neighboring microgrid(s). This can

Microgrids and their control

441

ISS

MG-1

MG-2 Load

DDER

MG-1 central controller

DDER Tertiary controller

Load

MG-2 central controller

ISS Load

DDER Coupled microgrid

DDER Tertiary controller

Load

MG-2 central controller

Figure 12.34 Operation of two microgrids in normal and overloaded condition, under the OMT. OMT, Overload management technique.

be realized by closing the normally open interconnecting static switches (ISSs), positioned between the microgrids (see Fig. 12.34). To this end, the overload management technique (OMT) needs to facilitate the interconnection of the microgrids. The OMT appears as a module of the network tertiary controller. The system of a standalone microgrid with an OMT offers more adaptability when overloaded. By coupling the microgrids with the help of the OMT, a portion of the microgrid demand will be supplied by the neighboring microgrid, and this will result in a reduced necessity and level of load-shedding [68
73]. Assume that MG-1 in Fig. 12.34 is overloaded, whereas MG-2 has surplus power. Under these conditions, the ISS between these microgrids must close so that the DDERs of MG-2 supply a percentage of MG-1 demand. To this end, first, the overloaded microgrid should be detected, and then, an interconnection after which the system of coupled microgrid may experience a power mismatch must be prevented. Once the condition and constraint for the interconnection are fulfilled, the OMT sends a command to the ISS to close after proper synchronization. Fig. 12.35 illustrates the operation flowchart of the OMT [68
73]. The microgrids are desired to interconnect when the unused active power capacity (UAPC) of MG-1 becomes lower than α2 times of the total active power capacity of all the DDERs of that microgrid (Pmax MG-1 ), that is, Pmax MG-1 2

NX DDER j51

PDER-1j , α1 Pmax MG-1

(12.85)

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Hybrid Renewable Energy Systems and Microgrids

Start Open ISS

Fetch fMG1, fMG2 and PTie-ine

Close ISS

Yes

Synchronization

ISS closed?

Yes

(12.97) or (12.98)

No

violated?

No Yes

(12.90) and (12.94)

No

satisfied?

Figure 12.35 Flowchart of the OMT. OMT, Overload management technique.

Since all DDERs have the same Δf, Eq. (12.85) is fulfilled when the UAPC of each DDER within MG-1 becomes lower than α1 times of the capacity of that DDER, that is, max Pmax DER-1j 2 PDER-1j , α1 PDER-1j

(12.86)

Henceforth, the criterion for the interconnection of these microgrids can be streamlined as PDER-1j . ð1 2 α1 ÞPcap DER-1j

(12.87)

As a result, the interconnecting necessity of two microgrids can be concluded by only considering the loading of one DDER in place of all the DDERs. Employing Eq. (12.87) in Eq. (12.9), the frequency of MG-1 becomes f , f max 2 mDER-1j ð1 2 α1 ÞPmax DER-1j

(12.88)

or f , f max 2

f max 2 f min ð1 2 α1 ÞPmax DER-1j Pmax DER-1j

(12.89)

Therefore overloading of MG-1 can be clear if the frequency at MG-1 side of the ISS (fMG-1) fulfills fMG-1 , f min 1 α1 Δf max

(12.90)

After interconnection, it is desired to have 2 X i51

Pmax MG-i 2

2 NX DDER X i51

j51

PDER-ij . α1

2 X i51

Pmax MG-i

(12.91)

Microgrids and their control

443

to avoid overloading of the coupled microgrids. Using Eq. (12.87) in Eq. (12.91) gives 2 X

Pmax MG-i 2

2 NX DDER max X f 2 fMG-i

i51

i51

mDER-ij

j51

. α1

2 X

Pmax MG-i

(12.92)

i51

which can be rewritten as ð1 2 α1 Þ

2 X

Pmax MGi 2

i51

NX DDER max

f

2 fMG-1

mDER-1j

j51

.

