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Microgrid Design and Operation
 9781630816711, 163081671X, 9781630811501

Table of contents :
Content: Microgrid Design and Operation
Foreword
Acknowledgments
1 Introduction
1.1 Overview
1.2 Traditional Electric Networks
1.3 The First Revolution: Power Electronics
1.4 The Second Revolution: The Distributed Energy Resources
1.5 The Third Revolution: Smart Grids and Microgrids
References
2 Technology Overview: Devices and Equipment
2.1 Overview
2.2 Introduction
2.3 Distributed Generation and Microgrids
2.4 Technologies for Electrical Energy Production
2.4.1 Photovoltaic Systems
2.4.2 Small Hydro Power Plants
2.4.3 Small Wind Power Plants. 2.5 Technologies for Thermal Energy Production2.5.1 Solar Thermal Systems
2.5.2 Boilers
2.5.3 Heat Pumps
2.6 Technologies for Cooling Energy Production
2.6.1 Compression Chillers
2.6.2 Absorption Chillers
2.7 Cogeneration and Trigeneration Technologies
2.7.1 Small Gas Turbines
2.7.2 Small Reciprocating Internal Combustion Engines (SRICEs)
2.7.3 Concentrating Solar Power Systems
2.7.4 Fuel Cells
2.8 Electrical Storage Systems
2.9 Power Electronic Converters
2.10 Conclusions
References
3 Microgrid Installations: State of the Art
3.1 Microgrids in America. 3.2 Microgrids in Europe3.3 Microgrids in Asia, Australia, and Africa
References
4 Communication and Monitoring Systems for Microgrids
4.1 Overview
4.2 Protocols for Microgrid Applications
4.2.1 Modbus
4.2.2 DNP3 and IEC 60870-5
4.2.3 IEC 61850
4.2.4 BACnet
4.2.5 LonWorks
4.2.6 KNX
4.2.7 Wireless Technologies: ZigBee and LoraWan
4.2.8 OPC
4.2.9 Interfaces Via Web Services: SOAP and REST
4.3 Supervision and Monitoring Systems: SCADA and BMS
4.4 Interoperability
References
5 Modeling and Simulation for Microgrids
5.1 Overview
5.2 Introduction. 5.3 Dynamic Modeling and Simulation of Multicomponent Energy Systems5.3.1 A Multicomponent Energy System
5.3.2 Equations Governing the Dynamic Behavior of the System
5.3.3 The Electrical Analogy
5.3.4 Dynamic Simulation of a Cogeneration Microturbine as a Multicomponent System
5.4 Electrical Devices Modeling for Islanded Microgrid Simulations
5.5 Conclusions
References
6 Optimization for Microgrid Planning
6.1 Overview
6.2 Introduction
6.3 State of the Art of the Optimal Planning Approaches
6.4 Optimal Design of Microgrids: The Decision Problem. 6.5 Decision Variables and Parameters6.5.1 Parameters Related to Power Flows
6.5.2 Parameters Related to Costs
6.5.3 Decision Variables
6.6 The System Model Description and Related Constraints
6.6.1 The PV Power Plant
6.6.2 The Solar Thermal Power Plant
6.6.3 The Wind Turbine Power Plant
6.6.4 The Combined Heat and Power Microturbines Plants
6.6.5 The Thermal Boilers
6.6.6 The Biomass Plants
6.6.7 The Heat Pumps
6.6.8 The Chillers
6.6.9 The Fossil Fuel Plants
6.6.10 The Electrical and Thermal Power Balance
6.7 The Optimization Problem
6.7.1 Operational Management Costs.

Citation preview

Microgrid Design and Operation Toward Smart Energy in Cities

For a complete listing of titles in the Artech House Power Engineering Series, turn to the back of this book.

Microgrid Design and Operation Toward Smart Energy in Cities

Federico Delfino Renato Procopio Mansueto Rossi Stefano Bracco Massimo Brignone Michela Robba

Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the U.S. Library of Congress. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library. Cover design by John Gomes

ISBN 13: 978-1-63081-150-1

© 2018 ARTECH HOUSE 685 Canton Street Norwood, MA 02062

All rights reserved. Printed and bound in the United States of America. No part of this book may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage and retrieval system, without permission in writing from the publisher.   All terms mentioned in this book that are known to be trademarks or service marks have been appropriately capitalized. Artech House cannot attest to the accuracy of this information. Use of a term in this book should not be regarded as affecting the validity of any trademark or service mark.

10 9 8 7 6 5 4 3 2 1

To the decision-makers of Italian government institutions, so that they can deeply understand the importance of fostering the technological evolution of the country toward the concept of sustainability, the true driving force to create new job positions and to promote and increase in value the historical, cultural, artistic, and landscape beauty of Italy.

Contents

Foreword

xv



Acknowledgments

1

Introduction

1

1.1

Overview

1

1.2

Traditional Electric Networks

1

1.3

The First Revolution: Power Electronics

4

1.4

The Second Revolution: The Distributed Energy Resources 5

1.5

The Third Revolution: Smart Grids and Microgrids References

xvii

7 10

2

Technology Overview: Devices and Equipment

13

2.1

Overview

13

2.2

Introduction

13

2.3

Distributed Generation and Microgrids

14

2.4

Technologies for Electrical Energy Production

17

vii

viii

Microgrid Design and Operation: Toward Smart Energy in Cities

2.4.1 2.4.2 2.4.3

Photovoltaic Systems Small Hydro Power Plants Small Wind Power Plants

17 22 24

2.5 2.5.1 2.5.2 2.5.3

Technologies for Thermal Energy Production Solar Thermal Systems Boilers Heat Pumps

29 29 30 32

2.6 2.6.1 2.6.2

Technologies for Cooling Energy Production Compression Chillers Absorption Chillers

33 33 34

2.7 2.7.1 2.7.2 2.7.3 2.7.4

Cogeneration and Trigeneration Technologies Small Gas Turbines Small Reciprocating Internal Combustion Engines Concentrating Solar Power Systems Fuel Cells

36 37 42 43 44

2.8

Electrical Storage Systems

45

2.9

Power Electronic Converters

46

2.10

Conclusions

48

References

48

3

Microgrid Installations: State of the Art

55

3.1

Microgrids in America

56

3.2

Microgrids in Europe

61

3.3

Microgrids in Asia, Australia, and Africa

67

References

75

4

Communication and Monitoring Systems for Microgrids 79

4.1

Overview

79

4.2 4.2.1 4.2.2 4.2.3 4.2.4

Protocols for Microgrid Applications Modbus DNP3 and IEC 60870-5 IEC 61850 BACnet

81 81 82 83 86



Contents

ix

4.2.5 4.2.6 4.2.7 4.2.8 4.2.9

LonWorks KNX Wireless Technologies: ZigBee and LoraWan OPC Interfaces Via Web Services: SOAP and REST

4.3

Supervision and Monitoring Systems: SCADA and BMS 90

4.4

Interoperability References

86 88 88 90 90

91 95

5

Modeling and Simulation for Microgrids

99

5.1

Overview

99

5.2

Introduction

99

5.3 5.3.1 5.3.2 5.3.3 5.3.4

Dynamic Modeling and Simulation of Multicomponent Energy Systems A Multicomponent Energy System Equations Governing the Dynamic Behavior of the System The Electrical Analogy Dynamic Simulation of a Cogeneration Microturbine as a Multicomponent System

5.4

Electrical Devices Modeling for Islanded Microgrid Simulations

114

5.5

Conclusions

120

References

101 102 103 106 109

120

6

Optimization for Microgrid Planning

123

6.1

Overview

123

6.2

Introduction

123

6.3

State of the Art of the Optimal Planning Approaches

124

6.4

Optimal Design of Microgrids: The Decision Problem 127

6.5 6.5.1 6.5.2

Decision Variables and Parameters Parameters Related to Power Flows Parameters Related to Costs

128 128 130

x

Microgrid Design and Operation: Toward Smart Energy in Cities

6.5.3

Decision Variables

6.6 6.6.1 6.6.2 6.6.3 6.6.4 6.6.5 6.6.6 6.6.7 6.6.8 6.6.9 6.6.10

The System Model Description and Related Constraints 132 The PV Power Plant 132 The Solar Thermal Power Plant 133 The Wind Turbine Power Plant 133 The Combined Heat and Power Microturbines Plants 134 The Thermal Boilers 134 The Biomass Plants 135 The Heat Pumps 135 The Chillers 136 The Fossil Fuel Plants 137 The Electrical and Thermal Power Balance 137

6.7 6.7.1 6.7.2 6.7.3

The Optimization Problem Operational Management Costs Installation Costs Objective Function

139 139 143 143

6.8

Optimal Planning Including Storage Systems

143

6.9

Examples

146

References

131

150

7

Optimization for Microgrid Management

153

7.1

Overview

153

7.2

Introduction

153

7.3 7.3.1 7.3.2 7.3.3 7.3.4 7.3.5 7.3.6

List of Symbols General Data Technical Data Electrical Network Forecasted Quantities Variables Involved in the Optimization Procedure Costs

154 154 155 155 156 156 157

7.4 7.4.1 7.4.2 7.4.3 7.4.4

The Energy Management System Component Models for Energy Management System Implementation Electric Network Models Optimization Problem Definition Forecasting Update Logic

157 159 162 165 169



Contents 7.4.5

Validation of the Proposed Energy Management System on the Smart Polygeneration Microgrid References

xi

172 181



Appendix 7A

182



Appendix 7B

183

8

Forecasting Tools

185

8.1

Overview

185

8.2

Introduction

185

8.3

Solar and Photovoltaic Production Forecasting

189

8.4

Wind Power Production Forecasting

192

8.5

Load Forecasting

195

References

199

9

Islanded Microgrids

203

9.1

Overview

203

9.2 9.2.1 9.2.2 9.2.3

Primary Control Communication-Based Control Droop-Based Control Droop-Based Control for Mainly Resistive Microgrids

205 206 213 217

9.3

Secondary Control

218

9.4

Tertiary Control

220

9.5 9.5.1 9.5.2 9.5.3 9.5.4 9.5.5

The Smart Polygeneration Microgrid in Islanded Configuration Description of the Off-Grid SPM Portion The Simplified Model Complete Model Built in PSCAD Experimental Campaign and Measurement Setup Validation References

222 222 223 232 232 233 238

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Microgrid Design and Operation: Toward Smart Energy in Cities



Appendix 9A: Conditions for Reaching the Same Angular Frequency with the Droop-Based Control Method

10

Commercial Tools for the Management of Microgrids 245

10.1

Overview

245

10.2

VERA (Honeywell)

246

10.3

DEMS (Siemens)

247

10.4

DER-CAM (Berkeley Lab)

249

10.5

Microgrid Plus (ABB)

250

10.6

EcoStruxure Microgrid Advisor (Schneider)

251

10.7

Micro Energy Management System (TOSHIBA)

252

10.8

Grid IQ Microgrid Control System (GE)

252

References

240

253

11

From Design to On-Field Installation: A Practical Case Study

255

11.1

Overview

255

11.2

Introduction

255

11.3 11.3.1 11.3.2 11.3.3

The Smart Polygeneration Microgrid 259 The Smart Polygeneration Microgrid Power Plants 259 The Smart Polygeneration Microgrid ICT Infrastructure 269 The Energy Management System 271

11.4 11.4.1 11.4.2 11.4.3

The Smart Energy Building The Smart Energy Building Thermal System The Smart Energy Building Electrical System The Smart Energy Building ICT Infrastructure

274 276 279 281

11.5

Smart Polygeneration Microgrid and Smart Energy Building: Building Timelines and Main Challenges

283

11.6

Conclusions

284

References

285



Contents

xiii

12

From Microgrids to Smart Cities

287

12.1

Overview

287

12.2

The Digital Utility Transformation

288

12.3

VPP, Aggregation of DERs, and Demand-Side Management

290

References

294



About the Authors

297



Index

301

Foreword While moving forward on rural electrification within concession areas by creating “off-grid” microgrids, over the last years Enel has also been assessing “ongrid” solutions as a golden opportunity to scout and test cutting-edge network technology in an urban environment. Affordable, sustainable and reliable access to electricity on one hand (according to SDG7 achievements); advanced urban design and development on the other, towards zero emissions urban services enabled by smart grids. A new customer experience, with energy end-user becoming leading actor and potential ancillary services provider: “open power” is the new keyword of the energy transition, and the on-grid microgrid can represent an immediately feasible and environmentally friendly solution to re-energize urban areas running through fast and deep redevelopment processes (residential, industrial, commercial, sports and leisure), bundling in a single interactive entity generators and loads, modern and traditional grid technologies, storage systems and electric vehicles. This results in the possibility to create close links among the distribution system operator (DSO) central control system and the local “brains” governing the microgrid and the intelligent buildings connected to it. Through maingrid/microgrid innovative protection and control solutions and the advanced management of this link, it is possible to plan and adjust in real-time power flows addressing the targets of (i) the minimization of the CO2 emissions; (ii) the reduction of the global energy cost for the end-users; (iii) the improvement of the power quality. The path has been clearly traced by now and the perspective market is expected to quickly grow in the next years; moreover, big synergies are present inside the development of smart grid and microgrid technologies and generally in the vision of the microgrid as a module of the smart grid. xv

xvi

Microgrid Design and Operation: Toward Smart Energy in Cities

In light of such considerations, this book presents a comprehensive and valuable analysis on all the technical aspects related to microgrid system’s design and operations, relying on the proven experience on the field of the authors, who devised and followed the construction of the Savona Campus microgrid – now a global “Living Lab” of Enel, too, an open air and freely accessible environment where developing and testing innovative solutions and technologies for energy generation, distribution and intelligent management in future cities: open, shared, sustainable. The volume is conceived as a reference book on microgrid topics, aimed at guiding the reader through the different phases of the process of sizing and operating such a complex integrated system. It firstly identifies the main blocks of the conceptual chain relevant to the microgrid architecture; furthermore, each single item is in depth analyzed, focusing on theoretical, practical and commercial aspects as well and, finally, an attempt to shape the smart city future is made, proposing a vision of a possible evolution of the electric distribution system, according to the real deployment of smart grid and smart microgrid models. Livio Gallo Head of Enel Global Infrastructure and Networks

Acknowledgments We would like to express our special thanks to the following colleagues. Paolo Comanducci, the University of Genoa rector, who immediately understood the potential of the Savona Campus in shaping the future as a living lab of the smart city. We give many thanks to Paolo for his constant support to accelerate such technological evolutionary processes, and for his ideas and suggestions. Paola Girdinio, acting as the former dean of the Engineering School of the University of Genoa, who strongly supported the development of our Energia 2020 project, which gave us the opportunity to increase and refine our scientific skills and experience in the microgrid sector, with a special focus on operating processes and procedures. We also give many thanks to Paola for her great attitude in creating close partnerships and fruitful interactions on innovation projects between our university and the industrial world. Paola Laiolo, the Savona Campus sustainability officer, who helped us to revise the book chapters in trying to make the text clear and simple to understand for nonexpert readers as well. Finally, Luca Barillari and Fabio Pampararo, the ICT and the power system managers of our Smart Polygeneration Microgrid, respectively, who made a contribution that has been fundamental in order to implement and test on the field the algorithms and the control strategies related to the main topics discussed in the book.

xvii

1 Introduction 1.1  Overview In the last few years, smart grids have attracted the attention of academic institutions and industry players, becoming one of the most promising technological developments, and at the same time, one of the most fascinating challenges. What is revolutionary about the smart grids concept? In this book, an answer to this question is proposed. This is done by providing a sketch of the historical evolution of the electric energy supply starting from traditional electric networks and accounting for the main technological innovations that have occurred during the last century and the present one.

1.2  Traditional Electric Networks Electric energy intrinsically has two main characteristics: (1) it can be easily transported at very long distances, and (2) it is not easily stored. As a consequence, in traditional systems, electric energy must be produced when it is needed. This fact has led researchers and engineers to consider electricity as a service energy that results from the transformation of another form of energy (chemical, mechanical, solar, among others) and requires a final transformation in another form to be effectively used. So, according to this concept, the traditional electric system has been thought as a very complex and wide infrastructure allowing energy to be produced at very long distances from the place in which it is needed. 1

2

Microgrid Design and Operation: Toward Smart Energy in Cities

Unfortunately, as any other physical system, the electric infrastructure consisting of transmission overhead lines (OHL) and buried cables presents some losses (basically due to the Joule effect on OHL/cable resistance) that have to be minimized to increase the overall system energy efficiency. Considering that the Joule losses are proportional to the square of the current flowing in the infrastructure while the power to be transmitted depends on the product between the voltage and the current itself, it is apparent that the smaller the current, the higher the energy efficiency of the whole infrastructure. This consideration should lead to the conclusion that the electric system should be operated at the highest possible voltage value. However, security issues imply a voltage-level upper bound depending on the specific network portion (an overhead transmission line can be safely operated at very high-voltage levels because the probability of a contact with people is quite modest; the distribution system that provides voltage to houses is instead characterized by a lower-voltage level because the safety issue is of primary importance). According to this reasoning, it is apparent that the optimal topology of an electricity network corresponds to a very wide infrastructure characterized by different portions with different voltage levels connected together. That is the reason why the classical electric network is divided into four main subsystems: (1) the generation subsystem (typical voltage level in the range of 10 to 20 kV), (2) the high-voltage (HV) transmission subsystem (voltage level of 400/230 kV or 132 kV for subtransmission subsystems), (3) the medium-voltage (MV) distribution subsystem (voltage level between 15 and 20 kV), and (4) and the low-voltage (LV) utilization subsystem (400/230V). To ensure the electric continuity of the whole system, it is necessary to position the devices that should receive in input one voltage level and produce in output another voltage level: the transformers. As the transformers’ working principle is based on the electromagnetic induction law, an electric infrastructure with the above-described characteristics cannot be operated in direct current (DC). That is probably the main historical reason why the traditional electric network is an alternating current (AC) system. A sketch of the typical traditional electric network can be found in Figure 1.1. As can be seen examining Figure 1.2, the transmission and distribution networks are different from a topological point of view; the structure of the first is typically meshed to ensure that any point of the network is fed in at least two alternative ways. However, the distribution network is normally radial with noteworthy advantages in the protection system design (see [1] for details). Moreover, the great majority of the traditional electric system is operated as a three-phase balanced system essentially for two main reasons: (1) from an economical point of view, this guarantees the possibility of transmitting the same amount of power as in a one-phase system with a significant cable material



Introduction

3

Figure 1.1  Traditional network structure.

Figure 1.2  Transmission and distribution networks topology.

saving (see for [1] details), and (2) the instantaneous power is constant, which becomes extremely important when dealing with induction machines.

4

Microgrid Design and Operation: Toward Smart Energy in Cities

Finally, in traditional networks, the possibility of regulating the quantities of interest at the desired values is limited to a few cases, namely: 1. The active power produced by synchronous generators, acting on the machine governors; 2. The voltage (or the reactive power) at the terminals of synchronous generators by means of an automatic voltage regulator (AVR) acting on their field voltage; 3. The voltage at specific points of the network, with the aid of on-load tap changer (OLTC) transformers or fixed capacitor banks. In such a structure, it is apparent that there is no way to control the active/ reactive power flows in the transmission and distribution networks, which are determined essentially by the solution of the load flow equations. Summarizing, the main characteristics of a traditional electric network are: 1. A very wide and interconnected infrastructure; 2. Energy sources consisting of a reduced number of plants producing a significant amount of power; 3. AC operating conditions to ensure the interconnection of different voltage-level subsystems by means of transformers; 4. Three-phase balanced system mainly for economical/power quality reasons; 5. A limited possibility of controls, mainly concentrated on synchronous generators, thus leading to a reduced degree of flexibility in the whole infrastructure.

1.3  The First Revolution: Power Electronics In 1948, Bardeen, Brattain, and Schockley from the Bell Telephone Laboratories invented the transistor, causing a revolution in electronics. In 1956, in the same laboratories, the first thyristor was produced and then was commercialized in 1958 by General Electric (GE). Since then, many different devices and converters have been invented, with a significant acceleration between the late 1980s and the early 1990s [2, 3]. Power electronics have changed the power system concepts and operation, introducing the possibility of converting an AC source into a DC one and vice versa. This has had a significant impact both on transmission and distribution systems. In the first case, the introduction of the Flexible AC Transmission



Introduction

5

Systems (FACTS) [4] has allowed the control of power flows in the transmission infrastructure and has opened the possibility of transmitting power in DC, with a meaningful reduction of voltage drops due to the absence of reactances (see Figure 1.3, where the unified power flow controller (UPFC) scheme is depicted, consisting of a series compensator, the static synchronous series compensation (SSSC), and a shunt one, the STATCOM). Distribution systems have increased their flexibility thanks to the custom power devices that have allowed solving a lot of power quality problems [5], like voltage sags, power factor correction, and harmonic active and passive filtering among others (see Figure 1.4). The introduction of power electronic devices has determined a significant change in the concepts of power systems, especially because it has relaxed the constraint of operating the network as a three-phase AC system (items 3 and 4 of the previously mentioned checklist) and because it has increased its flexibility and power quality, thanks to the introduction of new control actions (item 5).

1.4  The Second Revolution: The Distributed Energy Resources Distributed power technologies are not new. Before the development of largescale power plants in the early twentieth century, all energy requirements—including heating, cooling, lighting, and mechanical and electric power—were supplied at or near the point of use [6]. The movement to central station power plants started in 1891, when George Westinghouse assembled the first AC system in Telluride, Colorado. Since then, thanks to the lower power production

Figure 1.3  UPFC scheme as an example of FACTS devices. (After: [7].)

6

Microgrid Design and Operation: Toward Smart Energy in Cities

Figure 1.4  Custom power device example.

costs, central production units became more and more widely employed. The era of central station power was under way, and distributed power technologies were consigned to providing backup and remote power. However, technology change is constant. Today, technology advances have enabled the development of a new breed of distributed power technologies that have the ability to rival the cost and performance of central station power plants, but in a much smaller package. In [6], Owen distinguished among three phases in the global power system: the Legacy Distributed Power Era (1890–1910), the Central Power Era (1910–2000), and the Integrated Energy Systems Era (2000–present). The first phase was characterized by small and local power plants that provided energy to their local area through DC grids. Then the Central Power Era was determined by the economy of scale that produced the increase of larger power plants, some of them exceeding 1 GW. Finally, the present era is characterized by “the rise of distributed power transforming power networks around the globe into integrated energy systems” [6]. So, in the present era, item 2 of the initial checklist may not be respected. But what about item 1? In other words, is it possible to revolutionize the concept of electrical infrastructure by relaxing the constraint of a single infrastructure extended in space and interconnected? To answer this question, it is necessary to go deep into some properties of distributed energy resources (DERs). DERs can be basically divided into two broad categories: renewable energy sources (RESs) and conventional energy sources (CESs). If the first category is more appealing as its primary energy source is free of charge and does not negatively impact the environment, its intrinsic stochastic nature makes



Introduction

7

it impossible to dispatch a RES as a traditional power plant. Consequently, keeping in mind the concept according to which “the electric energy must be produced when it is needed,” it seems impossible to make the infrastructure disappear. Moreover, the wider diffusion of RES in the modern electric grid has opened the very important issue of integrating them into the network. As a matter of fact, the majority of control logics, protection schemes, and management systems that are consolidated in traditional networks fail whenever facing systems with a significant penetration of renewables. The following are some examples: • The classic dispatching based on the equality of incremental costs [1] is not valid anymore, as one has to account for a certain amount of unpredictable power production. • The usual protection schemes adopted for distribution networks rely on the concept that the power flow is unidirectional (i.e., from the transmission network to the loads). This idea can cause improper tripping when dealing with systems in which loads can be also fed by local generators. • The standard power/frequency regulation based on the droop technique [1] that ensures a proper subdivision of the load increase/decrease among the machines assumes that all the generators participating to the regulation can rely on a power reserve that can be used in any moment, which is not the case when dealing with a photovoltaic (PV) or a wind unit. Consequently, the possibility of changing item 1 of the initial checklist claims for a new revolution, which does not consist of the invention/production of new devices, but of a higher level of integration. It is to give a different answer to this question that the smart grids concept was born.

1.5  The Third Revolution: Smart Grids and Microgrids Smart grids and microgrids aims at moving from a centralized, monodirectional grid (Figure 1.�������������������������������������������������������������������� 5������������������������������������������������������������������� ) to a bidirectional grid characterized by distributed energy (Figure 1.6). A smart grid is an electrical grid which includes a variety of operational and energy measures including smart meters, smart appliances, renewable energy resources, and energy-efficient resources [8, 9]. Electronic power conditioning and control of the production and distribution of electricity are important aspects of the smart grid [8].

8

Microgrid Design and Operation: Toward Smart Energy in Cities

Figure 1.5  Unidirectional, centralized grid.

Figure 1.6  Microgrid.

The first official definition of smart grid was provided by the Energy Independence and Security Act of 2007 (EISA-2007), which was approved by the U.S. Congress in January 2007 and signed to law by President George W. Bush in December 2007. Title XIII of this bill provided a description, with 10 characteristics, that can be considered a definition for smart grid, as follows: It is the policy of the United States to support the modernization of the Nation’s electricity transmission and distribution system to maintain a reliable and secure electricity infrastructure that can meet future demand growth and to achieve each of the following, which together characterize a Smart Grid: (1) Increased use of digital information and controls technology to improve reliability, security, and efficiency of the electric grid. (2) Dynamic optimization of grid operations and resources, with full cyber-security. (3) Deployment and integration of distributed resources and generation, including renewable resources. (4) Development and incorporation of demand response, demand-side resources, and energyefficiency resources. (5) Deployment of smart technologies (real-time, automated, interactive technologies that optimize the physical operation of appliances and consumer devices) for metering, communications concerning grid operations and status, and distribution automation. (6)



Introduction

9

Integration of smart appliances and consumer devices. (7) Deployment and integration of advanced electricity storage and peak-shaving technologies, including plug-in electric and hybrid electric vehicles, and thermal storage air conditioning. (8) Provision to consumers of timely information and control options. (9) Development of standards for communication and interoperability of appliances and equipment connected to the electric grid, including the infrastructure serving the grid. (10) Identification and lowering of unreasonable or unnecessary barriers to adoption of Smart Grid technologies, practices, and services.

In the context of smart grid, particular attention is being given by academic and industrial researchers to microgrids. According to [10], the definition of microgrid is: “a cluster of loads and microsources operating as a single controllable system that provides both power and heat to its local area.” If one analyzes this definition, it readily follows that a smart grid is characterized by devices and infrastructures. The devices present in a smart grid can be classified in three main categories (see Figure 1.7): DERs (which can, in turn, be divided into RES and CES), distributed storage (DS) systems, and electric and thermal loads. From the infrastructure point of view, one typically has an electric infrastructure, a thermal infrastructure, and an information and communications technology (ICT) infrastructure.

Figure 1.7  A typical smart grid structure.

10

Microgrid Design and Operation: Toward Smart Energy in Cities

The electric and thermal infrastructure are coupled whenever combined heat and power (CHP) devices are present in the smart grid; moreover, the ICT infrastructure allows a central brain to optimally manage all the devices present in the smart grid. As specified earlier, it is the integration of all these devices and infrastructures that allows the possibility of changing the paradigm of the electricity (and thermal energy) supply. In particular, as will be outlined later on in this book, the energy management system (EMS), making use of the ICT network, dictates the production of CES, the charging/discharging of DS, and the eventual participation of specified loads (in case of demand/response strategies) the power reserve programs starting from the electric and thermal request and the RES production. This can optimize the energy behavior of the smart grid and, in principle, can lead to a situation in which it can be independent from the public network. The main aim of this book is to provide a state of the art of smart grids and in particular of microgrids both from a theoretical and a technological point of view and to give highlights on the future trends that appear more promising in the field. To do this, information will be drawn from both the scientific literature and from the experience of the authors in the design, construction, and management of the Smart Polygeneration Microgrid that is currently in operation at the University of Genoa Savona Campus. With this idea, the first part of the book will be devoted to the description of the main microgrid technologies (Chapter 2) and installations (Chapter 3) with particular attention to the communication infrastructures and systems (Chapter 4). Chapters 5 through 10 are dedicated to particular aspects of microgrid studies and management, that is to say, modeling and simulations (Chapter 5), planning (Chapter 6), production scheduling and management (Chapter 7), available models and technologies for loads and renewable forecast (Chapter 8), control methods for islanded microgrids (Chapter 9), and available commercial tools (Chapter 10). Finally, Chapter 11 presents in detail the case study of the smart polygeneration microgrid while Chapter 12 projects ourselves towards the future, highlighting how the concept of microgrid and smart grid can be extended to smart cities.

References [1] Kundur, P., N. J. Balu, and M. G. Lauby, Power System Stability and Control, New York: McGraw-Hill, 1994. [2] Rachid, M. H., Power Electronics Handbook: Devices, Circuits, and Applications Handbook, Third Edition, New York: Elsevier, 2011. [3] Erickson, R., and D. Maksimovic, Fundamentals of Power Electronics, Second Edition, Boston: Kluwer Academic, 2004.



Introduction

11

[4] Hingorani, N. G., and L. Gyugyi, Understanding FACTS: Concepts and Technology of Flexible AC Transmission Systems, New York: Wiley-IEEE Press, 1999. [5] Bollen, M. H. J., Understanding Power Quality Problems: Voltage Sags and Interruptions, New York: IEEE Press, 1999. [6] Owens, B., The Rise of Distributed Power, GE Company, 2014. [7] Pounraj, K., and S. Selvaperumal, “Dynamic Performance Investigation of D-Q Model Based UPFC with Various Controlling Techniques,” International Journal of Applied Engineering Research, Vol. 9, No. 21, 2014, pp. 10651–10670. [8] “Federal Energy Regulatory Commission Assessment of Demand Response & Advanced Metering,” United States Federal Energy Regulatory Commission, 2017. [9] Saleh, M. S., et al., “Impact of Clustering Microgrids on Their Stability and Resilience During Blackouts,” 2015 International Conference on Smart Grid and Clean Energy Technologies (ICSGCE), 2015, pp. 195–200. [10] Hatziargyriou, N., et al., “Microgrids,” IEEE Power and Energy Magazine, Vol. 5, 2007, pp. 78–94.

2 Technology Overview: Devices and Equipment 2.1  Overview The present chapter describes the main technologies adopted in distributed generation installations and microgrids. The attention is focused on plants that produce electrical energy (photovoltaic fields, hydro, and wind plants), thermal energy (solar thermal collectors, boilers, heat pumps), and cooling energy (compression and absorption chillers). Moreover, cogeneration and trigeneration technologies are analyzed, as well as electrical storage systems.

2.2  Introduction Renewable sources are increasingly exploited worldwide in combination with energy storage systems and high-efficiency cogeneration units [1–8]. As reported by Enerdata in [8], in 2016, the countries characterized by the highest share of renewables in the electricity production were: Norway (97.9%), New Zealand (84%), Colombia (82%), Brazil (81.2%), Canada (66.4%), Sweden (57.2%), Portugal (55.2%), Venezuela (54%), Romania (46.2%), Spain (40.1%), Chile (39.1%), and Italy (37.3%). Among renewable sources, solar and wind are those characterized by the highest rates of increase, also benefitting from incentives given by countries to photovoltaic and wind power plants and from the reduction in installation costs. If only solar and wind sources are taken into account, the ranking of greenest countries is different from that above reported. 13

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Microgrid Design and Operation: Toward Smart Energy in Cities

In [8], the following highest values of solar and wind share in the electricity production are indicated, always referring to 2016: New Zealand (23.5%), Spain (23.5%), Portugal (22.3%), Germany (18%), Italy (16.5%), Romania (16.2%), United Kingdom (14.1%), Belgium (10.8%), Sweden (9.8%), the Netherlands (9.4%), Australia (7.9%), and Poland (7.7%). The diffusion of renewable power plants is often associated with the development of distributed generation and microgrid concepts, both seen as a viable solution to reduce primary energy use and carbon dioxide emissions, as well as to increase the reliability and the flexibility of the electrical system [9–13]. Furthermore, it is important to consider that renewable power plants are characterized by the intermittency of the energy production that strongly depends on weather conditions (solar radiation, wind speed). Therefore, it is necessary to choose and design appropriately a renewable energy site [14–18], in terms of power plant best location and sizing, and, at a later time, to monitor energy production data to evaluate the plant performance as a function of time and ambient conditions. It follows that, first of all, it is necessary to have competences on technical aspects associated with different power plant technologies to well design a distributed energy system and a microgrid, without neglecting environmental and economic aspects. The present chapter aims at describing the main characteristics of the technologies currently adopted in microgrids to produce electrical, thermal, and cooling energy.

2.3  Distributed Generation and Microgrids The term distributed generation (DG) generally refers to small generation units (characterized by a power lower than 10 MW) installed on the territory next to energy users. In the following, the most important definitions of distributed generation are reported to highlight its main features: Distributed generation refers to a variety of technologies that generate electricity at or near where it will be used, such as solar panels and combined heat and power. Distributed generation may serve a single structure, such as a home or business, or it may be part of a microgrid (a smaller grid that is also tied into the larger electricity delivery system), such as at a major industrial facility, a military base, or a large college campus. When connected to the electric utility’s lower voltage distribution lines, distributed generation can help support delivery of clean, reliable power to additional customers and reduce electricity losses along transmission and distribution lines. —U.S. Environmental Protection Agency (https://www. epa.gov/energy/distributed-generation-electricity-and-its-environmentalimpacts).



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15

In a power system composed of distributed energy resources, much smaller amounts of energy are produced by numerous small, modular energy conversion units, which are often located close to the point of end use. These units can be stand-alone or integrated into the electricity grid. — Virginia Tech (www.dg.history.vt.edu/ch1/introduction.html). Distributed generation is an approach that employs small-scale technologies to produce electricity close to the end users of power. DG technologies often consist of modular (and sometimes renewable-energy) generators, and they offer a number of potential benefits. In many cases, distributed generators can provide lower-cost electricity and higher power reliability and security with fewer environmental consequences than can traditional power generators. —Virginia Polytechnic Institute (http://www.dg.history. vt.edu/ch1/introduction.html). Distributed Generation refers to small, modular electricity generators sited close to the customer load that can enable utilities to defer or eliminate costly investments in transmission and distribution (T&D) system upgrades, and provide customers with better quality, more reliable energy supplies and a cleaner environment. —U.S. Department of Energy (http:// www.iser.uaa.alaska.edu/Publications/akelectricpowerfinal.pdf ). Distributed generation (DG): the entirety of generation plants connected to the distribution system; Small-scale generation (SG): the entirety of electricity production plants, also operating in cogeneration mode, with a generation capacity no greater than 1 MW (this is not strictly a subset of DG); Microgeneration (MG): the entirety of electricity production plants, also operating in cogeneration mode, with a generation capacity no greater than 50 kWe (this is not strictly a subset of DG but is a subset of SG). —ARERA—Italian Regulatory Authority for Energy, Networks and Environment (http://www.arera.it/it/inglese/techprofile/16/304-16st. htm).

The concept of distributed generation is strictly related to that of microgrid, since microgrids are usually composed of several distributed generation units operated by a central energy management system. As reported by the U.S. Department of Energy (http://www.nrel.gov/international/pdfs/5a_ton_reif15. pdf ), a microgrid indicates a group of interconnected loads and distributed energy resources with clearly defined electrical boundaries that acts as a single controllable entity with respect to the grid, and can connect and disconnect from the grid to enable it to operate in both grid connected or island mode. The concept of microgrid can be applied to different domains: residential (less

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Microgrid Design and Operation: Toward Smart Energy in Cities

than 10 kW, single-phase), small commercial (from 10 kW to 50 kW, typically three-phase), and commercial (greater than 50 kW up to 10 MW). Microgrids not only are electrical grids but can also be electrical and thermal grids that provide electrical and thermal energy to different users by exploiting different primary sources (renewable sources, fossil fuels). The difference between a simple distributed generation installation (composed of several plants spread on the territory) and a microgrid is due to the smartness of the microgrid, constituted by an energy management system that daily manages plants and loads to reach an objective, such as the minimization of operating cost or the reduction of emissions [14, 16, 19]. As a consequence, the operation of a microgrid needs also a complex ICT infrastructure to communicate from the field (where plants and loads are located) to the central brain (energy management system) and vice versa. It follows that the architecture of a microgrid has a very complex structure characterized by the interaction of different domains (mechanical, electrical, ICT, social, environmental, economic) [12]. In Figure 2.1, the microgrid cloud is proposed to show the most important words dealing with microgrids. A majority of the depicted terms are analyzed in detail in this chapter and in the following ones. In particular, this chapter is focused on power plants used to produce electrical and thermal energy in microgrids or, more in general, in distributed generation facilities. In Figure 2.2 power plants are divided into three categories: • Plants that produce only electricity; • Plants that produce only thermal energy; • Cogeneration plants characterized by the simultaneous production of electrical and thermal energy.

Figure 2.1  The microgrid cloud.



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17

Figure 2.2  Power plants for microgrids.

In the following sections, the aforesaid technologies are described.

2.4  Technologies for Electrical Energy Production In the present section, the following technologies are analyzed: photovoltaic, small hydro, and wind. 2.4.1  Photovoltaic Systems

Photovoltaic is a fast-growing market in continuous expansion: as reported in [20], the compound annual growth rate of photovoltaic installations was 40% between 2000 and 2016. The solar photovoltaic market attained the record of new 77.3 GW installed during 2016, lifting the global total installed power to 320 GW; the annual market was nearly 10 times the world’s cumulative solar photovoltaic capacity of a decade earlier [21]. COP21 (held in Paris in 2015) announced that photovoltaics could contribute significantly to the decarbonization of the planet related to energy production, sooner than expected and at a reasonable cost. Concerning photovoltaic module production in 2016, China and Taiwan held the lead with a share of 68%, followed by the rest of the AsiaPacific region and Central Asia with 14%; Europe contributed with a share of 4%, and the United States and Canada contributed with 6% [20]. In 2016, Europe’s contribution to the total cumulative photovoltaic installations amounted to 33% (compared to 40% in 2015), whereas installations in China accounted for 26% (compared to 21% in 2015) [20]. At the end of 2016, the 5 top

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countries for total installed capacity were China (77.4 GW), Japan (42.8 GW), Germany (41.3 GW), the United States (40.9 GW), and Italy (19.3 GW). Photovoltaic cells may be based on either silicon wafers or thin-film technologies. The first are manufactured by cutting wafers from a solid ingot block of silicon while the second technology is obtained depositing a semiconductor material on low-cost substrates [22]. Photovoltaic cells can further be characterized according to the structure of the semiconductor material: monocrystalline, multicrystalline (also known as polycrystalline), or amorphous material [22]. Generally, the most efficient technology is represented by Mono-c-Si (crystalline silicon) cells, but these are also costlier than multi-c-Si cells. In [20], the authors assessed that Si wafer-based photovoltaic technology accounted for about 95% of the total production in 2017; the share of multicrystalline technology was about 62% of the total production. The main key performance indicators that can be measured and/or calculated to analyze the operation of a photovoltaic field are [23–30]: • The net electricity production Eel (expressed in kWh/h or kWh/day or kWh/month); • The solar radiation Rad measured on photovoltaic modules (expressed in kWh/(m2h) or kWh/(m2day) or kWh/(m2month)); • The final photovoltaic system yield Yf (h/day or h/month):

Yf =

E el Pnom

(2.1)

where Pnom is the photovoltaic plant-rated power; • The reference yield Yr (h/day or h/month):

Yr =

Rad Gref

(2.2)

where Gref is the standard radiation equal to 1 kW/m2; • The performance ratio Rp (%):

Rp =

Yf Yr

⋅ 100

• The overall photovoltaic system global efficiency ηg (%):

(2.3)



Technology Overview: Devices and Equipment



ηg =

E el ⋅ 100 A ⋅ Rad

19

(2.4)

where A is the overall array area of the photovoltaic plant. The aforesaid parameters can be also calculated on a yearly base: in this case, the yield Yf represents the number of annual equivalent operating hours that depend on different factors, among which the latitude and the photovoltaic panel installation have a great influence. As reported in [31], the average world equivalent operating hours of photovoltaic installations were equal to 1,168 from 2004 to 2014. Moreover, typical values of the performance ratio Rp are in the range of 70% to 80%. In Figure 2.3, some data collected during experimental tests on the photovoltaic fields installed at the Savona Campus of the Genoa University, in Italy, are reported in two different graphs: the first plot indicates the solar radiation, whereas the second one shows the specific production per square meter of photovoltaic panel, referring to four different months of the year (one per season). Rooftop photovoltaic systems mounted on residential buildings usually have a rated power around 5–20 kW, whereas those installed on office or commercial buildings can reach rated power values higher than 100 kW. Photovoltaic plants can be divided into two categories: grid-connected systems and stand-alone systems. The first ones are connected to the national distribution grid, whereas the second ones provide energy to areas not connected to the grid [32, 33]; in the latter case, they are coupled with storage batteries that are charged or discharged when the photovoltaic production is higher or lower than the load. Stand-alone systems are normally used to produce energy not only for users that are not connected to the grid because they are located in remote areas (small islands, in the mountains) but also in presence of a very low electrical demand that does not make cost-effective the connection to the grid. Consequently, this kind of system has to cover the totality of the user energy demand. The components of a stand-alone photovoltaic installation are the photovoltaic panels, the storage batteries, and the charge regulator [34]; since electricity is produced as direct current (DC), if the load is composed of equipment fed by the alternating current (AC), it is also necessary to install an inverter (i.e., a DC/ AC converter). Grid-connected photovoltaic systems can exchange electricity with the local or national power grid, and so electricity exchange in two directions is permitted: when the production of the photovoltaic field exceeds the load, and no storage system is installed, the energy surplus is injected into the grid; however, when the photovoltaic production is null (during the night) or lower than

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Figure 2.3  Hourly solar radiation and electricity specific production (for a typical month per season) of a rooftop photovoltaic field installed in the city of Savona (North of Italy).

the load, electricity is withdrawn from the grid. In grid-connected photovoltaic installations (for instance, in Italy) a bidirectional meter is installed to measure the electricity exchange in both directions, whereas a one-directional meter is used to quantify the photovoltaic field production. Moreover, all grid-connected photovoltaic systems are equipped with one or more inverters. Grid-connected photovoltaic systems can also be classified into the two following categories: photovoltaic power stations and photovoltaic systems installed in buildings. The first category is relative to large-scale power plants (rated power higher than 1 MW) that are installed in agricultural areas or industrial sites; they are also known as photovoltaic farms. In recent years, many photovoltaic farms have been installed benefitting from different incentive policies adopted by countries; these installations are characterized by a not negligible environmental and visual impact. However, photovoltaic systems



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21

installed in the tertiary and residential sectors are composed of photovoltaic panels mounted on roofs or facades of buildings and characterized by rated power from a few kilowatts to hundreds of kilowatts; in some cases, in the building-integrated photovoltaics, photovoltaic cells replace building materials and are incorporated in the building structure (solar shingles, solar facades, solar windows and skylights, platform roofs). In these cases, special attention must be paid to dimensional tolerances and to the proper installation of panels. In Figure 2.4, some photos of photovoltaic installations in Liguria Region (North of Italy) are shown. A photovoltaic field is composed of different strings connected in parallel, each one composed of some panels connected in series. A panel is made of mechanically assembled modules, each one composed of cells connected in series. During the design phase of a photovoltaic field, it is necessary to take into account: the desired rated power, the rated voltage (considering the same

Figure 2.4  Photovoltaic installations in Liguria Region (North of Italy). (a) A 288-kW photovoltaic field on the roof of the Ferrania Solis s.r.l. building that hosts a photovoltaic module production line. (Courtesy of Ferrania Solis s.r.l. (http://www.ferraniasolis.com/).) (b) A 190-kW photovoltaic field on the roof of an industrial building within the Port of Genoa. (Courtesy of Ferrania Solis s.r.l.) (c) A 5-kW photovoltaic field on the roof of a villa in Liguria. (Courtesy of Ferrania Solis s.r.l.) (d) A 121-kW photovoltaic field on the roof of Savona cruise terminal. (Courtesy of S.V. Port Service s.r.l. (http://www.svport.it).)

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Microgrid Design and Operation: Toward Smart Energy in Cities

power, a lower voltage implies high current and so higher electrical cable crosssections and costlier breakers, whereas a higher voltage determines more expensive protection systems) and the choice of the most suitable panel mounting system (that has to be flexible and cost-effective). Photovoltaic fields can have fixed modules or solar tracking systems (with one or two degrees of freedom, if they can vary tilt or azimuth angles or both). As a consequence, the energy production of a photovoltaic field depends on many factors, such as surface area of photovoltaic modules and their orientation (azimuth and tilt angles), solar radiation (that is a function of season, time during the day, latitude of the site), efficiency of the cells, efficiency of the balance of system (all components of the photovoltaic system other than the modules), ambient temperature, cleanliness of solar modules, shading effects, and mismatch losses. As reported in [20], in the past 10 years, the efficiency of average commercial wafer-based silicon modules increased from about 12% to 17% (Super-mono 21%), whereas CdTe module efficiency increased from 9% to 16%; moreover, in [20] maximum cell efficiency values obtained during laboratory tests were reported: 26.7% for monocrystalline and 22.3% for multicrystalline silicon wafer-based technology, 21.0% for CdTe and 21.7% for CIGS thin-film technologies. Higher values of cell efficiency (about 46%) can be obtained by multijunction solar cells [20]: using them, the module efficiency can reach maximum values of 39%. However, inverter-rated efficiency is typically equal to 98% or higher. 2.4.2  Small Hydro Power Plants

As reported by the World Energy Council (https://www.worldenergy.org/data/ resources/resource/hydropower/), hydropower is the leading renewable source for electricity generation globally, supplying 71% of all renewable electricity; reaching 1,064 GW of installed capacity in 2016, it generated 16.4% of the world’s electricity from all sources. The hydropower-installed capacity divided by region was equal to: 511 GW Asia (not including East Asia), 381 GW East Asia, 293 GW Europe, 193 GW North America, 159 GW Latin America and the Caribbean, 72.3 GW South and Central Asia, 57.8 GW Southeast Asia and the Pacific Islands, 22.9 GW Africa, and 20.6 GW Middle East and North Africa. In 2016, the top five countries producing electricity from the hydro source were: China (1,181 TWh per year), Brazil (410 TWh per year), Canada (380 TWh per year), the United States (266 TWh per year), and Russia (178 TWh per year) (https://www.statista.com/statistics/474799/ global-hydropower-generation-by-major-country/). As assessed in [35], hydro power can be one of the most reliable and costeffective methods to produce electricity. Furthermore, hydro power plants can be used to improve the grid stability and to mitigate the effect of the connection



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23

to the grid of other intermittent renewable energy sources, such as solar and wind [36]. In [37], it was noted that the golden age of hydro power was the first half of the twentieth century, before fossil fuel sources became the dominant primary energy source in the electricity generation sector of developed countries; in those years, many very large size dams and hydropower stations were built in North America and Europe. Today, large size hydro power plants are being built in emerging countries (China, India, Brazil), whereas in Europe the attention is directed to small hydro, defined in [35] as the sector of power plants characterized by a rated power lower than 10 MW. As highlighted in [38], in Europe small hydro is attracting growing interest because of the lack of suitable sites for large installations and the environmental effects of related civil structures; nevertheless, the small hydro potential is still largely unexploited because of strict legal rights for water use and environmental requirements, set out by environmental impact assessment and water framework directives [38]. However, a small hydro plant can blend in with its surroundings with no environmental impacts if it is well designed, taking into account compensatory measures for its implementation and the observance of regulatory requirements, such as those related to the minimum ecological flow of each river [35]. Moreover, hydro power on a small scale is also considered one of the most cost-effective energy technologies for rural electrification in less developed countries [37]. Most of the small hydro power plants are run-of-the-river plants, even if reservoir plants are also employed. The global efficiency of a hydro power plant is defined as:

ηg =

Pel ⋅ 100 = η p ⋅ ηt ⋅ ηm ⋅ ηel  ρ ⋅Q ⋅ g ⋅ H 0

(2.5)

where Pel is the rated electrical power, ρ is the water density, Q is the rated volumetric flow rate entering the turbine, g is the gravitational acceleration, and H0 is the gross head (difference between hydraulic heads in the upstream and downstream reservoirs) usually equal to the geodetic head. As shown by (2.5), the global efficiency ηg is given by the multiplication of four different terms: the pipe efficiency ηp (ratio between the net and the gross head, typically in the range of 90% to 95%), the turbine efficiency ηt (80% to 92%), the mechanical efficiency ηm (97% to 98%), and the electrical generator efficiency ηel (97% to 98%). The core of a hydro power plant is the turbine, which converts hydraulic potential energy into mechanical shaft power that can be used to drive an electrical generator or other machinery [37]. Turbines can be classified into two categories: impulse turbines and reaction turbines. As explained in [36], in

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Microgrid Design and Operation: Toward Smart Energy in Cities

impulse turbines, the hydraulic head is converted to kinetic energy before water enters the runner, this last operating at ambient pressure; however, reaction turbines are characterized by a runner fully immersed in water and by both fluid pressure and velocity decreasing from the runner inlet to outlet. The three parameters that describe a hydraulic turbine are: the rotational speed n, the volumetric flow rate Q , and the net head H. Typical impulse turbines are the Pelton, Turgo, and cross-flow types, whereas reaction turbines are Francis, Propeller, and Kaplan [36–42]; some photos of the aforesaid turbines are shown in Figures 2.5 and 2.6. Pelton, Turgo, and multijet Pelton are suitable for high-head (>50m) installations, and cross-flow, Turgo, multijet Pelton, and Francis (spiral case) are used for medium-head (10 to 50m) installations, whereas cross-flow, Francis (open-flume), Propeller, and Kaplan are adopted in case of low-head (500°C) is transferred to the engine working fluid (typically helium or hydrogen) and converted into mechanical energy (by expansion and compression of the fluid); mechanical energy is then converted into electrical energy with a permanent magnet synchronous generator. The heat discharged from the engine is usually used, in a cogeneration mode, for heating purposes. A typical small Dish Stirling system is characterized by a rated electrical power of 1 kW and a rated thermal power of 3 kW; the electrical efficiency is about 14%, while the thermal efficiency is about 42% [2, 12]. As reported in [87], small concentrating solar power systems have also a potential market for offgrid applications and rural areas. 2.7.4  Fuel Cells

Stoichiometric oxidation is the basis of the direct conversion from chemical to electrical energy in fuel cells. Fuel and oxidant are supplied continuously during operation; they react only through the intermediary of ionized molecules of the electrolyte, which is a good conductor for ions but an insulator for electrons, which are stripped from the reactants and flow through an external circuit producing work. The electrochemical reaction is exothermic and the released heat can be used in combined heat and power plants and combined cooling, heat, and power plants [91]. If fuel cells use natural gas as fuel source, then a reforming process is present to produce hydrogen, which takes part in the electrochemical reaction with the oxygen contained in the air. Depending on the used electrolyte, there are various types of fuel cells: alkaline, polymer, phosphoric, molten carbonate, and solid oxide [84, 91–93]. As assessed in [93], fuel cells are compatible with



Technology Overview: Devices and Equipment

45

renewable sources and modern energy carriers (i.e., hydrogen) for sustainable development and energy security, and they can be used in portable, stationary, and transportation power generation.