NX DDER max

f

j51

2 fMG-2 mDER-2j

(12.93)

Because f max
fMG-1 is equal for all DDERs of MG-1 and f max
f MG-2 is similar for all DDERs of MG-2, Eq. (12.93) can be expressed in the form of fMG-2 . f

max

2

ð1 2 α1 Þ

P2 i51

max Pmax 2 fMG-1 Þ MG-i 2 ðf PNDDER 1 j51 mDER-2j

PNDDER j51

1 mDER-1j

(12.94)

which is considered as the constraint for the interconnection. Therefore two microgrids will be interconnected when Eqs. (12.90) and (12.94) are both satisfied [68,69]. After the interconnection of the microgrids, the coupled microgrid has a frequency of fCMG and active power of PTie-line is supplied from MG-2 to MG-1. Thus the power consumed in MG-1 (PMG1) becomes PMG1 5

NX DDER

PDER-1j 1 PTie-line

(12.95)

j51

The coupled microgrid must be divided into two standalone microgrids if PTie-line becomes less than a limit. Substituting Eq. (12.95) in Eq. (12.85) gives Pmax MG-1

2

NX DDER max

f

j51

2 fCMG

mDER-1j

! 1 PTie-line , α1 ð1 1 α2 ÞPmax MG-1

(12.96)

which can be rewritten as [13,19] max PTie-line . Pmax 2 fCMG Þ MG-1 ð1 2 α1 ð1 1 α2 ÞÞ 2 ð f

NX DDER j51

1 mDER-1j

(12.97)

Also, the ISS must remain closed if fCMG is above a limit of fCMG . f min 1 α1 ð1 2 α3 ÞΔf max

(12.98)

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Hybrid Renewable Energy Systems and Microgrids

Figure 12.36 Performance of the considered two microgrid network, operating under the OMT. OMT, Overload management technique.

which is defined using Eq. (12.90). If either of Eqs. (12.97) or (12.98) is dissatisfied, the ISS will open immediately to isolate the microgrids. α2 and α3 are also used in Eqs. (12.97) and (12.98) to avoid chattering in the ISS operation [68,69]. Synchronization is necessary before interconnecting two microgrids as each microgrid may have a different voltage and frequency. Synchronization starts as soon as the ISS controller receives a close command from the OMT. The ISS will close when the difference of voltage angles and magnitudes across the ISS become very small (i.e., smaller than a predefined limit). In this way, significant distortions in the voltage, current, and power waveforms are avoided in the microgrids. The duration of synchronization is a function of the difference of the voltage angles at two sides of the ISS and the difference between the frequencies of the microgrids at the time of the command [74,75]. Example 12.10: Consider the network of Fig. 12.34 where each microgrid has two DDERs. At t 5 0, the microgrids are isolated and operating at steady state. At t 5 1 s, MG-1’s load increases by 94%. Thus PMG-1 increases to supply the load. The interconnection condition of Eq. (12.90) and constraint of Eq. (12.91) are fulfilled at t 5 1.2 s; thereby, the OMT sends the closing command to the ISS. Synchronization of microgrids accomplishes at t 5 2.28 s, and hence, the ISS closes, and a system of coupled microgrids is formed. MG-2’s load increases by 150% at t 5 3.3 s. As a result, P CMG increases to supply the load. At t 5 3.5 s, the tertiary controller detects that condition (12.98) is violated, and therefore ISS opens, and the microgrids get isolated. Fig. 12.36 illustrates the results of each microgrid in this example [68,69].

12.4.3 Coupling of more than two microgrids When coupling more than two microgrids, either of the decision-making or optimization approaches can be used. A decision-making-based approach can be very fast but not necessarily optimal. On the other hand, an optimization-based approach can help the network to observe the most optimal operational condition but may be

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slow, depending on the size of the microgrids and the number of decision variables. These approaches are discussed next.

12.4.3.1 Decision-making approach Consider the network of Fig. 12.37 consisting of NMG isolated microgrids among which N0 MG microgrids are overloaded. In general, the alternatives that can be considered to support the overloaded microgrids are combinations of single microgrids, two microgrids,. . ., and NMG 2 N0 MG microgrids among NMG 2 N0 MG microgrids. Therefore the number of the alternatives is [55,67] 0

N A 5 2N MG 2 N MG 2 1

(12.99)

As an example, for a distribution network with NMG 5 5 microgrids in which MG-1 is overloaded (i.e., N0 MG 5 1), the alternative microgrids are [{2}, {3}, {4}, {5}, {2,3}, {2,4}, {2,5}, {3,4}, {3,5}, {4,5}, {2,3,4}, {2,3,5}, {2,4,5}, {3,4,5}, {2,3,4,5}]. Thus the selection of the appropriate microgrid(s) to couple with the overloaded microgrid among the possible alternatives is a difficult task because of the large number of possible alternatives, several criteria for the selection and the different weightings for each criterion. To select the appropriate alternative, the OMT can employ a decision-making technique. To this end, the OMT, situated as a module within the network tertiary controller, has to send/receive information with the central controllers of the microgrid and the ISSs continuously. It will receive the power generated by the DDERs and the power consumed by the loads. In the first step, the OMT will recognize the microgrid(s) that are overloaded. Then, it will take a suitable action according to the number of overloaded microgrids and the available UAPC in the other microgrids. It computes the UAPC of MG-i from UPCi 5

X

Pmax MG-i 2

X

PMG-i

’ iAALT

MG-j

(12.100)

MG-N

MG-k

MG-1 MG-2

Figure 12.37 A network with multiple neighboring microgrids.