2.8  Electrical Storage Systems The massive diffusion of renewable energy source power plants within smart microgrids is usually coupled with the installation of electrical storage technologies that are acquiring more importance to compensate the fluctuation of the renewable energy source production and to provide ancillary services to the grid [94]; as highlighted in [95], small-distributed energy storage devices can be used to increase self-consumption of generated energy inside microgrids, helping also to flatten the daily load curve of the electrical power system. Electrical storage systems can be used to smooth the load peaks on the grid, participate in reactive power and voltage regulation and active power and frequency regulation, provide spinning reserve to the electricity market, contribute to solve grid congestions, and defer investments on the grid consequent to the increase in loads. There are a lot of electrical storage technologies (capacitors, batteries, flywheels, superconducting magnetic storage, compressed air, pumped storage hydro), each one suitable for a certain purpose, and so characterized by different values of storage capacity and charge or discharge time. In accordance with the classification provided in [95], electrical storage systems can be divided into two categories: dynamic energy storage devices having high power rates and small energy capacities, with the capability of very fast charge and discharge (flywheels, superconducting magnetic energy storage, electric double layer capacitors) and long-term energy storage devices with higher energy capacity and usually smaller power rating (batteries, pumped hydro-energy storage, compressed air energy storage). With regard to battery energy storage systems, extensively used in microgrids, different technologies are available on the market: Li-ions, Na/S, Na/ NiCl2, lead acid, Ni/Cd, Pb/Sb liquid metal, and so forth; batteries are made of stacked cells where chemical energy is converted to electrical energy and vice versa, and the desired battery voltage as well as current levels are obtained by electrically connecting the cells in series and parallel [96]. They are available in different sizes and capacities ranging from less than 100W to several megawatts, with estimated overall efficiency values in the range of 60% to 80%, depending on the operational cycle and the electrochemistry type within the batteries [97]. Moreover, it is important to note that vehicles with batteries (full-electric or hybrid) can also be considered as battery energy storage systems: indeed, in vehicle-to-grid applications, they can be used as both loads (when charged)

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Microgrid Design and Operation: Toward Smart Energy in Cities

and generator (when discharged), thus providing ancillary services to the power system [97]. The most important features of a battery energy storage system are: the size (number of battery modules, capacity, and energy density), the layout (number of batteries connected in series and number of batteries connected in parallel), the rated voltage and current, the limits in charging and discharging power levels (dependent on different possible charging and discharging modes), the minimum state of charge, the life span (expressed in terms of number of cycles), the operating temperature, and the self-discharge rate [96, 97].

2.9  Power Electronic Converters Distributed energy resource units, in terms of their interface with a microgrid, are divided into two groups. The first group includes conventional or rotary units that are interfaced to the microgrid through rotating machines [98]. The second group consists of electronically coupled units that utilize power electronic converters to provide the coupling media with the host system. The control concepts, strategies, and characteristics of power electronic converters, as the interface media for most types of distributed generation units (and storage devices), are significantly different than those of the conventional rotating machines. Therefore, the control strategies and dynamic behavior of a microgrid, particularly in an autonomous mode of operation, can be noticeably different than that of a conventional power system. The reasons why power electronic devices are employed to connect distributed energy resource units to a microgrid are essentially to: 1. Provide energy conversion that can be: (a) From DC to AC for storage systems and energy sources that produce DC voltage and current (e.g., the photovoltaic units); (b) From AC to AC, which is the case for example of combined heat and power units (e.g., cogenerative microturbines) or wind plants. This is essentially done to let the rotating machine at its optimal speed and, contemporarily, to generate voltages at the network frequency. 2. ������������������������������������������������������������������������� Exploit the power electronic converters flexibility, allowing the distributed generation or Dish Stirling unit to provide also ancillary services. More specifically, one has that: (a) For photovoltaic units (see Figure 2.14) and storage systems the connection is obtained by means of a DC/DC converter and a DC/AC converter (typically a PWM inverter [99]). The DC/DC



Technology Overview: Devices and Equipment

47

Figure 2.14  Photovoltaic microgrid connection.

converter guarantees that the inverter input DC voltage is constant no matter the fluctuations of the voltage across either the photovoltaic unit (due to the maximum power point tracking) or the storage device (due to the charging and discharging of the battery). (b) For wind turbines, one typically adopts two inverters in a back-toback configuration (see Figure 2.15). The machine-side converter aims at tracking the maximum power point and pursuing another control objective that depends on the employed electric machine. In particular, if a doubly fed induction generator configuration is used, the second machine-side converter degree of freedom controls the reactive power exchanged at the stator terminals. When using a synchronous generator, one often minimizes the stator current while providing the torque corresponding to the maximum power point; another possibility is to regulate the stator voltage according to the V/f control strategy (see [100–102] for details). The grid-side converter controls the DC voltage and the reactive power exchange with the main grid. (c) The case of combined heat and power units is often similar to the wind turbine equipped with a direct drive permanent magnet synchronous generator; as a consequence, the connection and the control aims are basically the same as what stated before.

Figure 2.15�  Wind turbine network connection via permanent magnet synchronous generator.

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2.10  Conclusions In this chapter, the main technologies adopted in microgrids to generate electrical and thermal energy have been analyzed. For each technology, the main technical features have been described and some considerations derived from the experience of the authors in the field have been proposed.

References [1] Bortolini, M., M. Gamberi, and A. Graziani, “Technical and Economic Design of Photovoltaic and Battery Energy Storage System,” Energy Conversion and Management, Vol. 86, 2014, pp. 81–92. [2] Bracco, S., et al., “A Pilot Facility for Analysis and Simulation of Smart Microgrids Feeding Smart Buildings,” Renewable and Sustainable Energy Reviews, Vol. 58, May 2016, pp. 1247–1255. [3] Punda, L., et al., “Integration of Renewable Energy Sources in Southeast Europe: A Review of Incentive Mechanisms and Feasibility of Investments,” Renewable and Sustainable Energy Reviews, Vol. 71, 2017, pp. 77–88. [4] Zhang, D., et al., “Present Situation and Future Prospect of Renewable Energy in China,” Renewable and Sustainable Energy Reviews, Vol. 76, 2017, pp. 865–871. [5] Schmidt, J., R. Cancella, and A. O. Pereira, “An Optimal Mix of Solar PV, Wind and Hydro Power for a Low-Carbon Electricity Supply in Brazil,” Renewable Energy, Vol. 85, 2016, pp. 137–147. [6] Angrisani, G., et al., “Performance Assessment of Cogeneration and Trigeneration Systems for Small Scale Applications,” Energy Conversion and Management, Vol. 125, 2016, pp. 194–208. [7] U.S. Energy Information Administration, Annual Energy Outlook 2017 with Projections to 2050 (#AEO2017), January 5, 2017. [8] Enerdata, “Global Energy Statistical Year Book,” 2017. [9] Yoldaş, Y., et al., “Enhancing Smart Grid with Microgrids: Challenges and Opportunities,” Renewable and Sustainable Energy Reviews, Vol. 72, 2017, pp. 205–214. [10] Wu, L., T. Ortmeyer, and J. Li, “The Community Microgrid Distribution System of the Future,” The Electricity Journal, Vol. 29, 2016, pp. 16–21. [11] Berry, A., D. Cornforth, and G. Platt, “Smart Grid: Integrating Renewable, Distributed and Efficient Energy – Part II,” Ch. 8 in What Role for Microgrids? New York: Academic Press/Elsevier, 2011, pp. 185–207. [12] Bracco, S., et al., “The University of Genoa Smart Polygeneration Microgrid Test-Bed Facility: The Overall System, the Technologies and the Research Challenges,” Renewable and Sustainable Energy Reviews, Vol. 18, 2013, pp. 442–459.



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[13] Basu, A. K., S. P. Chowdhury, and S. Paul, “Microgrids Research: A Review of Experimental Microgrids and Test Systems,” Renewable and Sustainable Energy Reviews, Vol. 15, 2011, pp. 4348–4356. [14] Bracco, S., G. Dentici, and S. Siri, “DESOD: A Mathematical Programming Tool to Optimally Design a Distributed Energy System,” Energy, Vol. 100, April 2016, pp. 298– 309. [15] Bracco, S., et al., “Optimal Planning of the Energy Production Mix in Smart Districts Including Renewable and Cogeneration Power Plants,” Proc. of 2016 IEEE Int. Smart Cities Conference (ISC2), Trento, Italy, September 12–15, 2016. [16] Mehleri, E., et al., “A Mathematical Programming Approach for Optimal Design of Distributed Energy Systems at the Neighbourhood Level,” Energy, Vol. 44, 2013, pp. 96–104. [17] Ren, H., and W. Gao, “A MILP Model for Integrated Plan and Evaluation of Distributed Energy Systems,” Applied Energy, Vol. 87, 2010, pp. 1001–1014. [18] Zhou, Z., et al., “A Two-Stage Stochastic Programming Model for the Optimal Design of Distributed Energy Systems,” Applied Energy, Vol. 103, 2013, pp. 135–144. [19] Bracco, S., et al., “A Dynamic Optimization-Based Architecture for Polygeneration Microgrids with Tri-Generation, Renewables, Storage Systems and Electrical Vehicles,” Energy Conversion and Management, Vol. 96, 2015, pp. 511–520. [20] Fraunhofer Institute for Solar Energy Systems (ISE), PSE AG, “Photovoltaics Report,” Freiburg, February 26, 2018. [21] Ren21, “Renewables 2017: Global Status Report,” Paris, France, 2017. [22] International Finance Corporation, “Utility-Scale Solar Photovoltaic Power Plants – A Project Developer’s Guide,” Washington, D.C., 2015. [23] Attari, K., A. Elyaakoubi, and A. Asselman, “Performance Analysis and Investigation of a Grid-Connected Photovoltaic Installation in Morocco,” Energy Reports, Vol. 2, 2016, pp. 261–266. [24] Bhakta, S., and V. Mukherjee, “Solar Potential Assessment and Performance Indices Analysis of Photovoltaic Generator for Isolated Lakshadweep Island of India,” Sustainable Energy Technologies and Assessments, Vol. 17, 2016, pp. 1–10. [25] Dolara, A., et al., “Performance Analysis of a Single-Axis Tracking PV System,” IEEE Journal of Photovoltaics, Vol. 2, No. 4, October 2012, pp. 524–531. [26] Su, Y., et al., “Real-Time Prediction Models for Output Power and Efficiency of GridConnected Solar Photovoltaic Systems,” Applied Energy, Vol. 93, 2012, pp. 319–326. [27] Tossa, A. K., et al., “Energy Performance of Different Silicon Photovoltaic Technologies Under Hot and Harsh Climate,” Energy, Vol. 103, 2016, pp. 261–270. [28] CEI EN 61724 Standard, “Photovoltaic System Performance Monitoring – Guidelines for Measurement, Data Exchange and Analysis,” 1999. [29] Sprenger, W., H. R. Wilson, and T. E. Kuhn, “Electricity Yield Simulation for the Building-Integrated Photovoltaic System Installed in the Main Building Roof of the

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[30] Madeti, S. R., and S. N. Singh, “Monitoring System for Photovoltaic Plants: A Review,” Renewable and Sustainable Energy Reviews, Vol. 67, January 2017, pp. 1180–1207. [31] World Energy Council, “World Energy Perspectives – Variable Renewables Integration in Electricity Systems: How to Get It Right,” London, U.K., 2016. [32] Fara, L., and D. Craciunescu, “Output Analysis of Stand-Alone PV Systems: Modeling, Simulation and Control,” Energy Procedia, Vol. 112, 2017, pp. 595–605. [33] Kulworawanichpong, T., and J. J. Mwambeleko, “Design and Costing of a StandAlone Solar Photovoltaic System for a Tanzanian Rural Household,” Sustainable Energy Technologies and Assessments, Vol. 12, 2015, pp. 53–59. [34] Mirzaei, A., et al., “Design and Construction of a Charge Controller for Stand-Alone PV/ Battery Hybrid System by Using a New Control Strategy and Power Management,” Solar Energy, Vol. 149, 2017, pp. 132–144. [35] European Small Hydropower Association (ESHA), “Small Hydropower Roadmap: Condensed Research Data for EU-27,” 2012. [36] Ardizzon, G., G. Cavazzini, and G. Pavesi, “A New Generation of Small Hydro and Pumped-Hydro Power Plants: Advances and Future Challenges,” Renewable and Sustainable Energy Reviews, Vol. 31, 2014, pp. 746–761. [37] Paish, O., “Small Hydro Power: Technology and Current Status,” Renewable and Sustainable Energy Reviews, Vol. 6, 2012, pp. 537–556. [38] Carapellucci, R., L. Giordano, and F. Pierguidi, “Techno-Economic Evaluation of Small-Hydro Power Plants: Modelling and Characterisation of Abruzzo Region in Italy,” Renewable Energy, Vol. 75, 2015, pp. 395–406. [39] Jawahar, C. P., and P. A. Michael, “A Review on Turbines for Micro Hydro Power Plant,” Renewable and Sustainable Energy Reviews, Vol. 72, 2017, pp. 882–887. [40] Sachdev, H. S., A. K. Akella, and N. Kumar, “Analysis and Evaluation of Small Hydropower Plants: A Bibliographical Survey,” Renewable and Sustainable Energy Reviews, Vol. 51, 2015, pp. 1013–1022. [41] The World Small Hydropower Development Report 2016: United Nations Industrial Development Organization, Vienna, and International Center on Small Hydro Power, Hangzhou, 2016. [42] European Small Hydropower Association (ESHA), “Guide on How to Develop a Small Hydropower Plant,” 2004. [43] Global Wind Energy Council (GWEC), “Global Wind Statistics 2016,” 2017. [44] Wind Europe, “Wind in Power – 2016 European Statistics,” February 2017. [45] Tummala, A., et al., “A Review on Small Scale Wind Turbines,” Renewable and Sustainable Energy Reviews, Vol. 56, 2016, pp. 1351–1371. [46] Bukala, J., et al., “Investigation of Parameters Influencing the Efficiency of Small Wind Turbines,” Journal of Wind Engineering and Industrial Aerodynamics, Vol. 146, 2015, pp. 29–38.



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[47] Grieser, B., Y. Sunak, and R. Madlener, “Economics of Small Wind Turbines in Urban Settings: An Empirical Investigation for Germany,” Renewable Energy, Vol. 78, 2015, pp. 334–350. [48] Staudt, L., “Design and Development of Small Wind Turbines,” in WIT Transactions on State of the Art in Science and Engineering, W. Tong (ed.), Vol. 44, Chap. 7, Southampton, UK: WIT Press, 2010. [49] World Wind Energy Association (WWEA), “2016 Small Wind World Report,” March 2016. [50] Casini, M., “Small Vertical Axis Wind Turbines for Energy Efficiency of Buildings,” Journal of Clean Energy Technologies, Vol. 4, No. 1, January 2016. [51] Castellano, R. N., Alternative Energy Technologies: Opportunities and Markets, Philadelphia, PA: Old City Publishing, 2012. [52] Earnest, J., Wind Power Technology, 2nd ed., Delhi, India: PHI Learning Private Limited, 2015. [53] Ragheb, M., and A. M. Ragheb, “Wind Turbines Theory - The Betz Equation and Optimal Rotor Tip Speed Ratio,” in Fundamental and Advanced Topics in Wind Power, R. Carriveau, (ed.), Rijeka, Croatia: InTech, 2011. [54] Lydia, M., et al., “A Comprehensive Review on Wind Turbine Power Curve Modeling Techniques,” Renewable and Sustainable Energy Reviews, Vol. 30, 2014, pp. 452–460. [55] AEE – Institute for Sustainable Technologies, “Thermal Use of Solar Energy,” Austria, 2009. [56] O’Hegarty, R., O. Kinnane, and S. J. McCormack, “Review and Analysis of Solar Thermal Facades,” Solar Energy, Vol. 135, 2016, pp. 408–422. [57] Vignali, G., “Environmental Assessment of Domestic Boilers: A Comparison of Condensing and Traditional Technology Using Life Cycle Assessment Methodology,” Journal of Cleaner Production, Vol. 142, 2017, pp. 2493–2508. [58] Rezaie, B., and M. A. Rosen, “District Heating and Cooling: Review of Technology and Potential Enhancements,” Applied Energy, Vol. 93, 2012, pp. 2–10. [59] Baldi, S., et al., “Real-Time Monitoring Efficiency and Performance Degradation of Condensing Boilers,” Energy Conversion and Management, Vol. 136, 2017, pp. 329–339. [60] Energy Saving Trust, Domestic Heating by Gas: Boiler Systems – Guidance for Installers and Specifiers, 2008 Edition, London, U.K., 2008. [61] Eriksen, V. L., Heat Recovery Steam Generator Technology, Woodhead Publishing Series in Energy, New York: Elsevier, 2017. [62] Fischer, D., and H. Madani, “On Heat Pumps in Smart Grids: A Review,” Renewable and Sustainable Energy Reviews, Vol. 70, 2017, pp. 342–357. [63] Harby, K., “Hydrocarbons and Their Mixtures as Alternatives to Environmental Unfriendly Halogenated Refrigerants: An Updated Overview,” Renewable and Sustainable Energy Reviews, Vol. 73, 2017, pp. 1247–1264.

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[64] Ertesvåg, I. S., “Uncertainties in Heat-Pump Coefficient of Performance (COP) and Exergy Efficiency Based on Standardized Testing,” Energy and Buildings, Vol. 43, 2011, pp. 1937–1946. [65] Self, S. J., B. V. Reddy, and M. Rosen, “Geothermal Heat Pump Systems: Status Review and Comparison with Other Heating Options,” Applied Energy, Vol. 101, 2013, pp. 341– 348. [66] Seyfouri, Z., and M. Ameri, “Analysis of Integrated Compression – Absorption Refrigeration Systems Powered by a Microturbine,” International Journal of Refrigeration, Vol. 35, 2012, pp. 1639–1646. [67] Park, C. W., J. H. Jeong, and Y. T. Kang, “Energy Consumption Characteristics of an Absorption Chiller During the Partial Load Operation,” International Journal of Refrigeration, Vol. 27, 2004, pp. 948–954. [68] Moya, M., et al., “Performance Analysis of a Trigeneration System Based on a Micro Gas Turbine and an Air-Cooled, Indirect Fired, Ammonia-Water Absorption Chiller,” Applied Energy, Vol. 88, 2011, pp. 4424–4440. [69] Deng, J., R. Z. Wang, and G. Y. Han, “A Review of Thermally Activated Cooling Technologies for Combined Cooling, Heating and Power Systems,” Progress in Energy and Combustion Science, Vol. 37, 2011, pp. 172–203. [70] Florides, G. A., et al., “Design and Construction of a LiBr-Water Absorption Machine,” Energy Conversion and Management, Vol. 44, 2003, pp. 2483–2508. [71] Shirazi, A., et al., “A Comprehensive, Multi-Objective Optimization of Solar-Powered Absorption Chiller Systems for Air-Conditioning Applications,” Energy Conversion and Management, Vol. 132, 2017, pp. 281–306. [72] Canova, A., et al., “Emission Characterization and Evaluation of Natural Gas-Fueled Cogeneration Microturbines and Internal Combustion Engines,” Energy Conversion and Management, Vol. 49, 2008, pp. 2900–2909. [73] Harvey, S., C. Carcasci, and T. Berntsson, “Gas Turbines in District Heating Combined Heat and Power Systems: Influence of Performance on Heating Costs and Emissions,” Applied Thermal Engineering, Vol. 20, 2000, pp. 1075–1103. [74] Meybodi, M. A., and M. Benhia, “A Study on the Optimum Arrangement of Prime Movers in Small Scale Microturbine-Based CHP Systems,” Applied Thermal Engineering, Vol. 48, 2012, pp. 122–135. [75] Rosa do Nascimento, M. A., et al., “Micro Gas Turbine Engine: A Review,” Ch. 5, in Progress in Gas Turbine Performance, E. Benini (ed.) IntechOpen, 2013, pp. 107–141. [76] Soares, C., Microturbines: Applications for Distributed Energy Systems, New York: Academic Press/Elsevier, 2007. [77] Bracco, S., and F. Delfino, “A Mathematical Model for the Dynamic Simulation of Low Size Cogeneration Gas Turbines Within Smart Microgrids,” Energy, Vol. 119, 2017, pp. 710–723. [78] Wang, W., R. Cai, and N. Zhang, “General Characteristics of Single Shaft Microturbine Set at Variable Speed Operation and Its Optimization,” Applied Thermal Engineering, Vol. 24, 2004, pp. 1851–1863.



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[79] Saravanamuttoo, H. I. H., et al., Gas Turbine Theory, 6th ed., New York: Pearson Education, 2009. [80] Cho, H. M., and B. Q. He, “Spark Ignition Natural Gas Engines – A Review,” Energy Conversion and Management, Vol. 48, 2007, pp. 608–618. [81] Aussant, C. D., et al., “Residential Application of Internal Combustion Engine Based Cogeneration in Cold Climate – Canada,” Energy and Buildings, Vol. 41, 2009, pp. 1288– 1298. [82] Ferrari, G., “Internal Combustion Engines,” II Edition, Società Editrice Esculapio, 2014. [83] U.S. Environmental Protection Agency, “Catalog of CHP Technologies – Section 2. Technology Characterization – Reciprocating Internal Combustion Engines,” March 2015. [84] Onovwiona, H. I., and V. I. Ugursal, “Residential Cogeneration Systems: Review of the Current Technology,” Renewable and Sustainable Energy Reviews, Vol. 10, 2006, pp. 389– 341. [85] Energy and Environmental Analysis Inc., an ICF Company, “Technology Characterization: Reciprocating Engines,” Virginia, December 2008. [86] Kwon, E. C., et al., “Performance of Small Spark Ignition Engine Fueled with Biogas at Different Compression Ratio and Various Carbon Dioxide Dilution,” Fuel, Vol. 196, 2017, pp. 217–224. [87] Giovannelli, A., “State of the Art on Small-Scale Concentrated Solar Power Plants,” Energy Procedia, Vol. 82, 2015, pp. 607–614. [88] Hoffschmidt, B., et al., “Concentrating Solar Power,” Comprehensive Renewable Energy, Vol. 3, 2012, pp. 595–636. [89] Behar, O., A. Khellaf, and K. Mohammedi, “A Review of Studies on Central Receiver Solar Thermal Power Plants,” Renewable and Sustainable Energy Reviews, Vol. 23, 2013, pp. 12–39. [90] Kadri, Y., and H. H. Abdallah, “Performance Evaluation of a Stand-Alone Solar Dish Stirling System for Power Generation Suitable for Off-Grid Rural Electrification,” Energy Conversion and Management, Vol. 129, 2016, pp. 140–156. [91] Elmer, T., et al., “Fuel Cell Technology for Domestic Built Environment Applications: State of the Art Review,” Renewable and Sustainable Energy Reviews, Vol. 42, 2015, pp. 913–931. [92] Zhao, T. S., K. D. Kreuer, and T. V. Nguyen, Advances in Fuel Cells, Vol. 1, New York: Elsevier, 2007. [93] Sharaf, O. Z., and M. F. Orhan, “An Overview of Fuel Cell Technology: Fundamentals and Applications,” Renewable and Sustainable Energy Reviews, Vol. 32, 2014, pp. 810– 853. [94] IRENA – International Renewable Energy Agency, “Renewables and Electricity Storage – A Technology Roadmap for Remap 2030,” June 2015.

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[95] Jarnut, M., S. Wermiński, and B. Waśkowicz, “Comparative Analysis of Selected Energy Storage Technologies for Prosumers-Owned Microgrids,” Renewable and Sustainable Energy Reviews, Vol. 74, 2017, pp. 925–937. [96] Divya, K. C., and J. Østergaard, “Battery Energy Storage Technology for Power Systems – An Overview,” Electric Power Systems Research, Vol. 79, 2009, pp. 511–520. [97] Tan, X., Q. Li, and H. Wang, “Advances and Trends of Energy Storage Technology in Microgrid,” Electrical Power and Energy Systems, Vol. 44, 2013, pp. 179–191. [98] Katiraei, F., et al., “Microgrids Management – Controls and Operation Aspects of Microgrids,” IEEE Power & Energy Management, May 2008, pp. 54–63. [99] Mohan, N., T. M. Undeland, and W. P. Robbins, Power Electronics, New York: John Wiley & Sons, 1995. [100] Li, S., T. A. Haskew, and L. Xu, “Conventional and Novel Control Designs for Direct Driven PMSG Wind Turbines,” Electric Power Systems Research, Vol. 80, 2010, pp. 328– 338. [101] Bonfiglio, A., et al., “Steady-State Assessments of PMSGs in Wind Generating Units,” International Journal of Electrical Power and Energy Systems, Vol. 90, 2017, pp. 87–93. [102] Bonfiglio, A., et al., “Modelling and Control of Wind Generating Units Equipped with Permanent Magnet Synchronous Generators,” Energies, Vol. 10, No. 1, 2017.

3 Microgrid Installations: State of the Art Microgrids are able to integrate different distributed and heterogeneous sources, either dispatchable or stochastic (these latter typically are the renewables like wind and solar), and require intelligent management methods and efficient design to meet the needs of the area in which they are located. Generally, microgrids are low-voltage internal distribution networks installed in small areas (like university campus sites or districts), but also buildings or industrial plants can themselves be seen as microgrids. Energy management systems are vital tools used to optimally operate and schedule microgrids. A microgrid can operate independently without connection to the main distribution grid during islanding mode. The opportunities and benefits of integrating DERs into a microgrid exist for end-users, electricity utilities, transmitters, and distributors for the satisfaction of different loads like residential, office, industrial parks, and university campuses. For these end-users, on-site microgrid implementation can provide improved electric service reliability, better power quality, and a reduction in electricity costs, which have been estimated in 20% to 25% [1]. Several works are present in literature that describe the role of microgrids, like [1], and that review their use around the world [2, 3]. Microgrids’ implementation can benefit local utilities by allowing system repairs without affecting customer loads, providing dispatchable load for use during peak power conditions, and lowering stress on the transmission and distribution system. They can also benefit users that become more independent from the main grid. Then, they are fundamental to test new technologies, methods, and tools for institutional and industrial stakeholders, distribution system operators, and research institutions in the smart grid area [4–6]. Microgrids are generally facilities with the aim of experimentation, even if in the past few years they entered the commercialization phases by the 55

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implementation of pilot projects. However, scaling up some of the solutions adopted in microgrids (e.g., electrical storage systems) to bigger applications is proving to be difficult because renewable energy and storage technologies are still expensive as far as capital expenditures are concerned, and pilots are demonstrating that several challenges exist in microgrid operation and control [1]. Generally, microgrids are compared on the basis of several issues like [2, 3]: • Distribution system (alternating current (AC), direct current (DC), low voltage, radial, ring); • Presence of renewables; • Presence and kind of storage systems; • Operation modes; • Presence and kind (centralized, distributed) of a controller; • Presence of combined heat and power and polygeneration; • Spatial dimension and load size. The focus of this chapter is more on microgrids that are bigger than the laboratory scale and/or that can be representative for distributed generation in a district. In the following sections, the main microgrid installations are reported and references are provided. Microgrids are described and grouped on a geographical basis (Americas, Europe, and finally Asia, Oceania, and Africa).

3.1  Microgrids in America In the United States, many research projects are focused on the development of microgrids for peak load reduction. Among them, it is worthwhile to mention the Consortium for Electrical Reliability Technology Solutions (CERTS) Microgrid Demonstration project, which is a full-scale test bed installed near Columbus, Ohio, and operated by American Electric Power [7]. The test bed is shown in Figure 3.1. Different feeders are present and some of them can be islanded from the utility using a static switch, while loads can be remotely controlled. The CERTS team has primarily focused on electrical characteristics of each source although an equally valuable set of thermal characteristics also exists, with the aim of better understanding how to pair sources together in beneficial ways. Among sources, it is possible to mention a directly coupled machine (generally consisting of synchronous or induction machines coupled to a prime mover such as an engine or a turbine) and an inverter-coupled machine. Each source can have a number of prime movers as their input such as solar, wind,



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Figure 3.1  The CERTS microgrid.

energy storage, fuel cell, engine, and turbine. With regard to loads, a shedding process can be performed centrally by an energy management system or in a distributed fashion with preset shedding criteria or a hybrid of the two systems. Two software tools for microgrid deployment have been developed in relation with CERTS project: mu-Grid, developed by the Georgia Institute of Technology, and the Distributed Energy Resources Customer Adoption Model (DER-CAM) in use at the Berkeley Lab [8]. The CERTS project belongs to the Renewable and Distributed Systems Integration (RDSI) program, managed by the U.S. Department of Energy, which is sponsoring several demonstration projects in the United States that deal with technologies like photovoltaics, fuel cells, hydro turbines, pumped water storage, plug-in hybrid vehicles, wind turbines, compressed air storage, and feeder automation systems. For example: • The Never-Failing Perfect Power Prototype, developed by the Illinois Institute of Technology, includes distributed generation, combined heat and power, and renewables [9].

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• The Fort Collins Demonstration Project is focused on the research, development, and demonstration of a coordinated and integrated system of 3.5 MW of mixed distributed resources in Fort Collins, Colorado, to achieve a 20% to 30% peak load reduction on two distribution feeders [10]. • The West Virginia Supercircuit Project includes different technologies such as biodiesel combustion engine, photovoltaics, energy storage, advanced wireless communication, and dynamic feeder reconfiguration [11]. Another significant installation is the University of Texas at Arlington (UTA) microgrid test bed [12]. The UTA Microgrid lab, though of small dimensions, includes three independent microgrids operating in grid-connected or islanded modes. Each grid has a 24-VDC bus as well as a 120-VAC to 60-Hz AC bus. The typical configuration relies on two 12-VDC lead-acid batteries, which are coupled in series as the primary energy storage. The batteries on each grid are recharged using solar panels and wind turbines, or a fuel cell and DC/ AC inverter. Figure 3.2 reports the interaction of the microgrid with the control system. The University of Texas at Austin houses [13] are often described as the most integrated and largest microgrid in the United States. Built in 1929 as a steam plant, the facility has evolved to provide 100% of the power, heat, and cooling for a 20-million square-foot campus with 150 buildings. The facility features a combined heat and power plant that provides 135-MW (62-MW peak) and 1.2 million pounds per hour of steam generation (300kW peak). The system also includes 45,000 tons of chilled water capacity in four plants (33kW peak), a 4 million gallon/36,000 ton-hour thermal energy storage tank, and 6 miles of distribution tunnels to distribute hot water and steam. The microgrid engages in real-time load balancing for steam and chilled water. The microgrid test bed at Albuquerque, New Mexico, realized by the Shimizu Institute of Technology (SIT) [14], is characterized by a radial type distribution system and serves residential and commercial loads. It consists of a gas engine generator (240 kW), fuel cell (80 kW), lead-acid battery (50 kW/100 kW), photovoltaic (50 kW), dummy load (100 kW), and electric load of 400 kW. The facility has a building energy management system, a heat source equipment controller, and a power supply equipment controller to regulate both supply and demand. In Canada, one of the main microgrids, described in [15, 16], was built by the British Columbia Institute of Technology (BCIT) with the goal to design and implement a 1.2-MW microgrid living laboratory on the Burnaby Campus in Vancouver (Figure 3.3). This campus-type microgrid consists of two wind



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Figure 3.2  The UTA microgrid layout.

Figure 3.3  The British Columbia microgrid.

turbines (5 kW each), photovoltaic modules (300 kW), thermal turbine (250 kW), lithium-ion battery (550 kWh), and campus loads (electric vehicle charging stations, industrial load, classrooms, offices, and residences).

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Other microgrids in Canada are described in [17]. It is worth noting that, to face increasing difficulties for power utilities, due to aging infrastructure, rising demand for electricity and concerns for the industry’s environmental footprint, it is planned that Canada’s smart grid will become a network of integrated smart microgrids capable of load side management, peak-shaving, power conservation, and integration of local renewable energy generation. Specifically, in [17], the following microgrids are mentioned: • Kasabonika is a microgrid about 1,300 km away from Toronto, equipped with diesel generators (1,000, 600, and 400 kW) and wind turbines (3 with nominal power of 10 kW and 1 of 30 kW) for a demand of 850kW peak and 12 MWh/day. Moreover, the following additional plants could be installed in the near future: storage, photovoltaic, and wind. • Bella Coola is the microgrid located 439 km away from Vancouver and is characterized by a peak load of 3,800 kW, hydropower (700 + 1,420 kW), diesel generator (7,200 kW), storage electrolizer (300 kW), fuel cell (125 kW), and batteries (125 kW). A centralized controller based on the model predictive control (MPC) approach is installed. It is worth mentioning that the University of Chile has developed Chile’s first microgrid project [18] in a remote Andes Mountains community of 150 residents called Huatacondo. Prior to the microgrid installation, the community had its own electric network (independent from the macrogrid) operating 10 hours per day with power provided from a single diesel generator. The microgrid includes a 150-kW diesel generator, a 22-kW tracking solar PV system, a 3-kW wind turbine, a 170-kWh battery, and an energy management system that provides online set-points for generation units looking at the minimization of the operating costs, taking into account renewable resource forecast, load, solar tracking, and water consumption. In the Chilean Desert, there is another microgrid in Ollague [19]: it is composed by photovoltaic (205 kW), sodium nickel chloride storage technology (725 kWh), a wind turbine (30 kW), and a backup diesel generator (410 kVA). The off-grid installation is intended to minimize the use of diesel generator and significant improvements in terms of costs have been demonstrated. Other microgrids are present in the United States and other ones are being developed [20–26]. They are relevant in the state of art of microgrids and are mainly developed at the campus level. However, for the sake of brevity, they are not detailed in this chapter.



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3.2  Microgrids in Europe In European countries, many research and development projects, benefitting from national or European funding, are focused on smart grids, energy efficiency, renewable sources, and distributed generation. About smart grids, several projects are described in [2–5]. In Denmark, in particular, the Energinet.DK company aims to build and demonstrate a complete prototype (EcoGrid EU project) of a system characterized by more than 50% energy production from renewable sources. The same company is developing a project to control 300 smart heat pumps as if they were a unique energy storage facility. The PV-Island Bornholm project in Denmark aims at installing photovoltaics (5 MW), in order to test how this technology could be implemented in a future intelligent power system. In Germany, the RWE DAG is developing a concept of virtual power plant (a number of generators connected to a third-party grid, managed as a single power plant), while in Austria, Salzburg AG is constructing a demonstration building to evaluate advantages and disadvantages of smart grids in connection with buildings. Other important demonstration projects in Europe are: • The PREMIO project developed by Electricité de France (EDF) in the PACA region to integrate distributed generation units, storage systems, renewable energy resources, and loads; • The Bronsbergen Holiday Park microgrid in Netherlands where there are 108 cottages with PV modules characterized by a peak generation capacity of 315 kW and by a central energy storage system; • The residential microgrid of Am Steinweg in Stutensee, Germany, where the main energy sources are: a 28 kWe CHP unit, different photovoltaic installation (35 kW), and a lead-free battery bank with a bidirectional inverter; about 100 apartments are linked to the grid. Regarding the smart grid development in Italy, apart from the University of Genoa Smart Polygeneration Microgrid described in this book, it is important to cite the CESI Ricerca DER test bed, which is a low-voltage microgrid (400V), connected to the medium-voltage grid (23 kV) by means of a 800-kVA transformer and a suitable converter, and constituted by several DERs and controllable loads [27]. Then, there is a smart grid project in the La Sapienza University in Rome, where internal combustion engines, photovoltaics, fuel cells, micro gas turbines, and absorption chillers are installed to produce electricity and thermal energy for the university departments.

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Bronsbergen Holiday Park in Netherlands [28], shown in Figure 3.4, gives power to 208 holiday family units for a total peak load of 105 kW. A portion of the holiday homes (108) have been equipped with photovoltaic arrays and the total generation capacity is 315 kW. There are also two batteries to store and resupply energy when photovoltaic is not available. A transformer is used to connect to the 10-kV medium-voltage network via a 630-A fuse. The infrastructure at the Université de Technologie de Compiègne (UTC), in France [29], is an example of building integrated microgrid. Its main local source is a roof-mounted photovoltaic field, integrated with a storage system. These devices are connected to a DC link, which is linked with the AC section of the grid by means of a converter. The implemented tertiary control allows maximizing the local consumption of the photovoltaic production. Islanding operation is also possible, as the installed local generation can satisfy all the local loads and the control equipment make it possible to regulate voltage and frequency. The University of Seville microgrid [30, 31], Spain, is designed as a smallscale, domestic-level microgrid and its control is based on programmable logic controllers. It includes an electrolyzer and a fuel cell generator as a storage system (Figure 3.5). The power in excess from the photovoltaic field, when present, is used by the electrolyzer to produce hydrogen, which is converted back

Figure 3.4  Bronsbergen Holiday Park in Netherlands.



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Figure 3.5  The University of Seville microgrid.

to electric power by the fuel cell generator, when the solar production is insufficient or absent. Lead-acid batteries are also employed to regulate the voltage. All the devices are connected to a main DC bus, each one with a dedicated DC/DC converter. The main DC bus is then linked to both local loads and the distribution network via a single inverter. In the microgrid test bed at the Universidade do Porto, Portugal [32], a variety of loads and generation equipment are clustered in a traditional European AC three-phase low-voltage distribution system (400V/50 Hz), connected to a medium-voltage grid by a transformer installed in the local medium-voltage/low-voltage substation. The set of generators includes a wind turbine (15 kW), a storage system (30 kW), two photovoltaic systems (18 kW), and three microturbines (two rated 30 kW, one rated 60 kW). The microgrid is managed by a microgrid central controller, which performs the optimal economic

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scheduling of its sources, based on forecasts for the load profile. In addition, it implements functions for the security assessment of the microgrid. The microgrid installed in Gaidouromantra [33], on Kythnos Island (Figure 3.6), permanently operates off-grid (the microgrid is 4 km away from the nearest point of connection with the distribution network), feeding a small settlement of 12 houses by means of a single-phase AC distribution line. It includes a battery system, a diesel genset, and the control and monitoring equipment, plus a number of photovoltaic fields integrated in some of the houses. The battery system uses three single-phase inverters connected in parallel in a master-slave configuration; each inverter is switched on or off according to the power that the system has to exchange with the microgrid. The rated power of each inverter is 3.6 kW. The inverters can operate in isochronous mode or in frequency droop mode: this latter mode is exploited in conjunction with switching load controllers and the control equipment of the photovoltaic inverters to perform load shedding when the battery state of charge is low and to curtail the photovoltaic generation when the state of charge reaches its maximum. The rated capacity of the battery is 53 kWh. The diesel genset has a rated power of 5 kVA. The total peak power of the photovoltaic fields integrated in the houses is 10 kWp. A second photovoltaic system rated 2 kWp, mounted on the roof of the system housing, with a dedicated inverter and a 32-kWh battery bank, feeds the monitoring and control equipment. The installation at Utsira Island [34], built between 2003 and 2004 by the Norwegian energy company Norsk Hydro and the German wind turbine manufacturer Enercon, is located in the municipality of Utsira, Norway. It can be considered as one of the first large-scale test implementations of a renewable energy-based system, permanently operating in islanding mode, where the storage system guaranteeing the energy balance relies on stored hydrogen. The hydrogen is produced by an electrolyzer when there is an excess of wind

Figure 3.6  The Kythnos Island pilot microgrid.



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generation and is converted back to electric energy when needed, by both a fuel cell and a hydrogen-fed engine. The microgrid provides energy for domestic loads in the area. At the Electrical Energy Systems (EES) laboratory of the National Technical University of Athens (NTUA), several facilities have been realized for research purposes in the field of distributed generation and microgrids [35]. These facilities include two laboratory-level microgrids, one single-phase and one three-phase, which can be connected with a third single-phase microgrid, belonging to the electrical machines laboratory (see Figure 3.7). The installed components consist of photovoltaic generators, wind generators, batteries and loads, and are supervised with a dedicated supervisory control and data acquisition (SCADA) and human-machine interface (HMI). Specifically, in the EES laboratory, a single-phase microgrid, a photovoltaic generator (1.1 kW), a small wind turbine (1.7 kW), and a battery energy storage system (3.3 kW) are installed. In addition, controllable loads are present. Fast power converters allow the connection with the main grid, and the microgrid can operate both in grid-connected and islanding modes. The three-phase microgrid includes a lead-acid battery bank (6 kW), two photovoltaic inverters (2 kW and 3 kW each), and a load bank. The third single-phase microgrid consists of a battery energy storage system (3.3 kW), a photovoltaic generator (1.1 kWp), and loads. The three microgrids together form the multi-microgrid laboratory Infrastructure, outlined in Figure 3.7. A platform to implement multi-agent systems for distributed controls, including smart load controllers, is also available. Another relevant initiative is in progress at Science Central [36], a 24-acre city-development site in the center of Newcastle, United Kingdom. It represents a £250 million flagship project bringing together academia, the public sector, communities, business, and industry. The vision behind the project is to create a new urban quarter in the center of Newcastle, aimed at representing a reference model for sustainability and attracting scientific organisations for developing leading edge technologies. Science Central includes an 11-kV smart grid, geothermal borehole, combined heat and power, a heat and cooling network, grid scale electrical and thermal storage, a smart building, a big data and cloud computing center, an urban observatory, an urban traffic management center, and an electric vehicle-recharging station. In particular, the smart grid laboratory and the energy storage test bed allow the study of interactions of these devices and the whole building hosting them, with those installed in the Science Central site and with the electricity network and, in addition, with emulated networks. This is performed through dedicated real-time network emulators, which exploit a technique called power network-in-the-loop. In this way, the building loads, the local generation, and storage can, in addition to the local electrical network, be virtually interfaced

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Figure 3.7  The NTUA multi-microgrid.

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to any distribution network, significantly expanding the scope and value of related studies. The one-line diagram of the main distribution network of Science Central is reported in Figure 3.8.

3.3  Microgrids in Asia, Australia, and Africa A quite diverse scenario characterizes Asian microgrid projects, owing to the different economical and industrial conditions of the various countries in this region. The main driving factors range from the desire to increase the renewable share in the energy mix in highly developed areas to the need to provide energy in remote sites or islands, in regions still characterized by a high number of rural communities where the electrification is almost absent. Many research infrastructures have been built in universities or research centers, but several real-scale examples have also been implemented. A relevant number of initiatives have been carried out in Japan, mostly promoted by the New Energy and Industrial Technology Development Organization (NEDO) of Japan [37]. The first one is the Aichi Project: it started operation in 2005 at the World Exposition and was then moved to the Central Japan Airport City near Nagoya in 2006, where it now supplies an office building and a sewage plant via a private distribution line. It uses fuel cells as main sources: two molten carbonate fuel cells, with rated power 270 kW and 300 kW, four 200-kW phosphoric acid fuel cells, and a 50-kW solid oxide fuel cell. The molten carbonate fuel cells are fed by biogas generated from the high

Figure 3.8  Proposed electrical distribution of phase 1 of Science Central.

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temperature (1,200°C) treatment of wood waste and plastic bottles. Both the molten carbonate fuel cells and solid oxide fuel cells are employed for satisfying the base load, while the phosphoric acid fuel cells are used for regulation. Photovoltaic generation for a total of 330 kWp is also present, and a 500-kW sodium-sulfur (NAS) battery is used for balancing purposes. The system can operate both in grid-connected and islanding modes. Another example is the Hachinohe microgrid project [38], outlined in Figure 3.9: its main features are, first, that it provides both electrical energy and thermal energy and, second, that it relies on renewable sources and biomass only. The system includes three 170-kW gensets (for a total of 510 kW of rated power) fed by sewage digester gas, a 100-kW lead-acid battery bank, and a biomass boiler fed by 1 tons per hour of wood; photovoltaic fields and small wind turbines are also present. The microgrid is able to provide energy for seven buildings, located in the city of Hachinohe. A 6-kV distribution line, 5.4 km long and with two circuits to guarantee redundancy, is the main connecting infrastructure. The whole system is linked to the main grid at a single point of common coupling (PCC). Considerable savings have been achieved thanks to this infrastructure: in the early period of operation, from November 2005 to July 2006, primary energy consumption reduction was estimated at 57.3%, thanks to the decrease in electricity purchases, while carbon emissions were reduced by 47.8%. A 1-week-long islanding test was also performed in this period. The microgrid energy management system, specifically developed in the context of this project, is in charge of optimally satisfying the building demands for electricity and thermal power, by controlling the output of the generators and boilers and the energy exchanged by the storage devices, with the goals of minimizing operating costs and carbon dioxide emissions, and maintaining a constant power flow at the PCC. An unusual project is the one started in 2005 in Kyotango, a city north of Kyoto, under the lead of the local municipality. This project was one of the early demonstrations of virtual microgrid, involving a number of sources, over an area about 40 km wide: 50 kWp of photovoltaic fields, wind power for approximately the same amount, five biogas gensets, each one rated 80 kW, a molten carbonate fuel cell rated 250 kW, and a 100-kW battery bank. No dedicated electrical network is present: each unit is connected to the legacy distribution system. The unifying element of the microgrid is its control and management: the system is managed by a central energy control center, which exchanges information and commands with the DERs via Internet Protocol (IP) on the legacy telecom network, with the main goal of balancing demand and supply. Finally, in Sendai, NEDO promoted another project aimed at analyzing the possibility of using microgrid-based technology to enhance the quality of service and reliability of the supply, also in comparison with traditional

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Figure 3.9  System configuration of Hachinohe microgrid project.

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solutions, like uninterruptable power supply equipment. The project had also the goal of evaluating the economic viability of providing a different level of quality of service and reliability within the same infrastructure. The microgrid has a dedicated network, with a single PCC with the distribution grid. The major DERs installed are a 250-kW molten carbonate fuel cell, two 350-kW natural gas gensets, and a 50-kW battery bank. The microgrid directly supplies a number of DC loads. Then a number of AC users are also served (a university, a high school, and a sewage plant), offering four different levels of quality: A, B1, B2, and B3. The A level guaranties no interruptions; in addition, voltage level and waveform are controlled. The B levels are differentiated according to the availability and kind of backup employed in each of them: storage (B1), gensets (B2), and no backup (B3). Microgrids like the project in Sendai, able to operate also in islanding mode, can substantially increase the reliability of the power supply for the loads that they feed, proving to be of fundamental importance in case of major adverse events affecting a national power grid. A practical example of this was given during the March 2011 Japanese earthquake and the subsequent Fukushima Daichi meltdown: during the event, the Sendai Microgrid continued to supply power to its load despite the extensive damage to the power system in its area. The operation of the microgrid during a massive and totally unforeseen event provided insights in critical aspects concerning microgrid design. A complete description and discussion of the performances of Sendai Microgrid during the event, together with a list of lessons learned, can be found in [39]. Jeju Island, Korea’s southernmost island, was selected in 2009 by the Korean Ministry of Trade, Industry and Energy (MOTIE) as the test bed for microgrid and smart grid applications [40, 41]. A total of 12 consortia and 168 companies were involved in the Jeju Smart Grid Demonstration Project, creating, according to MOTIE, the world’s largest smart grid test bed, allowing participants to test advanced smart grid technologies. Several actions were considered: the development of smart appliances capable of taking advantage of real-time pricing, electric vehicle rental service, advanced building energy management system, advanced demand response system, factory energy management system, and electric bicycle-sharing service at Jeju National University. In the context of the activities carried out at Jeju, the smart renewable pilot project has been realized, consisting of a smart grid test bed facility that includes a megawatt-size wind farm, photovoltaic generation, and lithium battery systems. Lead-acid batteries and a small hydro are also present on the island. Several projects are being realized in China also, both at the research test bed level, to investigate innovative technologic aspects, and at the real-world application level, where the microgrid is built to solve particular problems in some areas and actually feed communities. As an example, the Nanjing University of Astronautics and Aeronautics (NUAA) microgrid [42] was devised as a test bed



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to perform researches on microgrids designed to rely on the master-slave configuration for the inverters of the sources. The main focus of the research activity is the seamless transition between the on-grid operation (master inverter in the active-reactive power (P-Q) control mode) and the off-grid operation (master inverter in the voltage/frequency (V/f ) control mode). The test facility includes a 2-kW single-phase photovoltaic inverter, a 17-kW three-phase photovoltaic inverter (both fed by programmable DC supplies simulating solar panels), a 100-kVA passive load bank, a 30-kVA active load unit, and a 15-kW custommade wind simulation system. This latter consists of a permanent-magnet motor-generator set, a programmable converter to drive the motor to simulate different wind turbine performances, and a 15-kW, three-phase, grid-tied wind turbine inverter for the grid interface. The programmable DC power supplies and the wind simulation converter are used instead of actual solar panels and wind turbines to maximize the flexibility of the test bed. The master inverter is rated 100 kVA; its operation is controlled by a high-speed digital signal processor (150-MHz floating-point unit). Finally, a programmable logic controllerbased system controls the microgrid, providing additional various functions, such as information management and data acquisition. An actual application is the one in Luxi Island, located off the eastern coast of China [43]. About 8,000 people, mainly fishermen, reside on the island. Before the construction of the microgrid, whose online diagram is reported in Figure 3.10, the electrical supply was guaranteed only by a substation, equipped with a 10-MVA transformer, connected to the main grid via a 35-kV submarine cable. The rated capacity of the connection is about 2 MW, less than the island summer load peak, estimated as about 3.5 MW. Furthermore, Luxi grid is connected at the end of Wenzhou Power Grid, that is, in a peripheral weak point, and, in addition, frequent damages occurring to the submarine cable make this power supply unreliable. As a consequence, load shedding was often applied, especially during summer, to preserve the operation of the island power network. To address this problem, a 10-kV grid-connected system has been realized, composed of two microgrids, each one able to operate in the islanding mode, or connected with the other one, or in parallel with the main grid. In the two microgrids, a total of 1.86-MW renewable sources are installed, composed of 300 kWp of photovoltaics and two 780-kW wind turbine generators. Furthermore, four 1-MWh lead-acid battery storage banks (rated power: 500 kW each), equipped with four single-stage power converters, are present. They are able to operate both in P/Q control mode and V/f control mode; their size was chosen to ensure the possibility to cope with the island peak load, usually lasting for 1 to 1.5 hours, guaranteeing also a safety margin, together with the 2-MW connection with the main grid.

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Figure 3.10  Dual-microgrid structure of Luxi Microgrid.

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Another research test bed is the microgrid of the Institute of Nuclear Energy Research (INER), built in 2009 [44]. It includes a 100-kW-high concentration photovoltaic generator, two wind generators (25 kW and 150 kW), fuel cells (rated power 2 kW), and storage devices. The main objectives motivating the realization of this infrastructure were, first, to demonstrate developments performed by INER in renewable technologies, in particular concerning highconcentration photovoltaic systems and wind turbine generator; and second, to test the distributed generators behavior in several anomalous operating conditions. In the islanding mode, the battery system serves as a master controller. In India, an increasing attention is devoted to microgrids, as a mean of realizing electrification in rural areas, in a cheap and reliable way, guaranteeing at the same time a limited impact on the environment [45]. A practical implementation of this view is the set of solar mini-grid and solar hybrid mini-grid projects, which have been implemented in the Indian part of the Sundarbans Islands by the West Bengal Renewable Energy Development Agency (WBREDA), in cooperation with the Indian Ministry of New and Renewable Energy (MNRE). As an example, on Sagar Island, part of this area, many hybrids distributed generation projects were realized, combining the electricity generation with water supply and partially replacing the diesel generators previously used, which guaranteed the power supply only for few hours every day, with 300 kWp of photovoltaics, along with 400 kW of diesel generations and 500 kW from a wind-diesel hybrid power system, to meet increased energy needs. An example of the activity carried out in Australia at the university level is the microgrid test bed at Griffith University [46, 47]. This system, outlined in Figure 3.11, was built with a number of goals: to provide a backbone for testing all aspects of a microgrid, including different forms of distributed generation, renewable sources, and energy storage technologies, as well as of inverter technologies and communication techniques; to increase energy efficiency through an optimized configuration; to provide several levels of robustness to the system; and finally, to be flexible enough to allow future developments. From the point of view of its structure, this test bed represents a hybrid AC/DC microgrid, able to operate both in the grid-connected and islanding modes. The system is integrated in buildings N44, N05, and N74 on the Nathan Campus. One of the main sources is a 15.5-kW photovoltaic field on the roof of the N44 building. A microturbine is used as a backup power supply to provide an uninterruptible power supply system. A smart DC/AC inverter is also included, which allows for the charging and discharging of a battery energy storage system. A wind turbine, a new battery system, and electric vehicle charging stations with vehicle-to-grid capabilities are planned for installation. For rural Africa, microgrids, for example, in the form of simple photovoltaic plus storage installations, are considered a viable technique for new electrification, but more complex projects also exist, in urban areas. The microgrid

Figure 3.11  Hybrid microgrid system configuration of Griffith University.

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built by ABB at its company South African headquarters in Longmeadow, Johannesburg, is an example of this latter kind of installations [48]. The facility covers an area of about 96,000 square meters and includes a 1-MVA/380-kWh PowerStore storage device, a 75-kW rooftop photovoltaic field, controlled and supervised by a Microgrid Plus, ABB’s dedicated control system for microgrids.

References [1] Soshinskaya, M., et al., “Microgrids: Experiences, Barriers and Success Factors,” Renewable and Sustainable Energy Reviews, Vol. 40, 2014, pp. 659–672. [2] Hossain, E., et al., “Microgrids Testbeds Around the World: State of the Art,” Energy Conversion and Management, Vol. 86, 2014, pp. 132–153. [3] Ustun, T., C. Ozansoy, and A. Zayegh, “Recent Developments in Microgrids and Example Cases Around the World-A Review,” Renewable and Sustainable Energy Reviews, Vol. 15, 2011, pp. 4030–4041. [4] Lidula, N. W. A., and A. D. Rajapakse, “Microgrids Research: A Review of Experimental Microgrids and Test Systems,” Renewable and Sustainable Energy Reviews, Vol. 15, No. 1, January 2011, pp. 186–202. [5] Bracco, S., et al., “The University of Genoa Smart Polygeneration Microgrid Test-Bed Facility: The Overall System, the Technologies and the Research Challenges,” Renewable and Sustainable Energy Reviews, Vol. 18, 2013, pp. 442–459. [6] Bracco, S., et al., “A Dynamic Optimization-Based Architecture for Polygeneration Microgrids with Tri-Generation, Renewables, Storage Systems and Electrical Vehicles,” Energy Conversion and Management, Vol. 96, 2011, pp. 511–520. [7] Lasseter, R. H., et al., “CERTS Microgrid Laboratory Test Bed,” IEEE Transactions on Power Delivery, Vol. 26, No. 1, January 2011, pp. 325–332. [8] Hatziargyriou, N., H. Asano, R. Iravani, and C. Marnay, IEEE Power and Energy Magazine, Vol. 5, No. 4, July-August 2007, pp. 78–94. [9] Flueck, A., and Z. Li, “Destination: Perfection,” IEEE Power and Energy Magazine, Vol. 6, No. 6, November 2008, pp. 36–47. [10] Panwar, M., et al., “Dispatch in Microgrids: Lessons from the Fort Collins Renewable and Distributed Systems Integration Demonstration Project,” The Electricity Journal, Vol. 25, No. 8, 2012, pp. 71–83. [11] https://www.smartgrid.gov/files/USDOE_WVSC_Project_Final_Report_5-30-2014_ R1.pdf. [12] Turner, G., et al., “Design and Active Control of a Microgrid Testbed,” IEEE Transactions on Smart Grid, Vol. 6, No. 1, January 2015, pp. 73–81. [13] https://microgridknowledge.com/mice-microgrids-profile-one-us-largest-microgrids/. [14] Bayindir, R., et al., “Investigation on North America Microgrid Facility,” International Journal of Renewable Energy Research, Vol. 5, No. 2, 2015, pp. 558–574.

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[15] Fahrangi, H., Smart Microgrids: Lessons from Campus Microgrid Design and Implementation, Boca Raton, FL: CRC Press/Taylor and Francis, 2017. [16] Farhangi, H., “Intelligent Micro Grid Research at BCIT,” 2008 IEEE Canada Electric Power Conference, Vancouver, BC, 2008. [17] http://www.smart-microgrid.ca/about/. [18] https://building-microgrid.lbl.gov/huatacondo. [19] http://microgrid-symposiums.org/wp-content/uploads/2016/remote/18%20RemoteRodriguez-Ollagu%CC%88e%20Project.pdf. [20] http://www.raabassociates.org/Articles/Microgrids%20&%20District%20Energy%20 -%20Pathways%20to%20Sustainable%20Development%20-%20Case%20Studies.pdf. [21] https://building-microgrid.lbl.gov/examples-microgrids. [22] https://wp.nyu.edu/sustainability-nyusustainablog/2016/07/26/a-microgrid-grows-inbrooklyn-innovating-energy-solutions-through-revs-nyprize-competition/. [23] https://www.osisoft.com/uploadedFiles/Dynamic_Content/Media_Resources/Case_ Studies/case-study-university-san-diego-microgrid.pdf. [24] http://www.iitmicrogrid.net/microgrid.aspx. [25] https://facilities.princeton.edu/news/case-study-microgrid-princeton-university. [26] https://buildingsolutions.honeywell.com/en-US/newsevents/resources/Publications/ honeywell-hbs-white%20oak-case%20study.pdf. [27] Dondossola, G., et al., “A Laboratory Testbed for the Evaluation of Cyber Attacks to Interacting ICT Infrastructures of Power Grid Operators,” SmartGrids for Distribution IET-CIRED Seminar, 2008. [28] Bayindir, R., et al., “Microgrid Facility at European Union,” ICRERA 2014, Milwaukee, WI, October 19–22, 2014. [29] Sechilariu, M., B. Wang, and F. Locment, “Building Integrated Microgrid: Advanced Local Energy Management for Forthcoming Smart Power Grid Communication,” Energy and Buildings, 2013. [30] www.optimagrid.eu. [31] Valverde, L., C. Bordons, and F. Rosa, “Power Management Using Model Predictive Control in a Hydrogen-Based Microgrid,” IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society, Montreal, Canada, 2012. [32] Moreira, C. L., F. O. Resende, and J. A. P. Lopes, “Using Low Voltage MicroGrids for Service Restoration,” IEEE Transactions on Power Systems, Vol. 22, No. 1, February 2007, pp. 395–403. [33] Chatzivasiliadis, S. J., N. D. Hatziargyriou, and A. L. Dimeas, “Development of an Agent Based Intelligent Control System for Microgrids,” 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century, Pittsburgh, PA, 2008.



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[34] Ulleberg, Ø., T. Nakken, and A. Eté, “The Wind/Hydrogen Demonstration System at Utsira in Norway: Evaluation of System Performance Using Operational Data and Updated Hydrogen Energy System Modeling Tools,” International Journal of Hydrogen Energy, Vol. 35, No. 5, March 2010, pp. 1841–1852. [35] Messinis, G., et al., “A Multi-Microgrid Laboratory Infrastructure for Smart Grid Applications,” MedPower 2014, Athens, Greece, 2014. [36] Jenkins, M., et al., “Optimising Virtual Power Plant Response to Grid Service Requests at Newcastle Science Central by Coordinating Multiple Flexible Assets,” CIRED Workshop 2016, Helsinki, 2016. [37] Morozumi, S., et al., “Distribution Technology Development and Demonstration Projects in Japan,” 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century, Pittsburgh, PA, 2008. [38] Fujioka, Y., et al., “Regional Power Grid with Renewable Energy Resources: A Demonstrative Project in Hachinohe,” CIGRE 2006, Paris, August 2006. [39] Irie, H., et al., “The Sendai Microgrid Operational Experience in the Aftermath of the Tohoku Earthquake: A Case Study,” NEDO Microgrid Case Study: New Energy and Industrial Technology Development Organization, January, 2013. [40] Kim, T., S. K. Park and B. G. Lee, “What Is Appropriate Strategy for Smart Grid Business? A Case Study of Test Bed in Korea,” 2010 Proceedings of the 5th International Conference on Ubiquitous Information Technologies and Applications, Sanya, 2010. [41] Lee, J. M., E. H. Kim, and H. C. Kim, “Smart Wind Farm Using Grid. Energy Storage System in Jeju Island,” IEEE PES ISGT Europe 2012, Berlin, Germany, October 14–17, 2012. [42] Chen, X., Y. H. Wang, and Y. C. Wang, “A Novel Seamless Transferring Control Method for Microgrid Based on Master-Slave Configuration,” 2013 IEEE ECCE Asia Downunder, Melbourne, Victoria, 2013, pp. 351–357. [43] Zhao, B., et al., “Implementation of a Dual-Microgrid System with Flexible Configurations and Hierarchical Control in China,” Renewable and Sustainable Energy Reviews, Vol. 65, November 2016, pp. 113–123. [44] Hu, M. -C., et al., “Optimal Operating Strategies and Management for Smart Microgrid Systems,” Journal of Energy Engineering, Vol. 140, No. 1, March 2014. [45] Ulsrud, K., et al., “The Solar Transitions Research on Solar Mini-Grids in India: Learning from Local Cases of Innovative Socio-Technical Systems,” Energy for Sustainable Development, Vol. 15, No. 3, September 2011. [46] Leskarac, D., et al., “Testing Facility for Research and Development of Smart-MicroGrid Technologies,” 2015 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), Brisbane, Queensland, 2015. [47] Liu, J., et al., “Performance Investigation of Hybrid AC/DC Microgrids During Mode Transitions,” 2016 Australasian Universities Power Engineering Conference (AUPEC), Brisbane, Queensland, 2016. [48] “ABB Microgrid Solution in Johannesburg, South Africa Providing Uninterrupted Power Supply,” http://new.abb.com/microgrids/applications/grid-connected-microgrids.

4 Communication and Monitoring Systems for Microgrids 4.1  Overview Communication plays a key role in a microgrid infrastructure [1–3]. The selection of the appropriate set of technologies (protocols, physical layers) is closely related to the design of the infrastructure, such as the type of employed control architecture (centralized or decentralized), the required operation mode (gridconnected or islanding), and the characteristics of the devices to be installed (available interfaces, employed protocols). The “must” in the selection of the communication technologies is enclosed in one word: stability. The microgrid control system has to be stable independently of the operating modes, so the communication speed has to support all the possible configurations (grid-connected and/or islanding). More precisely, a microgrid that works in the islanding mode requires frequency and voltage controls to be implemented in the system; depending on the way these controls are built (see Chapter 9 for details), tight constraints could apply on the communication layer in terms of minimum speed and latency. If, on the contrary, the microgrid is designed for grid-tied operation only, the main duties of the management system are limited to the optimal dispatch or scheduling of the available resources, and to the online update of the dispatchable resources set points according to the scheduling. This implies a much slower dynamics and, as a consequence, less demanding requirements for the communication system performances. 79

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In this framework, selecting the appropriate control architecture becomes a crucial point. Although centralized and decentralized systems can be employed as a microgrid controller, one has to take into account advantages and drawbacks of both communication paradigms. If a microgrid is designed to use a centralized control architecture, all the operational decisions are executed by a software, simplifying all the components and minimizing the execution of conflicting operations; however, a centralized architecture implies a full dependence on the central controller and consequently a lack of redundancy in the system. Otherwise, if a decentralized control architecture is selected, devices are allowed to operate independently: the communication burden can be alleviated, since several pieces of information can be processed locally, without the need to be exchanged with an upper level, and redundancy protocols can be embedded in each devices for minimizing risk. There are many communication protocols that are at our disposal today for enabling data transmission inside a microgrid. The choice of the communication system and the protocols to be used depends in principle on the objectives to be controlled. As an example, although microgrids can be seen as a subset of the wider topic of smart grids, it should be underlined that they are characterized by some peculiarities. First, a large share of microgrids are actually low-voltage networks: at this voltage level, the penetration of IEC 61850-enabled equipment is still way lower than at medium-voltage and high-voltage levels, where this protocol shows nowadays a widespread application. As a consequence, microgrids very often must host devices and apparatuses (inverters, breakers, meters) not yet able to be interfaced with IEC 61850 and employing, instead, older and legacy protocols, such as Modbus (typically for inverters and meters), or even analog inputs/outputs (I/O) and digital I/O (as in the case of traditional low-voltage circuit breakers). Second, microgrids are often user networks, including heating, ventilation, and air conditioning (HVAC) equipment, heating and cogeneration sources, and the associated networks, actually making these infrastructures polygeneration systems. Furthermore, demand response strategies are becoming more important, thus triggering the need to closely interact with the loads, at building and home levels. As a consequence, the necessity arises to deal with protocols and physical layers typical, for instance, of the building automation and home automation world (KNX, BACnet, LonWork). Thus, from an ICT point of view, microgrids usually represent a very diverse environment, where many protocols and physical layers, intended for different domains of application and characterized by different levels of maturity, performances, use complexity, have to coexist and interoperate. In the following, the main features of the most common protocols for microgrid application will be highlighted. Next, the structure of monitoring systems (e.g., supervisory control and data acquisition (SCADA) and building



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management systems (BMS)) will be discussed, especially underlining the issues related to interoperability.

4.2  Protocols for Microgrid Applications 4.2.1  Modbus

Modbus [4] was originally introduced by Modicon in 1979 as a serial communication protocol for programmable logic controllers. Since then, the number of its applications considerably increased, ranging from meters and inverters to sensors and other industrial equipment. It is currently one of the most common options manufacturers offer for interfacing with their equipment. When introduced, the Modbus protocol was intended to be employed on serial communication links (typically RS485), with physical media such as twisted pairs. Alongside this solution, now referred to as Modbus RTU and still widely adopted, another one has been developed, namely Modbus TCP: in this solution, the Modbus messages, called frames or telegrams, are encapsulated in standard TCP/IP packets. The Modbus TCP allows establishing Modbus connections through a conventional local area network (LAN), or even over the internet, exploiting all the physical links on which the TCP/IP can be used. From a functional point of view, the main features of the protocol are the following: • The communication is the master-slave kind (or the client-server kind; see Figure 4.1), with one master and one or more slaves (up to 247 per each link; lower limits apply depending on the kind of physical connection), connected in a multidrop serial link architecture. • The communication is always initiated by the master by sending a telegram with an information request or a value setting to a slave; then

Figure 4.1  Master-slave communication scheme.

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the slave responds by sending a telegram with the requested data, an acknowledge message, or an error message. • All entities (read-only variables, editable parameters, commands) are addressed as entries in registers of different kinds; the type and maximum number of registers are defined by the standard, but the specification of which ones are actually used by a specific device and the meaning of the entity that they contain are left to the manufacturer: this information is usually provided in an interoperability table. The protocol is simple, robust, easy to deploy, openly published, and free from royalties; nevertheless, some drawbacks can be identified: • Serial communications over RS485 and on media such as twisted pairs imply limits on transmission rate and maximum length. • A device acting as a slave cannot initiate a communication, for instance, triggered by an event that it detects. • The circumstance that the registers actually used and their meaning are vendor-specific, which severely limit the devices’ interoperability (similar devices from different vendors could use totally different sets of registers and make available different variables). • No timestamp is provided for the values. • No security or authentication mechanisms are enforced on standard Modbus, although researches and implementations have been proposed to deal with this issue [5, 6]. In spite of these limitations, Modbus is still one of the few available options that manufacturers offer to interface devices such as inverters, meters, and other equipment, for especially low-voltage applications. 4.2.2  DNP3 and IEC 60870-5

DNP3 (Distributed Network Protocol 3) and IEC 60870-5 [7] are protocols developed for SCADA applications in electrical power systems, and they have been adopted worldwide, also in different areas of interest, such as water, gas, and oil plants. DNP was developed by Westronic, Inc. (now GE Harris) in 1990, and since 1993, its ownership has belonged to the DNP Users Group [8], now charged with maintaining the protocol. IEC 60870-5 is developed by the IEC Technical Committee 57, Working Group 03. It is organized in a number of sections: IEC 60870-5-101 (first



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released in 1995) defines the basic telecontrol tasks, IEC 60870-5-102 is dedicated to the transmission of integrated totals, IEC 60870-5-103 deals with interfacing protection equipment, and, finally, IEC 60870-5-104 defines the network access for IEC 60870-5-101 (i.e., enables the communication via a standard TCP/IP network). Even though DNP3 is not compliant with IEC 60870-5 specifications and the two protocols differ on a number of technical aspects, they also share several similarities and, most importantly, they both provide similar application functionalities. In particular, although a master-slave multidrop serial link is still used as basic architecture, just as the one adopted by Modbus, they both are characterized by functionalities that differentiate them from Modbus: • Time synchronization is provided, and values and events are timestamped; • Quality information can be associated with values; • Advanced functions, for instance, to manage counters, and procedures such as “select before operate” are offered; • Unsolicited responses by slaves are supported; • Data groups and data classes are defined. Furthermore, both protocols are characterized by a high level of conformance to ensure interoperability for each device to be plug-and-play-compatible directly into a SCADA system: each new functionality or feature is discussed by the appropriate technical committees and inserted in standard releases. In addition, security and authentication mechanisms can be implemented for these protocols: as an example, IEC TS 60870-5-7 defines a security extension to IEC 60870-5-101 and IEC 60870-5-104 protocols by applying IEC 62351. 4.2.3  IEC 61850

The first edition of IEC 61850 [9–11] was introduced in 2004 as a communication standard for substation automation, but it has also been used largely beyond the boundaries of electrical substations. The key feature that makes this protocol a step forward in interoperability between devices from different vendors is the introduction of standardized virtual data models and the definition of data exchange procedures based on these models. In other words, in addition to the specifications of the protocol elements, IEC 61850 defines models for how devices have to organize data, in a way that is consistent for all device types and manufacturers. Each physical intelligent electronic device (IED), actually connected to a transmission network and identified by an address, is considered

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as composed by one or more logical units, in turn consisting of one or more logical nodes. A logical node is a named group of data and associated services, related to power system functions that are listed in the standard. Strict naming conventions help to identify the functions of the logical nodes: for instance, all names of logical nodes related to automatic control begin with A, while names of logical nodes for metering and measurement begin with M, G is used for generic functions, and so on. Furthermore, the kind of node has to be specified in its name with an acronym dictated by the standard, along with an identifier that can be used to indicate the device, equipment, or section in the system to which the related information refers: for instance, MMXU2 indicates a three-phase measurement (acronym MXU), thus belonging to the set of metering and measurement functions (initial M), and related to a feeder number 2 (identification of the point in the electrical network to which the measure refers). Each logical node contains one or more elements of data, with a unique name, again, strictly determined by the standard according to its purpose. As an example, a circuit breaker, modeled as an XCBR logical node (X: set of switchgear functions, CBR: circuit breaker), contains, among others, the following items: Loc, for determining if the breaker operation set to remote or local; OpCnt, an operations counter; Pos, for the breaker position; BlkOpn indicating the block breaker open command; BlkCls indicating the block breaker close commands; and CBOpCap, for the circuit breaker operating capability. All these items must conform to the specification of a common data class (CDC) provided by IEC 61850–7-3. The virtual model of an IED is mapped to a specific protocol stack, such as Manufacturing Message Specification (MMS, ISO9506), TCP/IP, or Ethernet, as specified in IEC 61850–8-1. In this part, Sampled Measured Values (SMV) and Generic Object Oriented Substation Event (GOOSE) applications are also described: they map directly onto the Ethernet data frame (Figure 4.2) without any middle layer, to achieve the speed performances required by their functions. SMVs enable sharing analog I/O signals among IEDs and allow transmitting sampled measurements from transducers to IEDs. GOOSE is used for peer-to-peer communication between devices in substations, with performances in terms of guaranteed delivery times on the order of few milliseconds and checking mechanisms to verify that messages are correctly received for allowing protection purposes: functions such as breakers interlock, logical selectivity, and backup can be implemented by means of GOOSE messages over the Ethernet connection, without the need for the involved protection relays to be hardwired. In addition, the standard specifies that the entire configuration of the system has to be represented by using a Substation Configuration Language (SCL), based on the eXtensible Markup Language (XML). The SCL introduces a hierarchy of configuration files that enable multiple levels of the system



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Figure 4.2  IEC 61850 communication model.

to be described in standardized XML files: System Specification Description (SSD), IED Capability Description (ICD), Substation Configuration Description (SCD), and Configured IED Description (CID). SCL allows the off-line generation of files for IED configuration by means of dedicated tools and the sharing of IED configuration between users and suppliers to avoid configuration errors. This mechanism basically adds self-describing and self-configuring features to IEC 61850. Finally, security and authentication mechanisms can be added according to IEC 62351. Summing up, the main benefit of IEC 61850 can be stated in the following points: • Both protocol and device data models are based on standards, thus ensuring interoperability across manufacturers. • High-speed IED-to-IED and transducer-to-IED communications meet requirements for protection functions and fast sampled measurement transmission. • It is networkable over the Ethernet, thus taking advantage of the progress in transmission speeds. • Key features such as automatic configuration, security, and authentication are supported. IEC 61850-enabled devices (protection relays, breakers, transducers, meters, controllers) are today available for high-voltage application, and the

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protocol is making its way in medium-voltage networks as well. Unfortunately, this is still not the case for low-voltage applications: devices and protection equipment in such a context do not usually offer IEC 61850 compatibility, but they can be interfaced via Modbus or via standard digital and analog I/O with the remote terminal units of the control and supervision system. Along with IEC 61850, two other standard protocols are expected to gain more importance in the future: the IEC 61970 and the IEC 61968 [12, 13]. The IEC 61970, Energy Management System Application Program Interface (EMS-API), was developed to improve the interoperability of EMSs: it offers a standard framework in which both the information to make available and the methodologies to access them are defined. The IEC 61968, System Interfaces for Distribution Management, aims at standardizing interfaces for applications such as for network operations, network control, and meter reading in distribution systems. 4.2.4  BACnet

The Building Automation and Control Networking Protocol (BACnet) [14, 15] was issued in 1995 as ANSI/ASHRAE Standard 135; then, in 2003, it became an international standard, ISO 16484-5. It was developed to provide a universal way to exchange data such as sensors outputs, command for actuators, device schedules, alarms, and analogic variables in the building automation field. It is based on object-oriented programming (OOP) approaches to standardize the representation of data and processes within the building. Building data are accessed and actions are taken on its infrastructure by means of standard services. Different from IEC 61850, in order to guarantee the maximum flexibility, the internal data structures, the control logics, and the configuration of the field devices, as well as the physical communication links between the field devices, are not standardized: the BACnet standard determines how messages have to be exchanged (in a master-slave structure) and provides ways to represent device functions, but does not strictly point out the functions that a particular device must have. As far as the physical link is concerned, only the interface, by means of the BACnet network layer, is described; then different links can be used: from the Ethernet and ARCnet on a variety of physical media (coaxial cable, twisted pairs, fiber optic cable), to solutions characterized by lower costs and lower performances, such as MS/TP (master-slave/token-passing), designed for twisted pair wiring, and Echelon’s LonTalk links. 4.2.5  LonWorks

LonTalk technology [14, 16, 17] was developed by the Echelon Corporation, which initially designed a set of control chips, signaling technology, and routers,



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as its core. LonTalk communication protocol was accepted as standard ANSI/ CEA-709.1-B by the American National Standards Institute (ANSI) in 1999. The standard defines a number of functional device models to ensure interoperability among different vendors; data is represented as network variables, belonging to a set of standard network variable types (SNVTs). Both functional device models and SNVTs have been developed by the LONMARK Interoperability Association [16]. The communication between the devices connected on a network (nodes) exploits the network variables shared by them: some of the devices can send a network variable, while others can receive it; the network variables shared by a device are defined during its programming stage. When a device (e.g., a sensor) sends a new value for a network variable (for instance, because its value is changed), this value propagates across the network; each receiving device sharing it acquires the new value and takes actions accordingly (e.g., triggering an actuator) as defined in its program. No master is present on the network. The main characteristics of this technology are: • Peer-to-peer communication between devices (see Figure 4.3) and no master is required, which enhances reliability; • Event-based communication (e.g., a variable is sent only when its value changes, thus reducing the communication burden for the network); • A variety of physical media can be used, such as twisted pairs, power line carrier, and radio communications and devices can also be addressed over TCP/IP; • An authentication mechanism is included in the standard.

Figure 4.3  LonWork control network.

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4.2.6  KNX

KNX [14, 18] was created in 1999 from the merging of three previous European systems and their respective associations: the European Installation Bus (EIB), the Batibus Club International, and the European Home Systems Association (EHSA). KNX is now recognized as a standard at both European and international levels (CENELEC EN 50090, CEN EN 13321-1, and ISO/IEC 14543-3). Its stand in China is (GB/T 20965). KNX is focused on home and building automation (control of lights, blinds, and shutters, HVAC systems, appliances), privileging simplicity, modularity, and reliability; no master control is needed. As physical layers, the standard includes twisted pairs, power line carrier, radio frequencies, and the Ethernet. Devices connected to the bus can be subdivided in three main categories: sensors, actuators, and other devices (power supply units and controllers, if present). The standard specifies the functions a device can perform, according to its purpose (e.g., lights control, shutter control, ambient heating regulation) and the associated data. Functional relations between sensors and actuators are established by defining group addresses. As an example, if a switch has to turn on and off a number of lights, the switch and the lights’ actuators must share a common group address; when the switch is pressed, it sends a telegram specifying this group address; this latter is detected by the actuators that turn the lights on and off and send back an acknowledge telegram; the telegrams include the sender address, the values of variable if needed, and other information. Each device is normally in the receiving mode and sends a telegram only if an event occurs. Proper mechanisms are provided to avoid telegram collisions on the bus (a device cannot send a telegram if another is transmitting). KNX was developed with characteristics suitable for a home environment, with low cost, reliability, and simplicity in mind: thus, requirements and performances are very different from those of the previously discussed protocols, developed for electrical automation (IEC 61850, DNP3, and IEC 60870-5). 4.2.7  Wireless Technologies: ZigBee and LoraWan

ZigBee [19, 20] is a specification for a suite of high-level communication protocols using small, low-power digital radio signals based on the IEEE 802.15.4 standard for wireless personal area networks (WPANs). It was designed for applications requiring very low equipment and installation costs, low power consumption, and without tight constraints on the data rate.



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Development began in the late 1990s, focusing on self-organizing ad hoc digital radio networks. The IEEE 802.15.4 standard was issued in 2003, while ZigBee specifications were ratified at the end of 2004. In 2006, the ZigBee Alliance made available the specification for ZigBee 2006, and at the end of 2007, ZigBee PRO, an enhanced ZigBee version, was finalized. ZigBee 3.0 was announced in 2014. One of the distinguishing characteristics of this wireless technology is that it supports mesh networking: each ZigBee device is considered as a node; messages are received and retransmitted from node to node until they arrive at the destination node. This way, ZigBee networks allow transmitting messages over long distances, provided that a path exists whose distances between consecutive nodes are all within the limit of 10m to 100m (depending on environmental conditions and node power). The application span of this technology is very wide: besides home and office automation (for monitoring and controlling lights, appliances and even single sockets), ZigBee uses can be found in HVAC ������������������������������ control, medical monitoring, low-power sensors, communication, and even industrial automation. LoRaWAN [21] is a low-power wide area network (LPWAN) specification intended for wireless battery-operated devices in a regional, national, or global network. The specification is defined by the LoRa Alliance [22], a worldwide nonprofit organization clustering all companies working on LoRa technology. The network architecture is typically a star-of-stars; rather than resorting to the ability of each node to receive and retransmit a message, LoRaWAN relies on gateways as bridges between the end devices and a server. The gateways are connected to the server via standard IP connections, while end devices on the field exploit single-hop wireless communication to one or more gateways. Communication is generally bidirectional, but multicast is also supported. The key aspects of this technology are: • The required power is so low that the sensors can operate on coin size batteries for years (eventually, relying on energy harvesting techniques). • Ranges in the order of tens of kilometers can be achieved. • Applications such as localization are possible. More generally, ZigBee, LoRaWAN, and similar technologies aim at embodying the Internet of Things (IoT) paradigm, offering the possibility to add connection and processing capabilities also to simple devices, in a cheap, reliable, and simple way. The new IoT concept will potentially have a relevant impact on the building automation world.

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4.2.8  OPC

The OPC standard [23] is a series of specifications developed by industry vendors, end-users and software developers, aimed at allowing the interchange of data among industrial automation products (e.g., SCADA, PLCs) and other industrial systems (such as management software). The standard was first released in 1996 and was restricted to Windows systems (hence, the acronym OPC: Object Linking and Embedding (OLE) for Processing Control). The OPC Unified Architecture (UA), released in 2008, went beyond the former version, becoming an open-platform architecture, scalable and extensible. The standard is maintained by the OPC Foundation [24]. The specifications allow establishing a client/servers, or even servers/servers architecture, through which information such as real-time data, monitoring of alarms and events, historical data can be shared. Example of application range from controlling equipment to accessing PLC variables from a SCADA and accessing SCADA monitoring data from a third-party application. 4.2.9  Interfaces Via Web Services: SOAP and REST

Modern platforms offer the possibility to exchange data with third-party applications over the web by means of web services. Thus, this technology can be exploited for control and supervision or to overcome interoperability issues between different management systems. The two most common web services classes are Simple Object Access Protocol (SOAP) and Representational State Transfer (REST) [25, 26]. The former, originally developed by Microsoft, is based on XLM files to provide the messaging services. Each application interface (i.e., the way the application exposes data) is described by using the Web Service Description Language (WSDL). REST was subsequently developed to be more lightweight: in many cases, the communication can be performed by employing a simple URL, instead of a XLM, and the data can be exchanged in various ways (for instance, in simple csv files).

4.3  Supervision and Monitoring Systems: SCADA and BMS SCADA [27, 28] and BMS [29] gather information from the field and send commands or set points to the devices through communication links and protocols such as the ones discussed above. They visualize the system status, its main real-time data, alarms, and warning. They are the main human-machine interface (HMI) of the system, by means of which the operators can interact with all the equipment; furthermore, they allow archiving the most relevant data and variables for further processing.



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SCADA systems developed for applications such as substation/power systems monitoring and telecontrol usually offer the following functionalities: • Integrated user management (i.e., management of users accesses, offering different levels of permissions according to each user role); • Message system (e.g., to send alerts remotely); • Archiving system for relevant data and variables and visualization of historical trends; • Reporting and logging system for events and alarms; • Control functions (basic process control); • Scripting capabilities; • Possibility to run on twin servers working in parallel and constantly synchronized in hot-backup configuration to enhance reliability. Relational database management systems are typically used for archiving. Supported protocols usually include IEC 61850, DNP3, and IEC 60870-5. This means, for instance, that each SCADA variable, alarm, and event is characterized by an actual timestamp and quality information, thus allowing one to check for data consistency and, for instance, to precisely reconstruct a sequence of events leading to a fault. Although BMSs offer similar characteristics, some relevant differences exist: as the supported protocols do not usually provide timestamps, the recorded instant of a data sample or event corresponds to the instant when the information is received by the BMS (not the actual one when the event occurred or the data was detected by the sensor); the alarm and event management applications are usually simpler, as event and alarm handling is less critical, while other features are supported, such as advanced applications for daily, weekly, and annual scheduling of equipment, necessary, for instance, for the management of HVAC systems. Both SCADA and BMS now offer web-based and remote control applications.

4.4  Interoperability Summing up, it is possible to observe that there are two domains: the electric system automation (or, in general, utility automation) and building/home automation (see Figure 4.4). The different technologies developed over the years, with different goals and requirements, made it difficult to integrate and reach a full interoperability between these two worlds. As an example, SCADA systems

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Figure 4.4  Communication between SCADA and BMS.

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for substation automation can typically deal with IEC 61850, DNP3, and IEC 60870-5. OPC and web services are usually proposed as one of the methods to share information with third-party software. In addition, the associated remote terminal units can act as gateway between the aforementioned protocols and Modbus. On the other side, BMS and its field control units are usually able to manage BACnet, LonWorks, and KNX. Typically, they can exploit Modbus, OPC, and web services also. It straightforwardly follows that the only way to offer communication between the SCADA system and BMS is by means of Modbus, OPC, or web services. In microgrids, the necessity arises to bridge the separation between the two domains, for a number of reasons: • The core of the microgrid (decentralized electrical sources, switchboards, protection relays, secondary frequency, and voltage controllers, if present) is usually electrical, thus calling for an electrical automation SCADA; in addition, standards regulating the connection with the distribution grid are evolving towards more strict regulations about the exchange of information and commands between active users and distributed system operators, in order for the former to be able to deliver both mandatory and optional ancillary services: this communication will be implemented with IEC 61850 and companion protocols. • The loads (i.e., the buildings) are usually managed by BMSs with the associated field control units, belonging to the building automation domain. • There is ���������������������������������������������������������������� a need to monitor and control both electrical sources (photovoltaic (PV), wind generation, storage devices, diesel generation sets, microturbines) and thermal sources (solar thermal panels, boilers to be managed together with cogeneration microturbines, heat pumps, chillers). • Relevant information and feedbacks must be acquired from building loads, both for supervision purposes and to refine and continuously adjust forecasting models. • Demand response strategies are gaining a growing importance, and they have to be performed at the building level (i.e., involving the building loads), based on decisions and optimization results taken at the electrical system level of the microgrid. The issue of interoperability between electric system automation (or, in general, utility automation, i.e., SCADA) and building/home automation (i.e., BMS) can be addressed in different ways (Figure 4.5):

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Figure 4.5  Interoperability model between SCADA and BMS.

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• By employing Modbus as a “lingua franca,” for instance, by setting up a Modbus link between a remote terminal unit (RTU) connected to the electrical automation SCADA and a BMS field control unit; • By setting up a client/server link between SCADA and BMS, by using OPC; • By exploiting web services. The first solution, although providing ������������������������������������������� a�������������������������������� reliable field-level communication mechanism, is characterized by a number of drawbacks: the link inherits all the limitation in terms of data rates, latency, noise and communication errors (in case Modbus RTU over twisted pairs is used), unavailability of timestamps, and quality information, typical of Modbus. Furthermore, the implementation could result complex if the number of information and commands to be shared is relevant. The second solution could have more effective results, even though it requires appropriate coding both from the SCADA side and the BMS side, in order to manage the client/server connection. The third solution could be more flexible and general (as it can be employed also in cases when OPC is not available). In general, further efforts are needed to reach a higher level of unification between SCADA and BEMS to guarantee seamless operability and to offer the users a unique control and supervision platform.

References [1] Bani-Ahmed, A., et al., “Microgrid Communications: State of the Art and Future Trends,” 2014 International Conference on Renewable Energy Research and Application (ICRERA), Milwaukee, WI, 2014, pp. 780–785. [2] Emmanuel, M., and R. Rayudu, “Communication Technologies for Smart Grid Applications: A Survey,” Journal of Network and Computer Applications, Vol. 74, October 2016, pp. 133–148. [3] Martin-Martínez, F., A. Sánchez-Miralles, and M. Rivier, “A Literature Review of Microgrids: A Functional Layer Based Classification,” Renewable and Sustainable Energy Reviews, Vol. 62, September 2016, pp. 1133–1153. [4] http://www.modbus.org/. [5] Hayes, G., and K. El-Khatib, “Securing Modbus Transactions Using Hash-Based Message Authentication Codes and Stream Transmission Control Protocol,” 2013 Third International Conference on Communications and Information Technology (ICCIT), Beirut, 2013, pp. 179–184.

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[6] West, A., “Securing DNP3 and Modbus with AGA12-2J,” 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century, Pittsburgh, PA, 2008, pp. 1–4. [7] “Using DNP3 & IEC 60870-5 Communication Protocols in the Oil & Gas Industry,” Triangle MicroWorks, Inc., Raleigh, NC, 2001, www.TriangleMicroWorks.com. [8] https://www.dnp.org/default.aspx. [9] Mackiewicz, R. E., “Overview of IEC 61850 and Benefits,” 2006 IEEE PES Power Systems Conference and Exposition, Atlanta, GA, 2006, pp. 623–630. [10] Dawidczak, H., T. Dufaure, and H. Englert, “Compatibility of IEC61850 Edition 1 and Edition 2 Implementations,” 21st International Conference and Exhibition on Electricity Distribution, Frankfurt, June 6–9, 2011. [11] Elgargouri, A., M. M. Elfituri, and M. Elmusrati, “IEC 61850 and Smart Grids,” 2013 3rd International Conference on Electric Power and Energy Conversion Systems, Istanbul, 2013, pp. 1–6. [12] Latisko, G., D. Bhati, and V. Landenberger, “Application of IEC61970 and IEC61968 at KCP&L Smart Grid Demonstration Project,” ISGT 2014, Washington, DC, 2014, pp. 1–5. [13] Ling, L., Y. Hongyong, and C. Xia, “Model Differences Between IEC 61970/61968 and IEC 61850,” 2013 Third International Conference on Intelligent System Design and Engineering Applications, Hong Kong, 2013, pp. 938–941. [14] Kastner, W., et al., “Communication Systems for Building Automation and Control,” Proceedings of the IEEE, Vol. 93, No. 6, June 2005, pp. 1178–1203. [15] http://www.bacnet.org/. [16] http://www.lonmark.org/. [17] Echelon Corporation, “Introduction to the LonWorks® Platform,” 2009. [18] https://www.knx.org. [19] http://www.zigbee.org. [20] Asadullah, M., and A. Raza, “An Overview of Home Automation Systems,” 2016 2nd International Conference on Robotics and Artificial Intelligence (ICRAI), Rawalpindi, 2016, pp. 27–31. [21] Wixted, A. J., et al., “Evaluation of LoRa and LoRaWAN for Wireless Sensor Networks,” 2016 IEEE SENSORS, Orlando, FL, 2016, pp. 1–3. [22] https://www.lora-alliance.org. [23] Zheng, L., and H. Nakagawa, “OPC (OLE for Process Control) Specification and Its Developments,” Proceedings of the 41st SICE Annual Conference (SICE 2002), Vol. 2, 2002, pp. 917–920. [24] https://opcfoundation.org/.



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[25] Leksawat, S., et al., “Implementation of Communication Model and Web Services for Cluster-Based Power System Operation in Smart Grids,” 2015 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA), Bangkok, 2015, pp. 1–6. [26] Montanari, A., Y. A. Pignolet, and E. Ferranti, “REST Assured, We Manage Your Microgrid,” 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm), Venice, 2014, pp. 284–289. [27] http://w3.siemens.com/smartgrid/global/en/products-systems-solutions/substationautomation/hmi_and_archiving/pages/sicam-scc.aspx. [28] http://new.abb.com/substation-automation/products/software. [29] https://www.siemens.com/global/en/home/products/buildings/topics/desigo-cc.html.

5 Modeling and Simulation for Microgrids 5.1  Overview This chapter proposes a discussion on the techniques used to model energy systems and power devices within simulation tools. The chapter is divided into two main parts: in the first one the main fundamental thermo-fluid dynamic equations used to model the dynamic behavior of power plant components are reported, proposing as an example a simulation model of a cogeneration microturbine, whereas in the second one the attention is focused on the modeling of electrical power systems installed in islanded microgrids.

5.2  Introduction As reported in [1], simulation refers to the application of computational models to the study and prediction of physical events or the behavior of engineered systems. In the energy sector, simulation tools can be used for different purposes and in different situations [2–11]. First, a simulation tool can be used to verify if a plant behaves like it has been designed and the analysis of simulation results can highlight criticalities in the design phase [12]. Then simulation tools are also used to improve the operation of a power plant and its maintenance scheduling and to easily test several control strategies: indeed, with reduced simulation times, it is possible to run many operating conditions (start-up, shutdown, load modulation) to evaluate the physical quantities which describe the plant performance (temperature, pressure, voltage, efficiency) [5–8]. 99

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Specifically, when dealing with the simulation concept, it is necessary to distinguish between steady-state and dynamic simulation. The term steadystate refers to simulation models used to represent the plant behavior in static operating conditions that do not depend on time; for instance, the off-design or partial load simulation is a steady-state analysis aiming to examine the plant when it produces or absorbs a constant power that differs from the nominal value, this last representing the design point. Another example of steady-state simulation is the evaluation of the plant performance as a function of different boundary conditions, such as different values of ambient pressure and temperature, solar radiation, and wind velocity. However, the term dynamic refers to simulation models used to represent the plant behavior in transient operating conditions, during which the physical quantities that describe the plant vary with time; furthermore, to define the dynamics of the plant, important time constants have to be determined and experimentally validated. Finally, it is important to highlight that a third approach to simulate a power plant can be used: the quasi-steady-state analysis; this last has to be used when the time constants that characterize the plant dynamics are considerably lower than the change rate of exogenous sources that influence the aforesaid dynamics (i.e., requested power profile, variation in ambient conditions). As is well known, a simulation tool, usually called a simulator, derives from the implementation of a mathematical model that aims to represent a real physical system. In Figure 5.1 the main phases of a simulation project are sketched. First, it is necessary to define the object of the study, either the whole plant or a plant component, that is going to be simulated; moreover, the goals of the study have to be clearly defined to determine the approach to follow (i.e., steady-state or dynamic). The definition of the mathematical model and its implementation represent two crucial phases that can be carried out in different ways; indeed, it is possible to use programming codes or commercial simulation tools, these last being designed to simulate specific components and plant categories. As a consequence, the user does not always need to write equations since many commercial simulation tools are characterized by graphical interfaces where plant components are modeled by embedded equations already inserted by the software developer; in any case, several data have to be collected and used in order to define boundary conditions. Once built, the simulator needs to be tested to verify its robustness. The validation phase can be done using both literature results, related to the simulation of similar case studies, and results derived from experimental tests. If the validation phase is not satisfactory, namely the simulator does not accurately represent the real plant behavior, the mathematical model and/or its implementation need to be checked and modified. However, if the validation shows the accuracy of the model, a proper set of



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Figure 5.1  Simulation project scheme.

simulation runs is planned. Finally, the simulation results have to be analyzed in detail to deduce significant considerations about the plant performance.

5.3  Dynamic Modeling and Simulation of Multicomponent Energy Systems The dynamic simulation of complex energy systems is a topic of utmost importance in the current scenario where power plants need to operate in a more and more flexible and reliable way within the liberalized electricity market [5–8]. In smart grid and microgrid installations, analogous issues arise and, consequently, it is important to develop simulation tools able to model the behavior of power plants, both electrical and thermal, in different operating conditions [2, 13]; in particular, combined heat and power units, boilers, heat pumps, absorption chillers, thermal and electrical storage systems, and heat exchangers have to be modeled in a simple but effective way [2–4, 9, 10, 13–16]. The lumped-parameters theory is usually adopted to model the aforesaid plants as multicomponent

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systems, each one describable with a set of mathematical equations, algebraic and/or differential [2, 4, 8]. 5.3.1  A Multicomponent Energy System

A complex plant can be split into several components, each of them defined as elementary system (i.e., a spatial entity) contained within a control volume V delimited by a control surface S. The elementary system can exchange mass and/or energy (heat and/or work) with the environment. A system can be isolated (no mass and energy transfer through S), closed (only energy transfer through S), or open (both mass and energy transfer through S). In the lumpedparameters theory, adopting a one-dimensional flow modeling, each elementary system is described by state variables that depend on both the time and a spatial coordinate, the latter typically positive in the flow direction. As better explained next, using an electrical analogy, an elementary system can be modeled as a combination of one or more of the following thermo-fluid-dynamic components: • A capacitance Cf representing the mass accumulation; • An inductance Lf indicating the accumulation of quantity of motion; • A resistance Rf related to the steady-state characteristic curves of the system. Moreover, each elementary system is described by steady-state characteristic curves (i.e., the compressor and turbine flow and efficiency maps for a microturbine), and fluid properties (of water/steam, air) have also to be taken into account. The schematic representation of an elementary system is reported in Figure 5.2. The system is characterized by: • The control volume V (the volume of the component sketched in Figure 5.2); • The control surface S (indicated by the dashed line) that is impervious to mass except in correspondence of I and K inlet/exit ports; • I mass flow inlet ports having M ,..., M ,..., M entering mass flow rates;

in1

ini

inI

• K mass flow exit ports having M out1 ,..., M out k ,..., M out K exiting mass flow rates; • The heat rate Q considered positive if transferred from the environment to the system;



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Figure 5.2  Representation of an elementary system.��

• The mechanical power flow P (associated with rotating shafts or boundary displacements) considered positive if provided by the system (i.e., a turbine or an engine). In Figure 5.2 dm indicates the infinitesimal mass of the infinitesimal control volume dV. Moreover, the thermo-fluid dynamic properties that describe the system are: c (velocity), p (pressure), ρ (density), T (temperature), u (internal thermodynamic energy per mass unit), and s (entropy per mass unit). 5.3.2  Equations Governing the Dynamic Behavior of the System

In this section, the thermo-fluid-dynamic equations used to model a system in dynamic operating conditions are reported. 5.3.2.1  The Continuity Equation

The conservation of mass, or continuity equation, for the system sketched in Figure 5.2 can be stated as:

(5.1)

that is to say:

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∑ M out



k =1

∂ − ∑ M ini + ∫ ρ dV = 0 ∂V i =1 I

k

(5.2)

5.3.2.2  The Energy Balance Equation

The conservation of energy, or energy balance equation, represents the energy balance for the system sketched in Figure 5.2:



(5.3)

where the term total energy is used to indicate the sum of specific internal thermodynamic energy and specific kinetic energy. Rewriting (5.3) as a function of suitable thermo-fluid dynamic properties, one obtains:  ∑  M outk  k =1  K

  I  c2 ⋅  uk + k + gz k   − ∑  M ini 2    i =1 

  c2 ⋅  ui + i + gz i   2   

I  K  c2 ∂  + ∫  u + + gz  ⋅ ρ dV = Q − P −  ∑  M out k pk vk  − ∑  M ini pi vi  2 ∂V   k =1   i =1

(5.4)

where gz is the potential energy per mass unit related to the work done by volume forces. Since specific enthalpy h is defined as the sum of u and the term pv, (5.4) can be rewritten as:  ∑  M outk  k =1  K



  I  c2 ⋅  hk + k + gz k   − ∑  M ini 2    i =1 

 c2 ∂  + ∫  u + + gz  ⋅ ρ dV = Q − P 2 ∂V  

  c2 ⋅  hi + i + gz i   2   



(5.5)



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5.3.2.3  The Momentum Equation

The conservation of momentum, or momentum equation, for the system shown in Figure 5.2 states that:

(5.6)

from which the following vectorial equation can be derived:

∑ (M out K



k =1

k

I ∂ ⋅ ck − ∑ M out i ⋅ ci + ∫ ρ c dV = ∂t V i =1

)

(

)

F

∑ Fext

f =1

f



(5.7)

where the velocity terms c k and c i are vectors perpendicular to straight sections Ωout k and Ωini , whereas c indicates, in accordance with the proposed one-dimensional analysis, the velocity vector tangent to the streamline which represents the path of the fluid within the control volume V. As external forces, surface and volumes forces have to be taken into account. Volume forces typically refer to the gravitational field (if the control volume V is analyzed in a fixed reference system), whereas among surface forces it is possible to distinguish pressure forces from viscous ones. Hence:

F

I

f =1

i =1

(

)

K

(

)

∑ Fext f = FV + FS + ∑ pi Ωini ni − ∑ pk Ωout k nk k =1

(5.8)

where F V is the sum of volume forces, F S is the sum of pressure and viscous forces acting on the portion of the control surface S pervious to mass, whereas the last two terms on the right side of (5.8) refer to pressure forces on mass permeable surfaces (n i and n k being the versors perpendicular to straight sections Ωini and Ωout k and oriented as the fluid direction); however, viscous forces acting on mass permeable surfaces are often neglected. 5.3.2.4  The Entropy Balance

The entropy balance for the system represented in Figure 5.2 can be expressed as follows:

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(5.9)

that leads to:

∑ (M out K



k =1

k

I q q ∂ ⋅ s k − ∑ M ini ⋅ si + ∫ ρ s dV = ∫ dS + ∫ irr dV (5.10) T T ∂t V i =1 S V

)

(

)

where q is the heat flux per surface unit and q irr the heat dissipated per time unit and volume unit due to irreversibilities within the system. 5.3.3  The Electrical Analogy

As previously mentioned, in dynamic simulation models, it is customary to represent multicomponent energy systems by means of equivalent electrical circuits, where current stands for mass flow rate and voltage for pressure. In the aforesaid circuits, each energy system component is represented by a resistor described by its steady-state characteristic maps while its dynamic behavior is modeled by using the following additional blocks: a capacitor (characterized by a fluid-dynamic capacitance Cf) representing the accumulation of mass and an inductor (having a certain fluid-dynamic inductance Lf) indicating the accumulation of kinetic energy. Consequently, these electrical circuits are described by a set of algebraic and differential equations that can be solved in the time domain or in the frequency domain. In dynamic simulation models, there are also components used to represent controllable sources (i.e., fuel flow rate) as well as exogenous sources (ambient conditions, external heat sources). 5.3.3.1  Fluid-Dynamic Capacitance

Let’s consider a system like that depicted in Figure 5.2 and composed of one inlet port Ωin1 and one exit port Ωout1, respectively, crossed by M in1and M out1 mass flow rates of a fluid with null viscosity; moreover, let’s neglect the velocity of the fluid within the control volume V and consider a reversible adiabatic process



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without work exchange. Since the volume V is constant and the density ρ is assumed uniform within V, the continuity equation (5.2) becomes:

dρ M in1 − M out1 = V ⋅ dt

(5.11)

and, introducing the speed of sound in the fluid (a), it is possible to obtain:

1 dp M in1 − M out1 = V ⋅ 2 ⋅ a dt

(5.12)

Equation (5.12) reflects the classical equation used to model a capacitor characterized by a current flow equal to the difference (M in1 – M out1) and a voltage equal to p. As a consequence, it is possible to define the fluid-dynamic capacitance Cf as: Cf =



V a2

(5.13)

that, for a perfect gas, becomes equal to V , k and R being, respectively, the kRT adiabatic exponent and the gas constant. The same formula for the calculation of Cf can be obtained considering that Cf is defined as the fluid mass necessary to increase the pressure within the system of one unit. A fluid-dynamic capacitance is usually used in dynamic simulators to model components characterized by mass accumulation, such as combustion chambers, heat exchangers, and reservoirs. 5.3.3.2  Fluid-Dynamic Inductance

To introduce the concept of fluid-dynamic inductance, it is useful to consider as a control volume V a tube characterized by a constant cylindrical cross section Ω and rigid walls. Moreover, let’s assume that the properties (density, velocity) of the fluid flowing within the tube change with time but do not depend on the axial coordinate x along the tube; this hypothesis is valid if the perturbation wavelength is considerably higher than the tube length. Based on the aforesaid assumptions, the entering mass flow rate results equal to the exiting one and the momentum equation (5.7) can be rewritten in scalar form:

d l ρ Ω c dx = ( p1 − p2 ) ⋅ Ω dt ∫0

(5.14)

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where subscripts 1 and 2 indicate, respectively, the inlet and exit sections of the tube. The contribution of volume forces is not present in (5.14) since the momentum equation is written in a one-dimensional form (x being the axial coordinate varying from 0 to l, the latter indicating the tube length). Furthermore, the term F S is not present in (5.14) since pressure forces acting on the tube walls are null, due to the cylindrical geometry, whereas viscous forces are neglected. Since the term ρ Ω c is the mass flow rate M , (5.14) ������������������� can be rearranged as:

p1 − p2 =

l dM ⋅ Ω dt

(5.15)

Equation (5.15) reflects the classical equation used to model an inductor characterized by a current flow equal to M , and a voltage drop equal to (p1 – p2). As a consequence, it is possible to define the fluid-dynamic inductance Lf as:

Lf =

l Ω

(5.16)

If friction is taken into account, the momentum equation is equal to:

p1 − p2 =

FS ,x Ω

+ Lf ⋅

dM dt

(5.17)

where FS,x is the sum of viscous forces acting on the tube walls and parallel to the x-axis. Equation (5.17) indicates that the pressure difference between the inlet and the exit of the tube is given by the sum of two different terms: the pressure drop due to friction and the pressure difference needed to accelerate the fluid. The second of the two terms is that related to the inductance, and so this proves that fluid-dynamic inductance represents the accumulation of kinetic energy; in other words, to accelerate a fluid, the application of an external force is necessary. A fluid-dynamic inductance is usually used in dynamic simulators to model components characterized by long flow path, such as tubes and pipelines or certain machines (i.e., compressor and turbine stages) where fluid acceleration cannot be neglected.