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Hybrid Renewable Energy Systems and Microgrids

P in which ALT is the set of alternatives and PMG-i is the power demand of MG-i. It is preferred to keep the UAPC of every microgrid above the limit of [20] X Pmax UPCi $ α1 (12.101) MG-i Eq. (12.101) is considered as the overloading condition of a microgrid, and if it is valid for all microgrids of the distribution network, the OMT does not take any actions. But if it is invalid for one or some of the microgrids, the OMT assesses the availability of enough surplus power in the network from N MG X

UPCi $ α1

i51

N MG X X

Pmax MG-i

(12.102)

i51

Eq. (12.102) is assumed as the constraint for coupling the microgrids. If condition (12.101) shows that one or some of the microgrids are overloaded, and constraint (12.102) indicates that the distribution network has enough surplus power to support the overloaded microgrids, the OMT continues to select an appropriate alternative. If condition (12.101) is unacceptable for n1 5 NMG 2 1 microgrids but constraint (12.102) is valid, the OMT concludes that the only alternative is coupling all microgrids. It runs a power flow calculation to confirm that coupling all microgrids does not result in nonstandard deviations in the voltage and frequency of the coupled microgrid. The interconnection is allowed if VDI and FDI satisfy Eqs. (12.78a) and (12.78b). If condition (12.101) is unacceptable for 1 , n1 , NMG microgrids, the OMT concludes that several alternatives are possible, and decision-making is necessary. Then, it runs the decision-making function to frame all possible alternatives and to select the appropriate. After the selection of an appropriate alternative, it sends a closing command to the relevant ISS(s). Yet, if no appropriate alternative is chosen, load-shedding is unavoidable. Decision-making chooses the favorable alternative from a set of alternatives (A), based on P NC criteria (c), where each criterion may have a dissimilar weighting ðωÞ where ω 5 1. The multi-criteria decision-making problem can be formulated as a matrix in the form of [55,67] 2c1 ðω1 Þ A1 x11 A2 6 6 x21 ^ 4^ AN A xN A 1

c2 ðω2 Þ x12 x22 ^ xN A 2

... ... ... & ...

cN C ðωN C3Þ x1N C x2N C 7 7 ^ 5 xN A N C

(12.103)

where xuv is the performance of alternative Au from the criterion cv point of view. Eq. (12.103) will be altered to consider the weightings of the criteria as 2 c1 A1 ω1 x11 A2 6 6 ω1 x21 ^ 4 ^ AN A ω 1 x N A 1

c2 ... cN C 3 2 3 ω2 x12 . . . ωN C x1N C X1 6 7 ω2 x22 . . . ωN C x2N C 7 7 5 6 X2 7 5 4 ^ 5 ^ & ^ XN A ω 2 xN A 2 . . . ω N C xN A N C

(12.104)

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The evaluation results for the alternatives (X) are calculated as Xu 5

NC X ωv xuv =N C u51 NC

(12.105)

Xu 5 L ωv xuv u51

Xu 5 minðωv xuv Þv51;:::; N C Xu 5 β minðωv xuv Þ 1 ð1 2 βÞmaxðωv xuv Þ where β A [0,1] (e.g., β 5 0.75) is the optimist coefficient. The alternative that has the highest X will have the highest priority and will be chosen by the decisionmaking algorithm [55,67]. The outcome of the decision-making algorithm rests on the considered weightings for the criteria. Therefore these weightings need to be defined cautiously. The decision-making algorithm, first, evaluates the qualification of the alternatives based on the following criteria: Criterion-1 (consent/veto of a microgrid to couple) Criterion-2 (availability of surplus power in microgrids) Criterion-3 (VDI) Criterion-4 (FDI) The performance of alternative Au for these criteria is computed from  xu1 5

1 0

coupling approved coupling disapproved

8