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5.3.3.3  Fluid-Dynamic Resistance

As mentioned earlier, both the capacitance and the inductance refer to accumulation terms, thus requiring differential equations to model them. However, a fluid-dynamic resistance Rf can be defined, in accordance with the Ohm’s law, as:

Rf =

∆p M

(5.18)

In other words, the earlier algebraic equation shows that a mass flow rate variation determines a pressure variation. This component is used to model nozzles, valves, turbine stages (where pressure decrease), and pump or compressor stages (where pressure increase) when delay effects are neglected. Moreover, it is important to say that in some cases Rf is not constant but varies as a function of external parameters (valve lift, rotational speed of a machine, thermodynamic conditions); consequently, (5.18) can be used, if modified to take into account these aspects, to represent steady-state characteristic curves of turbomachines and valves. 5.3.4  Dynamic Simulation of a Cogeneration Microturbine as a Multicomponent System

To describe a possible application of the earlier equations to model a power plant in dynamic operating conditions, the case of a cogeneration microturbine is here briefly described referring to the study proposed by the authors in [2]. The microturbine, as shown in Figure 5.3 and also described in Chapter 2, is a multicomponent system that can be split into the following main components:

Figure 5.3  Cogeneration microturbine.��

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air filter, compressor, recuperator, combustion chamber, turbine, shaft, generator, and heat recovery boiler. 5.3.4.1  The Compressor

A compressor can be typically modeled as an algebraic system described by a set of steady-state performance maps that permit to evaluate efficiency hC and pressure ratio bC as a function of operating parameters such as mass flow rate M a and rotational speed n, in addition to environmental conditions (ambient pressure and temperature, respectively, indicated by p1 and T1) [2, 17]:

  T1 n  n  ηC = ηC  βC , β = β ⋅ M , , C C a   p1 T1  T1   

(5.19)

The fluid entering the compressor undergoes an adiabatic process and the outlet temperature T2′ can be calculated as:



kc −1   βc kc − 1  T2′ = T1 ⋅ 1 + ηc     

(5.20)

where kC is the adiabatic exponent. 5.3.4.2  The Accumulation of Mass and of Kinetic Energy within the Microturbine

Since in the proposed model the compressor, as well as the combustion chamber and the heat exchangers, are modeled neglecting mass and kinetic energy accumulation effects, that are present in the real plant operation, two additional components are inserted between the compressor and the recuperator, as shown in Figure 5.4, to model the aforementioned dynamic effects. In particular, a capacitor and an inductor described by fluid-dynamic capacitance Cf and inductance Lf are modeled, respectively, using (5.12) and (5.15) that here can be rewritten as:

(

)



dp2′ 1 = . M a − M X dt Cf

(5.21)



dM X 1 = ⋅ ( p2′ − pY ) dt Lf

(5.22)



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Figure 5.4  The fluid-dynamic capacitance and inductance components.

where: Cf =



Vˆ ka ⋅ Ra ⋅T2′

Lf =

lˆ ˆ Ω

(5.23)

ˆ are geometrical characteristics used to model the In (5.23), Vˆ , lˆ, and, Ω mass and kinetic energy accumulation in the microturbine; their estimation is a hard task that can be accomplished by the analysis of experimental tests. 5.3.4.3  The Combustion Chamber

As mentioned earlier, the combustion chamber is modeled as an algebraic component, since dynamic effects are already considered to be concentrated in the Cf and inductance Lf components reported in Figure 5.4. As a consequence, the continuity and energy equations described by (5.2) and (5.5) can be written for the combustion chamber in the steady-state form:

M X + M f − M eg = 0



M X .h2′′ + M f ⋅ h f + LHV f − M eg ⋅ h3 = 0

(

)

(5.24) (5.25)

where LHVf indicates the fuel lower heating value. Moreover, as reported in [2], a pressure drop in the combustion chamber is also considered, computing it as a function of the square of M X. 5.3.4.4  The Turbine

Analogous to the compressor, the turbine can be modeled using steady-state performance maps that express efficiency hT and pressure ratio βT as a function of mass flow rate M eg, rotational speed n, and pressure and temperature at the turbine inlet p3 and T3 as [2, 17]:

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  T3 n  n   , ηT = ηT  βT ,  , βT = βT  M eg ⋅  p3 T3  T3   

(5.26)

The fluid entering the turbine undergoes an adiabatic process and the outlet temperature T4′ can be calculated as:    1  T4′ = T3 − ηT ⋅T3 ⋅ 1 − k −1  T  kT  βT  



(5.27)

5.3.4.5  The Shaft

The law of conservation of angular momentum describes the dynamics of the shaft: Pm − PL = J ⋅ ω ⋅



dω dt

(5.28)

where PL is the mechanical power requested by the load, J and ω, respectively, the momentum of inertia and the angular rotational speed of the rotors on the same shaft, whereas Pm indicates the mechanical power produced by the microturbine equal to:

Pm = ηm ⋅ (PT − PC ) = ηm ⋅  M eg .(h3 − h4′ ) − M a ⋅ (h2′ − h1 ) (5.29)

with ηm being a mechanical efficiency. 5.3.4.6  The Heat Exchangers

Both the recuperator and the heat recovery boiler can be modeled as counterflow heat exchangers split into parts, each one characterized by the heat exchange between the cold fluid (air/water) and the hot one (exhaust gas); it is worthwhile to consider the thermal energy accumulation in the metal parts by writing the following differential equation:

jeg λ − jcf λ = C λ ⋅

dT λ dt

(5.30)



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where jeg λ and jcf λ indicate, respectively, the thermal flux transferred from the exhaust gas to the λth metal part of the heat exchanger and from this last to the cold fluid. Tλ is the temperature of the λth metal part and Cλ its thermal capacity, equal to the specific heat of the material multiplied by the metal mass. The thermal fluxes jeg λ and jcf λ can be calculated using the well-known equations of applied thermodynamics, written below for jeg λ:

(

)

jeg λ = M eg λ ⋅ hin λ − hout λ = Λeg ⋅ Seg λ ⋅ ∆Tlm _ eg λ λ

(5.31)

As shown in (5.31), the thermal flux can be calculated as a function of either fluid properties (mass flow rate and enthalpy) or global transmittance (Λeg ), heat exchange surface (Seg λ ) and logarithmic mean temperature differλ ence (∆Tlm _ eg λ) [18, 19]. 5.3.4.7  The Information Diagram

When building a simulation model, it is very useful to draw the information diagram, which graphically shows the connections among the different components that constitute the examined complex system. As far as the microturbine is concerned, in Figure 5.5 the pressure-mass flow rate information diagram is sketched. It is possible to note that the pressure values at the inlet and outlet of the compressor (p1 and p2′) are the input for the compressor C block to evaluate the air flow rate (M a) by means of the performance maps reported by (5.19). The Cf block is used to determine the pressure at the compressor outlet (p2′), in accordance with (5.21), whereas the Lf block permits to evaluate the air mass flow rate (M X) entering the recuperator through (5.22). However, the exhaust gas flow rate (M eg) is determined by combustion chamber CC block through the continuity equation, namely, (5.24), applied to the combustion chamber. The CC block also computes the pressure drop in the combustion chamber, whereas the turbine T block uses the turbine’s performance maps, represented

Figure 5.5  The pressure-mass flow rate information diagram.

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by (5.26), to determine the pressure p3 at the turbine inlet. However, the fuel mass flow rate (M f) is determined by the control system. 5.3.4.8  The Control System

It is important to remark that a simulation model of a power plant always contains the modeling of the control system [2, 11, 15, 16, 20]. A possible control system for the examined microturbine is represented in Figure 5.6, referring to the control of the microturbine in electrical priority operation, that is when a certain time profile of the electrical power production is set by the plant management system. The control system is characterized by the proportionalintegral controller PI-Reg-1 that varies the fuel flow rate as a function of the power error εP equal to the difference between the requested electrical power and the microturbine power output. Moreover, since this kind of microturbines are usually controlled in order to maintain constant the temperature T4′, the T4′ control block (based on a proportional-integral logic) also acts to vary the fuel flow rate as a function of the temperature error εT, given by the difference between the temperature T4′ and its set point. As shown in Figure 5.6, the outputs of PI-Reg-1 and T4′ control (indicated with ∆M Pf and ∆M Tf ), that vanish if εP and εT become null (namely, in steady-state conditions), are summed to that is the fuel flow rate entering the combustion gether with the quantity M f chamber when the microturbine generates a constant power equal to the one set by the plant management system.

5.4  Electrical Devices Modeling for Islanded Microgrid Simulations Among the various microgrid configurations, the off-grid one is the most promising, as it can constitute a reliable alternative to the construction of a complete network infrastructure in many different applications. Moreover, the highest

Figure 5.6  The control system.



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degree of flexibility can be reached when all the power generation is connected to the microgrid by means of power electronic devices (from now on this kind of microgrid will be labeled as no-inertia microgrids). However, the off-grid configuration of no-inertia microgrids is much more challenging, as it presents meaningful differences in terms of dynamics responses, regulation strategies and fault detection with respect to a grid-connected microgrid characterized by an inertial frequency response. For this reason, literature is rich of many different approaches to model this kind of microgrids (see [21–25]). The common feature characterizing these models is that they will concentrate their attention on the electric details of the infrastructure and of the converters, which has two main consequences: • An electromagnetic simulator environment is necessary, as the involved dynamics are very fast [26]. • The specific nature of the different energy sources present in the microgrid plays a negligible role (e.g., the thermodynamic process necessary to produce electric power from a combined heat and power unit is not accounted as its dynamics is much slower than the electric ones). As a consequence of the first issue, one possibility to model a no-inertia islanded microgrid is to set up a detailed circuital model on an electromagnetic simulator representing with a high degree of details all the involved components (e.g., all electronic devices (DC/DC and DC/AC) consist of controlled not ideal insulated gate bipolar transistor with pulse-width modulation allowing to evaluate all the harmonic spectra of voltages and currents both at DC and AC side). This approach is no doubt the most complete one, but it is quite cumbersome from both a setup and a CPU time point of view. An alternative approach is to provide a simplified and flexible representation of no-inertia microgrids, which is the object of the present section. The proposed model is a first harmonic model and is represented by of a set of ordinary differential equations under the following main assumptions: • The alternating current (AC) side portion of the microgrid is supposed to be at steady state (assuming that both the angular frequency of the sources and their voltage amplitude can vary), while all the direct current (DC) dynamics are fully considered. • Power electronics converters models neglect higher-order harmonics. • The shunt sections of inverters AC filters are neglected. • Loads are represented by a constant impedance model.

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In accordance to the first hypothesis listed here above, the AC distribution network of the microgrid is described by means of the extended admittance matrix YE (including load impedance, inverter transformers, and AC filters) [27]. Let us assume that the microgrid is composed by N power generating units and let us use the index k to represent the generic kth inverter. The overall schematic representation of the microgrid layout considered for the simplified model (SM) is depicted in Figure 5.7. For the generic kth inverter, under the second assumption of the SM it is possible writing the AC, line to ground, voltage VAC,k as [28]:

V AC ,k (t ) =

mk (t ) 2 2

V DC ,k (t ) e j δk (t )

(5.32)

where j is the imaginary unit, mk is the kth inverter modulation index assumed in the linear operational range (0 to 1.15), and δk is the corresponding angle defined as:

δk (t ) = ψk (t ) + jk (t )

(5.33)

d ψk (t ) = ωk (t ) dt

(5.34)

where

Figure 5.7  Microgrid layout considered for the application of the SM.



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with ωk and jk being the angular frequency and the phase of the kth converter, respectively. Thus, the active power injected by the kth power generating unit into the AC distribution grid is given by:

N  * * *  = PAC ,k = 3Re VAC ,k IAC 3Re V  AC ,k ∑ Y E ,kiV AC ,i  ,k i =1  

{

(

}

)

(5.35)

where YE,ki is the (k, i) element of the extended admittance matrix YE defined as: Y E ,ki = G ki + jBki



(5.36)

with Gki and Bki being the conductance and susceptance of the (k, i) element of the extended admittance matrix YE. It is then possible to rewrite (5.35) considering (5.32) and (5.36) as: PAC ,k = 3

mkV DC ,k 8

N

∑ {miVDC ,i cos ( δk − δi )Gki + sin ( δk − δi ) Bki }

i =1

(5.37)

On the DC side of the inverter a capacitor Ck is connected that has the aim of supporting the DC inverter voltage during the power transient. The capacitor power balance can be written as:

C kV DC ,k

dV DC ,k dt

= PDC ,k − PAC ,k

(5.38)

PDC,k is the power injected by the kth energy source at the DC link and, in this approach, represents the only quantity related to the specific energy source. Consequently, to evaluate it, it is necessary to enter into the details of the power production unit supplying the inverter (e.g., storage units, photovoltaics, wind turbines, microturbines). For example, for a wind power plant, one has [29]:

3 PDC ,k = kopt ωWT

(5.39)

with kopt and ωWT being the optimal coefficient of the maximum power point tracking curve and the turbine angular speed, while the microturbine links the produced power to the amount of gas [30]. Photovoltaic units are described according to the well-known currentvoltage curve depending on the solar irradiance [31]:

118

I PV (V PV

Microgrid Design and Operation: Toward Smart Energy in Cities V 1  b   V V α α − − max  b 1+ max min (V max + τ ν (T ))  V max αmax − αmin  1 − e    ) = αI SC τI (T )  −1 b 1−e      

(5.40)

where α is the is the per unit irradiation referred to 1,000 W/m2, Isc is the short circuit current, Vmax and Vmin are, respectively, and the maximum and minimum open-circuit voltage corresponding to the maximum and minimum irradiation αmax and αmin (provided by the photovoltaic panel data sheet). Moreover, b is a shape factor to be defined in order to match the specific datasheet maximum power point parameters and τI and τv are the voltage and current temperature corrections defined as:

τ I (T ) = 1 + TC I (T − 25°)

(5.41)



τv (T ) = TC v (T − 25°)

(5.42)

being TCI and TCv temperature coefficients provided by photovoltaic module manufacturer. Since the DC voltage produced by the photovoltaic plant is connected to the DC side of the inverter, the voltage VPV is the same as the DC inverter voltage VDC, thus allowing us to write the DC power PDC to be inserted into (5.38) as:

PDC = V DC I PV (V DC )

(5.43)

A storage unit is modeled as a nonideal DC voltage generator representing the battery units connected to the DC side of the inverter by means of an intermediate DC/DC chopper in a buck-boost configuration. The DC/DC converter is needed to keep a constant voltage at the DC side of the converter and the buck-boost configuration is needed to allow a bidirectional power flow. Between the battery and the DC/DC converter, a series inductor is considered, LST, to operate the DC/DC converter in a step-up configuration. The storage model equivalent circuit is depicted in Figure 5.8. The battery is represented by a Thevenin equivalent where the value of the voltage generator, namely E, is dependent on its state of charge SOC. This dependency can be expressed by means of an Nth order polynomial in the form:



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Figure 5.8  Schematic circuital representation of a storage unit. N

E (SOC ) = ∑ ai ⋅ SOC i



i =0

(5.44)

The values of ai coefficients vary in accordance to the specific battery technology. Details on the storage modeling are available in [32]. Neglecting the dependence of the state of change on the temperature (which is possible, for example, for the SPM battery technologies as specified in [31]), the DC voltage provided at the battery terminals, namely Vbatt, can be then written as:

Vbatt (SOC , I ST ) = E (SOC ) − I ST R int

(5.45)

where Rint is the battery internal resistance. The SOC is related to the storage current IST by: I dSOC = − ST dt NCC



(5.46)

where NCC is the nominal current capacity of the storage. Considering a first harmonic representation for the DC/DC converter too, it is possible to write the state equation of the storage inductor as:

LST

dI ST V = E (SOC ) − R int I ST − DC dt K ST

(5.47)

where KST is the DC/DC converter gain. As is well known, the DC/DC controller has the aim of keeping constant the storage DC link voltage [32]. For the storage unit, one can now define the invert DC power to be used in (5.38) as:

PDC =

V DC I ST K ST

(5.48)

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Equations (5.32), (5.33), and (5.37) through (5.48) represent the system behavior and completely describe the DC dynamics of the microgrid. The modeling of the network with the extended admittance matrix allows accounting for the influence of the network topology on the AC side power flows. The inputs of the proposed SM model can be divided into two categories: • Physical inputs that depend on the specific energy source (wind speed, solar irradiance and temperature, amount of fuel); • Control inputs that are the inverters modulation index mk, frequency ωk, and phase jk. Such inputs are provided by the controllers according to the specific strategy.

5.5  Conclusions In this chapter, the attention has been focused on the simulation of power plant components. The structure of a simulation project has been briefly described and the difference between steady-state and dynamic simulation has been highlighted. The main thermo-fluid dynamic equations used to study the dynamic behavior of a complex energy system have been explained and, as a case study, the dynamic simulation of a cogeneration microturbine has been reported. Finally, the main equations used to model electrical devices, such as photovoltaic units, wind turbines, and storage systems, in islanded microgrids have been analyzed in detail.

References [1] NSF, “Revolutionizing Engineering Science Through Simulation,” Report of the National Science Foundation Blue Ribbon Panel on Simulation-Based Engineering Science, May 2006. [2] Bracco, S., and F. Delfino, “A Mathematical Model for the Dynamic Simulation of Low Size Cogeneration Gas Turbines Within Smart Microgrids,” Energy, Vol. 119, 2017, pp. 710–723. [3] Bracco, S., I. Faccioli, and M. Troilo, “A Numerical Discretization Method for the Dynamic Simulation of a Double-Pipe Heat Exchanger,” International Journal of Energy, Vol. 1, No. 3, 2007, pp. 47–58. [4] Bracco, S., M. Troilo, and A. Trucco, “A Simple Dynamic Model and Stability Analysis of a Steam Boiler Drum,” Proceedings of the Institution of Mechanical Engineers (IMechE), Part A - Journal of Power and Energy, Vol. 223, No. 7, 2009, pp. 809–820.



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[5] Alobaid, F., et al., “Progress in Dynamic Simulation of Thermal Power Plants,” Progress in Energy and Combustion Science, Vol. 59, 2016, pp. 79–162. [6] Bracco, S., et al., “Dynamic Simulation of Combined Cycle Power Plant Cycling in the Electricity Market,” Energy Conversion and Management, Vol. 107, 2016, pp. 76–85. [7] Alobaid, F., et al., “A Comparative Study of Different Dynamic Process Simulation Codes for Combined Cycle Power Plants – Part A: Part Loads and Off-Design Operation,” Fuel, Vol. 153, 2015, pp. 692–706. [8] Bracco, S., G. Crosa, and A. Trucco, “Dynamic Simulator of a Combined Cycle Power Plant: Focus on the Heat Recovery Steam Generator,” Proceedings of Ecos 2007 (20th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems), Vol. I, Padova, Italy, June 25–28, 2007, pp. 189–196. [9] Chen, C., Z. Zhou, and G. M. Bollas, “Dynamic Modeling, Simulation and Optimization of a Subcritical Steam Power Plant. Part I: Plant Model and Regulatory Control,” Energy Conversion and Management, Vol. 145, 2017, pp. 324–334. [10] Hübel, M., et al., “Modelling and Simulation of a Coal-Fired Power Plant for Start-Up Optimization,” Applied Energy, Vol. 208, 2017, pp. 319–331. [11] Ordys, A. W., et al., Modelling and Simulation of Power Generation Plants, New York: Springer-Verlag, 1994. [12] Stoecker, W. F., Design of Thermal Systems, New York: McGraw-Hill, 1989. [13] Bracco, S., and F. Delfino, “The Role of High Efficiency Trigeneration Plants Within Sustainable Smart Microgrids: Performance Analysis and Experimental Tests,” Proceedings of AEIT International Annual Conference: A Sustainable Development in the Mediterranean Area, (AEIT 2015), Napoli, Italy, October 14–16, 2015. [14] Rezendes Tada, E. F., et al., “Investigation of Heat Transfer in Partially Filled Horizontal Drums,” Chemical Engineering Journal, Vol. 316, 2017, pp. 988–1003. [15] Maffezzoni, C., “Boiler–Turbine Dynamics in Power-Plant Control,” Control Engineering Practice, Vol. 5, 1997, pp. 301–312. [16] Lu, S., “Dynamic Modelling and Simulation of Power Plant Systems,” Proc. Inst. Mech Eng, Part A: J Power Energy, 1999, pp. 213–217. [17] Saravanamuttoo, H. I. H., et al., Gas Turbine Theory, 6th ed., Boston, MA: Pearson Education, 2009. [18] Kakac, S., and H. Liu, Heat Exchangers: Selection, Rating and Thermal Design, 2nd ed., Boca Raton, FL: CRC Press, 2002. [19] Kern, D. Q., Process Heat Transfer, New York: McGraw-Hill, 1990. [20] Ikonen, E., and K. Najim, Advanced Process Identification and Control, Control Engineering Series, New York: Marcel Dekker, 2002. [21] Davoudi, A., and A. Bidram, “Hierarchical Structure of Microgrids Control System,” IEEE Transactions on Smart Grid, Vol. 3, 2012. [22] Guerrero, J. M., et al., “Advanced Control Architectures for Intelligent Microgrids—Part I: Decentralized and Hierarchical Control,” IEEE Transactions on Industrial Electronics, Vol. 60, 2013, pp. 1254–1262.

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[23] Guerrero, J. M., et al., “Advanced Control Architectures for Intelligent Microgrids—Part II,” IEEE Transactions on Industrial Electronics, Vol. 60, 2013, pp. 1263–1270. [24] Guerrero, J. M., et al., “Decentralized Control for Parallel Operation of Distributed Generation Inverters in Microgrids Using Resistive Output Impedance,” IEEE Transactions on Industrial Applications, Vol. 54, 2007. [25] Tang, X., et al., “A Novel Frequency and Voltage Control Method for Islanded Microgrid Based on Multienergy Storages,” IEEE Transactions on Smart Grid, Vol. 7, 2016, pp. 410– 419. [26] Manitoba-HVDC, material.

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Library,

https://hvdc.ca/knowledge-library/reference-

[27] Kundur, P., N. J. Balu, and M. G. Lauby, Power System Stability and Control, New York: McGraw-Hill, 1994. [28] Erickson, R., and D. Maksimovic, Fundamentals of Power Electronics, Second Edition, Boston: Kluwer Academic, 2004. [29] Bonfiglio, A., et al., “Modeling and Maximum Power Point Tracking Control of Wind Generating Units Equipped with Permanent Magnet Synchronous Generators in Presence of Losses,” Energies, Vol. 10, 2017, p. 102. [30] Bendato, I., et al., “A Real-Time Energy Management System for the Integration of Economical Aspects and System Operator Requirements: Definition and Validation,” Renewable Energy, Vol. 102, 2017, pp. 406–416. [31] Boke, U., “A Simple Model of Photovoltaic Module Electric Characteristics,” 2007 European Conference on Power Electronics and Applications, 2007, pp. 1–8. [32] Labella, A., et al., “A Simplified First Harmonic Model for the Savona Campus Smart Polygeneration Microgrid,” 2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), 2017, pp. 1–6.

6 Optimization for Microgrid Planning 6.1  Overview This chapter deals with the planning of a microgrid from an economical point of view. A brief description of the different approaches present in the literature has been reported. The statement of the decision problem has been explained, as well as the list of the decision variables and the system model. The formalization of the overall optimization problem is discussed. Finally, the inclusion of storage systems and examples of the solutions have been presented.

6.2  Introduction The design of a microgrid implies the choice of location, size, and type of production plants (both from renewables and fossil fuels), storage systems, and thermal and electrical distribution systems, on the basis of the energy demand to be satisfied [1, 2]. Indeed, as highlighted by Mancarella [3] and by Chicco and Mancarella [4], the optimal utilization of local resources represents a strategic area to improve the environmental efficiency of cities. Within the aforementioned scenario, microgrids are expected to become part of the next electric power system evolution, not only in rural and remote areas, but also in urban communities. Gamarra and Guerrero [5] remarked that the planning of a microgrid is a complex process due to the different existing alternatives, goals, constraints, and uncertainties; the authors also proposed innovative planning methodologies focused on economic feasibility. Kolokotsa [6] highlighted that the role of buildings in microgrids is crucial; the author reported the state of 123

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the art of the building potential and communities integrated in smart grids, also analyzing future research prospects. Distributed energy systems and smart microgrids have to be designed in accordance with technical and economic constraints [5], without neglecting environmental and social aspects. Furthermore, during daily operation, they have to be handled by suitable energy management systems. In [7], the authors propose a mathematical programming tool to optimally design a distributed energy system, with the aim of minimizing capital and operating costs. In [3], attention is focused on distributed-multigeneration options to integrate natural gas, electricity, heat, and cooling at various decentralization levels, whereas Gu et al. [8] studied the economic operation of a system composed of wind power, fuel cells, photovoltaics, heat recovery boiler, and storage batteries. A widely used software package for optimization and planning of microgrids is HOMER [9], which adopts cycle charging and load following strategies to obtain the optimal sizing scheme. To minimize costs and maximize benefits in such a design procedure, the use of optimization algorithms is very important, since they allow finding the optimal solution and the same algorithm can be easily applied to different configurations, only by changing the values of the identified parameters. Optimization models are generally used both for planning purposes (i.e., to define the set of plants to be installed) [10, 11] and for management purposes (i.e., for a given plant’s configuration, to decide the optimal schedule of power production units and storages) [12, 13]. As described in the following, there are optimization models that provide as optimal results both the optimal configuration of the microgrid and the best operational management asset. The advantage is the possibility of taking into account the power balance in the short term (i.e., for example, each hour or even less), while the drawback is that the computational complexity increases. However, generally, the daily (or hourly) optimal plant schedule that results from the solution of the long term planning problem is not taken into account for operational management purposes. Instead, it is better to run an optimization model in the short term (for example, in the day ahead) in which the forecasting of renewables and demands are affected by less uncertainty (and in which plants are already sized).

6.3  State of the Art of the Optimal Planning Approaches In literature, the use of mathematical models for the design of energy systems is consolidated. Fewer contributions are related to microgrids, and, in general, there are not articles that describe a comprehensive approach including many different kinds of production plants and storage systems. Thus, attention is mainly focused on specific (and generally with few technologies) case studies. Moreover, there is still the need of facing the problem of uncertainty when



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defining the optimal mix of plants on the basis of long-term forecasting. This is a main issue that needs to be investigated and that suggests that optimal planning should be used for the definition of a general layout of the microgrid, and not for operational purposes (that should be faced in nearly real time). In the literature of the optimal planning of microgrids, Mehleri et al. [14] presented a mixed-integer linear programming super-structure model for the optimal design of distributed energy generation systems that satisfy the heating and power demand at the level of a small neighborhood; the objective function minimizes the overall investment and annual operating costs. The mathematical model described by [15] takes into account site energy loads, local climate data, utility tariff structure, characteristics of the candidate distributed energy resources, and geographical aspects. Both studies in [14, 15] presented methodologies for the planning and design of an urban microgrid, taking into account renewable and cogeneration sources and considering a local heating distribution network. In [16], the authors presented an optimal design of microgrids with multiple energy sources based on multi-agent systems for intelligent demand-side management where there is a limitation on the available capital, including gray prediction algorithms (i.e., algorithms based on both physically based models and data elaboration like in [17]) for better management. In [1], a mixed-integer linear programming model that includes electricity and heat transfer networks, as well as their physical and operational constraints, was presented to optimally design a microgrid, taking into account the localization of distribute energy resources inside the grid and optimal technology portfolio. In [18], a mixed integer linear model has been used for determining the optimal sizes of the equipment to be installed in each building, such as the photovoltaic system, the energy storage system, and inverters. In [19], a technique for the optimal planning and design of hybrid renewable energy systems for microgrid applications was proposed; in particular, the distributed energy resources customer adoption model is used to determine the optimal size and type of distributed energy resources. In [20], a multi-objective approach was adopted for optimizing planning and operation of a grid-tied microgrid equipped with different kind of distributed generators. In [21], a hybrid optimization method using a novel master-slave objective function was developed to derive optimal values of distributed sources capacity and operation strategy, by means of particle swarm optimization and quadratic programming. Finally, in [22], uncertainties in the technical parts and pecuniary information were encoded by means of a robust optimization approach for optimally design the microgrid. As an example, Ondeck et al. [23] analyzed the optimal integration of a combined heat and power plant as a utility producer for a residential district, highlighting its interaction with photovoltaic power generation. When setting up an optimization problem, one has to deal with the following entities:

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• Parameters: Fixed values, which, in the specific case of a microgrid, are, for example, energy efficiency in production plants or availability of loads and renewables. Typically, electrical and thermal demands and renewables’ availability are estimations that should be available before the run of the optimization problem. For long-term optimal planning problems (at least 1 year should be considered), such an estimation is based on historical data and is affected by uncertainty, whereas for short-term management problems, it is based on forecasting (in the day ahead, for instance), used to derive the hourly optimal schedule. • Decision variables: All those quantities that need to be determined to solve the problem and that correspond to the decisions to be taken. In the case of planning problems, examples of decision variables are size, location, number, and kind of available technologies to be used in the microgrid and the kind and physical dimensions of distribution systems in the microgrid. • Objective function: The goal to reach in terms of microgrid operation performances (for example, the minimization of economic costs or carbon dioxide emissions). It is a mathematical equation expressed as a function of parameters and decision variables. • Constraints: They represent technical, environmental, and legislative limits that are imposed for the solution of the optimization problem. For planning purposes, examples of constraints are available surface in the considered area for the installation of photovoltaics and wind plants (roofs of buildings, available areas), maximum power that can be purchased from the external grid (on the basis of contracts or commercial agreements), and electrical and thermal balance. As for the objective function, the constraints should be expressed as a function of the decision variables. In the following, the optimization problem for planning purposes will be formalized and some examples will be reported. Specifically, with the aim of building a simple decision problem, at first, storage systems will be neglected as well as the physical dimensions of the distribution networks. However, it should be noticed that the choice related to the specific distribution systems can be taken after the optimal design of production plants is performed, solving another, subsequent optimization problem. Storage systems are characterized by a state equation that relates the state of charge with the energy flows inside the battery. Thus, one has two possibilities: (1) to build a decision model relying on two suboptimizations, the first one related to the optimal size and kind of



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technologies, and the second one, given the set of plants, that optimizes storage size; and (2) to build a unique optimization problem.

6.4  Optimal Design of Microgrids: The Decision Problem The goal of the planning problem is to find the best sizing and combination of the available technologies, given estimated parameters, such as the photovoltaic and wind generation potential, the energy demand, and network constraints (under the single bus bar assumption). The planning is carried out by finding the sizes (or numbers) of the devices that minimize the sum of the overall installation and operative costs, over the considered time horizon of Y years. Hourly and seasonal variations of photovoltaic production, electrical demand, and energy prices are taken into account by considering, for each month of the year, the corresponding profile in a typical average day. Hourly prices are considered for the purchasing and selling of electricity. It is important to note that, in the following, 1 day per month (subscript m) is considered as an average typical day in month m, m = 1, …, 12. However, the subscript m can be considered as a general time period in which it is reasonable to find similar weather conditions. Thus, in the following, we consider m = 1, …, 12, but, for example, if the time period is chosen as a season or a day, then m = 1, …, 4 or m = 1, …, 365, respectively. The following technologies are considered in the formalization of the optimization problem described in the next sections: • Photovoltaic fields are characterized by the size of the installation in terms of the total panel surface, the azimuth (i.e., the orientation with respect to the south), the tilt angle of the panels, and the curves of the solar radiation in the site. The size of the photovoltaic installations in each site represents the decision variable. Different possibilities for the tilt angle are considered. • Combined heat and power units (micro gas turbines, combined heat and power) are capable of producing both electrical and thermal power. The decision variables here considered are the presence or absence of a certain kind of technology (to each different brands and rated power are referred), and the scheduling over time of the microturbines (i.e., power level in time interval (t, t + 1)). • Small-size wind turbines has the decision variable as the number of installed turbines of a certain technology. • Heat pumps are able to use electricity to produce cold air to be used during the summer or for rooms in which a low temperature is required.

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The decision variables are the presence or absence of the plant and its schedule over time (i.e., power level in the time interval (t, t + 1)). • Biomass production plants have different kinds of biomasses (agricultural, forest) that can be used in combustion, gasification, and pyrolysis power plants for the production of electrical and thermal power. This particular fuel has the characteristics of creating low carbon dioxide. Generally, it is assumed that the carbon dioxide that is emitted when the biomass is burnt is the same that the biomass has accumulated during growth. For this reason, together with the fact that its inherent energy comes from the Sun and it can regrow in a relatively short time, biomass is considered a renewable energy [24]. The considered decision variables are the presence or absence of a specific technology and the production schedule (i.e., power level in time interval (t, t + 1)). • Solar thermal technologies produce heat from the Sun. In this case, like for photovoltaics, the decision variable is related to the installed surface. • Fossil fuel plants produce only electrical power. As for the previous technologies, the decision variables are: the presence or absence of a specific technology with a given capacity and the production schedule (i.e., power level in the time interval (t, t + 1)). • Chillers are able to transform thermal power in cold air to satisfy the demand of cooling energy. Again, decisions are here related to the presence or absence of a specific technology with a given capacity and the production schedule (i.e., power level in the time interval (t, t + 1)). • Thermal power plants use natural gas to produce heat (boilers). Decisions are here related to the presence or absence of a specific technology with a given capacity and the production schedule (i.e., power level in the time interval (t, t + 1)).

6.5  Decision Variables and Parameters 6.5.1  Parameters Related to Power Flows

The main parameters and data relevant for the problem are the following: • y = 1, ..., Y: years of the considered installation expected life; • m = 1, ..., M: generic month in the year or time period represented by the typical day; • t = 1, ..., T: generic time interval in the day; • ∆t[h] time step in the typical day;



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• nm: number of days in month m; • D yel,m ,t [kW]: electrical demand in year y and month m in the time interval (t, t + 1); ,min ,max • D yheat , D yheat , D yheat ,m ,t ,m ,t ,m ,t [kW]: minimum and maximum and forecasted heat demand in year y and month m in the time interval (t, t + 1); ,min ,max • D ycold , D ycold , D ycold ,m ,t ,m ,t ,m ,t [kW]: minimum and maximum and forecasted cold demand in year y and month m in the time interval (t, t + 1);

• α = 1, …, Nα: index referring to the different photovoltaics considered for the installation; • SPV,max: maximum value of the photovoltaic field surface; •  p αPV, y ,m ,t [kW/m2]: specific production of the photovoltaic field installed if kind α, in year y, month m, and time interval (t, t + 1); • z = 1, ..., Nz: index for the different kinds of solar thermal technologies; •  pz,STy ,m ,t [kW/m2]: specific production of the solar thermal plant z installed, in year y and month m in the time interval (t, t + 1); • j = 1, ..., Nj: index for the different wind turbine technologies; • nW,max: maximum number of wind turbines that can be installed; •  pW j , y ,m ,t [kW]: power output of the wind turbine j in year y and month m in the time interval (t, t + 1); • β = 1, ..., Nβ : index for the different combined heat and power technologies; • CAPβCHP : maximum capacity of the βth microturbine; •  ηCHP β : efficiency for the combined heat and power of type β; •  θCHP : fraction between thermal and electrical power in the β-type comβ bined heat and power; • σ = 1, ..., Nσ: index of the different biomass plants; •  CAPσBIO : maximum capacity of the σth biomass plant; •  ηBIO σ : fraction between thermal and electrical energy in the σth biomass production plant; •  θBIO σ : efficiency to transform tons of biomass in electrical power in the σth biomass production plant; • f = 1, ..., Nf: index for the different chiller technologies; •  CAPfCHI : maximum capacity of the fth chiller; •  ηCHI f : efficiency to transform heat power in cold power through the fth chiller;

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• d = 1, ..., Nd: index for the different technologies that use fossil fuels to produce electrical energy; •  CAPdFF : maximum capacity of the dth plant; •  ηdFF : efficiency for the dth plant. • b = 1, ..., Nb: index of the different boiler technologies; •  CAPbBOI : maximum capacity of the bth boiler; •  ηbBOI : efficiency of the bth boiler; • x = 1, ..., Nx: index of the different technologies for heat pumps; •  CAPxHP : maximum capacity of the xth heat pump; •  ηHP x : efficiency to transform electrical energy in thermal power through the xth heat pump; • M: a big number with respect to the possible values of the decision variables; 6.5.2  Parameters Related to Costs

The parameters related to costs are as follows: el

•  c y ,m ,t [€/kWh]: purchasing price of electricity in year y and month m in the time interval (t, t + 1); el •  b y ,m ,t [€/kWh]: selling price of electricity, in year y and month m in the time interval (t, t + 1); CHP •  c y ,m [€/kWh]: fuel cost per unit of primary energy for the combined heat and power in year y and month m; FF •  c y ,m [€/kWh]: fuel cost per unit of primary energy for the fossil fuel plants in year y and month m; BIO •  c y ,m [€/kWh]: fuel cost per unit of primary energy for the biomass plants in year y and month m; BOI •  c y ,m [€/kWh]: fuel cost per unit of primary energy for the boiler in year y and month m; CHP •  µ β [€/kWh]: maintenance costs per unit of produced electrical energy for the type β combined heat and power; CHP , fix •  µ β [€]: fixed maintenance costs for the βth combined heat and power; •  µPV α [€/kWh]: maintenance costs per unit of produced energy for the photovoltaic fields of kind α;



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, fix •  µPV [€]: fixed maintenance costs for the photovoltaic fields of kind α α; W •  µ j [€/kWh]: maintenance costs per unit of produced energy for the wind turbines of kind j; W , fix •  µ j [€]: fixed maintenance costs for the wind turbines of kind j;

•  µST z [€/kWh]: maintenance costs per unit of produced energy for the solar thermal fields of kind z; , fix •  µST [€]: fixed maintenance costs for the solar thermal fields of kind z z;

•  k αPV [€/m2]: installation costs for the photovoltaic fields, per unit of panel surface; CHP •  k β [€]: installation costs of the βth combined heat and power; W

•  k j [€]: installation costs of the jth wind turbine; •  kzST [€/m2]: installation costs of the zth solar thermal plant, per unit of panel surface; •  k σBIO [€]: installation costs of the σth biomass plant; CHI •  k f [€]: installation costs of the fth chiller; •  kbBOI [€]: installation costs of the bth boiler; •  kxHP [€]: installation costs of the xth heat pump; •  kdFF [€]: installation costs of the dth fossil fuel plant; • i: discount rate. 6.5.3  Decision Variables

The decision variables are as follows: ,el • Pygrid ,m ,t [kWh]: power exchanged with the external grid (unrestricted in sign, positive if bought) in year y and month m in the time interval (t, t + 1);

• S αPV [m2]: surface covered by photovoltaic panels with tilt α; • Pz,STy ,m,th,t [kW]: power generated by solar thermal plants of kind z in year y and month m in time interval (t, t + 1); •  S zST [m2]: surface covered by solar thermal panels of kind z; ,el • PαPV , y ,m ,t [kW]: power generated by photovoltaic plants of kind α in year y and month m in time interval (t, t + 1);

• nWj : number of installed wind turbines of kind j;

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• PjW, y ,m ,t [kW]: power generated by wind turbines of kind j in year y and month m in time interval (t, t + 1); ,el • PβCHP , y ,m ,t [kW]: electric power generated by the βth combined heat and power installed in year y and month m in time interval (t, t + 1); ,th • PβCHP , y ,m ,t [kW]: thermal power generated by the βth combined heat and power installed in year y and month m in time interval (t, t + 1); ,th •  PbBOI , y ,m ,t [kW]: thermal power generated by the bth boiler installed in year y and month m in time interval (t, t + 1); ,th •  PσBIO , y ,m ,t [kW]: thermal power generated by the σth biomass plant installed in year y and month m in time interval (t, t + 1); ,el • PσBIO , y ,m ,t [kW]: electric power generated by the σth biomass plant installed in year y and month m in time interval (t, t + 1);

•  PdFF , y ,m ,t [kW]: electric power output of the dth production plant based on fossil fuels in year y and month m in time interval (t, t + 1); ,el • D xHP , y ,m ,t [kW]: electric power used to feed the xth heat pump in year y and month m in time interval (t, t + 1); ,cold •  PxHP , y ,m ,t [kW]: thermal power produced by the xth heat pump in year y and month m in time interval (t, t + 1); ,cold • P fCHI [kW]: thermal power for cold produced by the fth chiller in , y ,m ,t year y and month m in time interval (t, t + 1); ,in •  P fCHI , y ,m ,t [kW]: thermal power for cold entering chiller f in year y and month m in time interval (t, t + 1); BOI HP CHI , δdFF , δCHP •  δBIO σ , δb β , δx , δ f , binary variables (either 0 or 1). Each one is equal to 1 when the corresponding technologies are installed and 0 otherwise.

6.6  The System Model Description and Related Constraints 6.6.1  The PV Power Plant

To determine the optimal surface S αPV covered by a photovoltaic plant of kind α, the constraint related to the available maximum surface SPV,max should be considered: Nα



Sα ∑ α =1

PV

≤ S PV ,max



(6.1)



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133

Since the covered surface is strictly related to the electrical power produc,el tion PαPV , y ,m ,t , then for each interval (t, t + 1), in the generic month m and in year y, we get that

,el PV PV PαPV , y ,m ,t = p α, y ,m ,t S α



(6.2)

where p αPV, y ,m ,t is the production per square meter. 6.6.2  The Solar Thermal Power Plant

For optimally fixing the surface S zST covered by the installed solar thermal plant of kind k, similarly to the photovoltaic plant, the following constraint has to be taken into account: Nz



∑ S zST

z =1

≤ S ST ,max

(6.3)

where SST,max is the maximum surface that can be covered by the plant. Since the covered surface is strictly related to the thermal power produc,th tion PzST , y ,m ,t , for each interval (t, t + 1), in the generic month m and in year y, we get

,th ST ST PzST , y ,m ,t = pz , y ,m ,t S z

(6.4)

where pzST, y ,m ,t is the specific production per square meter. 6.6.3  The Wind Turbine Power Plant

For optimally fixing the (integer) number nWj of the installed wind turbines for any kind j, the condition that the total installation number cannot be greater than an a priori fixed maximum can be written as: Nj



∑ nWj j =1

≤ nW ,max

(6.5)

Since the number of installation is strictly related to the total electrical power production PjW, y ,,mel ,t , then for each interval (t, t + 1), in the generic month m and in year y, we get

W PjW, y ,,mel ,t = pW j , y ,m ,t n j

(6.6)

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Microgrid Design and Operation: Toward Smart Energy in Cities

where pW j , y ,m ,t is the average power delivered by the wind turbine. 6.6.4  The Combined Heat and Power Microturbines Plants

In the case of combined heat and power, as said before, a binary variable is used to state the presence/absence of the βth technology. It is assumed in this work that an a priori fixed maximum number of microturbines can be installed. That is: Nβ



∑ δCHP β

β =1

≤ nCHP ,max

(6.7)

,el The electrical power produced by βth combined heat and power (PβCHP , y ,m ,t ) should be less than or equal to its capacity of production. Moreover, binary ,el and continuous variables should be linked (i.e., if δCHP = 0, then PβCHP β , y ,m ,t =0; CHP ,el otherwise, Pβ, y ,m ,t can assume any values within its constraint of capacity). This means that the following equations are necessary:



,el CHP 0 ≤ PβCHP , y ,m ,t ≤ CAPβ

(6.8)



,el CHP PβCHP ≤0 , y ,m ,t − M δ β

(6.9)

where CAPβCHP is its maximum rate and M is a sufficiently big number with respect to all variables of the optimization model. ,th The thermal power PβCHP , y ,m ,t produced is strictly related to the electrical power one, according to a relation that, for the purposes of this chapter, is assumed to be linear according to the constant θβ, that is,

,th CHP CHP ,el PβCHP , y ,m ,t = θ β Pβ, y ,m ,t

(6.10)

6.6.5  The Thermal Boilers

In strict analogy to the case of cogenerators, also in this case we add a constraint related to the maximum number of installed technologies, the maximum power production, and the relation between binary and continuous variables: Nb



∑ δbBOI

b =1

≤ n BOI ,max

(6.11)



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135



,th BOI 0 ≤ PbBOI , y ,m ,t ≤ CAPb

(6.12)



,th BOI PbBOI ≤0 , y ,m ,t − M δb

(6.13)

where CAPbBOI is its maximum rate and M is a sufficiently big number. 6.6.6  The Biomass Plants

For optimally fixing which biomass plant should be installed, it is useful to introduce the binary variable δBIO σ , so that the condition that the total installation number cannot be greater than an a priori fixed maximum can be written as: Nσ



≤ n BIO ,max ∑ δBIO σ

σ =1

(6.14)

The presence of the σth biomass plant or its absence determines the upper bound of electrical and thermal power production. More precisely for each time interval (t, t + 1), in the generic month m and in year y, the electrical power ,el generated PσBIO , y ,m ,t has to be limited by its rating according to

,el BIO 0 ≤ PσBIO , y ,m ,t ≤ CAPσ

(6.15)



,el BIO PσBIO ≤0 , y ,m ,t − M δσ

(6.16)

where CAPσBIO is its maximum rate and M is a sufficiently big number. ,th The thermal power PσBIO , y ,m ,t generated by the σth biomass plant is here assumed to be linearly dependent to the electrical power according to the constant θBIO σ , that is,

,th BIO BIO ,el PσBIO , y ,m ,t = θ σ Pσ , y ,m ,t

(6.17)

6.6.7  The Heat Pumps

For optimally fixing which of the xth heat pumps should be installed, it is useful to introduce the binary variable δHP x , so that the condition that the total installation number cannot be greater than an a priori fixed maximum can be written as: Nx



∑ δHP x

x =1

≤ n HP ,max

(6.18)

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Microgrid Design and Operation: Toward Smart Energy in Cities

The presence of the xth heat pump or its absence determines the upper bound of the cold power production. More precisely, for each time interval (t, t + 1), in the generic month m and in year y, the electrical power generated ,cold PxHP , y ,m ,t has to be limited by its rating according to

,cold HP 0 ≤ PxHP , y ,m ,t ≤ CAPx

(6.19)



,cold HP PxHP , y ,m ,t − M δx ≤ 0

(6.20)

where CAPxHP is its maximum rate and M is a sufficiently big number. ,el The required electrical power DxHP , y ,m ,t is here supposed to be linearly linked to the cold production by means of the efficiency coefficient ηHP x , that is,

,cold HP HP ,el PxHP , y ,m ,t = ηx Dx , y ,m ,t

(6.21)

6.6.8  The Chillers

For optimally fixing which of the fth chiller should be installed, it is useful to introduce the binary variable δCHI , so that the condition that the total installaf tion number cannot be greater than an a priori fixed maximum can be written as:

∑ δCHI f f

≤ nCHI ,max

(6.22)

The presence or the absence of the fth chiller determines the upper bound of the cold power production. More precisely, for each time interval (t, t + 1), ,cold in the generic month m and in year y, the electrical power generated PfCHI , y ,m ,t has to be limited by its rating according to

,cold CHI 0 ≤ PfCHI , y ,m ,t ≤ CAP f

(6.23)



,cold CHI PfCHI ≤0 , y ,m ,t − M δ f

(6.24)

where CAPfCHI is its maximum rate and M is a sufficiently big number. The generation of the cold power is performed by thermodynamically ,in converting a greater quantity of hot thermal power PfCHI , y ,m ,t , by means of a relation that here is supposed to be linear according to the efficiency coefficient ηCHI f , that is,



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137

,cold CHI CHI ,in PfCHI , y ,m ,t = η f P f , y ,m ,t



(6.25)

6.6.9  The Fossil Fuel Plants

For optimally fixing which of the dth fossil fuel plants should be installed, it is useful to introduce the binary variable δdFF , so that the condition that the total installation number cannot be greater than an a priori fixed maximum can be written as:

∑ δdFF



d

≤ n FF ,max

(6.26)

The presence or the absence of the dth fossil fuel determines the upper bound of the electrical power production. More precisely, for each time interval (t, t + 1), in the generic month m and in year y, the electrical power generated ,el PdFF , y ,m ,t has to be limited by its rating according to

, el FF 0 ≤ PdFF , y , m ,t ≤ CAPd

(6.27)



,el FF PdFF , y ,m ,t − M δd ≤ 0

(6.28)

where CAPdFF is its maximum rate and M is a sufficiently big number. 6.6.10  The Electrical and Thermal Power Balance

The electric and the thermal power balance should be guarantee for each time interval (t, t + 1), in the generic month m and in year y. Concerning the electric constraint, the following equality between power generation and power demand has to be satisfied: Pygrid ,m ,t



+∑ α

,el PαPV , y ,m ,t

Nj

+∑ j

PjW, y ,,mel ,t



+∑ σ

,el PσBIO , y ,m ,t



,el + ∑ PβCHP , y ,m ,t + β

Nd

+∑ d

PdFF , y ,m ,t

= D yel,m ,t

Nx

+∑ x

(6.29) ,el DxHP , y ,m ,t

A similar one has to be fulfilled by the thermal power, that is,

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Microgrid Design and Operation: Toward Smart Energy in Cities Nβ



Nz

Nb

β =1

σ =1

z =1

b =1

BIO ,th ST ,th BOI ,th ,th ∑ PβCHP , y ,m ,t + ∑ Pσ , y ,m ,t + ∑ Pz , y ,m ,t + ∑ Pb , y ,m ,t



Nf

−∑

f =1

,in PβCHI , f , y ,m ,t

Nx

Nf

x =1

f =1

,cold ∑ PxHP , y ,m ,t +





(6.30)

= D yhot,m ,t

,cold ∑ PfCHI , y ,m ,t

= D ycold ,m ,t

(6.31)

with:

,in CHP ,CHI BIO ,CHI ST ,CHI BOI ,CHI PβCHI , f , y ,m ,t = Pβ, f , y ,m ,t + Pσ , f , y ,m ,t + Py , f ,m ,t ,z + Pb , f , y ,m ,t

(6.32)

BIO,CHI BOI,CHI CHP ,CHI where PfST,CHI ,z , y ,m ,t , P f , σ , y ,m ,t , P f ,b , y ,m ,t , P f , β, y ,m ,t , represent the thermal power that is required by the chillers to produce cold. If the grid architecture is able to reject excess heat to the environment, in order to guarantee more flexibility to the model in the optimal solution research, the equality constraint has been substituted with a weaker inequality one, that is,

D yhot,m,min ≤ ,t

Nb

+∑

b =1







Nz

σ =1

z =1

BIO ,th ST ,th ,th ∑ PβCHP , y ,m ,t + ∑ Pσ , y ,m ,t + ∑ Pz , y ,m ,t

β =1

,th PbBOI , y ,m ,t



Nf



f =1

,in PβCHI , f , y ,m ,t

Nx

Nf

x =1

f =1

,min ,cold D ycold ≤ ∑ PxHP ,m ,t , y ,m ,t +



(6.33)

≤ D yhot,m,max ,t

,cold ∑ PfCHI , y ,m ,t

,max ≤ D ycold ,m ,t

(6.34)

It is important that to calculate constraints (6.35) and (6.36) it is, in gen,in eral, necessary to quantify PβCHI , f , y ,m ,t as a function of the thermal production and the chillers’ operation. The thermal power for cold is here considered as taken from the overall district heating system (that is supposed to connect production plants that give thermal power). A general formalization of the optimization problem is here provided by the use of some auxiliary decision variables:

,th ST ,hot PzST , y ,m ,t = Pz , y ,m ,t +

Nf

∑ PyST, f ,,CHI m ,t ,z

f =1

(6.35)



Optimization for Microgrid Planning



,th PβCHP , y ,m ,t

,hot = PβCHP , y ,m ,t



,th PbBOI , y ,m ,t

,hot = PbBOI , y ,m ,t

Nf

,CHI ∑ PβCHP , f , y ,m ,t

+

f =1

+



(6.36)



(6.37)

Nf

,CHI ∑ PbBOI , f , y ,m ,t

f =1

,th BIO ,HOT PσBIO + , y ,m ,t = Pσ , y ,m ,t



139

Nf

,CHI ∑ PσBIO , f , y ,m ,t

f =1

(6.38)

,HOT BOI,HOT where PβCHP , PσBIO,HOT , PzST,HOT is thermal power of the differ, y ,m ,t , y ,m ,t , y ,m ,t , Pb , y ,m ,t ent technologies that is used to produce heat.

6.7  The Optimization Problem The objective function here formalized is characterized by operational management and installation costs. However, by using the same approach, other performance indicators can be selected (such as carbon dioxide emissions). 6.7.1  Operational Management Costs

The operational management costs can be obtained by considering the following contributions coming from the different component of the microgrid: grid

•  C y ,m ,t : costs for purchasing/selling energy from/to the external grid in year y, and month m and time interval (t, t + 1); CHP •  C y ,m ,t : microturbine costs for producing thermal and electrical power in year y, and month m and time interval (t, t + 1); FF •  C y ,m ,t : fossil fuel plant costs for producing electrical power in year y, and month m and time interval (t, t + 1); BIO •  C y ,m ,t : biomass plant costs for producing thermal and electrical power by using biomass in year y, and month m and time interval (t, t + 1); BOI •  C y ,m ,t : boiler costs for producing thermal power in year y, and month m and time interval (t, t + 1); CHI •  C y ,m ,t : chillers costs for producing cold thermal power in year y, and month m and time interval (t, t + 1);

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Microgrid Design and Operation: Toward Smart Energy in Cities HP

•  C y ,m ,t : heat pumps costs for producing thermal power in year y, and month m and time interval (t, t + 1); PV W ST • C y ,m ,t ,C y ,m ,t ,C y ,m ,t : renewable (photovoltaics, wind turbines, solar thermal, respectively) costs, essentially due to maintenance, for producing thermal and/or electrical power in year y, and month m and time interval (t, t + 1).

The overall operational management cost, C OM y ,m ,t , is thus provided by the sum of the previous defined quantities:



grid PV W ST C OM y ,m ,t = C y ,m ,t + C y ,m ,t + C y ,m ,t + C y ,m ,t + FF BIO BOI HP CHI + C CHP y ,m ,t + C y ,m ,t + C y ,m ,t + C y ,m ,t + C y ,m ,t + C y ,m ,t

(6.39)

Each contribution in has to be related to the proper power production due to suitable coefficients. More precisely: • The purchasing electricity from the grid (or the benefit obtained by its sale) can be expressed as:

grid grid el el C ygrid ,m ,t = c y ,m ,t max(Py ,m ,t ,0)∆t − b y ,m ,t max( −Py ,m ,t ,0)∆t el

(6.40)

el

where c y ,m ,t and b y ,m ,t represent the price of the bought and sold energy, respectively. • The operating costs for the combined heat and power can be considered as the sum of two contributions, one coming from fuel consumption (which can be expressed as the cost of the primary energy consumed) and the second due to maintenance costs (related to the produced electrical energy), that is,



C CHP y ,m ,t =

   c CHP  y ,m CHP , fix CHP CHP ,el  P δ β ∆t  + µβ ∑ β, y ,m ,t  ηCHP + µCHP β  β =1   β  



(6.41)

CHP CHP where c y ,m is the fuel price per unit of primary energy, η β is the efficiency coefficient, and µCHP is the maintenance costs per unit of β , fix electrical energy produced, while µCHP is a fixed cost of maintenance β per time unit.



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141

• The operating costs for the production plants from fossil fuels can be expressed again as



C yFF,m ,t

 ,el = ∑ PdFF , y ,m ,t  d =1  Nd

  c FF  y ,m FF , fix FF FF δd ∆t  FF + µd  + µd   ηd 

(6.42)

FF where c FF y ,m is the fuel price per unit of primary energy, ηd is the effiFF ciency coefficient, and µd is the maintenance costs per unit of electrical energy produced, while µdFF , fix is a fixed cost of maintenance per time unit.

• The operating costs for biomass plants can be expressed again as



C yBIO ,m ,t

   c BIO  y ,m BIO , fix BIO BIO ,el  = ∑ Pσ , y ,m ,t  BIO + µBIO δσ ∆t σ  + µσ  σ =1   ησ   Nσ

(6.43)

BIO where c BIO is the efy ,m is the fuel price per unit of primary energy, η σ ficiency coefficient, and µBIO is the maintenance costs per unit of elecσ , fix trical energy produced, while µBIO is a fixed cost of maintenance per σ time unit.

• The operating costs for the boilers can be expressed similarly as



Nb    c BOI  y ,m BOI ,th  C yBOI P = ∑ b , y ,m ,t  ηBOI + µbBOI  + µbBOI , fix δbBOI ∆t ,m ,t  b =1   b  

(6.44)

BOI where c BOI is y ,m denotes the fuel price per unit of primary energy, ηb BOI the efficiency coefficient, and µb is the maintenance costs per unit of thermal energy produced, while µbBOI , fix is a fixed cost of maintenance per time unit.

• The operating costs for chillers are related only to maintenance, that is,



C CHI y ,m ,t =

Nf

,cold CHI ∑ PfCHI , y ,m ,t µ f

f =1

, fix CHI  + µCHI δ f ∆t f

(6.45)

CHI

where µ f is the maintenance costs per unit of cold thermal energy CHI , fix produced, while µ f is a fixed cost of maintenance per time unit. • The operating costs of heat pumps are again related only to maintenance, that is,

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Microgrid Design and Operation: Toward Smart Energy in Cities Nx

HP , fix HP   HP ,cold HP C yHP δx ∆t ,m ,t = ∑ Px , y ,m ,t µx + µx

(6.46)

x =1

where µHP is the maintenance costs per unit of cold thermal energy x , fix produced, while µHP is a fixed cost of maintenance per time unit. x • The operating costs of the photovoltaics are again related only to maintenance, that is,

C yPV ,m ,t =



∑ PαPV, y ,m,el,t µPV α

α =1

, fix PV  S α ∆t + µPV α

(6.47)

where µPV α is the maintenance costs per unit of electrical energy pro, fix duced, while µPV is a fixed cost of maintenance per time unit and α per unitary surface. • The operating costs for solar thermal plants are again related only to maintenance, that is, Nz



ST , fix ST  ST ,el C yST,m ,t = ∑ PzST S z ∆t , y ,m ,t µz + µz z =1



(6.48)

where µST z is the maintenance costs per unit of thermal energy pro, fix duced, while µST is a fixed cost of maintenance per time unit and z per unitary surface. • The operating costs of the wind turbines are again related only to the maintenance, that is, Nj



W , fix  W  W W CW y ,m ,t = ∑ n j  p j , y ,m ,t µ j + µ j ∆t j =1

(6.49)

where µWj is the maintenance costs per unit of electrical energy proW , fix duced, while µ j is a fixed cost of maintenance per time unit and per unitary surface. In this framework, the annual operating costs of the whole installation can be formalized as:

Cy =

M T

∑ ∑ nmC OM y ,m ,t

m =1 t =1

(6.50)



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143

6.7.2  Installation Costs

The total installation cost is given by the sum of the capital costs of all the considered technologies. That is: K =





α =1



k αPV S αPV +

Nz

+∑

Nf

z =1

kzST S zST

Nj

+∑

j =1

W kW j nj



+ ∑ k CHP δCHP + β β

Nd



d =1

σ =1

β =1

δCHI + ∑ kdFF δdFF + ∑ k σBIO δBIO ∑ k CHI f f σ

f =1 Nb

Nx

b =1

x =1

(6.51)

+ ∑ kbBOI δbBOI + ∑ kxHP δHP x CHP CHI where the constant k W , k f , kdFF , k σBIO , kbBOI , kxHP represents the instalj ,k β

lation cost per unit and k αPV , kzST represents the installation cost per unitary surface. 6.7.3  Objective Function

Finally, the objective function is relative to the minimization of the sum of installation costs and annual discounted operating costs, that is, Y  C y  min K + ∑ y  y =1 (1 + i )   



(6.52)

where i is the discount rate.

6.8  Optimal Planning Including Storage Systems In the previous section, storage systems have not been taken into account in the problem formalization. As mentioned in Section 6.2, there are two options for the representation and design of these technologies in a microgrid: • Adding to the formalization reported above the state equation for the storage systems, related constraints, and the cost (in the objective function) of installing storage in the microgrid as a function of technology and size. Unfortunately, this procedure has the drawback to be very cumbersome from a computational viewpoint; the presence of a storage system introduces a relationship between contiguous interval time so that the dimension of the problem blows up.

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Microgrid Design and Operation: Toward Smart Energy in Cities

• Considering the overall planning problem as a two-step optimization procedure, first, the optimization problem that does not include the storage systems is performed, and, second, the optimal storage size is found on the basis of the known size of the other technologies. Note that, in this case, the fossil fuel plants can work at different power levels and not only at the rated power. This option solves the initial problem in an acceptable approximate way by reducing the number of optimization variables and consequently the computational time. In the following, the two-step optimization procedure will be considered. This choice is mainly done for an easier presentation of the optimal planning problem for a microgrid. However, one can easily formalize the desired decision problem on the basis of the characteristics of specific case studies. In any case, decision variables, constraints, and objective function related to the storage systems are necessary and reported in the following. Thus, in this case, it is supposed that the technologies to be used for production (in number and size) have been chosen and the decision is now limited to the storage systems. In other words, the previous introduced variCHP CHI BOI ables S αPV , S zST ,nW , δdFF , δBIO labeled as decisional , δHP j , δβ , δf σ , δb x , ones, have now been fixed from the previous step. The main decision variable for the storage systems is the power exchanged between the storage and the grid. Using a similar notation to the one used in the previous sections, the decision variables are: S ,el

• Pk ,y ,m ,t : the power exchanged (unrestricted in sign) between the storage of technology k, in year y, month m and time interval (t, t + 1). S • SOC k ,y ,m ,t : the state of charge of the storage of technology k, in year y, month m and time interval (t, t + 1). • δ k : binary variable that represents the presence or absence of the storage system of technology k. S

The auxiliary binary variable δ kS is useful to write the condition that the total installation number cannot be greater than an a priori fixed maximum can be written as: Nk



∑ δSk ≤ nS ,max

k =1

(6.53)

The presence of the kth storage system or its absence determines the upper and lower bounds of electrical and power PkS, y,el,m ,t exchanged into the grid for the year y, the month m and the time interval (t, t + 1), that is,



Optimization for Microgrid Planning

145



−PkS ,max ≤ PkS, y,el,m ,t ≤ PkS ,max

(6.54)



− M δSk ≤ PkS, y,el,m ,t ≤ M δkS

(6.55)

where PkS ,max is the maximum power injected to or received by the electrical grid and M is a sufficiently big number. A similar relation holds true for its state of charge bounded between a minimum SOC kS ,min and a maximum SOC kS ,max value SOC kS ,min δkS ≤ SOC kS, y ,m ,t ≤ SOC kS ,max δkS



(6.56)

State of charge and electrical power are related by the continuity equation through the battery efficiency coefficient ηSk , that is, SOC kS, y ,m ,t +1 − SOC kS, y ,m ,t =

=

(6.57) ηSk ∆t ∆t S ,el S ,el max ,0 max ,0 − P − P k , y ,m ,t k , y ,m ,t CAPkS ηSk CAPkS

(

)

(

)

where CAPkS is the maximum size, in terms of electrical energy stored, of the storage system. The new storage variables have an influence on the power balance and it has to be rewritten as Nα

Nj





β =1

σ =1

,el PV ,el W ,el CHP ,el BIO ,el Pygrid ,m ,t + ∑ Pα, y ,m ,t + ∑ P j , y ,m ,t + ∑ Pβ, y ,m ,t + ∑ Pσ , y ,m ,t + α =1

j =1

Nd

Nk

d =1

k =1

(6.58)

Nx

S ,el el el ,HP ,el + ∑ PdFF , y ,m ,t + ∑ Pk , y ,m ,t = D y ,m ,t + ∑ Dx , y ,m ,t x =1



Nj

,el W ,el Note that, for this second step, the quantity ∑ PαPV , y ,m ,t + ∑ P j , y ,m ,t is α=1 j =1 completely known. As concerns the objective function, the total operational management cost in will be unchanged while the only installation costs of the storage system has to appear in . More precisely, it will be substituted by the following

Nk



K = ∑ kxSCAPkS δkS k =1

where kxS is the storage cost per unit of energy.

(6.59)

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6.9  Examples In this section, it is explained how the above described optimization model can be applied to real case studies. Specifically, three simple examples have been considered. A subset of possible production plants has been chosen, mainly for the sake of clarity in the presentation of results. However, the extension to more general case studies can be easily obtained. In particular, the possible technologies to be installed to satisfy electrical and thermal (heat) demands are: photovoltaics with two possible tilt angles, combined heat and power, and wind turbines. The study considers small-size horizontal axis wind turbines as the suitable choice for the installation in urban areas. The rated power of the considered horizontal axis wind turbine is Pr = 20 kW. Boilers are considered as already installed in the project urban area. The photovoltaic production mainly depends on the site solar radiation, on the technology efficiency, and on the azimuth and title angles used for the installation. As far as their technology is concerned, polycrystalline modules having a rated efficiency of 15% are considered. For the three examined sites the solar radiation has been evaluated by means of the PVGIS software, and the photovoltaic production has been estimated taking into account appropriate correction factors that consider azimuth and tilt angles. In particular, the second and the third sites are south-oriented (azimuth equal to zero), while the first one is characterized by an azimuth of 45°. The combined heat and power units considered in the present analysis are Capstone C30 and C65 models, characterized by a rated electrical power equal to 28 kW and 65 kW, respectively. At full-load rated conditions, the microturbine electrical efficiency is 25% for the C30 and 29% for the C65. The first step is to calculate renewables availability and energy demands that are different for the various cases. In particular, three areas of the Savona Province (Northwest Italy) have been considered. The first step is the collection of available data for renewables availability (power per square meter over time) and electrical demand in each time interval (one hour) of the day representative of the desired time period. In this case, we consider M = 12, and the necessary data are inserted in a database for m = 1, …, M (January, …., December), that is, each hour for 1 day representative of each month. For the specific case study, in Figures 6.1, 6.2, and 6.3, the daily demands for 4 representative days of January, April, August, and October for the three considered sites are reported. As far as the electrical load is concerned, the three sites are characterized by different daily electrical load profiles, which also vary as a function of the season. In Figures 6.1, 6.2, and 6.3, the aforementioned profiles are reported referring to four different months (January, April, August, and October), each one representing a different season.



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Figure 6.1  Electrical load profiles of Site 1.

Figure 6.2  Electrical load profiles of Site 2.

Figure 6.3  Electrical load profiles of Site 3.

The described optimization problem has been solved by the use of the Lingo 9.0 optimization package. Table 6.1 reports the optimal values of decision

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Microgrid Design and Operation: Toward Smart Energy in Cities Table 6.1 Optimal Results Technology Layout Variable Results Number of wind turbines [20 kW] Site 1: 0, Site 2: 1, Site 3: 3 Number of C65 microturbines Site 1: 1, Site 2: 0, Site 3: 1 Number of C30 microturbines Site 1: 0, Site 2: 0, Site 3: 0

Variable

Results 2 PV surface (tilt 0°) Site 1: 270 m2, Site 2: 223 m , Site 3: 450 m2 2 PV surface (tilt 30°) Site 1: 0 m2, Site 2: 0 m , Site 3: 0 m2 Annual electricity 825,344 kWh withdrawn from the grid

variables and the layout of the production mix. Sites 1 and 3 are characterized by higher loads during daytime, while the load of Site 2 exhibits two peaks, during morning and evening. This means that, for the first and third sites, the power generated by combined heat and power (running during daytime) covers the local load, while in the case of the second site, a high share of the generated power would be sold to the grid (which is less convenient, as the benefit for selling energy to the grid is considerably lower than the avoided cost due to not purchasing energy from the grid). This explains the choice to install combined heat and power in Sites 1 and 3, but not in Site 2. The C65 model was preferred over the C30 as, for both the selected sites, the magnitude of the loads justifies the installation of the combined heat and power model characterized by higher rated power and efficiency values. One combined heat and power only was selected, as if two or more were installed, the total generated power would exceed the demand in many hours. As far as the photovoltaic is concerned, a tilt of 30° is the optimal one (at the site’s latitude) from the point of view of the energy generated by a single panel, but with a tilt of (almost) 0° the panels can be installed closer to one another (less mutual shadowing) and, as a consequence, for a given location, the total surface actually covered in panels can be higher; the maintenance costs are also lower in this case. The latter alternative, more favorable as the panels cost decreases, was the one actually selected by the algorithm. The higher load of the third site and the fact that the second one is not equipped with combined heat and power allow for the installation of wind turbines in these two sites, as their production can contribute to the local demands, not fully satisfied by the other local sources. The optimal configuration implies a total cost of 2,540 k€; the annual operating costs are equal to 155 k€ and the installation costs are equal to 767 k€. The overall costs have been calculated with a discount rate over 20 years.



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In order to assess the correlation between the demand and the configurations proposed by the algorithm, two additional scenarios were considered. The first one was obtained by a 50% decrease of the loads (see Table 6.2), while, in the second one, the load was increased by 50% (see Table 6.3). The results confirm the close relation between the convenience to install combined heat and power and the magnitude of the local load: with lower loads, the C65 rated power results too high (and the C30 is deemed not sufficiently convenient, due to the lower efficiency). With no combined heat and power, two wind turbines are proposed for Site 1, while three are again proposed for Site 3. With higher loads, one more combined heat and power is proposed for Site 3 and a higher amount of renewables (both photovltaics and wind turbines) can be installed. For the first one of the modified scenarios, the optimal configuration implies a total cost of 1,234 k€; the annual operating costs are equal to 60 k€ and the installation costs are equal to 542 k€, while for the second one, the optimal Table 6.2 Optimal Results, �Load Decreased by 50% Technology Layout Variable Results Variable Number of wind turbines [20 kW] Site 1: 2, PV surface (tilt 0°) Site 2: 0, Site 3: 3 Number of C65 microturbines Site 1: 0, Photovoltaic Site 2: 0, surface (tilt 30°) Site 3: 0 Number of C30 microturbines Site 1: 0, Annual electricity Site 2: 0, withdrawn from Site 3: 0 the grid

Results Site 1: 270 m2, Site 2: 150 m2, Site 3: 450 m2 Site 1: 0 m2, Site 2: 0 m2, Site 3:0 m2 353,262 kWh

Table 6.3 Optimal Results, Load Increased by 50% Technology Layout Variable Number of wind turbines [20 kW] Number of C65 microturbines Number of C30 microturbines

Results Site 1: 3, Site 2: 2, Site 3: 3 Site 1: 1, Site 2: 0, Site 3: 2 Site 1: 0, Site 2: 0, Site 3: 0

Variable Photovoltaic surface (tilt 0°)

Results Site 1: 0 m2, Site 2:270 m2, Site 3: 450 m2 Photovoltaic Site 1: 450 m2, surface (tilt 30°) Site 2: 0 m2, Site 3:0 m2 Annual electricity 1,206,389 kWh withdrawn from the grid

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configuration implies a total cost of 3,844 k€, the annual operating costs are equal to 226 k€ and the installation costs are equal to 1,250 k€.

References [1] Mashayekh, S., et al., “A Mixed Integer Linear Programming Approach for Optimal DER Portfolio, Sizing, and Placement in Multi-Energy Microgrids,” Applied Energy, Vol. 187, 2017, pp. 154–168. [2] Zhao, B., et al., “Optimal Sizing, Operating Strategy and Operational Experience of a Stand-Alone Microgrid on Dongfushan Island,” Applied Energy, Vol. 113, 2014, pp. 1656–1666. [3] Mancarella, P., “Distributed Multi-Generation Options to Increase Environmental Efficiency in Smart Cities,” Proc. of the 2012 IEEE Power and Energy Society General Meeting, San Diego, CA, 2012, pp. 1–8. [4] Chicco, G., and P. Mancarella, “Distributed Multi-Generation: A Comprehensive View,” Renewable and Sustainable Energy Reviews, Vol. 13, 2009, pp. 535–551. [5] Gamarra, C., and J. M. Guerrero, “Computational Optimization Techniques Applied to Microgrids Planning: A Review,” Renewable and Sustainable Energy Reviews, Vol. 48, 2015, pp. 413–424. [6] Kolokotsa, D., “The Role of Smart Grids in the Building Sector,” Energy and Buildings, Vol. 116, 2016, pp. 703–708. [7] Bracco, S., G. Dentici, and S. Siri, “DESOD: A Mathematical Programming Tool to Optimally Design a Distributed Energy System,” Energy, Vol. 100, 2016, pp. 298–309. [8] Gu, W., Z. Wu, and X. Yuan, “Microgrid Economic Optimal Operation of the Combined Heat and Power System with Renewable Energy,” Proc. of IEEE PES General Meeting, Minneapolis, MN, 2010, pp. 1–6. [9] HOMER software, www.homerenergy.com. [10] Baghaee, H. R., et al., “Reliability/Cost-Based Multi-Objective Pareto Optimal Design of Stand-Alone Wind/PV/FC Generation Microgrid System,” Energy, Vol. 115, 2015, pp. 1022–1041. [11] Magrassi, F., et al., “Optimal Planning of Sustainable Buildings: Integration of Life Cycle Assessment and Optimization in a Decision Support System (DSS),” Energies, Vol. 9, No. 7, 2016, pp. 490–502. [12] Bracco, S., et al., “A Dynamic Optimization-Based Architecture for Polygeneration Microgrids with Tri-Generation, Renewables, Storage Systems and Electrical Vehicles,” Energy Conversion and Management, Vol. 96, 2015, pp. 511–520. [13] Minciardi, R., and M. Robba, “A Bi-Level Approach for the Stochastic Optimal Operation of Interconnected Microgrids,” IEEE Transactions on Automation Science and Engineering, Vol. 14, No. 2, 2017, pp. 482–493.



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[14] Mehleri, E., et al., “A Mathematical Programming Approach for Optimal Design of Distributed Energy Systems at the Neighbourhood Level,” Energy, Vol. 44, 2012, pp. 96–104. [15] Mehleri, E., et al., “Optimal Design and Operation of Distributed Energy Systems: Application to Greek Residential Sector,” Renewable Energy, Vol. 51, 2013, pp. 331–342. [16] Kyriakarakos, G., et al., “Intelligent Demand Side Energy Management System for Autonomous Polygeneration Microgrids,” Applied Energy, Vol. 103, 2013, pp. 39–51. [17] Zhiqiang, A., C. Xiao, and J. Wang, “Applying the Grey Forecasting Model to the Energy Supply Management Engineering,” Systems Engineering Procedia, Vol. 5, 2012, pp. 179– 184. [18] Zenginisa, I., et al., “Cooperation in Microgrids Through Power Exchange: An Optimal Sizing and Operation Approach,” Applied Energy, Vol. 203, 2017, pp. 972–981. [19] Junga, J., and M. Villaranb, “Optimal Planning and Design of Hybrid Renewable Energy Systems for Microgrids,” Renewable and Sustainable Energy Reviews, Vol. 75, 2017, pp. 180–191. [20] Chen, J., et al., “Optimal Sizing for Grid-Tied Microgrids with Consideration of Joint Optimization of Planning and Operation,” IEEE Transactions on Sustainable Energy, Vol. 9, Issue 1, January 2018, pp. 237–248. [21] Atia, R., and N. Yamada, “Sizing and Analysis of Renewable Energy and Battery Systems in Residential Microgrids,” IEEE Transactions on Smart Grid, Vol. 7, No. 3, 2016, pp. 1204–1213. [22] Gazijahani, F. S., and J. Salehi, “Robust Design of Microgrids with Reconfigurable Topology Under Severe Uncertainty,” IEEE Transactions on Sustainable Energy, Vol. 9, Issue 2, April 2018, pp. 559–569. [23] Ondeck, A. D., T. F. Edgar, and M. Baldea, “Optimal Operation of a Residential DistrictLevel Combined Photovoltaic/Natural Gas Power and Cooling System,” Applied Energy, Vol. 156, 2015, pp. 593–606. [24] Shikha, S. S., “Renewable and Nonrenewable Energy Resources: Bioenergy and Biofuels,” Principles and Applications of Environmental Biotechnology for a Sustainable Future, 2016, pp. 293–314.

7 Optimization for Microgrid Management 7.1  Overview This chapter discusses an energy management system to minimize the overall production costs while satisfying all the thermal and electric network constraints within a microgrid. To do this, first an adequate model of all the installed components is necessary, while different electric network models could be used.

7.2  Introduction As outlined earlier, microgrids can be either grid-connected or islanded. In the first case, the presence of the connection to the public network always ensures the active and reactive power balance, so the main objective of a microgrid control system is to schedule the microgrid production to optimize specified key performance indicators. Such a control system is often called an energy management system and is typically organized into two levels: • The inner level is basically the controller placed in each device and its purpose is to track the reference signals produced by the outer level. For example, if a photovoltaic unit is connected to a microgrid, the outer level dictates the reference signals for its active and reactive power production, while the inner level receives such signals and produces in

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output the inverter input (e.g., the amplitude modulation index, the frequency, and the phase angle) to meet the higher-level controller request. • The outer level produces the reference signal for the inner one to minimize or maximize suitable objective functions. This can be done in many different ways, according to the components of the microgrid, to its topology, to the market rules, and to the object of the optimization. The aim of this chapter is to analyze some possibilities of implementation for the outer level of a grid-connected microgrid energy management system. As outlined in previous chapters, the main components of a microgrid can be classified into the following categories: • Renewable energy sources, whose active power production is determined by the availability of the primary source and so cannot be dispatched; • Traditional generators that are often combined heat and power units (in this case, both active and reactive power can be programmed); • Storage devices; • Loads. As a consequence, the wide variety of possibilities claims for a specific treatment case by case. In the following, the optimization problem formulation is presented in a very general case and applied to the Smart Polygeneration Microgrid test bed facility.

7.3  List of Symbols In this section, a list of all the variables and symbols involved in the problem formulation is presented. 7.3.1  General Data

The general data is as follows: • j is the imaginary unit. • Nt are the time intervals in the considered horizon (t denotes the generic time instant and ∆t is the length of each time interval). • NDU is the number of the dispatchable units. • NHG is the number of the heat generator units.



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• NST is the number of the electrical storage units. • NRES is the number of the renewable energy source units. • NEL is the number of the electric loads. • Nb is the number of buses in the electric network. • NL is the number of branches in the electric network. 7.3.2  Technical Data

The technical data is as follows. With dispatchable units, the values of all the following quantities are typically present in the dispatchable unit datasheets: el ,max • PDU ,i is the full load maximum electric power. • SDU,i is the inverter rating, if the ith dispatchable unit is connected to the grid by means of a power electronics converter.

With heat generator units, the values of all the following quantities are th ,max typically present in the heat generator datasheets: PHG, is the maximum theri mal power. With storage units, the values of all the following quantities are typically present in the storage unit datasheets: in

in

•  ηST,i and ηST,i represent the efficiency values for the charging and discharging of the battery. el ,disch el ,ch and PST, • PST,i i are the maximum power allowed for the discharging and the charging phase, respectively. el ,min el ,max • W ST,i and W ST,i are the minimum and maximum energy stored in the storage unit. • SST,i is the inverter rating, if the storage unit is connected to the grid by means of a power electronics converter. With renewable energy source units, the values of all the following quantities are typically present in the renewable energy source datasheets: SRES,i is the inverter rating, if the ith renewable energy source is connected to the grid by means of a power electronics converter. 7.3.3  Electrical Network

The electrical network is as follows:

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• is the hk-entry of the network admittance matrix. • Ahr the hr-entry of the network nodal incidence matrix. • Vmax and Vmin are, respectively, the maximum and the minimum bus voltage amplitudes. • δmax and δmin are, respectively, the maximum and the minimum bus voltage phases. • Rr and Xr are the resistance and the reactance of the rth branch, and Zr is its longitudinal impedance. •  I rmax is the current rating of the device or cable present in branch r. • MDU is the allocation matrix of the dispatchable units (size NCHP × Nb), and its ih-entry is 1 if the ith dispatchable unit is connected to the hth bus; otherwise, it is 0. • MST is the allocation matrix of the storage units (size NST × Nb), and its ih-entry is 1 if the ith storage unit is connected to the hth bus; otherwise, it is 0. • MRES is the allocation matrix of the renewable energy source units (size NRES × Nb), and its ih-entry is 1 if the ith renewable energy source is connected to the hth bus; otherwise, it is 0. • MEL is the allocation matrix of the electric load (size NEL × Nb), and its ih-entry is 1 if the ith electric load is connected to the hth bus; otherwise, it is 0. 7.3.4  Forecasted Quantities

The following are the forecasted qualities: th •  PTL, t is the global thermal load at time t. el el • PEL ,i ,t and Q EL ,i ,t are the active and reactive power injections by the ith electric load at time t. el • PRES, i ,t is the active power of the ith renewable energy sources at time t.

7.3.5  Variables Involved in the Optimization Procedure

The following are the variables involved in the optimization procedure. ep th el el • PDU, i ,t , PDU,i ,t , PDU,i ,t , and Q DU,i ,t are, respectively, the primary energy per time unit used, the thermal power produced, the electrical active



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power generated, and the reactive power by the ith dispatchable unit at time t. ep th • PHG,i ,t and PHG,i ,t are, respectively, the primary energy per time unit used and the thermal power produced by the ith heat generator at time t. el el el • PST,i ,t ,Q ST,i ,t , and W ST,i ,t are, respectively, the injected active and reactive power and the storage energy content of the ith storage unit at time t. el • Q RES,i ,t is the reactive power injection of the ith renewable energy source unit at time t. el el • Ph ,t and Q h ,t are the total active and reactive powers injected at bus h and time t. • Vh,t and δh,t are, respectively, the voltage amplitude and phase at bus h and time t. • I is the current phasor flowing in the branch r at time t. r ,t

7.3.6  Costs

The following are the costs: • CNET,t is the cost or revenue coming from the energy buying or selling from the external grid at time t. • CDU,i,t is the fuel cost of the ith dispatchable unit at time t. • CHG,i,t is the fuel cost of the ith heat generator unit at time t. • Ctot is the overall energy cost. • Csell,t and Cbuy,t represent, respectively, the cost and revenue, depending on the time slot t, of the sold and purchased electricity to and from the main grid.

7.4  The Energy Management System In grid-connected microgrids, the energy management system typically recalls the principles of optimization of traditional electricity transmission networks, achieved by classical optimal power flow algorithms [1, 2]. The simplest optimal power flow problem is the one that allows to dispatch the production of (two) conventional sources. Consider the situation in which two sources (Pg1 and Pg2 being their active power productions) and an assigned load (PL) are connected to the same bus. If one wants to define the

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optimal dispatching of the two sources, the following optimization problem has to be solved:

( )

( )

min C1 Pg 1 + C 2 Pg 2    subject to Pg 1 + Pg 1 = PL

(7.1)

having indicated with C1 and C2 the production cost of the first and second sources. The application of the Lagrange multipliers theorem allows one to find out the optimal power productions, solving the following algebraic system:



 dC1 dC 2  dP = dP = λ g2  g1 P + P = P L  g1 g1

(7.2)

being the Lagrange multiplier. However, such methods can not consider: 1. The limits on the power production; 2. �������������������������������������������������������������������� The network topology, which determines the possibility to check whether voltages and currents lay in their feasibility range; 3. ���������������������������������������������������������������������� Eventual capability constraints (which, for example, have to be considered for sources connected to the grid by means of power electronic converters); 4. ������������������������������������������������������������������ The presence of dynamic components like storage devices, which increases the computational complexity of the algorithm too much as the power production of one time instant is not independent anymore on the previous samples; 5. The combined presence of thermal and electric constraints. So, for the case of microgrids, ad hoc optimization procedures must be defined. In the following, suitable models for the typical components and infrastructure characterizing a microgrid are presented and some possible optimization problems are formalized highlighting advantages and disadvantages. In all the cases, the core of the optimization algorithm is not analyzed as it is beyond the scope of these lectures, but some hints will be given to choose commercially available tools.



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7.4.1  Component Models for Energy Management System Implementation

Microgrids typically consist of many different devices that can be divided into the following categories: • Dispatchable units, generators that can produce electric and eventually thermal power relying on a primary energy source (as a consequence, those units imply a cost for the energy production, which can be scheduled according to the specific microgrid needs); • Heat generators, which produce only a dispatchable thermal power relying on a primary energy source; • Storage systems, which can store the energy produced at one time to be used at a later time; • Renewable energy sources, which are generators whose electric active power is typically free but cannot be dispatched as it depends on the availability of the primary source; • Electric loads and thermal loads that represent the final users existing in the microgrid. In this section, the basic equations describing these objects are presented, which will be part of the constraints of the optimization procedure. 7.4.1.1  Dispatchable Units

The ith dispatchable unit (i = 1, …, NDU) employs a primary energy-per-time ep unit PDU, i ,t (coming from fuel consumption), to produce the electric active powel th er PDU,i ,t and (only for cogenerative units) the thermal power PDU, i ,t . Moreover, assuming that the dispatchable unit is connected to the grid by means of a power el electronic converter, its reactive power production Q DU, i ,t can be dispatched too. The electric active power is limited by [3]:

el el ,max 0 ≤ PDU ,i ,t ≤ PDU ,i

(7.3)

and is related to the primary energy per time unit by means of a suitable nonlinear function fi, that is:

(

)

ep el PDU ,i ,t = f i PDU ,i ,t

(7.4)

If the ith dispatchable unit is cogenerative, the following relationship between electric and thermal power holds:

(

)

th el PDU ,i ,t = g i PDU ,i ,t

(7.5)

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Moreover, the dispatchable unit capability curve constraint must be satisfied, that is:

(P

) + (Q

2 el DU ,i ,t



) ≤ (S )

2 el DU ,i ,t

2

DU ,i



(7.6)

where SDU,i is the converter rating of the ith dispatchable unit. In principle, (7.4) and (7.5) are nonlinear functions; whenever the optimization algorithm requires linear or piecewise linear functions, they can be approximated by piecewise linear relations (see [4] for details). The cost of the ith dispatchable unit is related to the fuel cost to produce ep the primary energy PDU, i ,t and can be expressed as: C DU,i ,t =



fuel C DU, i ∆t

LHV DU,i

ep PDU, i ,t

(7.7)

fuel

where C DU,i is the fuel cost of the ith dispatchable unit and LHVDU,i is its lower heating value. 7.4.1.2  Heat Generators

The ith heat generator (i = 1, …, NHG) representation can be performed once ep one knows the link between the primary energy per time unit PHG ,i ,t and the th thermal power PHG ,i ,t . The thermal power is limited by:

th th ,max 0 ≤ PHG, i ,t ≤ PHG,i

(7.8)

and is related to the primary energy by means of a suitable nonlinear function [4, 5] hi, that is:

(

)

ep th PHG, i ,t = hi PHG,i ,t

(7.9)

In principle, (7.9) is a nonlinear function; whenever the optimization algorithm requires linear or piecewise linear functions, it can be approximated by suitable piecewise linear relations (see [4] for details). The cost of the ith heat generator unit is related to the fuel cost to produce ep the primary energy PHG ,i ,t and can be expressed as: fuel



C HG,i ,t =

C HG ,i ∆t LHV HG ,i

ep PHG ,i ,t

(7.10)

fuel where C HG, i is the fuel cost of the ith heat generator unit and LHVHG,i is its lower heating value.



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7.4.1.3  Storage Systems

In the following, the technological detail of the storage system is neglected and the device is basically modeled according to the following continuity equation el that relates the power exchange PST ,i ,t with the storage state of charge (i.e., its el energy content W ST,i ,t [6]):



el PST ,i ,t

el el  1 −WST ,i ,t + WST ,i ,t −1 el el WST  in ,i ,t −1 < WST ,i ,t ∆ t η  =  ST ,i el el  out −WST W + ,i ,t ST ,i ,t −1 el el WST  ηST ,i ,i ,t −1 ≥ WST ,i ,t ∆t 

(7.11)

Both the power exchange and the energy content are limited by the following: • Limit in the power charge and discharge phase

el ,ch el el ,disch −PST, i ≤ PST,i ,t ≤ PST,i

(7.12)

• Limit in the energy content

el ,min el el ,max W ST, ≤ W ST, i i ,t ≤ W ST,i

(7.13)

Finally, the presence of a power electronic converter makes it available the possibility of dispatching the reactive power exchange, provided that the following capability constraint is not violated:

(P ) + (Q 2 el ST,i ,t

)

2 el ST,i ,t

≤ (S ST,i ) 2

(7.14) el

where SST,i is the converter rating of the ith storage unit and Q ST,i ,t is the reactive power injected into the microgrid. 7.4.1.4  Renewable Energy Sources el The ith renewable energy source (i = 1, …, NRES) active power PRES, i ,t is an input for the energy management system algorithm (i.e., it is supposed to be known, which can be done by means of suitable forecasting algorithms). Moreover, the renewable energy source inverter capability constraint establishes the el lower and upper bounds for its reactive power production Q RES, i ,t as follows:

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(

el − (S RES,i ) − PRES, i ,t 2

)

2

el ≤ Q RES, i ,t ≤

el (S RES,i )2 − (PRES, i ,t )

2

(7.15)

where SRES,i is the converter rating of the ith renewable energy source unit. 7.4.1.5  Electric Loads

The optimization algorithm contained in the energsystem needs to know the el ith electric load (i = 1, …, NEL) active and reactive power requests PEL, i ,t and el Q EL,i ,t at any time t of the optimization horizon. For this reason, as it is not possible to know them exactly in advance, suitable forecasting algorithms are necessary to increase the energy management system performances. 7.4.1.6  Thermal Loads th The same applies for the global thermal power request PTL, t . Moreover, in the proposed ���������������������������������������������������������������� energy management system���������������������������������������� , the following very simple thermal constraint will be considered, expressing the thermal power balance at any time sample t: N DU





i =1

N HG

th th th PDU, i ,t + ∑ PHG,i ,t ≥ PTL,t i =1

(7.16)

which means that the total thermal request must be at the least satisfied either by the heat generator or by the cogenerative dispatchable units. If the energy management system includes this model, an effective forecasting algorithm for the thermal power request is needed, which is not very simple. Some approaches can be found in the literature that allow defining an analytical relation between the desired temperature and the thermal power necessary to produce it, thus allowing one to input the energy management system with the temperature request (which is easily available information to be predicted; see [7]). 7.4.2  Electric Network Models

Another important aspect that deeply affects the performances and the computational impact of the energy management system is the representation of the electric network. In the present section, four alternative models for the electric network will be presented: the single busbar model, the complete load flow model, the linearized load flow model, and the decoupled load flow model highlighting positive aspects and drawbacks. 7.4.2.1  Single Busbar Model

As introduced before, the first and simplest model adopted to describe the microgrid electric network is the single bar approximation, which assumes that all the generations and loads are positioned to the same busbar.



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As a result, the only power balance equation is the one in which the sum of active powers must be zero, as follows: for any t = 1, …, Nt,

el PNET ,t +

N ST

N DU

i =1

i =1

∑ PSTel ,i ,t +



el PDU ,i ,t +

N RES



i =1

el PRES ,i ,t −

N EL

∑ PELel ,i ,t

i =1

= 0 (7.17)

el with PNET being the power injected into the microgrid by the external network. Equation (7.17) allows finding out the amount of active power exchanged with the main network, which is necessary to estimate the cost or gain coming from the energy exchange with the grid. Such cost or gain is given by: el C sell,t PNET, t ∆t C NET ,t =  el C buy,t PNET,t ∆t



el PNET, t 1.1

where Qmax is a positive number defined in [17]. The fulfillment of such requirement has been verified taking for each time sample the couple (P1,t, Q1,t) and reporting it in the power quality plane together with the graph of (7.46) (see Figures 7.9, 7.10, and 7.11).

Figure 7.11  Active or reactive power relationship at the point of interconnection. Comparison between economic dispatch (dots) and economic dispatch and power quality algorithm (crosses).



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As can be seen, the points obtained with both modules of the energy management system (crosses) belong to the desired curve, differently from the ones provided by only the economic dispatch one (dots).

References [1] Del Toro, V., Electric Power Systems, Englewood Cliffs, NJ: Prentice-Hall, 1992. [2] Gabash, A., and P. Li, “Active-Reactive Optimal Power Flow in Distribution Networks with Embedded Generation and Battery Storage,” IEEE Transactions on Power System, Vol. 27, No. 4, November 2012, pp. 2026−2035. [3] “Technical Reference: Capstone Model C65 Performance,” Capstone Turbine Corporation, USA, 2008. [4] Bracco, S., et al., “An Energy Management System for the Savona Campus Smart Polygeneration Microgrid,” IEEE Systems Journal, Vol. 99, 2015. [5] Bonfiglio, A., et al., “An Optimization Algorithm for the Operation Planning of the University of Genoa Smart Polygeneration Microgrid,” Proceedings of IREP 2013 SymposiumBulk Power System Dynamics and Control–IX, Rethymnon, Greece, August 25−30, 2013. [6] Brekken, T. K. A., et al., “Optimal Energy Storage Sizing and Control for Wind Power Applications,” IEEE Transactions on Sustainable Energy, Vol. 2, 2011, pp. 69–77. [7] Bonfiglio, A., et al., “Definition and Experimental Validation of a Simplified Model for a Microgrid Thermal Network and Its Integration into Energy Management Systems,” Energies, Vol. 9, 2016, p. 914. [8] Lasseter, R., et al., “Integration of Distributed Energy Resources - The CERTS MicroGrid Concept,” U.S. Department of Energy, April 2002. [9] Karami, H., et al., “An Optimal Dispatch Algorithm for Managing Residential Distributed Energy Resources,” IEEE Transactions on Smart Grids., Vol. 5, No. 5, September 2014, pp. 2360−2367. [10] Coffrin, C., and P. Van Hentenryck, “A Linear Programming Approximation of AC Power Flows,” INFORMS J. on Comput., May 2014. [11] Beyer, H., et al., “Report on Benchmarking of Radiation Products,” Sixth Framework Programme MESOR, Management and Exploitation of Solar Resource Knowledge, 2009, http://www.mesor.org/docs/MESoR_Benchmarking_of_radiation_products.pdf. [12] Bonfiglio, A., et al., “Rossi Day Ahead Microgrid Optimization: A Comparison Among Different Models,” Proceedings of OPT-i An International Conference on Engineering and Applied Sciences Optimization, Kos Island, Greece, June 4−6, 2014. [13] IBM, “IBM ILOG CPLEX Optimization Studio Software,” 2012, http://www-01.ibm. com/software/integration/optimization/cplex-optimization-studio. [14] Potra, A., A. Florian, and S. J. Wright, “Interior-Point Methods,” Journal of Computational and Applied Mathematics, Vol. 124, No. 1–2, 2000, pp. 281–302.

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[15] Siemens AG, “Ten Reasons for a Smart Grid with DEMS,” Energy Sectore, Nürnberg, Germany, 2008, http://w3.siemens.com/smartgrid/global/en/products-systems-solutions/ gridapplications/dems. [16] Bendato, I., et al., “A Real-Time Energy Management System for the Integration of Economical Aspects and System Operator Requirements: Definition and Validation,” Renewable Energy, Vol. 102, 2017, pp. 406–416. [17] “CEI 0-16 Reference Technical Rules for the Connection of Active and Passive Consumers to the HV and MV Electrical Networks of Distribution,” December 2012.

Appendix 7A Let n ∈ N, and a0 ≤ a1 ≤ … ≤ an. If f is the piecewise continuous function defined as:



c1x + b1 a 0 < x ≤ a1  f (x ) =   c x + b a ≤ x ≤ a n n −1 n n

(7A.1)

and g is the linear function in 2N variables n



g ( y1 ,, yn , z1 ,, z n ) = ∑ bi yi + c i z i i =1

(7A.2)

such that for any i = 1, …, n

yi ∈{0,1}

(7A.3)



ai −1 yi ≤ z i ≤ ai yi

(7A.4)



∑ yi = 1

n

i =1

(7A.5)

and n

then

∑ zi = x

i =1

(7A.6)



Optimization for Microgrid Management

g ( y1 ,, yn , z1 ,, z n ) = f (x )



183

(7A.7)

Appendix 7B Let n ∈ N, Y ⊂ Rn a compact set and c > 0. If f, g: Y → R are two continuous functions, then the following three minimum problems are equivalent min  f ( x ) + cg ( x )



(7B.1)

x ∈Y

min

 f ( x ) + cs 



(7B.2)

min

 f ( x ) + cs 

(7B.3)



( x ,s )∈Y ×[sm ,s M ] s = g (x )



( x ,s )∈Y ×[sm ,s M ] s ≥ g (x )

where sm = min g ( x ) e s M = max g ( x ) . x ∈Y

x ∈Y

• Let us prove that (7B.1) implies (7B.3): ∀(x,s) ∈ Y × [sm , sM] such that, s ≥ g(x) one gets f (x) + cs ≥ f (x) + cg(x). Let x0 ∈ Y be a solution of (7B.1), if s0 = g(x0), one has that (x0, s0) satisfies the constraints of (7B.3), and moreover f (x) + cs ≥ f (x) + cg(x) ≥ f(x0) + cg(x0) = f (x0) + cs0. This means that (x0, s0) solves (7B.3). • Let us prove that (7B.3) implies (7B.2). Let (x0, s0) ∈ Y × [sm, sM] be a solution of (7B.3) and let us suppose that s0 > g(x0). Then, if s 0′ = g(x0), one gets that (x0, s 0′)satisfies the constraints of (7B.3) and the following holds true f (x0) + cs 0′ < f (x0) + cs0, which is in contradiction with the initial hypothesis. This means that if (x0, s0) solves (7B.3), then s0 = g(x0). Hence, (x0, s0) solve also (7B.2). • Straightforwardly, we have that (7B.2) implies (7B.1).

8 Forecasting Tools 8.1  Overview The present chapter describes the state of the art of the main forecasting methods used to predict the energy production from renewable sources, in particular, solar and wind, and the thermal and electrical loads. These methods can be efficiently employed to improve both the planning and the management of smart energy infrastructures characterized by the presence of variable renewable sources and loads.

8.2  Introduction The decisions related to the design and operational management of microgrids strongly depend on the estimation of thermal and electrical demands, power from renewables, and energy costs. The forecasting of these variables is necessary to run optimization tools, such as energy management systems, used to optimally schedule the operation of dispatchable power plants in microgrids. Indeed, with regard to microgrid operational management discussed in Chapter 7, day-ahead optimization of plant production is made on the basis of forecasting power generated by noncontrollable renewable sources and energy demands. As an example, Figure 8.1 shows the interaction between the energy management system of a smart microgrid (equipped with solar and wind renewable power plants, storage systems, dispatchable plants such as fossil-fueled cogeneration plants, and loads) and forecasting tools used to predict the power production from renewable sources, the loads, and the electricity or gas prices. 185

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Figure 8.1  The interaction between a smart microgrid energy management system and forecasting tools.

It is important to note that forecasted parameters are affected by uncertainties and thus it is generally necessary to update their values whenever more reliable values are available during the optimization horizon. For these reasons, forecasting models are generally combined with model predictive control approaches for the solution of the optimization problem described in Chapter 7 [1, 2]. From a theoretical point of view, Ghiani et al. [3] defined forecasting as the attempt to determine in advance the most likely outcome of an uncertain variable. To develop a good forecasting, the variable to be estimated needs to show some degree of regularity and to depend on some quantities involved in the examined phenomenon: for instance, the production of a photovoltaic plant depends on the solar radiation, the ambient temperature, and other quantities that show typical trends as a function of the month and of the daily hour. As highlighted by Ikonen and Najim [4], forecasting tools are useful in different applications: during the design phase of a process, in process control, in plant optimization, and in fault detection. As described in the following, it is possible to develop long-term, medium-term, and short-term forecasting models. Ghiani et al. [3] specified that, in the majority of cases, short-term forecasts are more accurate than medium-term and long-term forecasts since these last strongly depend on a higher probability of unexpected events.



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When dealing with methods and tools for renewable resources and energy demand forecasting, the attention is mainly focused on two classes of models: physically based models and data-driven models. Physically based methods rely on physical principles and detailed information on the system model; they are usually referred as white-box models. The other type of prediction methods (i.e., data-driven methods) mainly relies on operational data to find the relationship between predicted output and relevant variables (e.g., the outdoor temperature, solar radiation, wind velocity); the models developed in such a manner are known as either gray-box or black-box models. The latter ones are based only on available data, while the former ones combine measurements and physical or theoretical knowledge. In literature, the interest in forecasting techniques for energy systems has grown in the last 20 years. Dimakis et al. [5] reviewed methods and tools to estimate the potential of different renewable sources (i.e., solar, wind, wave, biomass, and geothermal), whereas other authors have focused on a specific renewable source: solar [6–8], wind [9–11], and so forth. Obviously, quantitative forecasting methods can be applied when there is a sufficient amount of historical data that describe the trend of the examined variable [3]. In the aforesaid cases, causal methods or time series extrapolation can be used. Time series extrapolation methods assume that the past behavior of the examined variable will be replicated in the future [3, 12]. Among them, the time series decomposition method considers that the time history profile of a certain variable depends on four different effects: trend, cyclical variation, seasonal variation, and residual variation [3]. The well-known moving average method uses the average of the most recent values of the examined variable as the forecast for the first period ahead [3]; the aforesaid method becomes the exponential smoothing method if all historical data are taken into account and lower weights are assigned to older data [3]. In presence of seasonal effects, as suggested by Ghiani et al. [3] and by Alfares and Nazeeruddin [13], different methods can be applied, among which the revised exponential smoothing method, for constant trends, and the Winters method, for linear trends with seasonal effects, are preferably used. For Sobri et al. [6], time series statistical methods can be divided into artificial neural network, support vector machine, Markov chain, and autoregressive (autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA)) and regression models. As defined in [3, 14], artificial neural networks are composed of a set of elementary nonlinear systems approximating the behavior of biological neurons; when properly trained by historical data, they can be used for forecasting purposes. The design of an artificial neural network involves input layers, hidden layers, output layers, connection weights and biases, activation functions, and summation nodes [15]. Its operation to forecast the examined variable needs to be preceded by a learning phase, called artificial neural network training [8,

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16], during which the weights are modified until a predetermined condition is satisfied [6]; finally, the artificial neural network needs to be validated with historical data not used during the training phase. Support vector machines, based on Vapnik-Chervonenkis’ theory, are machine learning techniques also used to forecast photovoltaic production and solar irradiance [6, 17–20]; they are based on the concept of decision planes that define decision boundaries and, primarily, they represent a method that performs classification tasks by constructing hyperplanes in a multidimensional space that separates cases of different class labels. Markov chain models are based on the assumption that, in a sequence of stochastic events, the current state of a variable is independent of all past states, except the immediately previous one [6, 21, 22]; as defined in [6], the process is identified by the initial state, the state space, and the transition matrix. Regression is a statistical method that relates a dependent variable to some casual variables whose value is known or can be predicted; such a relation can be linear or nonlinear [3, 14]; this method determines the regression line or curve that best fits the available historical data. However, autoregressive models assume that the output variable depends linearly on its own previous values and on a stochastic term. Autoregressive models for time series provide a description of a (weakly) stationary stochastic process in terms of two polynomials, one for the autoregression and the second for the moving average. A polynomial model uses a generalized notion of transfer functions to express the relationship between the input, the output, and the noise. Depending on the structure of such polynomials, models belong to different classes (autoregressive with exogenous input (ARX), ARMA, and autoregressive moving average with exogenous input (ARMAX)) and their parameters should be determined through identification techniques. Regarding forecasting tools adopted in the Smart Polygeneration Microgrid described in Chapter 11, some forecasting functions are implemented in the energy management system of the microgrid, called DEMS [23, 24]. In particular, the DEMS Weather Forecast tool is based both on historical data and input from weather service: the external imported weather forecast is adapted to the local site measurements by using a moving average correction algorithm that minimizes the difference of the deviation between external forecast and local measured weather data. Moreover, there is a DEMS Generation Forecast tool that calculates the expected output of renewable energy sources as a function of the forecasted weather conditions; in particular, the forecast algorithm is a piecewise linear transformation that correlates two weather variables to the expected power output according to a given transformation matrix that can be parameterized according to the unit technical specifications and historical data by using an artificial neural network model.



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In the following sections, the main forecasting methods used to predict renewable source power production (solar and wind) and loads (electrical and thermal) are briefly analyzed referring to more recent literature papers.

8.3  Solar and Photovoltaic Production Forecasting In smart grid and microgrid, for operational management purposes, the forecasting of power production from plants that take energy from the Sun such as photovoltaics and solar thermal plants is crucial. Forecasting is needed at different temporal scales: yearly and monthly for planning purposes, 1 day ahead and intra-day for operational management, from seconds to minutes for real-time control. In Figure 8.2, a flow diagram is reported to highlight inputs and outputs of forecasting tools used to predict solar radiation and power production of a photovoltaic plant. As shown in Figure 8.2, the solar radiation forecasting tool needs, as input, historical solar radiation and weather data, weather forecast (information about ambient temperature and pressure, humidity, wind),

Figure 8.2  Solar radiation and photovoltaic production forecasting tools.

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real-time experimental measurements (coming from a real plant if present), and site status (information about unusual climate and environmental conditions). The output of the aforesaid tool (i.e., the solar radiation) is used together with other input (historical photovoltaic production data and information on the plant status if present, such as aging factors and maintenance issues) to estimate the photovoltaic power production. The forecasting of the photovoltaic production is an important challenge within the electrical system, since the accurate estimation of the solar radiation and, as a consequence, of the photovoltaic production determines benefits from the economic and technical point of view for grid operators and power system designers [6, 25]; photovoltaic forecasting tools will be more necessary due to the increasing installation of photovoltaic fields all around the world, both in islanded or grid-connected modes. The aforesaid forecast can be used for both planning and daily operation purposes. In other words, to accurately design a photovoltaic field, it is necessary to estimate its daily, monthly, and annual production; moreover, to manage a photovoltaic field installed within a more complex infrastructure (with storage systems and other generation units), it is essential to have a precise forecasting of its production. This is not a simple task since, as is well known, the solar source is characterized by high variability and intermittency. Two main issues should be considered for the photovoltaic production prediction: environmental parameters (i.e., temperature and solar radiation) and the model for conversion technologies. Numerical Weather Predictions models are operationally used to forecast the evolution of the atmosphere in the following days and thus help in obtaining useful data for the quantification of renewable production and loads. Satellite-derived solar radiation images are a useful tool for quantifying solar irradiation at ground surface for large areas, but they need to set an accurate radiance value under clear-sky conditions and under dense cloudiness. These limitations have placed time series analysis as the dominant methodology for short-term forecasting horizons from 5 minutes up to 6 hours. In [6, 7], the authors proposed a detailed review of solar photovoltaic generation forecasting methods. In particular, in [6], they classified the aforesaid methods into three main categories: time series statistical methods, physical methods and ensemble methods; furthermore, they proposed some metrics to evaluate the performance of forecasting methods. However, in [25], the authors assessed that the solar radiation forecasting methods can be divided into two categories: the cloud imagery combined with physical models, and the machine learning models. In literature, a lot of works based on data-driven approaches do not use the physical model of production plants but directly measured data on power production. For example, Anguita et al. [26] dealt with the problem of nowcast



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and forecast the photovoltaic production on the basis of available real data. Nowcasting deals with the problem of predicting a few hours ahead or in real time the photovoltaics for continuous control and anomaly detection, while forecasting is used for operational management for longer time windows. In this work, forecasting results are compared considering different prediction horizons (i.e., from 1 day ahead to 7 days ahead). Three solutions based on state-of-the-art methods are compared: kernel methods, extreme learning machines, and random forests. The purpose of this comparison is to understand which one is the better approach for these particular problems among the best algorithms in three different families of algorithms: kernel methods, ensemble methods, and neural networks. Data-driven approaches include artificial neural networks [27], ARIMA models and the support vector machine method [28], analog ensemble (AnEn) method [29], and dynamic harmonic regression [30]. As aforesaid, in the literature, many papers are relative to photovoltaic forecasting tools based on artificial neural networks [8, 15, 16]. In [31], the authors proposed a practical method for solar irradiance forecast based on the Multilayer Perceptron (MLP) model: in particular, they consider as input the mean daily solar irradiance, the mean daily air temperature, and the day of the month, whereas the output gives as parameters the 24 hours of solar irradiance for the next day. Chen et al. [32] showed that by using fuzzy logic and neural network together, the solar radiation forecast results can follow the real values very well under different sky and temperature conditions, whereas in [33], the authors proposed an optimization approach to find the number of neurons and discuss the dependence of forecasting on the chosen training year. Literature models relative to the use of artificial neural networks for photovoltaic forecasting usually differ for the data input set. For instance, Almonacid et al. [34] forecast the photovoltaic production as a function of solar irradiance and air temperature, while in [35] several input parameters are taken into account to forecast direct, diffuse and global irradiance: latitude, longitude, altitude, local mean time, month of the year, monthly mean hourly relative humidity, monthly mean hourly total rainfall, and mean duration of sunshine per hour. Different artificial neural network models for global radiation forecasting have been analyzed by Kashyap et al. [36], where wind parameters (direction and speed) are also considered as input data. In [37], the authors presented a support vector machine method to predict photovoltaic power output considering four different weather conditions: cloudy, foggy, sunny, and rainy, whereas in [20], seven support vector machine models were proposed, each one characterized by different input parameters used to forecast daily solar radiation. In [19], the input data referred to the atmospheric transmissivity in two-dimensional form and other meteorological variables, including sky cover, relative humidity, and wind speed, whereas in

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[18] a comparison between support vector machine models and artificial neural networks was reported, highlighting the efficacy of the first ones for short-term predictions. A novel method named as support vector machine-Firefly Algorithm (SVM–FFA) was developed in [17] by hybridizing the support vector machines with the Firefly Algorithm to predict the monthly mean horizontal global solar radiation using three meteorological parameters such as sunshine duration, maximum temperature, and minimum temperature as input. With regard to the use of the Markov chain to predict the photovoltaic production, some studies were reported in [21, 22, 38], whereas many other papers are focused on univariate/multivariate regression to find a functional relationship between dependent and independent parameters [6, 39, 40].

8.4  Wind Power Production Forecasting Forecasting of wind power is a significant issue of smart grid and microgrids. Accurate forecasting of wind power generation is an efficient tool to deal with the randomness and intermittence of the wind resource interacting with the power system [11, 41]. In Figure 8.3, a flow diagram is shown to report input and output of forecasting tools used to predict wind conditions and the production of a wind power plant. A wind forecasting tool typically needs, as input, historical wind and weather data, weather forecast (information about ambient temperature and pressure, humidity, and so forth), real-time experimental measurements (coming from a real plant if present), and site status (information about unusual climate and environmental conditions). The predicted wind conditions are then used to forecast the power production of a wind plant. Analogously to photovoltaic power production forecasting, to predict the production of a wind power plant, it is also necessary to know other input data such as historical production data and information on the plant status if present (aging factors and maintenance issues). As underlined by Exizidis et al. [42], member states of the European Network of Transmission System Operators for Electricity (ENTSO-E) are required to publish, among other information, aggregate wind power forecasts. Moreover, as highlighted by Pinson et al. [43] and Taylor and Jeon [44], accurate forecasts of wind generation permit to increase revenues for wind power producers, since they can operate in the electricity market with more reliable bids. Power from wind turbines depends both on wind and the turbine model. Like in the case of photovoltaics, forecasting can be performed through physically based models (both for data generation and production plant modeling) or data-driven models from real measurements. A starting point for the review of forecasting tools for wind power can be found in [5, 9, 10]. Methods for



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Figure 8.3  Wind and wind power production forecasting tools.

wind power forecasting can be classified in four categories [41, 45]: physical models, conventional statistical models, spatial correlation models, and artificial intelligence and new models. A physical model uses physical or meteorology information such as temperature, pressure, and orography to predict the future speed of wind. It has advantages in long-term prediction, but it does not give accurate results for short-term prediction. Nowadays, wind maps and global databases have been developed for many regions around the world, containing wind speed, temperature, and pressure at several heights. However, available techniques are not suitable for the assessment of local energy production because of the low resolution of some existing data and lack of data. Other approaches are based on the combination of flow and mesoscale models. Flow modeling techniques estimate the theoretical wind energy at a resolution of the order of few kilometers and can be suitable only for a raw evaluation of the wind potential of a region. Based on the theory of flow over small hills, some linearized flow models are currently used (for example, Wind Atlas Analysis and Application Program (WasP) [46]). A different type of models is the atmospheric mesoscale models: they were developed for general weather prediction at fine resolution (1–10 km) and in

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particular for air pollution studies and aviation purposes. To combine flow and mesoscale models, one can stop the mesoscale modelling at a resolution of approximately 5 km and local predictions can be made with a wind flow model. When multi-year-long measurements for the target site are available, statistical evaluations are performed to determine the probability density function (PDF) of the wind. The use of this frequency distribution approach can provide a simple method to evaluate the theoretical wind energy, because it provides useful information about wind speed. The Weibull distribution has a number of advantages with respect to the other PDFs even if it cannot describe all the wind regimes encountered in nature such as, for example, those with high percentages of null wind speeds and bimodal distributions. A conventional statistical model uses historical wind speed data for training. The goal is to find the relationship between certain explanatory variables and future wind speed. The representative statistical models are autoregressive model, moving average model, ARMA, and ARIMA. A spatial correlation model takes into account the spatial relationship of the wind speed in different wind speed measurement stations. The wind speed time series of the predicted points and its neighboring observation points are employed to predict the wind speed. In addition, with the development of artificial intelligence and other forecasting methods, various new models have been proposed, such as artificial neural networks, support vector regression machines, and various hybrid methods. Data-driven models are widespread in the literature of wind forecasting and power production [9, 10, 47, 48]. Both linear and nonlinear methods have been widely applied to wind forecasting. Linear models, such as ARMA methods, Box-Jenkins methods, Kalman filter, and Markov chain models, are most widely used in the literature. Artificial neural networks and support vector machine are the two most popular nonlinear methods for wind forecasting. Hybrid methods have been shown in the literature to produce more accurate forecasts than any of the individual forecasting models. For example, Fang and Chiang [49] proposed a two-layer multimodel forecasting methodology, which utilizes multiple characteristically different machine learning algorithms with different kernels in both layers. The first layer is composed of multiple machine learning models that generate individual forecasts, on the basis of inputs determined by a deep feature selection framework. A blending algorithm is applied in the second layer to create an ensemble of the forecasts produced by first-layer models and to generate both deterministic and probabilistic forecasts. Numerical results show that the developed multimodel framework with a deep feature selection procedure has improved the forecasting accuracy by up to 30%. Renani et al. [48] compared two different approaches in wind power forecasting that are indirect and direct prediction methods. In indirect method, several times series are applied to forecast the wind speed, whereas the logistic function with five parameters is then used to forecast the wind power. The backtracking



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search algorithm with novel crossover and mutation operators is employed to find the best parameters of the five-parameter logistic function. A new feature selection technique, combining the mutual information and neural network, is proposed to extract the most informative features. Since data-driven models, to be trained, need a large amount of data, the effectiveness of these approaches strongly depends on the characteristics of the specific case study. Thus, a question arises regarding whether the prediction model trained by data coming from older farms (where more data are available) is also effective for a newly built farm. Hu et al. [45] proposed a trial of transferring the information obtained from data-rich farms to a newly built farm. In particular, they introduced deep neural networks, trained by data from data-rich farms, to extract wind speed patterns, and then finely tune the mapping with data coming from newly built farms. The experimental results show that prediction errors are significantly reduced using the proposed technique.

8.5  Load Forecasting The term load forecasting refers to the prediction of different types of demand: electrical, thermal (heating, steam, domestic hot water), and cooling. Focusing on electric load, Alfares and Nazeeruddin [13] assessed that load forecasting is relative to the prediction of both the magnitudes and the geographical locations of electric loads over the different periods of the examined horizon. As highlighted by Hong and Fan [50], electric load forecasting involves different stakeholders (electric utilities, industrial and big commercial companies, banks, trading firms, insurance companies); moreover, they suggested grouping forecasting models into four categories depending on their horizon: very short term (shorter than 1 day), short term (between 1 day and 2 weeks), medium term (between 2 weeks and 3 years), and long term (longer than 3 years). Very short-term models can be used in demand response and hour/dayahead scheduling problems, whereas short term models can be adopted when dealing with energy trading and unit commitment. However, medium-term and long-term forecasting models are usually used in system planning and energy policies. Considerations on forecasting time horizons were also drawn by Kuster et al. [14]. They assessed that short-term models (1 hour to several days) are useful for demand response, whereas mid-term (1 month to a season) and long-term (1 year or more) models are employed for power system planning and maintenance; furthermore, they indicated that long-term prediction with a large time step is often applied for policy-making, large-scale planning, and business plan. Forecasting models use as input load historical data and, in some cases, exogenous variables such as building and occupancy characteristics and

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environmental data [14]; in particular, the authors in [14] divided the exogenous variables into four categories: socioeconomic, environmental, building and occupancy, and time index. Also, Almeshaiei and Soltan [51] highlighted that social and environmental factors need to be taken into account since they are big sources of randomness on load patterns. Indeed, as also assessed by Alfares and Nazeeruddin [13], electric load is a random nonstationary process composed of thousands of individual components, and different factors have to be considered in load forecasting models: economic factors, time, day, season, weather, and random effects. A typical flow diagram representing input and output of load forecasting tools is shown in Figure 8.4. A load forecasting tool, used to predict different kinds of loads (electrical, thermal, and cooling) usually needs, as input, historical load data, weather forecast (information about ambient temperature and humidity), real-time experimental measurements (collected through smart meters installed in buildings), and site status (information about unusual climate and environmental conditions, special events); furthermore, the possibility to apply demand response strategies has also to be taken into account to identify flexible loads. Finally, information about building occupancy levels has also to be considered. In literature, many studies are present for the forecasting of the electric load at a large scale such as the whole building, university campuses, utility

Figure 8.4  Load forecasting tools.



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companies, and districts. Most studies are carried at hourly or higher time steps. Very few studies are related to the forecast of the electric demand at the system level [52]. Moreover, it is necessary to distinguish between point load forecasting and probabilistic load forecasting: the first one refers to the prediction of one value of the electric load for each time instant, whereas the second one is used to provide load probabilistic distributions for each time instant [50]. The second approach is very useful in the present electricity market, characterized by market competition and a massive presence of power plants fed by variable renewable sources. Similar considerations were reported in [53], where the authors directed the attention on short-term load forecasting models that provide probability distribution outputs. Different techniques can be used in load forecasting and they can be mainly divided into two categories: statistical and artificial intelligence [50]. Among statistical techniques, Hong and Fan [50] described multiple linear regression models, semi-parametric additive models, ARMA models, and exponential smoothing models. However, among artificial intelligence techniques, they considered artificial neural networks, fuzzy regression models, support vector machines, and gradient boosting machines. Kuster et al. [14] emphasized that regression models are efficient for long and very long-term prediction, where periodicity and changes are less significant, while for short-term and very short-term prediction machine learning algorithms such as artificial neural network, support vector machine, and time series analysis are preferable. An interesting review on load forecasting techniques was also reported in [13], where the Winters method is reported as one of the several exponential smoothing methods used to analyze seasonal time series; the same paper even described the mathematical models used by autoregressive, ARMA, and ARIMA techniques. About artificial neural network use, an interesting review regarding load forecasting was reported in [54], whereas in [55] two artificial neural network forecasters were described, to predict, respectively, the base load and the change in load. Furthermore, it is important to highlight the necessity to develop preanalysis and preprocessing of forecasting input data and to adopt metrics to evaluate load forecasting errors [14, 50]. For local systems, the authors in [52] focused the attention on buildings and in particular on the forecast of electric demand of existing heating, ventilation, and air conditioning subsystems at subhourly time steps, which is of high interest for demand response programs; this study suggested data-driven (neural networks) forecasting models for this specific application. Always referring to local (small) applications, Bassamzadeh and Ghanem [56] proposed a probabilistic data-driven predictive model for consumption forecasting in residential buildings based on a Bayesian network framework, which is able to discover dependency relations between contributing variables. The performance of the model is tested in a multiscale setting by considering various temporal

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(i.e., 15 minutes, hourly intervals) and spatial (i.e., all households in a region, each household) resolutions for analyzing data. As highlighted by the authors, Bayesian network models can have significant implications: first, the structure of the model is purely learned from the data and no prior assumptions are inserted; then the full probability distribution of the demand variable can be inferred even if all predictor variables are not observed; and finally, prediction at high granularity levels, specifically at the households’ scale, can provide benefits for utility companies in designing targeted and personalized policies. It is also important to report the bottom-up approach proposed in [14]: to estimate the household electric load, the aggregation of all appliances is considered, each one characterized by the probability to be on at every time step of the day, depending also on household members’ characteristics. In buildings, not only is the forecast of power demand important, but also that of thermal demand. Cheng et al. [57] focused the attention on short-term building cooling load prediction. In line with other authors, they stated that conventional methods, which heavily rely on physical principles, have limited power in practice as their performance is subject to many physical assumptions. Thus, they prefer data-driven methods and in particular deep learning, in predicting 24 hours ahead for building cooling load profiles. Deep learning refers to a collection of machine learning algorithms that are powerful in revealing nonlinear and complex patterns in big data. The authors compared its performance in cooling load prediction with typical feature extraction methods and popular prediction techniques in the building field. The results showed that deep learning can enhance the performance of building cooling load prediction. In [58], data-driven methods applied to thermal load forecasting were analyzed, as well as in [59] where a data-driven approach for analysis and forecast of aggregate space and water thermal load in buildings is proposed. As for electric load forecasting, thermal load forecasting models are based on different input data: in [60], appropriate input variables for data-driven predictive models are obtained from building energy management system sensors as well as from weather data, while the hybrid method proposed in [61] combines the time series model and artificial intelligence and uses as input data historical load information and real-time meteorological data. The DEMS energy management system of the Smart Polygeneration Microgrid described in Chapter 11 contains a load forecast function that calculates the forecasted demand schedules for all energy or media end use demands (e.g., electricity, heat, gas, water) of the system [23, 24]. Load forecast is based on weather, historical data, typical consumptions according to the day of the week, holiday, or other variables (e.g., related to academic activity). A piecewise linear model is being set up for modeling the demand behavior as a function of influencing variables like day types, weather variables or building occupation levels. The model equation coefficients are estimated cyclically each day after



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new measurements are available; the mathematical method for calculating the model coefficients is a Kalman filter.

References [1] Parisio, A., E. Rikos, and L. Glielmo, “A Model Predictive Control Approach to Microgrid Operation Optimization,” IEEE Transactions on Control Systems Technology, Vol. 22, No. 5, 2014, pp. 1813–1827. [2] Bracco, S., et al., “���������������������������������������������������������������� A Dynamic Optimization-Based Architecture for Polygeneration Microgrids with Tri-Generation, Renewables, Storage Systems and Electrical Vehicles,” Energy Conversion and Management, Vol. 96, 2015, pp. 511–520. [3] Ghiani, G., G. Laporte, and R. Musmanno, Introduction to Logistics Systems Planning and Control, New York: Wiley, 2004. [4] Ikonen, E., and K. Najim, Advanced Process Identification and Control, Control Engineering Series, New York: Marcel Dekker, 2002. [5] Dimakis, A., et al., “Methods and Tools to Evaluate the Availability of Renewable Energy Sources,” Renewable and Sustainable Energy Reviews, Vol. 15, 2011, pp. 1182–1200. [6] Sobri, S., S. Koohi-Kamali, and N. A. Rahim, “Solar Photovoltaic Generation Forecasting Methods: A Review,” Energy Conversion and Management, Vol. 156, 2018, pp. 459–497. [7] Antonanzas, J., et al., “Review of Photovoltaic Power Forecasting,” Solar Energy, Vol. 136, 2016, pp. 78–111. [8] Yadav, A. K., and S. S. Chandel, “Solar Radiation Prediction Using Artificial Neural Network Techniques: A Review,” Renewable and Sustainable Energy Reviews, Vol. 33, 2014, pp. 772–781. [9] Giebel, G., and G. Kariniotakis, “Wind Power Forecasting – A Review of the State of the Art,” in Renewable Energy Forecasting: From Models to Applications, Elsevier–Woodhead Publishing Series in Energy, Cambridge, UK, 2017, pp. 59–109. [10] Wang, X., P. Guo, and X. Huang, “A Review of Wind Power Forecasting Models,” Energy Procedia, Vol. 12, 2011, pp. 770–778. [11] Zhang, Y., J. Wang, and X. Wang, “Review on Probabilistic Forecasting of Wind Power Generation,” Renewable and Sustainable Energy Reviews, Vol. 32, 2014, pp. 255–270. [12] Guo, H., et al., “A Monthly Electricity Consumption Forecasting Method Based on Vector Error Correction Model and Self-Adaptive Screening Method,” Electrical Power and Energy Systems, Vol. 95, 2018, pp. 427–439. [13] Alfares, H. K., and M. Nazeeruddin, “Electric Load Forecasting: Literature Survey and Classification of Methods,” International Journal of Systems Science, Vol. 33, No. 1, 2002, pp. 23–34. [14] Kuster, C., Y. Rezgui, and M. Mourshed, “Electrical Load Forecasting Models: A Critical Systematic Review,” Sustainable Cities and Society, Vol. 35, 2017, pp. 257–270.

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[15] Zhu, H., et al., “A Power Prediction Method for Photovoltaic Power Plant Based on Wavelet Decomposition and Artificial Neural Networks,” Energies, Vol. 9, No. 1, 2016, pp. 1–15. [16] Ogliari, E., et al., “Hybrid Predictive Models for Accurate Forecasting in PV Systems,” Energies, Vol. 6, No. 4, 2013, pp. 1918–1929. [17] Olatamiwa, L., et al., “A Support Vector Machine–Firefly Algorithm-Based Model for Global Solar Radiation Prediction,” Solar Energy, Vol. 115, 2015, pp. 632–644. [18] Mellit, A., A. Massi Pavan, and M. Benghanem, “Least Squares Support Vector Machine for Short-Term Prediction of Meteorological Time Series,” Theoretical and Applied Climatology, Vol. 111, No. 1-2, 2013, pp. 297–307. [19] Zeng, J., and W. Qiao, “Short-Term Solar Power Prediction Using a Support Vector Machine,” Renewable Energy, Vol. 52, 2013, pp. 118–127. [20] Chen, J. L., G. S. Li, and S. J. Wu, “Assessing the Potential of Support Vector Machine for Estimating Daily Solar Radiation Using Sunshine Duration,” Energy Conversion and Management, Vol. 75, 2013, pp. 311–318. [21] Bhardwaj, S., et al., “Estimation of Solar Radiation Using a Combination of Hidden Markov Model and Generalized Fuzzy Model,” Solar Energy, Vol. 93, 2013, pp. 43–54. [22] Sanjari, M. J., and H. B. Gooi, “Probabilistic Forecast of PV Power Generation Based on Higher Order Markov Chain,” IEEE Transactions on Power Systems, Vol. 32, No. 4, 2017, pp. 2942–2952. [23] Werner, T. G., and R. Remberg, “Technical, Economical and Regulatory Aspects of Virtual Power Plants,” Proc. of 3rd International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT 2008), Nanjing, China, April 6–9, 2008. [24] http://w5.siemens.com/italy/web/ic/sg/ea/applicazioni/ gestionedimicrogridevirtualpowerplant/pages/dems.aspx. [25] Voyant, C., et al., “Machine Learning Methods for Solar Radiation Forecasting: A Review,” Renewable Energy, Vol. 105, 2017, pp. 569–582. [26] Anguita, D., et al., “Data-Driven Photovoltaic Power Production Nowcasting and Forecasting for Polygeneration Microgrids,” IEEE Systems Journal, Vol. 99, 2017, pp. 1–12. [27] Izgi, E., et al., “Short-Mid-Term Solar Power Prediction by Using Artificial Neural Networks,” Solar Energy, Vol. 86, No. 2, 2012, pp. 725–733. [28] Bouzerdoum, M., A. Mellit, and A. M. Pavan, “A Hybrid Model (SARIMA-SVM) for Short-Term Power Forecasting of a Small-Scale Grid-Connected Photovoltaic Plant,” Solar Energy, Vol. 98, 2013, pp. 226–235. [29] Alessandrini, S., et al., “An Analog Ensemble for Short-Term Probabilistic Solar Power Forecast,” Applied Energy, Vol. 157, 2015, pp. 95–110. [30] Trapero, J. R., N. Kourentzes, and A. Martin, “Short-Term Solar Irradiation Forecasting Based on Dynamic Harmonic Regression,” Energy, Vol. 84, 2015, pp. 289–295.



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[31] Mellit, A., and A. Massi Pavan, “A 24-H Forecast of Solar Irradiance Using Artificial Neural Network: Application for Performance Prediction of a Grid-Connected PV Plant at Trieste, Italy,” Solar Energy, Vol. 84, No. 5, 2010, pp. 807–821. [32] Chen, S. X., H. B. Gooi, and M. Q. Wang, “Solar Radiation Forecast Based on Fuzzy Logic and Neural Networks,” Renewable Energy, Vol. 60, 2013, pp. 195–201. [33] Cornaro, C., M. Pierro, and F. Bucci, “Master Optimization Process Based on Neural Networks Ensemble for 24-H Solar Irradiance Forecast,” Solar Energy, Vol. 111, 2015, pp. 297–312. [34] Almonacid, F., et al., “A Methodology Based on Dynamic Artificial Neural Network for Short-Term Forecasting of the Power Output of a PV Generator,” Energy Conversion and Management, Vol. 85, 2014, pp. 389–398. [35] Kaushika, N. D., R. K. Tomar, and S. C. Kaushik, “Artificial Neural Network Model Based on Interrelationship of Direct, Diffuse and Global Solar Radiations,” Solar Energy, Vol. 103, 2014, pp. 327–342. [36] Kashyap, Y., A. Bansal, and A. K. Sao, “Solar Radiation Forecasting with Multiple Parameters Neural Networks,” Renewable and Sustainable Energy Reviews, Vol. 49, 2015, pp. 825–835. [37] Shi, J., et al., “Forecasting Power Output of Photovoltaic Systems Based on Weather Classification and Support Vector Machines,” IEEE Transactions on Industry Applications, Vol. 48, No. 3, 2012, pp. 1064–1069. [38] Barnes, A. K., J. C. Balda, and J. K. Hayes, “Modelling PV Clouding Effects Using a Semi-Markov Process with Application to Energy Storage,” Proceedings of the 19th World Congress IFAC, Cape Town, South Africa, August 24–29, 2014. [39] Kumar, R., R. K. Aggarwal, and J. D. Sharma, “Comparison of Regression and Artificial Neural Network Models for Estimation of Global Solar Radiations,” Renewable and Sustainable Energy Reviews, Vol. 52, 2015, pp. 1294–1299. [40] Ibrahim, S., et al., “Linear Regression Model in Estimating Solar Radiation in Perlis,” Energy Procedia, Vol. 18, 2012, pp. 1402–1412. [41] Wang, Q., et al., “The Value of Improved Wind Power Forecasting: Grid Flexibility Quantification, Ramp Capability Analysis, and Impacts of Electricity Market Operation Timescales,” Applied Energy, Vol. 184, 2016, pp. 696–713. [42] Exizidis, L., et al., “Impact of Public Aggregate Wind Forecasts in Electricity Market Outcomes,” IEEE Transactions on Sustainable Energy, Vol. 8, No. 4, 2017, pp. 1394–1405. [43] Pinson, P., C. Chevallier, and G. Kariniotakis, “Trading Wind Generation from ShortTerm Probabilistic Forecasts of Wind Power,” IEEE Transactions on Power Systems, Vol. 22, No. 3, 2007, pp. 1148–1156. [44] Taylor, J. W., and J. Jeon, “Forecasting Wind Power Quartiles Using Conditional Kernel Estimation,” Renewable Energy, Vol. 80, 2015, pp. 370–379. [45] Hu, Q., R. Zhang, and Y. Zhou, “Transfer Learning for Short-Term Wind Speed Prediction with Deep Neural Networks,” Renewable Energy, Vol. 85, 2016, pp. 83–95. [46] http://www.wasp.dk/wasp.

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[47] Cong, F., et al., “A Data-Driven Multi-Model Methodology with Deep Feature Selection for Short-Term Wind Forecasting,” Applied Energy, Vol. 190, 2017, pp. 1245–1257. [48] Renani, E. T., M. F. M. Elias, and N. A. Rahim, “Using Data-Driven Approach for Wind Power Prediction: A Comparative Study,” Energy Conversion and Management, Vol. 118, 2016, pp. 193–203. [49] Fang, S., and H. D. Chiang, “Improving Supervised Wind Power Forecasting Models Using Extended Numerical Weather Variables and Unlabelled Data,” IET Renewable Power Generation, Vol. 10, No. 10, 2016, pp. 1616–1624. [50] Hong, T., and S. Fan, “Probabilistic Electric Load Forecasting: A Tutorial Review,” International Journal of Forecasting, Vol. 32, 2016, pp. 914–938. [51] Almeshaiei, E., and H. Soltan, “A Methodology for Electric Power Load Forecasting,” Alexandria Engineering Journal, Vol. 50, 2011, pp. 137–144. [52] Le Cam, M., R. Zmeureanu, and A. Daoud, “Cascade-Based Short-Term Forecasting Method of the Electric Demand of HVAC System,” Energy, Vol. 119, 2017, pp. 1098– 1107. [53] Feinberg, E. A., and D. Genethliou, “Load Forecasting,” Applied Mathematics for Restructured Electric Power Systems: Optimization, Control, and Computational Intelligence, 2005, pp. 269–285. [54] Hippert, H. S., C. E. Pedreira, and R. C. Souza, “Neural Networks for Short-Term Load Forecasting: A Review and Evaluation,” IEEE Transactions on Power Systems, Vol. 16, 2001, pp. 44–55. [55] Khotanzad, A., and R. Afkhami-Rohani, “ANNSTLF – Artificial Neural Network ShortTerm Load Forecaster Generation Three,” IEEE Transactions on Power Systems, Vol. 13, No. 4, 1998, pp. 1413–1422. [56] Bassamzadeh, N., and R. Ghanem, “Multiscale Stochastic Prediction of Electricity Demand in Smart Grids Using Bayesian Networks,” Applied Energy, Vol. 193, 2017, pp. 369–380. [57] Cheng, F., X. Fu, and Z. Yang, “A Short-Term Building Cooling Load Prediction Method Using Deep Learning Algorithms,” Applied Energy, Vol. 195, 2017, pp. 222–233. [58] Geysen, D., et al., “Operational Thermal Load Forecasting in District Heating Networks Using Machine Learning and Expert Advice,” Energy and Buildings, Vol. 162, 2018, pp. 144–153. [59] Idowu, S., et al., “Applied Machine Learning: Forecasting Heat Load in District Heating System,” Energy and Buildings, Vol. 133, 2016, pp. 478–488. [60] Kapetanakis, D. S., E. Mangina, and D. P. Finn, “Input Variable Selection for Thermal Load Predictive Models of Commercial Buildings,” Energy and Buildings, Vol. 137, 2017, pp. 13–26. [61] Liu, T., et al., “A Hybrid Model of AR and PNN Method for Building Thermal Load forecasting,” in Zhang L., X. Song, and Y. Wu, (eds.), Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems, AsiaSim 2016, SCS AutumnSim 2016. Communications in Computer and Information Science, Vol. 643, Springer, Singapore, 2016.

9 Islanded Microgrids 9.1  Overview In a recent report by Navigant Research [1] the main uses for microgrids have been pointed out to be��������������������������������������������������������� commercial and industrial, community and utility, institutional and university campus, military, and remote systems (villages). The common feature of all these categories is either the permanent lack of the connection to a public grid (in the case of remote systems) or the possibility of working in the islanding mode, which is more seen as mandatory feature in applications (like �������������������������������������������������������������� university campuses ��������������������������������������������������� or military installation) where the continuity of operation, also in case of out of services of the main grid, is essential. As a consequence, it is apparent that probably the greatest challenge posed by microgrids is the possibility of managing them in an islanded configuration. This opens two basic problems: • Design a proper control system (as an islanded microgrid cannot rely on the main grid for frequency and voltage control, these tasks have to be performed by local control systems). • Design a proper protection architecture against faults. Concerning the second item, this is related to the fact that for an islanded microgrid it is difficult to ensure the protection selectivity usually guaranteed in a standard network. Most of the sources typically employed in a microgrid are interfaced with power electronic inverters, whose controls limit the magnitude 203

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of the currents they deliver when a fault such as a short circuit occurs in the network. This limitation, usually on the order of 1.5 to 2 times the rated current, is required to protect the electronic switches of the inverters, but implies that a network fed only by inverter-interfaced sources is characterized by levels of short-circuit currents that are so low, if compared to those of a conventional network, that the standard techniques employed to ensure the protection coordination result ineffective; even detecting the fault can be difficult. This problem is addressed in detail in [2]. As far as the first issue is concerned, the first idea is to mimic what happens in traditional networks. As is well known [3, 4], power systems are equipped with active power or frequency control and voltage or reactive power control. Both of them are organized in a hierarchical structure where the outer level produces the reference signals for the inner one. In particular, the active power or frequency control is organized on three levels: the primary regulation is local and is responsible to ensure the active power balance after a contingency. To guarantee a proper power sharing among the machines participating to the regulation, the droop method has been proposed that, unfortunately, does not guarantee the possibility to restore the frequency. For this reason, a centralized secondary regulation is implemented that this responsible for zeroing the frequency error. The tertiary regulation is rather an optimal dispatch aimed at minimizing the energy production cost. As far as the voltage control is concerned, the possible control actions can be classified in three main categories [3]: 1. Purely local actions are those performed by the primary voltage control of generators, static compensator controllers, and on-load tap changers. In this case, they absorb or generate reactive power on the basis of the local voltage, that is to say, the voltage at the point where they are placed. 2. Area level centralized action is also called secondary voltage control. This action varies the reactive powers delivered by the units in order to maintain a fixed voltage profile in the high-voltage transmission system under steady-state conditions. This is obtained by compensating the voltage deviations due to disturbances and load variations in important network nodes, called pilot nodes (this practice is typically applied in European transmission systems). 3. The tertiary control is a centralized action performed at the central dispatching center of a country (or, in general, of the independent system operator managing the transmission network of a given region), which is aimed at setting the voltage profile that secondary control has to keep fixed.



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Unfortunately, microgrids present some different characteristics with respect to traditional networks that do not allow replicating these concepts. The main difference lies in the fact that the majority of microgrid sources are connected to the infrastructure by means of power electronic converters; therefore, there is no link between the speed of the rotating machines (if present) and the system frequency that is dictated by the inverters. It should be observed that the hierarchical structure is maintained both in on-grid and off-grid configurations (see [5] for details), but, in the case of grid-connected microgrids, the presence of the public network is sufficient to guarantee that the frequency value is kept within the prescribed range. For this reason, only the tertiary level of control is often addressed in this case and, in this book, has been thoroughly described in Chapter 7. In this latter case, all the devices are seen as active and reactive power sources, which follow the generation schedules computed by the tertiary control and transmitted from the microgrid supervision system to their local controllers. The only possibility is for them to offer frequency support to the main grid eventually specified by local standards that can require the controller to implement some frequency and active power control laws [6]. The hierarchical structure and, in particular, primary and secondary control become essential when the microgrid operates in the islanding mode. For these reasons, this chapter is devoted to the state of the art of control for islanded microgrids, focusing the attention on primary and secondary controls.

9.2  Primary Control As pointed out in [5], the primary control is designed to satisfy the following requirements: • To stabilize the voltage and frequency. As a consequence of an islanding event, the microgrid may lose its voltage and frequency stability due to the mismatch between the power generated and consumed; furthermore, contingencies on generators or sudden variation of the loads could compromise the frequency stability. • To offer plug-and-play capability for DERs and properly share the active and reactive power among them, preferably without any communication links. • To mitigate circulating currents that can cause over-current phenomenon in the power electronic devices and damage the direct current (DC) link capacitor.

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There are two main categories of primary controllers: the first one requires communication among the source’s controllers, while the second one ensures a proper power sharing without the need for any communication system. 9.2.1  Communication-Based Control

This category of controllers has been historically developed to manage pulse width modulator (PWM) inverters for parallel operation of uninterruptible power supply with the aim of avoiding the use of very large uninterruptible power supply units. The first idea was to consider all the units as voltage sources to be connected by means of suitable inductors and synchronized. The voltage magnitude and phase angle were then adjusted to control the production of active and reactive power [7]. The main drawback of the method is that the synchronization was performed by a phase-locked loop (PLL) characterized by an inherent slow response. For this reason, in 1995, Chen and Chu [8] proposed the master-andslave method, in which one of the inverters (the master) was considered as a voltage-controlled PWM inverter and the remaining n-1 (slaves) PWM inverters were treated as current-controlled PWM inverters whose reference was dictated by the power distribution center. Thus, with a suitable power distribution center (PDC) algorithm, the slaves share the load currents while the master keeps the voltage at the desired value and acts as a slack bus to compensate for any missing or exceeding power. This way, the only necessary PLL was the one to synchronize the master with the external network (when present). In the following, the working principle of the master-and-slave method is presented. Consider the situation depicted in Figure 9.1, where one master (by way of example, no. 3) and two slaves (no. 1 and no. 2) are present. The master is to be intended as a voltage source; consequently, its controllers should regulate the voltage v0, which can be done as follows. Consider the situation depicted in Figure 9.2, in which the AC side of the master inverter is represented by the first harmonic of the voltage source e and the output filter consists of a series inductor and a shunt capacitor. In the following, the filter resistances are neglected for the sake of simplicity; if one wants to account for them or for another filter topology, the transfer function between e and the output voltage v0 will change according to the specific layout. In a Park reference frame synchronized with the external network and such that the network voltage has only direct axis component, the relationship between the axis components of the PWM inverter output voltage and the modulating signals is:



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Figure 9.1  Equivalent circuit of three parallel inverters in the master-and-slave configuration.

Figure 9.2  Master circuit.



 e d 3 =   e =  q 3

3 v DC 3 ud 3 2 2 3 v DC 3 uq 3 2 2

(9.1)

with ud3 and uq3 being the Cartesian components of the modulating signals linked to the modulating amplitude index ma3 and the phase angle δ3 according to the following relationships:

ud 3 = ma 3 cos δ3  uq 3 = −ma 3 sin δ3

(9.2)

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In the following equations, subscript d(q) will refer to the direct axis (quadrature) component of a voltage or current. Furthermore, for the sake of brevity, all the quantities appearing in the electric circuits we not be defined explicitly after the equations involving them. The Kirchhoff ’s voltage law (KVL) at the master AC side mesh states that: did 3  e d 3 − vod = L f 3 dt + ωL f 3iq 3  di q 3 e − v = L − ωL f 3id 3 f3  q 3 oq dt



(9.3)

but  id 3 = C f  i = C f  q 3



dvod + ωC f voq + iod′ dt dvoq − ωC f vod − ioq′ dt

(9.4)

Combining (9.3) and (9.4), one has:



 d 2vod = + 1 − ω2L f 3C f vod + 2 ωL f 3C f e L C  d3 f3 f 2 dt  diod  L f 3 dt + ωL f 3ioq′   2 e = L C d voq + 1 − ω2L C v − 2 ωL C f3 f f3 f oq f3 f  q3 dt 2  L dioq − ωL i ′ f 3 od  f 3 dt

(

)

dvoq

(

)

dvod + dt

dt

+

(9.5)

So, defining



one has that:

 v d 3 = e d 3 − 2 ωL f 3C f  v = e + 2 ωL C f3 f  q 3 q 3

dvoq

diod′ − ωL f 3ioq′ dt dt dioq′ dvod − Lf 3 + ωL f 3iod′ dt dt − Lf 3

(9.6)



Islanded Microgrids

 v d 3 = L f 3C f    vq 3 = L f 3C f



209

d 2vod + 1 − ω2L f 3C f vod 2 dt d 2voq + 1 − ω2L f 3C f voq dt 2

(

)

(

)

(9.7)

which results in the two decoupled control loops depicted in Figure 9.3, in which two conventional proportional integral (PI) controllers (Rd and Rq) can be used for the two axis channels. It should be observed that to compensate the cross-coupling between the two channels, one should use (9.6), which would imply the estimation of the time derivative of the load voltage and current. As far as the voltage is concerned, the time derivative calculation can be avoided by solving (9.4) with respect to such derivative; however, the load current time derivative appearing in (9.6) can be eventually estimated according to the specific in nature of the load. The final control scheme appears in Figure 9.4, in which the last block represents the Cartesian to polar transformation described in (9.2). As each slave inverter is intended to be a current source, the aim of its controller should be to regulate its output current. Consider, for example, the first of the two slave inverters appearing in Figure 9.1; the KVL at its AC side mesh states that:



did 1  e d 1 − vod = L f 1 dt + ωL f 1iq1  diq1 e − v = L − ωL f 1id 1 f1  q1 oq dt

(9.8)

v d 1 = e d 1 − ωL f 1iq1 − vod  vq1 = e q1 + ωL f 1id 1 − voq

(9.9)

So, defining

one has the following two scalar relationships existing between the fictitious inputs vd1 and vq1 and the current axis components

Figure 9.3  Direct and quadrature axis control loops.

Microgrid Design and Operation: Toward Smart Energy in Cities

Figure 9.4  Master controller.

210



Islanded Microgrids



did 1  v d 1 = L f 1 dt  diq1 v = L f1  q1 dt

211

(9.10)

which results in the two decoupled control loops depicted in Figure 9.5, in which two conventional PI controllers (Rd and Rq) can be used for the two axis channels. The final control scheme appears in Figure 9.6, in which again the last block represents the Cartesian to polar transformation described in (9.2). Finally, in the original work by Chen and Chu, the PDC controller implemented the following algorithm to define the current references for the slave inverters:

Figure 9.5  Direct and quadrature axis control loops.

Figure 9.6  Slave controller.

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* id ,t = iod    iq*,t = ioq   

SiWi

n −1

∑ S jW j j =1

i = 1,..., N inv − 1

SiWi

(9.11)

n −1

∑ S jW j j =1

where Ninv is the number of machines connected to the grid via a power electronics converter, Si is the ith inverter rating, and Wi is a weight factor that is set to 1 if the ith inverter is active and to 0 if it is switched off. In the off-grid mode, all the weighting factors are 1, while, in presence of the external network, some of them are 0 and suitable activating and deactivating logics were proposed. In the microgrid application, the current reference depends on the source that is behind the inverter. If, for example, one is dealing with a photovoltaic unit, such a reference comes out of the active and reactive power references dictated, respectively, by the maximum power point tracking (MPPT) algorithm and/or the higher control level requirements. Later on in this chapter, the SPM islanded configuration will be presented, consisting of two photovoltaic units and the storage system. In this case, the storage is the master and the two photovoltaics are the slaves controlled according to the MPPT algorithm. Another method was proposed by Sun et al. in 2003 [9] and is known as the average current sharing scheme. According to this approach, each inverter is treated as a master but its reference voltage comes from an external current loop fed by the error between the average current and the measurement of the inverter output current. The resulting scheme is depicted in Figure 9.7 (accounting for two inverters), where the inner voltage loops have been incorporated in the blocks G1 and G2: The reference voltage ui* of the ith inverter is given by:

ui* = u * + H (s ) (is − ii )

(9.12)

with N inv



is =

∑ ii

i =1

n



(9.13)

H being the current sharing controller transfer function, and u* being a common reference voltage. This way, if the system is stable, each individual current equals, at steady state, the average current is. In [9], conditions on H and on the transfer



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213

Figure 9.7  Average current sharing control scheme.

functions of the individual converters have been derived to meet the stability requirements. Some years later [10], the same authors reformulated the method as an optimal control problem and showed that the current sharing can be reached adopting a controller that relies only on the load current knowledge, thus requiring only one common signal line to share information on the total current. Other methods have been proposed later on in the literature; a survey on them can be found in [5]. 9.2.2  Droop-Based Control

The droop control method was first proposed by Chandorkar et al. in 1993 [11] and then was thoroughly discussed in [12, 13] among others. The droop control method idea is to mimic what happens in traditional high-voltage networks, where, due to the mainly inductive nature of the infrastructure, the active power is mostly influenced by the angular frequency (via its integral, that is, the voltage phase angle), while the reactive power mainly affects the voltage amplitude. To show this, let us consider the situation depicted in Figure 9.8, where a voltage source Ee jδ representing an inverter is connected to an infinite bus Ve j0 (chosen as phase reference) by means of impedance Ze jθ. The active power P and reactive power Q injected into the bus can be evaluated as follows:

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Microgrid Design and Operation: Toward Smart Energy in Cities

Figure 9.8  Equivalent circuit of a converter connected to the microgrid.



 VE V2 cos ( θ − δ) − cos ( θ ) P = Z Z  2 Q = VE sin θ − δ − V sin θ ( ) ( )  Z Z

(9.14)

If the line impedance can be considered as inductive (θ = 90°), then one has:



VE   P = Z sin δ  2 Q = V E cos δ − V  Z

(9.15)

and if the phase difference between the inverter voltage phasor and the network phasor is small, one can state that sinδ ≈ δ e cos δ ≈ 1, which means that:



VE   P = Z δ  Q = V (E − V )  Z

(9.16)

which suggests the possibility of controlling the active power using the phase angle (and so the frequency) and the reactive power using the voltage amplitude E. Consequently, the droop control laws are the following ones:

( ωi − ω0 ) = mi (Pi 0 − Pi ) with i = 1,..., N inv   E i − E i 0 = ni (Qi 0 − Qi )

(9.17)

with Ninv being the number of machines connected to the grid via power electronics converter, while the subscript 0 defines a specific working point (that can be either the precontingency value or the device active or reactive powerrated value).



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215

Examining the first part of (9.17), it is apparent that, at steady state, if the angular frequencies of all the machines are equal, the active power variations are inversely proportional to the coefficients mi. Moreover, if one chooses such coefficients such that:

ml P01 = m2P02 =  = mN inv P0N inv

(9.18)

ml P1 = m2P2 =  = mN inv PN inv = k

(9.19)

then:

with k being a constant. As the active power balance states that: N inv

∑ Pi = P



(9.20)

i =1

with P being the load active power, it follows that:

N inv

k

i =1

i

∑m

=P

k=

P

(9.21)

So N inv

1

i =1

i

∑m





(9.22)

N inv

1

(9.23)

i =1

i

and

P

Pi = mi

∑m

which means that it is possible to distribute the load power among the machines in the desired way without requiring them to communicate with each other. In Appendix 9A, the conditions under which, at steady state, the machine frequencies are equal are discussed for the case of two sources. The meaning of (9.17) suggests some comments:

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Microgrid Design and Operation: Toward Smart Energy in Cities

• The droop logics basically implement two scalar controllers relying on the fact that it is the physical system that basically decouples the active and reactive power dynamics. • The active power droop controller measures the deviation of the device active power production from the reference value (due to any contingency) and orders the inverter to change its frequency according to the first of (9.17). This will, in turn, change the phase angle of its phasor and, consequently, its active power production. • The reactive power droop controller measures the deviation of the device reactive power production from the reference value (due to any contingency) and orders the inverter to change the PWM inverter modulation index. This will, in turn, change the amplitude of the AC voltage according to the second part of (9.17). • This way, the idea of the droop control is the same as what happens in traditional networks, but, especially for the active power channel, the roles of angular frequency and active power are exchanged, as now the active power is the controller input and the angular frequency is its output. The overall scheme is depicted in Figure 9.9, where mai and δi are the modulating index and the phase angle of the ith inverter modulating signal. As opposed to the active load-sharing technique, the conventional droop method can be implemented with no communication links and therefore does not rely on a reliable communication system. However, it has some drawbacks, as listed here: • Since there is only one control variable for each droop characteristic (e.g., for frequency droop characteristic), it is impossible to satisfy more than one control objective. As an example, a design trade-off needs to be considered between the time constant of the control system and the

Figure 9.9  Droop control scheme.



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217

voltage and frequency regulation (speed of response versus steady state error in frequency). This issue has been discussed in ������������������� [14]��������������� , where a solution has been proposed in which the angle is calculated multiplying the integral of the angular frequency by an assigned gain Kp. This allows adjustment of the dynamic properties of the control system without affecting the droop coefficient that can then be used for the steady-state power sharing. • The conventional droop method is developed assuming highly inductive effective impedance between the voltage-souce converter (VSC) and the infinite bus. However, this assumption is challenged in microgrid applications since low-voltage transmission lines are mainly resistive. Thus, (9.16) is not valid for microgrid applications. In literature, many different solutions have been proposed for low-voltage microgrids (see [15, 16]). In the following, two of them will be presented in detail. • In case of nonlinear loads, the conventional droop method is unable to distinguish the load current harmonics from the circulating current. Moreover, the current harmonics distorts the DER output voltage (see [17, 18] for possible sharing of harmonics currents). 9.2.3  Droop-Based Control for Mainly Resistive Microgrids

In the following, two possible solutions are presented for resistive infrastructures. 9.2.3.1  VPD-FQB Method [19]

As low-voltage transmission lines are mainly resistive, one can reconsider (9.14) and set θ = 0. Assuming again that the phase shifting among the voltage phasors is small, one can write:



 VE − V 2 P ≈  Z  VE Q = − δ  Z

(9.24)

which suggests the possibility of regulating the active power through the voltage amplitude and reactive power with the phase angle (and thus the frequency). The minus sign in the second part of (9.24) implies that the reactive power control characteristics should not be a droop but a boost, as follows:

( ωi − ω0 ) = −mi (Qi 0 − Qi ) with i = 1..., N inv   E i − E i 0 = ni (Pi 0 − Pi )

(9.25)

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Microgrid Design and Operation: Toward Smart Energy in Cities

which gives the name of the control logics: VPD, voltage active power droopFQB, and frequency Q reactive power boost. 9.2.3.2  Virtual Impedance Method [20, 21]

A typical control strategy can be adopted in order to make the control system see an inductive network, while the real one cannot. This can be done with an intermediate control loop that inserts a virtual impedance. Starting from the voltage amplitude and angular frequency dictated by (9.17), one can construct a fictitious voltage E   ′i whose time-domain voltage waveform is given by:

ei′ (t ) = 2E i′ (t ) cos  δi′ (t )

(9.26)

being t

δi′ (t ) = ∫ ωi (s ) ds + δi′0



(9.27)

0

If now one measures the device current ii(t) and constructs the time-domain waveform of the real voltage Ei as: ei (t ) = ei′ (t ) − Lv



dii (t ) dt

(9.28)

with Lv being the virtual inductance, then it is apparent that, in phasor notations:

(

)

Ei′ = Ei + j ωLv Ii = V + j ωLv + Z Ii

(9.29)

Consequently, a proper choice of the virtual inductance makes the controller deal with an inductive system. The overall scheme of the virtual impedance method is depicted in Figure 9.10, where the block phasor/time-domain (PHAS/TD) converts a phasor in the corresponding time-domain function.

9.3  Secondary Control The secondary control, realized by a dedicated centralized controller, has the goal of restoring the microgrid voltage and frequency after a perturbation, compensating for the deviations with respect to the reference values that are present once the transients of the primary controllers have reached the steady state. As a consequence, its dynamics are, by design, slower than that of the primary

Islanded Microgrids

Figure 9.10  Virtual impedance method.

219

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Microgrid Design and Operation: Toward Smart Energy in Cities

controllers. This allows considering the two control loops as decoupled, thus facilitating their tuning. In Figure 9.11, a general block diagram of interaction between primary, secondary, and tertiary controls is reported. In its simplest form, the secondary control consists of simple PI controllers, both for the frequency and for the voltages. The voltage of each generator E and the frequency are compared with the corresponding reference values, Eref and ωref; the errors δE and δω are the input to the secondary controllers, whose outputs are sent to the primary controller of the generators to compensate for the frequency and voltage deviations:



( (

) )

( (

) )

 δω = K P ω ωref − ω + K I ω ∫ ωref − ω dt + ∆ωs   ref ref  δE = K PE E − E + K IE ∫ E − E dt

(9.30)

The constants KPω, KIω, KPE, and KIE are the controllers’ parameters. The additional term is considered in the frequency controller to allow the synchronization of the microgrid to the main gird: this term zero in the islanded operating mode, while, during synchronization with an external grid, it is obtained from the output of a PLL. Moreover, during the grid-tied operation, voltage and frequency of the main grid are considered as the references.� Recently, other schemes have been proposed [5], such as potential function-based optimization technique: in this method, a potential function is introduced for each DER, consisting of a scalar cost function that depends on all the information on the generator measurements, constraints, and control objectives. An optimizer calculates the set points that minimize these cost functions: when the functions are close to their minimum values, the microgrid operates close to the desired states. It should be underlined that this technique requires bidirectional communication between the generators and the optimizer; this communication is affected by delays, but these are usually acceptable, given the slower dynamics of the secondary control.

9.4  Tertiary Control In a microgrid, the tertiary control has the duty to optimally manage the available sources, computing the daily optimal scheduling of the dispatchable units. Details of the available techniques, as well as the employed models for the equipment, are discussed in Chapter 7.

Islanded Microgrids

Figure 9.11  Block diagram of the secondary and tertiary controls.

221

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Microgrid Design and Operation: Toward Smart Energy in Cities

9.5  The Smart Polygeneration Microgrid in Islanded Configuration The aim of this section is to present the results of the research conducted on the Smart Polygeneration Microgrid (SPM) in the Savona Campus of the University of Genova in islanded configuration (see Chapter 7). As far as the normal operation mode is concerned, this will be done in three steps: • Development and implementation of a simplified model of the power system that can be easily interfaced with different controllers to become a useful tool for the design of the controllers and the tuning of their parameters; • Validation of the proposed model comparing its results with the ones provided by the electromagnetic simulator called PSCAD-EMTDC [22] that allows detailed representation of all the components; • Experimental validation of the two models comparing their results with the measurements collected in an experimental campaign done on the SPM. 9.5.1  Description of the Off-Grid SPM Portion

In 2017, a portion of SPM was tested in an islanded configuration and analyzed in terms of stability and load sharing. Such a portion is represented in Figure 9.12 and consists of: • The public grid connected to switchgear (SG) Q01;

Figure 9.12  SPM islanded portion.�



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223

• Three photovoltaic (PV) units connected through a unique cable to the SG Q01, namely, PV1; • One PV unit (with its inverter and transformer) connected to SG Q02, namely PV2; • The storage unit produced by FIAMM (with DC/DC converter, inverter, and transformer) connected to SG Q02; • Resistive-inductive loads connected to SG Q02. As far as the load is concerned, it is composed by an uncontrollable (and unbalanced) portion due to the power absorption by the building in which the SPM control room is located [23] and a set of adjustable resistors. The source ratings and the main data of the network components are reported in Table 9.1. Moreover, both PV2 and the storage have a transformer, whose rated values are 80 kVA, 6%, 200/400V and 70 kVA, 4%, 400/400V, respectively. 9.5.2  The Simplified Model 9.5.2.1  The Sources

In the present section, a mathematical modeling of the involved SPM components is presented. Photovoltaic Unit Model

The photovoltaic modules are modeled as DC dipoles whose voltage-current characteristic curve (dependent on the irradiance α, the temperature T, and the voltage V) in the I-V plane is described as follows [24]:

I ( α,T ,V ) =

 1 − e c ( α,V ,T )/b  α I SC τi (T )   1000  1 − e −1/b 

with Table 9.1 Source Data PV 1 Rated Power 3 × 5 kWp Cable Resistance to Q02 157.2 mΩ Cable Inductance to Q02 3,023.7 µH

PV 2

Storage

Load

77 kWp

62 kW



20.8 mΩ 14.15 µH

43.5 mΩ 12.42 µH

181 mΩ 14.59 µH

(9.31)

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Microgrid Design and Operation: Toward Smart Energy in Cities

c ( α,V ,T ) =

V  V MAX − V MIN α − αMAX ⋅ 1 + V MAX αMAX − αMIN

  ⋅ (V MAX + τV (T ))

−1



where the meaning of symbols and data are reported in Table 9.2 (the number p of parallel modules and s of series modules is: p = 3 and s = 22 for PV1 and p = 14 and s = 24 for PV2). Moreover, τI and τv are the voltage and current temperature corrections defined as:

τ I (T ) = 1 + TC I (T − 25)

(9.32)



τv (T ) = TCV (T − 25)

(9.33)

being TCI and TCV temperature coefficients provided by photovoltaic module manufacturer. The only nontabulated variable of (9.31) is the shape factor b that can be numerically determined imposing that the photovoltaic panel described in (9.31) provides the maximum power if vPV = VMPP. Electric Storage Model

The electric storage system is a FIAMM SoNick battery (once known as ZEBRA (Zeolite Battery Research Africa or Zero Emissions Batteries Research Activity)), with rated capacity of 141 kWh, 228 Ah of nominal current capacity, rated power of 62 kW when suppling and 30 kW when absorbing. It is structured in Nm = 6 modules in parallel each one composed by the series of Nc = Table 9.2 Photovoltaic Module Parameters Short-circuit current in standard test conditions (STC) Rated external temperature

ISC TN

Temperature coefficient of the short-circuit current Temperature coefficient of the open module voltage

TCI TCV

Minimum solar radiation to supply energy Maximum solar radiation to supply energy

αmin αmax Vmin Vmax VMPP PMPP

Open voltage module at αmin Open voltage module at αmax Maximum power point voltage in STC Maximum power in STC

8.75A 25°C 0.06 −0.31 0.2 kW/m2 1 kW/m2 35V 37.11V 29.7V 240W



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225

240 cells [25]. The storage is represented by a nonideal DC voltage generator, where the produced voltage V is a function of its state of charge [26], its internal resistance Rint, and the current I injected by the storage, according to the following equation:

V ≡ V (SOC , I ) = E (SOC ) − IR int

(9.34)

where the internal voltage (E) is an unknown function of the state of change, to be deduced from measured data. The model proposed in (9.34) is justified observing Figure 9.13, where it can be noticed that discharging the battery at different (constant) currents results in a rigid translation of the curve. Under this assumption, for a fixed value of the SOC, the voltage V depends proportionally on the current. In terms of the electric equivalent, if there is a linear relationship between voltage and current, there is an internal resistance, whose value can be estimated by simply knowing two voltage-current couples. For each current I, a set of N measurements is available in Figure 9.13, formally encoded in the following

{(SOC

k ,IP

}

)

,Vk ,I p : k = 1,, N and p = 1,, N i

(9.35)

where, for our measurements, NI = 3 and I1 = 2A, I2 = 4A, and I3 = 6A. The cell resistance value can be calculated as the following average



Rcell

1 = NI N

  N i  Vk ,I p − Vk ,I q ∑ ∑  I −I q p k =1  p ,q =1  I q >I p N

Figure 9.13  V/SOC cell characteristic [26].

    

(9.36)

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Microgrid Design and Operation: Toward Smart Energy in Cities

which, for the experimental data, results in Rcell=0.028 Ω. As a consequence, the pairs (SOCk, Ecell,k) can be obtained as follows: E cell ,k = Vk ,I 1 + Rcell ⋅ I 1



(9.37)

Finally, the possible analytical expression for the link between the internal voltage and the state of change can be obtained fitting the pairs (SOCk, Ecell,k) with the following polynomial formula: 6

E cell = ∑ ai ⋅ SOC i



(9.38)

i =0

where the coefficients ai have been found with a least square method aimed at minimizing the difference between the polynomial output and the measured sequence Ecell,k. The numerical values of the coefficients are reported in Table 9.3 and the resulting curve is depicted in Figure 9.14. Then the maximum voltage of a single cell results in: E cell (SOC = 100%) = 2.66V



(9.39)

therefore, the maximum voltage of the overall storage is E (SOC = 100%) = E cell (SOC = 100%) ⋅ N c = 2.66V ⋅ 240 = 640 V (9.40) and the internal resistance is R int =



Rcell ⋅ N c Rcell ⋅ 240 = = 1.12Ω Nm 6

(9.41)

The dependence of the voltage from the temperature is neglected according to Zebra features [27]. Often, and thus also in this microgrid, between the storage and its inverter, a DC/DC bidirectional converter is interposed (which will be discussed in detail in the following). Table 9.3 Polynomial Coefficients [A] 0 [A]

a6 1.6

a5 10−11

−4.0

a4 10−9

6.0

a3 10−7

−3.3

a2 10−5

7.5

a1 10−4

5.2

10−4

a0 2.42



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227

Figure 9.14  E(SOC) cell characteristic.

Inverter Model

The aim of the inverter is to couple the DC sources to the AC grid correctly. Each inverter is modeled by the mean values’ input/output relationship [28]:

mV DC e VAC = 2 2





(9.42)

where the m is the PWM modulation index, VDC is the DC voltage, and δ is the phase angle with respect to a suitable reference (j, as usual, is the imaginary unit). Each inverter presents a filter posed at its AC terminals to attenuate highfrequency harmonics and a capacitor of 2.5 µF at the DC terminals to stabilize the voltage. Figure 9.15 and Table 9.4 present the configuration and the parameters of the filter.

Figure 9.15  Inverter AC filter configuration.��

228

Microgrid Design and Operation: Toward Smart Energy in Cities Table 9.4 AC Filter Parameters Lse 1 [mH]

Rse 0.314 [mΩ]

Lsh 0.0166 [mH]

Csh 1 [µF]

Rsh 2.61 [kΩ]

DC/DC Converter Model

To allow a proper operation of the inverter, it is necessary to keep its DC voltage as constant as possible. Thanks to the MPPT algorithm, photovoltaic inverters are controlled in a way that keeps the DC voltage almost constant, since the MPP voltage does not vary so much [29]. The storage inverters are characterized by a significant variation of the voltage on the battery; thus, they need a dedicated control of the voltage obtained by the insertion of a DC/DC bidirectional (buck-boost) chopper. The DC/DC converter has the structure depicted in Figure 9.16 [30]. The well-known chopper input/output relationship [30] is given by: V DC = K ST VST



(9.43)

with VDC and VST being the DC/DC output (inverter side) and input (battery side) voltages, respectively, while KST is related to the switch duty cycles D1 (boost) and D2 (buck) according to the following:



K ST

 1 1 − D PST ≥ 0 1 = 1  PST < 0  D2

(9.44)

with PST being the power injected by the storage into the network. Moreover, a filter consisting of a series inductor and a shunt capacitor have been inserted, whose values are 1 mH and 0.5 mF, respectively.

Figure 9.16  DC/DC converter circuital layout.



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229

Network Model

The aim of this work is to find an approximate model able to adequately represent both the transient and the steady state of any microgrid, after a contingency. To do this, the following simplifications are introduced: • Each input or output power electronics converter relationship neglects the presence of the higher-order harmonics. • The shunt sections of inverters AC filters are neglected. • The AC side portion of the microgrid is supposed to be at steady state (assuming that both the angular frequency of the sources and their voltage amplitude can vary), while all the DC dynamics are accounted. • The load is supposed to be a constant impedance. Consequently, the AC portion of the microgrid is described by means of the extended admittance matrix YE [3]. Thus, in the case of the SPM, the portion described in Figure 9.12 collapses in the one shown in Figure 9.17. The first assumption allows one to write that [30]: m (t ) VAC ,k (t ) = k V DC ,k (t ) e j δk (t ) 2 2



k = 1,2,3

(9.45)

where for the kth source mk is the modulation index (in accordance to its linear meaning [30], it lies in the range of 0 to 1.15) and δk is the angle such that:

δk (t ) = ψk (t ) + jk (t )

(9.46)

d ψk (t ) = ωk (t ) dt

(9.47)

where

while ωk and φk are the angular frequency and the phase of the kth source, respectively. The main network is an independent voltage source, whose phasor is given by:

VAC ,4 = V AC ,4e j δ4

(9.48)

Thus, the active power injected by the kth source (k = 1, 2, 3) is given by:

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Microgrid Design and Operation: Toward Smart Energy in Cities

Figure 9.17  Simplified model of the islanded portion of the SPM. 4  * * *  PAC ,k (t ) = 3Re VAC ,k IAC ,k = 3Re V AC ,k ∑ Y E ,kiV AC ,i  = i =1   cos ( δk (t ) − δi (t ))G ki   m (t )V DC ,k (t ) 3  =3 k m t V t   (9.49) ( ) ( ) ∑  i DC ,i  8 + − sin t t B δ δ ( ) ( )    ( ) i =1  k i ki  

{

+3

}

(

)

cos ( δk (t ) − δ4 (t ))G k 4   mk (t )V DC ,k (t )   V AC ,4  2 2   + sin ( δk (t ) − δ4 (t )) Bk 4  

where YE,ki = Gki + jBki is the (k, i) element of the extended admittance matrix. Moreover, for each photovoltaic capacitor, the power balance can be written as:



Islanded Microgrids

C kV DC ,k

dV DC ,k dt

231

{

* = V DC ,k I PV ,k (V DC ,k ) − 3Re VAC ,k IAC ,k

} k = 1,2

(9.50)

while the storage capacitor power balance results in:

C 3V DC ,3

dV DC ,3 dt

=

V DC ,3I ST K ST

{

}

* − 3Re VAC ,3IAC ,3

(9.51)

Furthermore, the storage device current dynamic equations are:



LST

V dI ST = E (SOC ) − R int I ST − DC ,3 dt K ST

(9.52)

d SOC −I ST = dt NCC

(9.53)

with LST and Rint appearing in Figure 9.17. Inserting (9.46) into (9.49) and then (9.49) into (9.50) and (9.51), one finally gets the system of ordinary differential equations (9.47), (9.50), (9.51), (9.52), and (9.53), that completely describes the DC dynamics, and so the complete dynamics of the microgrid under the aforementioned assumptions. A differential equations (ODE) system can be written as:

X = f ( X ,U )

(9.54)

where f collects (9.47), (9.50), (9.51), (9.52), and (9.53), while the vectors X and U are given by:

X = V DC ,1 V DC ,2 V DC ,3 I ST SOC ψ1 ψ2 ψ3  T

U = m1 m2 m3 ω1 ω2 ω3 j1 j2 j3 

T



(9.55)

The initial equilibrium point can be obtained by solving the nonlinear algebraic system, obtained by zeroing all the time derivatives in (9.54). Starting from an assigned equilibrium point, a structure perturbation (encoded in a variation in one or more elements of the extended admittance matrix) causes the dynamics. It is useful to underline that, to exclude a source from the analyzed grid, it is sufficient to cancel the related admittances, highlighting the flexibility of the proposed approach to be applied at different microgrid structures.

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Microgrid Design and Operation: Toward Smart Energy in Cities

9.5.3  Complete Model Built in PSCAD

To represent with a high degree of details all the devices present in the SPM, the electromagnetic simulator PSCAD has been chosen, also due to its user-friendly interface, with the idea to achieve a very highly detailed model, in which the inverters are built with a bridge of IGBT and modulated with a PWM technique. Moreover, the DC/DC converter is an inverter branch and modulated through a comparison between a triangle carrier and a reference. Thus, the whole harmonic spectrum is accounted both at the DC side and at the AC one, contrarily to what is done in the simplified model. 9.5.4  Experimental Campaign and Measurement Setup

To achieve the target of acquiring a complete collection of meaningful data, it was decided to use two different measurement instruments, both posed downstream of the storage transformer or at the point of interconnection with the distribution system operator. The first one is a Jupiter Power Quality Analyzer, depicted in Figure 9.18(a); its main goal is to obtain measurements in terms of mean values on a long period horizon due to its capability to show one value per second. The second one is a Fluke 190-104/S ScopeMeter, capable of showing voltages and currents waveforms in a precise way. It will be used mainly to record currents and voltages in a narrow temporal window due to its 16-µs sampling time. The Fluke 190-104/S ScopeMeter is depicted in Figure 9.18(b). The photovoltaic inverters in the SPM can only be controlled as fixed P-Q generators, while the storage is controlled to keep the voltage and the frequency as constant as possible. Consequently, the overall control system is a master-and-slave type.

Figure 9.18  (a) Jupiter Power Quality Analyzer and (b) Fluke 190-104 ScopeMeter.



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9.5.5  Validation

As specified before, the developed a simplified model is now validated comparing its results with the ones provided by the PSCAD simulation and the experimental measurements. Three cases have been reported here, characterized by initial conditions and perturbations reported in Table 9.5. In all the cases, three quantities will be monitored, namely: the storage system AC phase current, the storage system active (or reactive) power and the storage system AC output voltage. Case A

The storage active power is plotted in Figure 9.19. As could be expected, the storage absorbed power decreases to supply the increased active power load request. As far as the comparison is concerned, it is possible to highlight that the simplified model is able to capture both the transient behavior and the steadystate mean value, with some deviations in the first transient with respect to the measurements that produce a slightly slower dynamic. It should be observed that the dotted line in Figure 9.19 has been obtained starting from the phase currents and voltages measurement and applying the theory of the instantaneous power [3] as a direct power measure was not available with such a fine sampling time. The good agreement among the approaches is confirmed in the plots of the phase current (������������������������������������������������������������� Figure 9.20�������������������������������������������������� ) and the phase voltage (������������������������� Figure 9.21�������������� ). In particular, it can be noted that the AC storage voltage is practically not affected by the contingency. Table 9.5 Validation Cases Validation Case Initial Point A PV1 PV2

Perturbation Storage

Load Uncontrollable (control room) loads connected

t = 1 second, 10 kW* resistor load connection t = 1 second, PV2 disconnection

P = 9 kW, Q = 0 kVAr

P = 20 kW, Q = 0 kVAr

SOC = 60%, P = −17 kW, Q = 5 kVAr

B

P = 9 kW, Q = 0 kVAr

P = 20 kW, Q = 0 kVAr

C

P = 9 kW, P = 20 kW, Q = 0 KVAr Q = 0 kVAr

SOC = 60%, (1) Uncontrollable P = −7.4 kW, (control room) loads connected; (2) 10 kW Q = 5 kVAr load connected SOC = 60%, Uncontrollable (control P = −17 kW, room) loads connected Q = 4 kVAr

t = 1 second, QPV2 = −10 kVAr

*The controllable loads are constant impedance loads. The sentence 10-kW resistor means that three resistors have been connected that absorb 10 kW at rated voltage.

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Microgrid Design and Operation: Toward Smart Energy in Cities

Figure 9.19  Case A storage active power.��

Figure 9.20  Case A storage phase current.�

Case B is aimed at simulating a sudden decrease of the solar irradiance that claims for some power injection by the storage. Figure 9.22 depicts the active power injected by the storage that switches from the charging mode to the discharging one. From the comparison standpoint, the same considerations as before can be done in terms of transient and steady-state behavior. The good agreement among the approaches is confirmed by the plot of the current (Figure 9.23) and of the voltage (Figure 9.24); in particular, in the current waveform, some deviations appear especially in the first transient.�



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235

Figure 9.21  Case A Q02 voltage.

Figure 9.22  Case B storage active power.�

Case C aims at simulating a distribution system operator request that orders the second photovoltaic unit to absorb some reactive power that, with this control system, has to be provided by the storage. Figure 9.25 plots the reactive power waveform, highlighting the ability of the simplified model for representing well the final steady-state value. Some deviations appear in the transient behavior; in particular, the simplified model has a much faster dynamics than the other two, which can be explained recalling that one of the hypotheses of the model was to neglect all the AC portion dynamics.

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Microgrid Design and Operation: Toward Smart Energy in Cities

Figure 9.23  Case B storage phase current.�

Figure 9.24  Case B Q02 voltage.

Figures 9.26���������������������������������������������������������� and 9.27������������������������������������������������� ����������������������������������������������������� represent the phase current and voltage, respectively, highlighting a good agreement among the approaches. �



Islanded Microgrids

Figure 9.25  Case C storage reactive power.�

Figure 9.26  Case C storage phase current.�

237

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Microgrid Design and Operation: Toward Smart Energy in Cities

Figure 9.27  Case C Q02 voltage.

References [1] Navigant Research, “Microgrid Deployment Tracker 2Q13: Commercial/Industrial, Community/Utility, Institutional/Campus, Military, and Remote Microgrids: Operating, Planned, and Proposed Projects,” 2013. [2] Hooshyar, A., and R. Iravani, “Microgrid Protection,” Proceedings IEEE, Vol. 105, 2017, p. 22. [3] Marconato, R., Electric Power Systems: Dynamic Behaviour, Stability and Emergency Controls, Vol. 3, Milan, Italy: CEI, 2008. [4] Kundur, P., Power System Stability and Control: New York: McGraw-Hill, 1994. [5] Bidram, A., and A. Davoudi, “Hierarchical Structure of Microgrids Control System,” IEEE Transactions on Smart Grid, Vol. 3, 2012, pp. 1963–1976. [6] Comitato Elettrotecnico Italiano, “CEI 0-16 Standard: Reference Technical Rules for the Connection of Active and Passive Consumers to the HV and MV Electrical Networks of Distribution Company,” 2008. [7] Kawabata, T., and S. Higashino, “Parallel Operation of Voltage Source Inverters,” IEEE Transactions on Industry Applications, Vol. 24, 1988, pp. 281–287. [8] Chen, J. -F., and C. -L. Chu, “Combination Voltage-Controlled and Current-Controlled PWM Inverters for UPS Parallel Operation,” IEEE Transactions on Power Electronics, Vol. 10, 1995, pp. 547–558. [9] Sun, X., Y. -S. Lee, and D. Xu, “Modeling, Analysis, and Implementation of Parallel Multi-Inverter Systems with Instantaneous Average-Current-Sharing Scheme,” IEEE Transactions on Power Electronics, Vol. 18, 2003, pp. 844–856.



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[10] Sun, X., et al., “Design and Analysis of an Optimal Controller for Parallel Multi-Inverter Systems,” IEEE Transactions on Circuits and Systems II: Express Briefs, Vol. 53, 2006, pp. 56–61. [11] Chandorkar, M. C., D. M. Divan, and R. Adapa, “Control of Parallel Connected Inverters in Standalone ac Supply Systems,” IEEE Transactions on Industry Applications, Vol. 29, 1993, pp. 136–143. [12] Guerrero, J. M., et al., “Advanced Control Architectures for Intelligent Microgrids— Part II: Power Quality, Energy Storage, and AC/DC Microgrids,” IEEE Transactions on Industrial Electronics, Vol. 60, 2013, pp. 1263–1270. [13] Guerrero, J. M., et al., “Advanced Control Architectures for Intelligent Microgrids—Part I: Decentralized and Hierarchical Control,” IEEE Transactions on Industrial Electronics, Vol. 60, 2013, pp. 1254–1262. [14] Sao, C. K., and P. W. Lehn, “Autonomous Load Sharing of Voltage Source Converters,” IEEE Transactions on Power Delivery, Vol. 20, 2005, pp. 1009–1016. [15] Rokrok, E., and M. Golshan, “Adaptive Voltage Droop Scheme for Voltage Source Converters in an Islanded Multibus Microgrid,” IET Generation, Transmission & Distribution, Vol. 4, 2010, pp. 562–578. [16] Guerrero, J. M., et al., “Hierarchical Control of Droop-Controlled AC and DC Microgrids—A General Approach Toward Standardization,” IEEE Transactions on Industrial Electronics, Vol. 58, 2011, pp. 158–172. [17] Borup, U., F. Blaabjerg, and P. N. Enjeti, “Sharing of Nonlinear Load in ParallelConnected Three-Phase Converters,” IEEE Transactions on Industry Applications, Vol. 37, 2001, pp. 1817–1823. [18] Marwali, M. N., J. -W. Jung, and A. Keyhani, “Control of Distributed Generation Systems-Part II: Load Sharing Control,” IEEE Transactions on Power Electronics, Vol. 19, 2004, pp. 1551–1561. [19] Guerrero, J. M., et al., “Decentralized Control for Parallel Operation of Distributed Generation Inverters Using Resistive Output Impedance,” IEEE Transactions on Industrial Electronics, Vol. 54, 2007, pp. 994–1004. [20] Guerrero, J. M., et al., “Output Impedance Design of Parallel-Connected UPS Inverters with Wireless Load-Sharing Control,” IEEE Transactions on Industrial Electronics, Vol. 52, 2005, pp. 1126–1135. [21] Yao, W., et al., “Design and Analysis of the Droop Control Method for Parallel Inverters Considering the Impact of the Complex Impedance on the Power Sharing,” IEEE Transactions on Industrial Electronics, Vol. 58, 2011, pp. 576–588. [22] PSCAD-EMTDC version 4.5, The Professional’s Tool for Electromagnetic Transients Simulation, Manitoba HVDC Research Centre Inc., Manitoba, Canada. [23] Bonfiglio, A., et al., “The Smart Polygeneration Microgrid Test-Bed Facility of Genoa University,” 2012 47th International Universities Power Engineering Conference (UPEC), 2012, pp. 1–6. [24] Böke, U., “A Simple Model of Photovoltaic Module Electric Characteristics,” European Conference on Power Electronics and Applications, 2007.

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[25] FIAMM, SoNick ST523, 2015, fiamm.com/media/20150209-st523_datasheet.pdf. [26] Rexed, I., M. Behm, and G. Lindbergh, “Modelling of ZEBRA Batteries,” Royal Institute of Technology, 2010. [27] Sudworth, J. L., “Zebra Batteries,” Journal of Power Sources, Vol. 51, 1994, pp. 105–114. [28] Mohan, N., and T. M. Undeland, Power Electronics: Converters, Applications, and Design: New York: John Wiley & Sons, 2007. [29] Seyedmahmoudian, M., et al., “Analytical Modeling of Partially Shaded Photovoltaic Systems,” Energies, Vol. 6, 2013, p. 128. [30] Erickson, R., and D. Maksimovic, Fundamentals of Power Electronics, 2nd ed.: Boston, MA: Kluwer Academic, 2004.

Appendix 9A: Conditions for Reaching the Same Angular Frequency with the Droop-Based Control Method Let us consider a network at rated angular frequency ωn, with NG independent voltage sources with fixed amplitude and angular frequencies ωi (i = 1, ..., NG) in per unit on the basis ωn, and with NL linear loads (described in principle as a combination of resistances, capacitances, and inductances). Let us describe the system in the Park reference frame with the angle θ defined as: t



θ = ωn ∫ ω0dt + θ0

(9A.1)

0

where ω0 is a real positive number and θ0 is the initial angle. Neglecting the electrical transients (time derivatives associated with inductances and capacitances are zero), one is able to describe the network with the classic admittance matrix Y: T



I 1 , I 2 ,, I N ,I N +1 ,, I N + N  G G G L  

T

= Y V1 ,V 2 ,,V N G ,V N G +1 ,,V N G + N L 



(9A.2)

where Vi (I i ) for i = 1, …, NG are the Park phasors of generators bus voltages (injected currents), and Vi (I i ) for i = NG + 1, …, NG + NL are the Park phasors of load bus voltages (injected currents). Moreover, due to the linearity of the loads, it is possible to write the following relation:



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241

I i = −Y LiVi , ∀i = N G + 1,, N L + N G



(9A.3)

where Y Li is the admittance of the ith load. So, it is possible to write the admittance matrix Y with the following block structure:  YGG Y=  YLG



YGL  YLL 

(9A.4)

where YGG is the matrix that combines the generators currents and voltages; YLG combines the loads currents with the generators voltages; YGL represents the relation between generators currents and loads voltages; and finally, YLL links load currents and voltages. Thus, combining (9A.2) and (9A.3), one has: T

T

− YL V N G +1 ,V N G + N L  = I N G +1 , I N G + N L  T

T

T

      I N G +1 , I N G + N L  = YLG V1 ,V N G  + Y LL V N G +1 ,V N G + N L  T

T

− {YLL + YL } V N G +1 ,V N G + N L  = YLG V1 ,V N G  T

(9A.5)

T

V N +1 ,V N + N  = − {YLL + YL }−1 YLG V1 ,V N  G L  G   G  T

{

}

T

I 1 , I N  = YGG − YGL {YLL + YL }−1 YLG V1 ,V N  G  G    T

T

I 1 , I N  = YE V1 ,V N  G  G   

where the matrix YE provides a linear relationship between the generators injected currents their bus voltages. Consequently, the real power of the generic source i is defined as:

{ }

Pi = Re Vi I i

*

*   N G   Pi = Re Vi  ∑ YE ,ik Vk       k =1 

(9A.6)

where Vi = Vi e j δi and the generic element YE,ik of the matrix YE is defined as

YE ,ik = G E ,ik + jBE ,ik i ∈1,, N G and k ∈1,, N G Therefore, the active power can be written as:

(9A.7)

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Microgrid Design and Operation: Toward Smart Energy in Cities NG   Pi = Re Vi e j δi ∑ (G E ,ik − jBE ,ik )Vk e − j δk  k =1  



NG



(9A.8)

= Vi ∑Vk (G E ,ik cos ( δi − δk ) +BE ,ik sin ( δi − δk )) k =1

Now, from the Park transformation, one can define for any i = 1, …, NG δi′ ≡



d δi = ( ωi (t ) − ω0 ) ωn dt

(9A.9)

The droop control law imposed by the inverters controller states that for any i = 1, …, NG ωi − ω0 = mi (Pi − Pi 0 )



(9A.10)

where mi is a negative coefficient that represents the slope of the droop characteristic of the ith inverter and Pi0 is the rated active power of the machine. It is useful to recall that ωi can be considered the instantaneous frequency of the ith inverter (i.e., no control system delay time is accounted for). So, from (9A.8), one gets the following system of NG ordinary differential equations NG     δi′= ωnmi Pi 0 − Vi ∑Vk G E ,ik cos( δi − δk ) + BE ,ik sin( δi − δk )     (9A.11) k =1  δi (0) = δi 0 

To show the effectiveness of the droop control method, one has to show that:

lim δi′ (t ) = ω = k ∀i = 1,, N G t →∞



(9A.12)

that is, each frequency approaches the same (constant) value. If NG = 2, it is possible to write the previous ordinary differential equation (ODE) system as

 δ − δ ′ t = f δ − δ ( ( 1 2) 2) ( )  1 ( δ1 − δ2 ) (0) = δ10 − δ20

(9A.13)



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243

where

f ( δ1 − δ2 ) = A + B cos ( δ1 − δ2 ) + C sin ( δ1 − δ2 )

(9A.14)

in which

(



)

(

)

A = ωn m1 Pn1 − V12G E ,11 − m2 Pn 2 − V 22G E ,22    B = ωnV1V 2G E ,12 (m2 − m1 )

(9A.15)

C = − ωnV1V 2BE ,12 (m2 + m1 ) Observing that (9A.13) is an ODE system that depends only on the difference δ1 – δ2, it is possible to rewrite it in the simpler way once introduced the new variable y = δ1 – δ2 and the constant value y0 = δ10 – δ20 as  y ′ (t ) = f ( y )   y (0 ) = y 0



(9A.16)

Being, f ∈ C(∞) (R) (9A.14) is a Cauchy problem that satisfies the existence and uniqueness of the solution. So, if f (y0) = 0, then y(t) = y0 (constant solution) is the (unique) solution, that is, δ1(t) – δ2(t) = δ10 – δ20 for any value of t; therefore, the two frequencies are equal all through the transient. Otherwise, if f (y0) ≠ 0, then, due to continuity of f, one can assume that exists Ω > 0 such that f (y) > 0 (or f (y) < 0) for any y ∈ I = (y0 – Ω, y0 + Ω); therefore, one gets: t





0

t y ′ (s ) ds = ∫ ds f ( y (s )) 0

(9A.17)

that (applying the change of variable theorem) becomes y (t )





y0

ds =t f (s )

(9A.18)

The left side is an integral monotonic function (f has constant signum in I), that, for the sake of simplicity, can be denoted as:

H ( y (t )) =

y (t )



y0

ds f (s )

(9A.19)

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so that: y (t ) = H −1 (t )



(9A.20)

The domain of the function H could be computed observing the zeros (if exist) of f. Due to the definition of f in (9A.14), one can conclude that: If there exists y ∈  such that f  (y ) = 0, then it is a zero of order 1; hence, the integral function H does not converge in that point; moreover, due to periodicity of f, indicating with y1 , y 2 ∈R , y1 < y 2 the zeros of f closer to y0, then the domain of H is ( y1 , y 2 ) ⊇ I . So it is possible to conclude that lim H ( y ) = −∞and lim− H ( y ) = +∞ (if f is negative in I, then lim H ( y ) = +∞ +

y → y1+

y → y2

y → y1

and lim− H ( y ) = −∞ ). This means that lim y (t ) = lim H −1(t ) = y 2 (or y → y2

t →+∞

t →+∞

lim y (t ) = lim H −1(t ) = y1 if f is negative in I. In this case, δ1(t) – δ2(t) be-

t →+∞

t →+∞

comes constant for t enough great and so ω1(t) = ω2(t) for any t after in which the angle difference is constant. Moreover, (9A.11) allows one to conclude that lim δi′(t ) = K for i =1, 2.

t →∞

If f (y) ≠ 0 for any y ∈ R, then the domain of the function H is I. Moreover, f is bounded (from (9.69) it is a linear combination of sine and cosine functions), this means that H grows up; hence, lim y (t ) = lim H −1(t ) = +∞ t →+∞

t →+∞

(or lim y (t ) = lim H −1(t ) = −∞ if f is negative in I), and so the angle differt →+∞

t →+∞

ence will grow up indefinitely. In this case ω1(t) ≠ ω2(t) for any t. The fact that all the frequencies are equal at steady state leads to know how the two inverters share the active power, using (9A.10).

10 Commercial Tools for the Management of Microgrids 10.1  Overview An extensive literature exists on the comparison of commercial and research tools developed over the years for the planning or sizing and optimal siting of decentralized resources, as well as for screening new projects for new installations, comparing different scenarios, or computing the optimal generation mix for a district, a city, or a region. The analysis of different tools and methods can be made according to several aspects, such as: • The scale of the considered initiatives with which the tool can deal (nationwide, regional, district, single project); • The goals of the underlining optimization procedures, such as the minimization of operational and installation costs, the minimization of carbon dioxide emissions, the increase in reliability, or the improvement of the stability in islanded operation; • Whether the addressed problem is the siting of new resources or the addition of new resources in existing locations; • The possibility to take into account technical, environmental, and regulatory constraints;

245

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Microgrid Design and Operation: Toward Smart Energy in Cities

• The possibility to deal with uncertainties (forecast of renewable production, variation in time of the electrical and thermal demands, fuel prices, energy costs from the network). A detailed comparison of more than 30 tools (most of them developed by universities or research organizations) was performed in [1], with the aim to provide useful information for the identification of a suitable energy tool to analyze the integration of renewable energy into various energy systems under different objectives. A similar goal was pursued in [2], where 13 tools were compared with reference to the application to urban areas. An extended description of available models and software tools can be found also on the Energyplan.eu web site [3]. Of these tools, some are intended for large-scale applications (e.g., finding the optimal energy mix for a large area or a nation), so they are not suitable for the planning or assessment of a single microgrid project. Others that were specifically developed for microgrid sizing or were aimed at the detailed analysis of a single project can be used also for this application; examples are HOMER [4], DER-CAM [5], and EnergyPlus [6]. Different from the previously mentioned literature, in this chapter, attention is focused on commercial tools specifically developed for microgrid management. Generally speaking, the software tools that will be described in the following comply with the generic layout of an energy management system outlined in Chapter 7, usually formulating the optimal scheduling problem as a linear, or mixed integer linear, or mixed integer-nonlinear programming problem and employing various solutions for the forecast of energy demands and renewable production, needed as input data. Some of the tools have been developed to be integrated in a specific supervision and automation framework, with a given supervisory control and data acquisition (SCADA) system and specific controllers; others are conceived as stand-alone programs, able to be interfaced with a variety of control and supervision subsystems or even cloudbased applications offered following the software as a service paradigm. In the following, their main characteristics will be outlined. This list is by no means intended to be exhaustive; rather, it aims at giving an overview of the most typical features of the products available on the market by briefly describing some of them, which cover quite a large variety of solutions and applications.

10.2  VERA (Honeywell) Honeywell VERA (Versatile Energy Resource Allocation) [7] is energy management software that closely follows the general centralized energy management



Commercial Tools for the Management of Microgrids

247

system layout outlined in Chapter 7. It consists in a software solution that is focused on the optimization of microgrids with renewable generation, storage, various forms of cooling, heating, as well as conventional power generation, such as combined heat and power plants. It was developed primarily for microgrid systems feeding buildings, campuses, military bases, hospitals and residential neighborhoods. VERA is able to perform the optimal scheduling (unit commitment and economic dispatch) of dispatchable sources, using demand and renewable production forecasts as an input. Problems such as fuel switching and optimal utilization of the capacity of storage devices are also addressed. The goal of underlying optimization task is the reduction of operating costs, considering the energy balances and technical constraints (satisfaction of all energy demands, equipment capacity curves), as outlined in Chapter 7. The cost function takes into account the time-dependent costs of purchased energy and fuels, penalties for emissions, earnings from selling energy, start-up and shutdown costs of equipment, and fixed costs of operation. The nonlinear optimization problem is solved via a Sequential Quadratic Programming (SQP) solver. As far as the load forecasting is concerned, VERA employs a regression model built by using data from an historical database. The model correlates the energy consumption with a number of independent influencing factors such as weather conditions, seasonal effects, time of the day, day of the week, holidays, and special day indicators. VERA operates as an off-line optimizer, which has to be interfaced with process control and real-time optimization solutions [8] that directly control the field level.

10.3  DEMS (Siemens) DEMS (Decentralized Energy Management System) [9, 10] is an energy management solution created by Siemens to manage virtual power plants and microgrids. The product was developed using Siemens WinCC (Windows Control Center) as a basic SCADA engine and the user interface was realized with the WinCC interface builder. In other words, DEMS can be considered as composed of a number of scripts, graphical interfaces, and other components, integrated in a WinCC-based SCADA. DEMS provides functions for both off-line optimal scheduling of the available resources and real-time online optimization. At the off-line stage, the optimal dispatch is performed, based on weather and demand forecast. In the management (real-time) stage, the program controls whether the prescribed profiles for power generations are met and, if necessary, takes corrective actions.

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Microgrid Design and Operation: Toward Smart Energy in Cities

DEMS also follows the typical structure of a centralized energy management system; it is organized in a number of functions; the most important of them are briefly described in the following. The weather forecast function is based both on input from a weather service and on historical data collected by the system by means of a local weather station. The availability of this latter data is exploited to refine the information provided by the weather service, usually intended as mean values on quite a wide area and thus affected by a certain degree of error if used to obtain local conditions on a 15-minute time scale, for example. The weather forecast is one input for other scheduling functions. The load forecast is essentially based on weather, historical measures of thermal and electrical demands, typical consumptions according to the day of the week, and information about holidays. The time behavior of the demand is expressed as a piecewise linear function of a number of selected influencing variables, like day types (working or not working), and weather variables (such as temperature). The coefficients for this representation are recursively estimated, based on the available historical data of demand and influencing variables, with a mathematical method that employs a Kalman filter. The generation forecast function calculates the expected production of the renewable energy sources, based on the forecasted weather conditions. The forecast algorithm is based on a piecewise linear function (specified as a lookup table), which expresses the power generated by the renewable source as a function of two weather variables (e.g., wind speed and direction for wind power units, radiation, and ambient temperature for photovoltaic systems). The function can be set according to the technical specifications of each source (e.g., photovoltaic field). An off-line tool is provided to refine the forecast parameters based on the actual measurements collected over time. The Unit Commitment function calculates the optimal dispatch schedules for all flexible units. These latter include: • Contracts (i.e., unidirectional or bidirectional electrical energy exchange with the grid and natural gas fed by the local gas utility, characterized by given prices that can be variable over time, for example, taking into account peak and off-peak hours, as in the case of electrical energy, and, eventually, constraints on maximum fluxes); • Dispatchable generation units, such as generators, characterized by the given relation between output power (thermal for boilers, electrical for electrical generators) and fuel consumptions, and cogenerators, characterized by given curves for electrical energy versus fuel consumptions and thermal energy versus electrical energy; furthermore, rated data,



Commercial Tools for the Management of Microgrids

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such as maximum value and maximum gradient for the generated power have to be specified; • Storages (both thermal and electrical, modeled taking into account their rated capacity and charge/discharge efficiencies); • Flexible demands (if any). The scheduling considers a time period of 1 to 7 days, with a time step of 15, 30, or 60 minutes. It can run at prescribed times, for instance, every day to compute the scheduling for the next day, or can be initiated manually, or can be triggered automatically, in case some conditions happen that require recalculating the generation schedules. The underlining application algorithms were developed with SIEMENS ECANSE (Environment for Computer Aided Neural Software Engineering). The optimization is formulated as a mixed integer linear programming problem; software named CPLEX is used as a basic engine for its solution. The online optimization and coordination function monitors the power exchange with the network and, if the actual value differs from the scheduled one, dispatches correction values to all controllable units. These correction values are calculated with an algorithm that takes into account the actual unit constraints and operates trying to minimize the correction cost: this is achieved by subdividing the total power correction over the available units in inverse proportion to their incremental power costs at the current operating point. DEMS makes use of Microsoft Windows software components; in particular, it uses standard Microsoft Excel spreadsheets for input and output of time series.

10.4  DER-CAM (Berkeley Lab) The Distributed Energy Resources Customer Adoption Model (DER-CAM) [11, 12] is defined as “an economic and environmental model of customer DER adoption,” which has been in development at Berkeley Lab since 2000. As an analysis and design tool, its first objective is to find the most effective configuration of distributed generation technologies that a specific customer can install and what the appropriate size is of the installed capacity for each one of these technologies that minimizes costs. In addition to this application, which is referred to as investment and planning, DER-CAM can also be used as an energy management system: this second application is called operations. In this second case, the program

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determines the optimal week-ahead scheduling for the installed equipment, taking into account the forecasted behavior for the loads, the weather conditions, and the energy tariffs. The scheduling is performed with a time step from 1 minute to 1 hour; existing load information and weather forecasts are used to forecast demands and renewable production. Different from the previous example, DEMS, which is integrated in a SCADA system, DER-CAM works as an independent program, so its results must be transmitted to a third-party building management system or SCADA. From a mathematical viewpoint, DER-CAM formulates the optimization problem as a mixed integer linear problem; this means that all of the employed model and the objective function must be linear (possibly obtained by linearizing nonlinear models). Models for a number of devices are included in the program (combustion engines, fuel cells, microturbines, combined heat and power, photovoltaic, solar thermal panels, wind turbines, energy storage, stationary storage, electric vehicles, heat storage, and cooling storage). An OpenADR communication architecture is provided to manage demand response.

10.5  Microgrid Plus (ABB) The Microgrid Plus control system by ABB [13, 14] slightly differs from the previous examples, since its architecture is closer to the distributed control paradigm, rather than to the centralized one. The system is based on the MGC600 series of controllers. Each source or load (or group of loads) is equipped with a given controller type: MGC600G, MGC600P, MGC600W, and MGC600H for diesel generators (or similar equipment, such as gas turbines), photovoltaic fields inverters, wind turbines, and hydro plants, respectively, MGC600F for protection relays, MGC600L for large loads (crushers, boilers), MGC600E for storage devices like flywheels and batteries, and, finally, MGC600N for monitoring and controlling the interface to other microgrids or larger grids. Each controller publishes information about the power generated or absorbed by the device it supervises, as well as information about the generation or load type, its status and its availability for the network. In this way, these data are made available to all the other controllers in the network. The optimization of the system is carried out based on this information and on rules coded in each controller. No master controller is present; in addition, methods are provided to automatically reconfigure the control system when a new component is added.



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The optimization is performed with the aim of achieving goals such as the maximization of the average renewable penetration and the minimization of fuel consumption; constraints related to availability and maintenance of the assets, as well as to reserve margins, are taken into account. Voltage and frequency regulations can be performed for the islanding-mode operation. The system employs the Optimax PowerFit tool [15] to compute an optimal dispatch based on load and renewable generation forecasts. This tool uses the OpenModelica environment, compliant to the Modelica multiphysics modeling language, which can be used to model complex dynamic systems. The optimal dispatch is in this case reformulated as a nonlinear programming problem (rather than a linear programming one). The main applications include islanded microgrids (defense sites, remote communities, research centers, or industries), as well as grid-connected microgrids in commercial and industrial complexes and campuses.

10.6  EcoStruxure Microgrid Advisor (Schneider) Schneider EcoStruxure Microgrid Advisor (EMA) [16] has been developed in the context of EcoStruxure solution [17] for the management of complex infrastructures. According to its developers, EcoStruxure follows a system-of-systems approach, trying to combine expertise from different domains (such as power management, building management, building security) in a single architecture to control, supervise, and manage all the infrastructures of an enterprise. To achieve this goal, EcoStruxure gathers energy-related data from different subsystems, including power monitoring and process control, metering, substation automation, and process automation. EMA is offered as a cloud-based service, following the software as a service paradigm. It consists of an engine, based on predictive and learning algorithms, and a user interface. The platform includes on-site hardware for secure communication and algorithms for up to 3 days of off-line decentralized energy resource optimization. Real-time data from sources such as photovoltaic solar, building heating, ventilation, and air conditioning, electric vehicle charging, battery storage, cogeneration, and backup generation are collected via a web interface. As in the previous examples, the operation of the infrastructure (consumptions, productions, and energy storage) is scheduled to minimize operating costs. Demand response actions can be managed via the Open ADR 2.0 protocol. EcoStruxure Microgrid Advisor is in operation at the Schneider Electric’s Boston One Campus (BOC) and Research Center.

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10.7  Micro Energy Management System (TOSHIBA) The Micro Energy Management System (μEMS) [18, 19] by TOSHIBA is a software tool for the scheduling and real-time operation of microgrids with electrical and thermal sources. It is organized in a number of functions: • μSCH: Overall energy supply and demand planning (every 30 minutes, 24 hours per day). This function performs the tasks of managing heat and electricity, forecasting photovoltaic output and demand, and scheduling the production accordingly. • μELD: Aimed at performing an economic dispatch every minute, with a time horizon of 30 minutes. • μLFC: Controls the frequency in real time (every second), using the batteries installed in the microgrid. • μDAS: Provides fault recovery network reconfiguration capabilities. As far as its architecture is concerned, the solution consists of two main components: a server on which the central application runs, performing the real-time control and visualization of the network, and a set of distributed controllers for monitoring and controlling the field devices. The μEMS requires a front to connect to the field and acquire the realtime measurements. To perform this task, a product by Landis+Gyr can be used, the S650 Smart Grid Terminal, which provides remote terminal unit-like functionality for controlling the actuators on the grid. The system has been demonstrated in a number of test sites. An example is a substation in Rome (belonging to the Italian utility ACEA SpA), which feeds an electric vehicle charging station, with a 10-kW PV array and a 45kWh energy storage system. The goal of the demonstration is to stabilize power grid fluctuations, by controlling and balancing the photovoltaic generation and energy storage using the μEMS, while also providing a stable power supply for the electric vehicle charging station. Another example is the microgrid on the remote Japanese island of Miyako, where the μEMS is used to improve the stability and the quality of electricity power supply in a system characterized by a high penetration of renewable sources.

10.8  Grid IQ Microgrid Control System (GE) The Grid IQ Microgrid Control System (MCS) by GE [20, 21] is built around the Multilin U90Plus Generation Optimizer, which provides the supervisory and control architecture. The other components of the system are all the intelligent



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electronic devices controlling the generators, the gateways, a human machine interface, and a communication network. This product is intended for providing a simple solution for microgrids with fossil fuel-based DERs (including combined heat and power for mixed electrical and thermal systems), renewable sources, and energy storage that can be operated both in grid-connected and islanding modes. In addition to performing the optimal dispatch of the available sources, it provides the voltage and frequency regulation function necessary for islanding operation. Voltage/ reactive power regulation with network losses minimization can be performed. Typical applications of this solution are off-grid remote communities, military bases, and mining communities. The U90Plus integrates the functions of daily load forecast, generation forecast, and optimal dispatch, gathering field data and computing the set point for the sources, each of them controlled by a local intelligent electronic device installed near the generator. The communication system between the U90Plus and the local intelligent electronic devices is based on Modbus TCP/IP; it can employ either Ethernet connections or an industrial wireless network, depending on constraints concerning the infrastructure costs and the distances among the equipment. The U90Plus minimizes the amount of information to be transmitted by optimizing the requirements in terms of data needed for each load or generation location. The D400 controller, part of the MCS system, can act as a gateway to communicate with the utility system (i.e., the control and supervision system of the main grid to which the microgrid is connected) by means of the standard DNP 3.0 communication protocol.

References [1] Connolly, D., et al., “A Review of Computer Tools for Analysing the Integration of Renewable Energy into Various Energy Systems,” Applied Energy, Vol. 87, No. 4, 2010, pp. 1059–1082. [2] van Beuzekom, I., M. Gibescu, and J. G. Slootweg, “A Review of Multi-Energy System Planning and Optimization Tools for Sustainable Urban Development,” 2015 IEEE Eindhoven PowerTech, Eindhoven, 2015, pp. 1–7. [3] http://www.energyplan.eu/othertools/. [4] https://www.homerenergy.com/. [5] https://building-microgrid.lbl.gov/projects/der-cam. [6] Karagevrekis, A., et al., “Interconnected Microgrids: An Energyplus Simulation Test Case,” Machines Review, Vol. 1, 2014, pp. 7–13. [7] Stluka, P., D. Godbole, and T. Samad, “Energy Management for Buildings and Microgrids,” 2011 50th IEEE Conference on Decision and Control and European Control Conference, Orlando, FL, 2011, pp. 5150–5157.

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[8] Vohryzek, J., “Process Data and Advanced Control Systems for Industrial Energy Management and Real-Time Energy Optimization,” WEC Central & Eastern Europe Regional Energy Forum - FOREN 2016, Costinesti, Romania, June 12–16, 2016. [9] Werner, T. G., and R. Remberg, “Technical, Economical and Regulatory Aspects of Virtual Power Plants,” 2008 Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies, Nanjing, 2008, pp. 2427–2433. [10] Rideout, B., T. Sahin, and D. Shereck, “Implementation of a Virtual Power Plant: The Integrated Load Management System,” The 2014 2nd International Conference on Systems and Informatics (ICSAI 2014), Shanghai, 2014, pp. 192–196. [11] Marnay, C., et al., “Optimal Technology Selection and Operation of Microgrids in Commercial Buildings,” 2007 IEEE Power Engineering Society General Meeting, Tampa, FL, 2007, pp. 1–7. [12] https://building-microgrid.lbl.gov/projects/der-camBB. [13] Jansen, M., “Microgrid Plus: A Networked, Easy to Apply Power Flow Control System for Stable Integration of Intermittent Renewable Generation into Microgrids,” 2013 Renewable Energy World Asia Conference and Exhibition, Bangkok, Thailand, October 2–4, 2013 [14] http://new.abb.com/microgrids/our-offering/microgrid-plus-system. [15] Franke, R., “Mathematical optimization of dynamic systems with OpenModelica,” 7th Annual OpenModelica Workshop, Linkoping University, Linkoping, Sweden, February 2, 2015. [16] https://www.schneider-electric.com/en/work/solutions/microgrids/. [17] Mora, D., M. Taisch, and A. W. Colombo, “Towards an Energy Management System of Systems: An Industrial Case Study,” IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society, Montreal, QC, 2012, pp. 5811–5816. [18] http://www.toshiba-tds.com/tandd/technologies/smartgrid/en/ems.htm. [19] http://eu.landisgyr.com/blog/landisgyr-and-toshiba-launched-microems-solution. [20] https://www.cse-uniserve.com.au/pdf/GE_Digital_Energy_%20Grid%20IQ_ Microgrid_Control_System_Product_Brochure%20.pdf. [21] https://www.gegridsolutions.com/multilin/catalog/mcs.htm.�

11 From Design to On-Field Installation: A Practical Case Study 11.1  Overview This chapter is focused on the sustainable energy infrastructures present at the Savona Campus of the University of Genoa in Italy. In particular, the Smart Polygeneration Microgrid and the Smart Energy Building projects are described in order to show technical features and operating procedures of a real test-bed facility, representing an example of the sustainable energy district according to the paradigm of the smart city.�

11.2  Introduction The Savona Campus of the University of Genoa in Italy, developed over an area of about 60,000 m2, is 3 km from the Savona city center (Figure 11.1) [1, 2]. It hosts research laboratories of the university, research centers, and small and medium sized enterprises (SMEs), as well as the Centro Internazionale in Monitoraggio Ambientale (CIMA Research Foundation) National Centre for Civil Protection on hydrogeological risk [3]. Approximately 2,000 students attend lessons at the Campus, where different courses relating to the Polytechnic School, the Medicine and Surgery School, and the Social Sciences School are offered. The Savona Campus is also provided with a library, a cafeteria, sports facilities, student residences, and other services. The research activities in the 255

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Figure 11.1  The location of the Savona Campus.

sustainable energy sector represent one of the main aspects that characterize this academic compound. The project of transforming the old Bligny Military Compound, dating back to the 1930s, to a university campus was first conceived in 1990, as a joint initiative of the University of Genoa and local stakeholders (Municipality of Savona, Savona Province, the Savona’s Chamber of Commerce, Industry, Agriculture, and Artisanship, and the local chapter of the General Confederation of Italian Industry). In 1992, the urban regeneration activities began and a special-purpose company was created by the stakeholders to manage the Campus. The research and teaching activities began shortly after, with the first university courses. Since 2007, the Campus has hosted the CIMA Research Foundation, an important research center, active on the international level on the topics of natural environment, protection against environmental risks, and risk management. In the aforesaid context, the University of Genoa (UNIGE) developed the Energia 2020 project, an important research and development project related to the concepts of sustainable energy (renewable energy, energy saving, and reduction of carbon dioxide emissions) and smart city (Figure 11.2) [4]. The Energia 2020 project is managed by an ad hoc-created UNIGE technical and administrative structure, called Savona Campus Service Center (CENS), to which is also assigned the scientific and operative management of the Campus [2]. Research activities are performed by UNIGE faculty staff working on: electrical networks and power systems modeling and control; integration of renewables and storage systems into the power delivery system; planning, design,



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Figure 11.2  The Energia 2020 project.

and management of smart microgrids; simulation and optimization of energy systems; and power systems engineering and economics. The Energia 2020 project, which was developed due to a full public financing, has been conceived to install, within the Savona Campus, innovative energy systems aimed at reducing operating costs and carbon dioxide emissions and, at the same time, creating a comfortable working environment for the Campus users. Due to the aforesaid initiatives, as reported in Figure 11.3, since 2016, the University of Genoa has been a member of the International Sustainable Campus Network (ISCN) [5]. The Energia 2020 project consists of three different subprojects: Smart Polygeneration Microgrid, Smart Energy Building, and Energy Efficiency Measures (EEM). In particular, the Smart Polygeneration Microgrid is relative to the construction of a smart microgrid to provide both electrical and thermal energy to the Savona Campus. The Smart Polygeneration Microgrid was inaugurated at the beginning of February 2014, and in 2015 the European Electricity Grid Initiative (EEGI) label was assigned to it [6]. The Smart Polygeneration Microgrid was funded by the Italian Ministry for Education, University and Research with 2.4 million euros [7]. The Smart Energy Building deals with the construction of a sustainable smart building connected to the Smart Polygeneration Microgrid as a prosumer. The Smart Energy Building was unveiled in December 2017. It was funded by the Italian Ministry of the Environment and Protection of Land and Sea with 2.7 million euros [8]. Finally, the EEM project is relative to some interventions necessary to upgrade some existing buildings of the Savona Campus to increase their energy performance and guarantee the safety of users. The EEM has been funded by the Liguria Region with 1.5 million euros [9]. In the following, attention is focused on Smart Polygeneration Microgrid and Smart Energy Building projects, since they concern sustainable energy and microgrid concepts.

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Figure 11.3  The Savona Campus history.

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11.3  The Smart Polygeneration Microgrid The Smart Polygeneration Microgrid is a three-phase, low-voltage (400V line-to-line) intelligent distribution system, coupled with a thermal network, composed of electrical/thermal loads and generation units [10–16]. The Smart Polygeneration Microgrid electrical grid topology, as shown in Figure 11.4, is that of a ring (600m long) with one main switchgear (QGEN) and four other switchgears (Q01, Q02, Q03, Q04) to which the power plants and the loads are connected; in 2017, a fifth switchgear (Q05), that of the Smart Energy Building, was added. A dedicated medium voltage/low voltage (MV/LV) transformer (indicated by T2 in Figure 11.5), linked to the MV busbar of the Savona Campus MV/LV substation (indicated by Bus #MV in Figure 11.5), connects the Smart Polygeneration Microgrid to the 15-kV distribution network. All the buildings (named canteen, Marchi, Lagorio, library, Branca, Locatelli, Delfino, residences), except the Smart Energy Building, are not directly connected to the Smart Polygeneration Microgrid���������������������������������������������� : in this way, the Smart ��������������������������� Polygeneration Microgrid can be easily disconnected from the other systems without compromising the electricity supply to the buildings. 11.3.1  The Smart Polygeneration Microgrid Power Plants

The following power plants compose the Smart Polygeneration Microgrid: • No. 3 cogeneration microturbines (no. 2 Capstone C65 and no. 1 Capstone C30) fed by natural gas; • No. 2 gas boilers fed by natural gas; • No. 2 absorption chillers; • No. 2 photovoltaic fields; • No. 3 concentrating solar power systems; • No. 2 electrical storage (ES) systems (sodium/nickel chloride and lithium-ion batteries); • No. 3 electric vehicle (EV) charging stations. In Figure 11.6, the location of the aforesaid power plants inside the Savona Campus is reported; moreover, the pipelines reported on the map on the right indicate the heat distribution network fed by the microturbines and the boilers. As reported in Figure 11.7, the Smart Polygeneration Microgrid can be split into different subsystems:

Figure 11.4  The Smart Polygeneration Microgrid single-line wiring diagram.

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Figure 11.5  The Savona Campus single-line wiring diagram.



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Figure 11.6  The Smart Polygeneration Microgrid power plants (map on the left) and the heat distribution network (map on the right).

• The electrical network to which the following plants and infrastructures are connected: the PV fields, the microturbines, the concentrating solar power systems, the storage batteries, and the EV charging stations; • The thermal network that provides thermal energy (as hot water), produced by microturbines and boilers, to the building’s heating system; • The cooling network that provides cooling energy (as chilled water), produced by absorption chillers fed by microturbines, to some buildings (namely the library and the Delfino building); • The domestic hot water circuit for the residence building fed by concentrating solar power systems and some solar thermal collectors.



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Figure 11.7  The Smart Polygeneration Microgrid energy infrastructure (PV: photovoltaic fields, ES: electrical storage systems; EV: electric vehicle charging stations; CSP: concentrating solar power systems; µGT: microturbines; AC: absorption chiller; and B: boilers).

11.3.1.1  The Microturbines

The microturbines are fed by natural gas and operate in the cogeneration mode typically from November until April: electricity is injected into the grid of the Smart Polygeneration Microgrid, whereas exhaust gas exiting the machines is used to heat water for heating purposes. However, during the summer, the microturbines operate in connection with the absorption chillers, thus providing both electricity and cooling energy. The microturbines usually remain off in May and in October. The main rated data of the installed microturbines are shown here [17]: • Capstone C30: • Electrical power = 28 kW; • Thermal power = 54 kW; • Electrical efficiency = 25%; • Thermal efficiency = 48%. • Capstone C65 (Figure 11.8): • Electrical power = 65 kW; • Thermal power = 112 kW; • Electrical efficiency = 29%; • Thermal efficiency = 50%.

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Figure 11.8  The C65 microturbines of the Smart Polygeneration Microgrid.

11.3.1.2  The Boilers

Two traditional gas boilers are installed in the Smart Polygeneration Microgrid (Figure 11.9). Each one is characterized by a rated thermal power equal to 450 kW. Since the boilers are not cogeneration units, it is convenient to use them only when the microturbines are not sufficient to completely satisfy the thermal demand of the Campus. If in operation, the boilers produce hot water that is first mixed with that produced by microturbines and then conveyed to the heat distribution network of the Campus. 11.3.1.3  The Absorption Chillers

Regarding air conditioning systems, the majority of the Campus buildings are equipped with traditional compression chillers, arranged on the roofs, except the library and Delfino buildings, which are cooled by two absorption chillers

Figure 11.9  One of the boilers of the Smart Polygeneration Microgrid.



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(Figure 11.10) fed by the two Capstone C65 microturbines. The ���������������� main characteristics of the aforesaid chillers are: • Absorption chiller Carrier Sanyo TSA-16LJ-01E-LC model for the library (Figure 11.10(a)) [18]: • Technology: single-effect water/lithium bromide absorption chiller; • Thermal power input = 105 kW (hot water temperatures: 95°C inlet, 85°C outlet); • Cooling power output = 70 kW (chilled water temperatures: 12°C inlet, 7°C outlet); • Coefficient of performance = 0.67. • Absorption chiller Systema PKSYHH150 model for Delfino building (Figure 11.10(b)) [19]: • Technology: double-effect water/lithium bromide absorption chiller; • Thermal power input = 130 kW (hot water temperatures: 145°C inlet, 135°C outlet); • Cooling power output = 150 kW (chilled water temperatures: 12°C inlet, 7°C outlet);

Figure 11.10  The absorption chillers: (a) Library chiller, (b) Delfino building chiller.

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• Coefficient of performance = 1.15. 11.3.1.4  The Photovoltaic Fields

The two photovoltaic (PV) fields of the Smart Polygeneration Microgrid are characterized by the following main rated data: • PV1 field (Figure 11.11): • Technology: polycrystalline silicon modules; • Peak power: 80.64 kW; • Number of modules: 336 (manufactured by Ferrania Solis, 240W each) [20]; • Installation: roof-mounted (on the Delfino building), azimuth = −30°, tilt angle = 15°. • PV2 field: • Technology: polycrystalline silicon modules; • Peak power: 15 kW; • Number of modules: 60 (manufactured by Ferrania Solis, 250W each); • Installation: roof-mounted (on the Delfino building), azimuth = −30°, tilt angle = 15°. 11.3.1.5  The Concentrating Solar Power Systems

The three concentrating solar power systems are cogeneration plants which produce, at rated conditions, 1 kW of electrical power and 3 kW of thermal power each (Figure 11.12). The thermal power is used to produce domestic hot water for the residence building. Each concentrating solar power system is equipped with a free-piston Stirling engine having helium as working fluid. Each concentrating solar power system is characterized by rated electrical and thermal efficiency values, respectively, equal to 14% and 41%.

Figure 11.11  The PV1 field.



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Figure 11.12  The concentrating solar power system plants.

11.3.1.6  The Electrical Storage Systems

In the Smart Polygeneration Microgrid, two different electrical storage technologies are installed: lithium-ion batteries (Hitachi Chemical [21]) supplied by Loccioni [22] and sodium nickel (SoNick) batteries by FZSoNick (Figure 11.13(a)) [23]. The Hitachi electrical storage system is composed of 15 batteries (CH756 type) connected in series and having a total capacity of 25 kWh. A maximum power (AC side) of 70 kW can be charged or discharged into or from the storage system. However, the FZSoNick electrical storage system is composed of 6 batteries (ST523 SoNick type) connected in parallel and having a total capacity of 141 kWh. A maximum power (AC side) of about 36 kW can be charged or discharged into or from the storage system. The arrangement of SoNick batteries inside the container and the power electronic equipment supplied by Nidec [24] are, respectively, shown in Figure 11.13(b, c). 11.3.1.7  The E-Mobility

The e-mobility infrastructure (Figure 11.14) of the Savona Campus consists of: • Four charging stations: • Two Enel pole stations (equipped with Scame and Mennekes sockets) characterized by a maximum charging power of 22 kW [25]; • Two Enel V2G station (equipped with a CHAdeMO socket) characterized by a maximum charging power of 10 kW and a maximum discharging power of 9 kVA. • Two full-electric vehicles:

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Figure 11.13  The electrical storage systems: (a) Whole container, (b) batteries, (c) Nidec inverter and other power conversion equipment.

• One Renault Twizy (battery capacity: 6.1 kWh, driving range: 80 km, average consumption: 87 Wh/km, maximum speed: 80 km/h) [26];



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Figure 11.14  The e-mobility.

• One Nissan Leaf Tekna (battery capacity: 40 kWh, driving range: 389 km considering Worldwide Harmonized Light Vehicles Test Procedure (WLTP) urban driving cycle, average consumption: 102 Wh/ km, maximum speed: 144 km/h) [27]. • Two electric bikes. 11.3.2  The Smart Polygeneration Microgrid ICT Infrastructure

The Smart Polygeneration Microgrid is controlled and managed by a three-level system (Figure 11.15) composed of: • Field data acquisition and local automation devices; • A Supervisory Control And Data Acquisition (SCADA) system; • An energy management system. The SCADA systems (SIMATIC WinCC [28] and DESIGO [29] by Siemens) and the energy management system (DEMS by Siemens [30]) are installed inside two identical servers located in the control room (Figure 11.16) where technicians monitor all the equipment installed in the Smart Polygeneration Microgrid. In particular, generation units and loads are constantly monitored and the collected data (relative to electrical and thermal measurements) are used by the energy management system and also by researchers to assess the technical performance of power plants. The operators access the monitoring system through the workstations of the control room by means of a web interface made available by the WinCC environment of the SCADA system. The collected data are stored into a network-attached storage and a dedicated uninterruptible power supply guarantees the supply for the control room [15]. As shown in Figure 11.15, the communication system consists of two switches installed in the control room (Figure 11.16) and one switch installed in each switchgear of the Smart Polygeneration Microgrid. The aforesaid switches, compliant with the IEC 61850 protocol, are connected via a double-fiber optic

Figure 11.15  The Smart Polygeneration Microgrid ICT infrastructure.

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Figure 11.16  The control room of the Smart Polygeneration Microgrid.

ring, thus obtaining a dual counterrotating ring configuration with one fiber transmitting in one direction and the other fiber transmitting in the opposite one [15]. Two sets of power meters are installed on the Smart Polygeneration Microgrid: nonfiscal power meters for data acquisition and supervision, and fiscal energy meters, installed to measure the energy produced by each generation unit and the energy absorbed by the loads connected to each busbar of the Smart Polygeneration Microgrid. It is important to note that some devices directly communicate using the IEC 61850 protocol, while other ones need the adoption of remote terminal units to convert a modbus signal into an IEC 61850 one. 11.3.3  The Energy Management System

The Smart Polygeneration Microgrid is daily operated by an energy management system with the main aim of minimizing operating costs that are given by the sum of the natural gas expense (to feed microturbines and boilers) and of the electricity expense (due to the electricity withdrawn from the public grid). The energy management system acts as the intelligence of the energy infrastructure and interacts with both the hardware in the field (with different communication protocols) and a variety of software tools for simulation and control. The energy management system uses as input: • Cost functions (natural gas price, electricity price, plant maintenance cost, fees for the electricity local production); • Technical and environmental constraints (related to the performance of power plants: microturbine primary and thermal power as a function of generated electrical power, behavior of microturbines and boilers at

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partial loads, minimum and maximum values of power generated by the different plants); • The Savona Campus electrical and thermal load forecast (estimated on the basis of weather forecast and historical data); • Estimation of power production from renewable sources based on weather forecast and historical data. The goal of the energy management system is that of scheduling the operation of fossil-fueled power plants (microturbines and boilers) and electrical storage systems with the aim of minimizing daily operating costs and/or carbon dioxide emissions. The energy management system’s objective function can also take into account other issues such as the minimization of the energy withdrawn from the public grid or the maximization of the self-consumption. The optimization algorithm is characterized by a time horizon of 24 hours, with 15-minute time intervals. It is also important to mention the flexibility of the energy management system in real time: basing on the dispatched plan, any deviation that occurs during operation concerning with the energy exchange with the public grid is shared at minimum cost among generators, storage systems, and loads, which can be controlled, so that the planned value can be met. For instance, if the photovoltaic production is lower than the expected one, the lack of energy is compensated by increasing the power delivered by the energy storage systems or by the microturbines. As an example, in Figure 11.17 the recorded electrical load profiles of a winter nonworking day (February 22, 2015) are plotted, highlighting how the different power plants have been managed by the energy management system. The electrical load attains maximum values (around 150 kW) at midday, whereas the base load of 100 kW (almost constant during the entire year) occurs during the night. The power plants are managed as follows: • The two Capstone C65 microturbines mainly operate from the early morning to the evening. • The contribution of concentrating solar power system plants is limited, and thus it is not easy to distinguish it in Figure 11.17. • The photovoltaic production is higher during the central hours of the day. • Electricity is withdrawn from the public grid during the night and when one of the two Capstone C65 microturbines is off. • The sodium/nickel chloride storage system is charged in hours when the Smart Polygeneration Microgrid production exceeds the load, whereas

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Figure 11.17  Savona Campus electrical energy profiles for a winter nonworking day.

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it is discharged during the night to limit the electricity withdrawn from the public grid. The Savona Campus thermal load shown in Figure 11.18, is covered by the two Capstone C65 microturbines and one of the two boilers is used only at the beginning of the morning, due to the load peak. It is important to point out that the weather forecast is based both on inputs from a weather service and on historical data collected by the system by means of a local weather station, while the load forecast is essentially based on weather, historical measures of thermal and electrical demands, typical consumptions according to the day of the week, and information about holidays.

11.4  The Smart Energy Building The Smart Energy Building is an environmentally sustainable building connected to the Smart Polygeneration Microgrid as a prosumer, equipped with renewable power plants and characterized by energy efficiency measures [12, 13]. In Figure 11.19 some photos of the building are shown. The building has two floors, it is characterized by a floor area of 510 m2 (for each floor), and it is 10m high. It is made of reinforced concrete with Predalles prestressed slabs and the ground floor elevation has been adopted to prevent flood water entering the building; �������������������������������� high performance thermal insulation materials for building applications (ventilated facades and claddings) and acoustic insulation systems are applied. The building is certified as A4, which is the most efficient energy class in accordance with Italian Classification of Building Energy Efficiency [31]. Moreover, the Smart Energy Building is a zero emission building since its electrical loads are satisfied by a photovoltaic field and the storage systems of the Smart Polygeneration Microgrid, while its

Figure 11.18  Savona Campus thermal energy profiles for a winter nonworking day.



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Figure 11.19  The Smart Energy Building.

thermal and cooling loads are covered by a geothermal heat pump and solar thermal collectors. The Smart Energy Building hosts three laboratories, a gym, two classrooms, and some offices. The following technologies are installed in the Smart Energy Building (Figure 11.20): • A geothermal heat pump; • Solar thermal collectors; • An air-handling unit;

Figure 11.20  The Smart Energy Building energy infrastructure.

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• A domestic hot water heat pump; • A photovoltaic field; • Extremely low-consumption light-emitting diode (LED) lamps; • A rainwater harvesting system (Figure 11.21(a)); • A hydroponic system (Figure 11.21(b)). 11.4.1  The Smart Energy Building Thermal System

The main devices that constitute the thermal system of the Smart Energy Building are: the geothermal heat pump, the air source heat pump for domestic hot water production, the solar thermal collectors, and the air-handling unit.

Figure 11.21  (a) The rainwater harvesting system and (b) the hydroponic system.



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The geothermal heat pump, also called the ground source heat pump, is a system used to heat and/or cool a building transferring heat from or to the ground (characterized by a nearly constant temperature during the year), respectively during winter or summer months. Consequently, ground-source heat pumps are more efficient than air-source heat pumps because they take advantage of the relatively constant ground temperature. Heat exchangers for specific applications with ground water or geothermal closed loops can be adopted in ground-source heat pump systems. In open loop systems, well or surface body water is used as the heat exchange fluid that circulates directly through the heat pump system; however, closed systems consist of underground continuous piping loops filled with a water and antifreeze solution which constitutes the working fluid that is used to transfer heat from or to the ground to or from the geothermal heat pump, this last being typically installed inside the building. Closed loop systems can be: horizontal, vertical, or a pond or lake. Horizontal loops are a cost-effective solution for residential installations, in particular for new buildings where sufficient land is available; vertical loops are preferable for commercial buildings and schools, whereas pond/lake systems are installed when an adequate body of water is available. The ground source heat pump plant installed in the ������������������� Smart Energy Building is characterized by a close-loop vertical configuration. Eight borehole heat exchangers are buried about 120m deep in the soil. The geothermal heat pump is a Clivet WSHN-XEE2 MF 14.2 having a rated thermal/cooling power equal to 46/44.3 kW and a coefficient of performance at full load equal to 4.4 [32]. The dimensions of the heat pump are 900 × 1,700 × 1,870 mm and the weight is about 400 kg. In Figure 11.22, the scheme of the thermal system that provides the Smart Energy Building�������������������������������������������������������������� heating and ������������������������������������������������� domestic hot water������������������������������� services is reported. The geothermal heat pump produces hot water that is used both for heating and domes������ tic hot water purposes. Regarding the heating system, the hot water produced by the geothermal heat pump is conveyed to an inertial tank (having a volume of 500 liters) and to the collector that feeds the heating circuit (composed of the pipeline network and several two-pipe fan-coils and radiators). The hot water exiting the geothermal heat pump is also used to produce domestic hot water. Indeed, the geothermal heat pump is hydraulically connected to the domestic hot water storage tank (having a volume of 500 liters) that is equipped with other two heat exchangers, respectively, connected to the domestic hot water heat pump and to the solar thermal collectors installed on the roof of the building. Consequently, three different devices (geothermal heat pump, domestic hot water heat pump, and solar thermal collectors) are used to produce domes������ tic hot water. In particular, the domestic hot water is preferably produced using the solar source that is free. When the solar source is not available or is very low, the geothermal heat pump and/or the air-source heat pump are used. In

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Figure 11.22  The Smart Energy Building thermal system.

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particular, when the building cooling system is on (from the beginning of June to the end of September), the geothermal heat pump contributes to the domes������ tic hot water production by conveying to the domestic hot water storage tank a part of the water flow coming from the building cooling circuit. However, when the building heating system is on (from the beginning of November to the middle of April), the geothermal heat pump contributes to the domestic hot water production by conveying to the domestic hot water storage tank a part of its hot water flow production. In the remaining months, when the building needs neither to be heated nor to be cooled, the domestic hot water is produced by the solar thermal collectors and the air-source heat pump, since the geothermal heat pump remains off. The domestic hot water heat pump is a Clivet WSAN-XIN 51 PRM characterized by a rated thermal power of 11.5 kW and a coefficient of performance equal to 3.4 [32]. The dimensions of the heat pump are 445 × 1,087 × 1,230 mm and the weight is about 170 kg. The two vacuum collectors (Kloben Sky Pro 10 Advanced type) of the forced circulation solar system have a total surface of 3.84 m2 [33]. As mentioned earlier, the Smart Energy Building is also equipped with an air-handling unit, installed on the roof, which performs functions such as circulating, cleaning, and humidifying of air within the building; the air-handling unit, a Clivet Zephir3 CPAN-XHE3, is a complete packaged primary air supply system with thermodynamic energy recovery [32]. The dimensions of the air-handling unit are 2,465 × 1,735 × 1,810 mm, and the weight is about 1,070 kg. The air-handling unit is characterized by a rated thermal power of 21 kW and a rated cooling power of 38.7 kW; its maximum absorbable power is 35.3 kW. In Figure 11.23, some photos of the geothermal heat pump plant are reported, while in Figure 11.24 the air-handling unit plant is shown. However, the solar thermal collectors are shown in Figure 11.25. 11.4.2  The Smart Energy Building Electrical System

The Smart Energy Building is directly connected to the Smart Polygeneration Microgrid through a dedicated switchgear (called Q05 in Figure 11.4). The main electrical loads of the Smart Energy Building are represented by: • The LED lamps (no. 111 LED 20W each for rooms, no. 10 LED 50W each for the labs, no. 8 LED 60W each installed outside); • The auxiliary systems of electrical/thermal power plants; • The gym equipment.

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Figure 11.23  The geothermal heat pump plant.

Figure 11.24  The air-handling unit plant.

Figure 11.25  The solar thermal collectors.

As shown in Figure 11.26, it is interesting to mention that in the gym there are elliptical machines and bikes [34] electrically equipped in order to transform the human energy of people working out into electricity for the Smart Polygeneration Microgrid.



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Figure 11.26  The gym.

As far as electrical production is concerned, the photovoltaic field installed on the Smart Energy Building roof is characterized by a rated power equal to 21.25 kW. It is composed of 85 polycrystalline modules (Futura Sun FU250P type [35]) installed with 0° tilt and −30° azimuth; the photovoltaic field is visible in Figure 11.19. With regard to electric mobility, an Enel V2G station (equipped with a CHAdeMO socket), characterized by a maximum charging power of 10 kW and a maximum discharging power of 9 kVA, is installed inside the ��������� Smart Energy Building [25]. 11.4.3  The Smart Energy Building ICT Infrastructure

As shown in Figure 11.27, the Smart Energy Building is managed by a building management system that interacts with the energy management system of the Smart Polygeneration Microgrid [29]; as a consequence, thermal/electrical generation units and loads of the building are monitored and managed in order to reduce the whole energy expense and the carbon footprint. Furthermore, different indoor comfort levels can be set by the building management system to control the energy demand of the building. Regarding research activities, the main goal is that of operating the Smart Energy Building in island mode (i.e., disconnected from the public grid), using as electricity sources the photovoltaic fields (its own and those of the Smart Polygeneration Microgrid) coupled with storage systems. Indeed, experimental activities are ongoing in order to shift the Smart Polygeneration Microgrid and Smart Energy Building infrastructures from the grid-connected mode of operation to the energy-island one. It is worth mentioning that, in 2017, the Enel company [36] decided to develop at the Savona Campus, jointly with the University of Genoa, the Living Lab Microgrid, an open-air lab devoted to testing

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Figure 11.27  Smart City interaction between EMS and BMS.

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and making visible and tangible to public and private stakeholders energy and ICT technologies for the future smart cities [37, 38].

11.5  Smart Polygeneration Microgrid and Smart Energy Building: Building Timelines and Main Challenges Figures 11.28 and 11.29 outline the main phases, from the development of the initial idea to the final design and construction of Smart ��������������������������� Polygeneration Microgrid and Smart Energy Building. The conceptual design of both the Smart Polygeneration Microgrid and the Smart Energy Building was first devised by the research team of the University of Genoa, which is now operating and performing research activities on them. The concept of the Smart Polygeneration Microgrid was awarded with a grant by the Italian Ministry of Education and Research [7], while the Smart Energy Building was submitted to the Italian Ministry of the Environment and Protection of Land and Sea [8], in the context of a special call aimed at promoting energy savings and advanced building technologies in the public sector;

Figure 11.28  Smart Polygeneration Microgrid construction timeline.

Figure 11.29  Smart Energy Building construction timeline.

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also, in this latter case, the concept won the required grant (90% of the total amount), together with a funding from the University of Genoa (the remaining 10%) [1]. These two special research infrastructures are now run directly by a dedicated university team and are at the core of several projects, both at Italian and European levels, involving the University of Genoa [1] and the Savona Municipality [39], for which the Campus represents the starting point in the evolution towards the smart city concept. Different challenges had to be faced both for the Smart Polygeneration Microgrid������������������������������������������������������������������ and the Smart Energy Building. First, the management of the projects and of the related tenders resulted absolutely far-from-easy tasks, in terms of size and complexity, for the university bureaucratic staff, which put a great effort in operating towards their construction. The bureaucratic issues were solved with a constant relation between the research team and the university administrative personnel. Technical issues during the construction phase and first operation of the Smart Polygeneration Microgrid were solved thanks to a joined activity between the research team and experts of the company in charge of the construction and installation works. As far as the Smart Polygeneration Microgrid is concerned, regulatory issues arose, related to the connection of the microgrid to the local distribution system operator network, due to the exceptionality of the project (different kinds of generators connected to the same point of common coupling), which hardly fitted with the standard cases on which the connection regulations were modeled; furthermore, another difficult aspect was the presence of storage devices, not yet clearly regulated by the Italian authority at that time. These problems were solved by means of a special agreement between the university and the Italian distribution system operator e-distribuzione [40], which recognized the importance of the research activity and, since then, has been cooperating with it in a number of projects. For the Smart Energy Building, a close interaction with the municipality was required, in order to overcome issues concerning limitations due to flooding risks (related to the presence of a nearby stream), which demanded that the positioning of the Smart Energy Building on the Campus to be carefully chosen. Also the development of the interface between the Smart Energy Building’s building management system and the ��������������������������������������������� Smart Polygeneration Microgrid��������������� energy management system was a very complex task since no commercial products on the market were available.

11.6  Conclusions In this chapter, the description of the Smart Polygeneration Microgrid and the Smart Energy Building projects of the University of Genoa, in Italy, has been reported with the aim of showing how the concept of microgrid and, more



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generally, sustainability can be really applied in a public site such as a university campus. In particular, the strong interaction among cogeneration, renewable sources, and storage systems has been highlighted and the importance of ICT infrastructures and energy management systems has been underlined. Furthermore, challenges and criticalities encountered during the aforesaid projects have been also reported to provide a comprehensive view of the activities performed.

References [1] https://www.unige.it/en/. [2] http://www.cens.unige.it/en/. [3] http://www.cimafoundation.org/. [4] http://www.energia2020.unige.it/en/home/. [5] https://www.international-sustainable-campus-network.org/. [6] http://www.gridplusstorage.eu/eegi-documents. [7] http://www.miur.gov.it/. [8] http://www.minambiente.it/. [9] https://www.regione.liguria.it/. [10] Bracco, S., and F. Delfino, “A Mathematical Model for the Dynamic Simulation of Low Size Cogeneration Gas Turbines Within Smart Microgrids,” Energy, Vol. 119, 2017, pp. 710−723. [11] Bracco, S., et al., “The University of Genoa Smart Polygeneration Microgrid Test-Bed Facility: The Overall System, the Technologies and the Research Challenges,” Renewable and Sustainable Energy Reviews, Vol. 18, 2013, pp. 442−459. [12] Bracco, S., et al., “A Pilot Facility for Analysis and Simulation of Smart Microgrids Feeding Smart Buildings,” Renewable and Sustainable Energy Reviews, Vol. 58, 2016, pp. 1247−1255. [13] Bracco, S., et al., “The Smart City Energy Infrastructures at the Savona Campus of the University of Genoa,” Proc. of AEIT 2016 - International Annual Conference: A Sustainable Development in the Mediterranean Area, Energy and ICT Networks of the Future, Capri (Naples), Italy, October 5−7, 2016. [14] Bracco, S., et al., “Smart Microgrids in Smart Campuses with Electric Vehicles and Storage Systems: Analysis of Possible Operating Scenarios,” Proc. of IEEE 2nd International Smart Cities Conference: Improving the Citizens Quality of Life, ISC2 2016, Trento, Italy, September 12−15, 2016. [15] Barillari, L., et al., “The Smart Microgrid Pilot Project of the University of Genoa: Power and Communication Architectures,” Proc. of AEIT 2013 - Annual Conference: Innovation and Scientific and Technical Culture for Development, Palermo, Italy, October 3−5, 2013.

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[16] Bracco, S., and F. Delfino, “The Role of High Efficiency Trigeneration Plants Within Sustainable Smart Microgrids: Performance Analysis and Experimental Tests,” Proc. of AEIT 2015 - International Annual Conference: A Sustainable Development in the Mediterranean Area, Naples, Italy, October 14−16, 2015. [17] https://www.capstoneturbine.com/. [18] https://www.carrier.com/. [19] https://www.systema.it/. [20] http://www.ferraniasolis.com/. [21] http://www.hitachi-chem.co.jp/english/index.html. [22] http://www.loccioni.com/. [23] http://www.fzsonick.com/it/. [24] http://www.nidec.com/en-Global/. [25] https://www.esmartlife.it/mobilita-elettrica/. [26] https://www.renault.fr/vehicules/vehicules-electriques/twizy.html. [27] https://www.nissan-global.com/EN/index.html. [28] http://w3.siemens.com/mcms/human-machine-interface/en/visualization-software/ scada/pages/default.aspx. [29] https://www.siemens.com/global/en/home/products/buildings/automation/desigo.html. [30] http://w5.siemens.com/italy/web/ic/sg/ea/applicazioni/gestionedimicrogridevirtual power plant/pages/dems.aspx. [31] https://www.iea.org/beep/italy/codes/decree-for-energy-efficiency-requirements-inbuildings-2015.html. [32] https://www.clivet.com/. [33] http://en.kloben.it/. [34] https://www.technogym.com/. [35] http://www.futurasun.com/it/. [36] https://www.enel.com/. [37] https://corporate.enel.it/it/storie/a/2017/12/smart-city-futuro-living-lab-savona. [38] Bracco, S., et al., “Controllo e gestione di microreti il Living Lab Microgrid,” AEIT Journal, March-April 2018, pp. 6–15. [39] http://www.comune.savona.it/. [40] https://www.e-distribuzione.it/it/homepage.html.

12 From Microgrids to Smart Cities 12.1  Overview This book has until now addressed and explored many technical and operational issues concerning the concept of microgrid, focusing the attention on the new opportunities related to the employment of such a network architecture in the presence of consolidated and marketable technologies to exploit renewable energy sources and underlining the role of ICT as an enabling factor of the future evolution of the energy system. Why should microgrids increase their potential application value, moving from the classical uses (in remote areas with limited grid access or in the presence of weak distribution networks) to a scenario of significant urban deployment? This question has a twofold answer. First, one should take into account the emerging business opportunities for the utilities in terms of exploiting digitalization directly related to the mainstream of the energy chain [1, 2], especially concerning the aggregation of distributed generation (such as wind, photovoltaic (PV), biogas, biomass, small hydro, combined heat and power (CHP)) and its combination with a flexible demand and with energy storage systems [3–5]. Second, a more widespread awareness at the population level on sustainability issues results in a strong push towards the use of renewables and the implementation of energy-saving/energy-efficiency procedures in civil, industrial, commercial, and transportation contexts. Undoubtedly, both these factors are projected to have a positive impact over the next 5 to 8 years in the growth of the global microgrid market, which

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is expected to reach $17.5 billion in U.S. dollars by 2025, increasing at a compound annual growth rate (CAGR) of 17% [6]. Let us examine in detail how the digital utility transformation and the new platforms for the aggregate management of generators and loads can facilitate microgrid penetration.

12.2  The Digital Utility Transformation The world of production, distribution, and use of electricity has been significantly changed, both in the technical and operations management and in the choice of the most profitable business sectors, by the massive adoption of two digital technological evolutions, strictly linked to each other, namely, the use of Internet of Things (IoT) devices, such as smart meters, intelligent protections, and energy controllers, and the management of Big Data, directly coming from IoT exploitation and extremely interesting for customer profiling aims, which can turn into new competitive benefits. It is worth adding that the interaction environment for these two actors is typically the “cloud,” and the communication channels, from TCP/IP to mobile, play a crucial role in the global efficiency of the data processing and their protection. Digitalization reflects on new opportunities for the today’s utilities in the following key action areas: • Distributed generators (DG) management; • Intelligent and predictive maintenance; • Network control and automation; • Smart and connected buildings (SBs); • Demand response (DR); • Electricity market business models; • Digital billing; • Electric mobility; • Customer interaction and related energy-efficiency services; • Workforce productivity and safety. From a quick look at the list above, one can observe that the smart microgrid concept bundles together all the items, with one important peculiarity: the microgrid is an entity, involving generators and loads, totally controllable by the distribution system operator (DSO), which is more at ease interacting with the microgrid control center instead of providing specific signals to each DER



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connected to its networks [7]. In addition, the microgrid can operate both gridtied and in islanded mode and can manage directly DR programs on behalf of the DSO, with economic benefits for all its connected customers (earnings from flexibility in electricity consumptions and reduction in the global operation costs of the network due to the exploitation of such flexibility). Therefore, trying to give a mid-term to long-term vision (5 to 10 years), it is not so idealistic to conceive the future Smart City MV distribution network made of integrated smart microgrids, including SBs right inside, and separate DER (Figure 12.1), with the digital DSO playing the role of the technical and commercial coordinator of the whole system, negotiating with smart microgrids and DER the ancillary services useful for the safe and reliable operation of the power infrastructure and potentially reselling the same services (e.g., active power reserve) to the national transmission system operator (TSO). According to an urban development planning perspective, it is clear that the smart microgrids will refer to specific redevelopment operations on underused or decaying urban areas or to the redesign of city centers [8–10] from a traditional urban core, very often overcrowded, to a more environmentally sustainable and comfortable space, where the public community can work and spend free time, experiencing a healthy lifestyle. In light of these considerations, the integrated smart microgrids will be related to well-delimited industrial, commercial, financial, educational, and sport districts or to the first experiences of smart city areas, where new and

Figure 12.1  Possible evolution of the electric distribution system according to the smart grid and smart microgrid paradigms.

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eco-friendly SBs are surrounded by green areas technologically equipped for people connectivity and safety and can benefit from the presence of electric mobility infrastructures. It should be noticed at this point that the model sketched in Figure 12.1 could be further enhanced by introducing the concept of virtual microgrid or local virtual power plant (VPP) [11], which can be thought as a cluster of DER and loads geographically close to each other and connected to the public distribution network. The DSO can virtually (software-based) group these electricity market actors, by configuring a suitable energy controller, which can be used to exploit again ancillary services (like voltage support, congestion resolution, frequency regulation) but, this time, without resorting to the islanding operation.

12.3  VPP, Aggregation of DERs, and Demand-Side Management We have stated many times throughout this book that microgrids allow one to better exploit the flexibility of dispersed generation and loads by clustering them under a coordinated management. This idea is further extended with the concepts of VPPs, mentioned in the previous section, and aggregators. In [12], a VPP was introduced by proposing an approach “whereby DER (including responsive loads) are aggregated into controllable virtual power plants (VPP). When aggregated, these groups of DER would have system visibility, controllability and impact similar to a transmission-connected generator.” A similar definition was proposed in [13], where a VPP is considered as an aggregation of DG units, controllable loads, and storage devices connected to a certain cluster in a single entity responsible for managing the electrical energy flows within the cluster and their exchange with the main network (Figure 12.2). As far as the aggregator is concerned, this has been defined in [14] as a subject that at a higher level “interacts with the ISO (Independent System Operator) by participating in both day-ahead and real-time markets, whereas at a lower level [...] interacts with energy consumers by dispatching loads according to pre-established bilateral contracts”; while this definition refers to flexible demand only, a more general one is given in [15], where the aggregator is identified as a subject that “bundles flexibility capabilities of distributed generation (DG) and demand response (DR) and offers the collective resources for smart grid services such as ancillary, e.g. congestion management, as well as active electricity market participation” (Figure 12.3). From these definitions, it is apparent that the two concepts overlap, with differences possibly being related to the facts that, first, VPP put the emphasis on generation (but flexible demand is also considered), while the aggregator emphasizes demand response (but generators are included too), and, second, that when referring to a VPP, the focus is frequently more concentrated on



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Figure 12.2  Decentralized resources in a VPP.

Figure 12.3  Aggregator interactions and participation to energy and balancing markets.

technical and management aspects, while when speaking about the aggregator, the attention is more centered on trading mechanisms on energy and balancing markets. Despite the differences in the proposed definitions, three main points can be identified that represent the key advantages for small or medium energy resources (both generations and flexible demand units), dispersed on a wide area, to join an aggregator or VPP: • The possibility of participating in wholesale energy markets;

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• The possibility of participating in balancing markets and in ancillary services provision with independent system operators (TSO in the first case and DSO in the second one) as a counterpart; • The possibility of taking advantage of optimal management services (e.g., generation scheduling, load shifting) offered by the aggregator or VPP. The third point is conceptually analogous to the advantage brought by an energy management system in a microgrid, except that it can involve hundreds or thousands of resources spread across a region or a country. Nevertheless, some of the concepts discussed in this book (generation forecast, load forecast, formalization and solution of an optimal scheduling problem) still apply, at a larger scale, while the communication infrastructure has to include the interface with the market platforms and the communication equipment with the customers’ installations, usually based on programmable controllers and simple units to establish an encrypted link based on General Packet Radio Service (GPRS), Universal Mobile Telecommunications System (UMTS), or similar technologies. Instead, the first two points are related to the possibility of overcoming the size barrier (i.e., the regulatory constraints on rated power and power regulation capabilities) that historically prevented small generation units (and customers characterized by small demand) from directly participating to wholesale energy and balancing markets: for instance, in Italy, before the introduction of a new regulation concerning aggregation, currently in an experimental phase, only generation units with rated power greater or equal to 10 MVA were considered eligible for the participation in balancing markets [16]. The removal of the size barrier through aggregation offers two major advantages. First, from a system-wide perspective, it unleashes a whole new set of resources, for which a dire need exists to solve the increasingly difficult problems of balancing a growing demand with a more unpredictable and nondispatchable generation and of avoiding the associated network congestions. Second, from a user perspective, it makes available a new stream of revenues for the single user or prosumer, which originates from the aggregator or VPP trading its flexibility. Concerning the first aspect, according to [17], in Germany, the costs for curtailing renewable generation rose from €43.7 million in 2013 to €82.7 million in 2014, due to the need to resolve network congestions caused by the high wind power generation in the northern regions to be delivered to the southern industrial regions. Furthermore, the high availability of low-price renewable energy in some periods makes traditional power plants unprofitable for long periods, while at the same time their regulation capability is highly stressed by the necessity to compensate frequent power fluctuations: in this respect, the



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Figure 12.4  Microgrids and the future smart cities.

new flexibility offered by aggregated decentralized resources becomes essential to progressively replace the traditional reserve and regulation capacities offered by conventional plants [17]. Regarding specifically demand response, practical estimations of its technical potential and expected economic benefits for users have been estimated, for instance, in [18]; roughly speaking, practical experience from a demand aggregator suggests that “any facility that can reduce at least 100 kilowatts of electricity is typically a good candidate for demand response” [19]. It is important to underline at this point that the market opening to aggregation, currently promoted by ongoing regulatory initiative with a different degree of evolution across the world (see, by way of example, [20], focused on the European context), bring a new paradigm in the power system, with many customers moving from simple users to prosumers with an active involvement in the system management. In such an evolving context, the smart microgrids are destined to play a crucial role, since they will become the first step in building a hierarchical structure of highly interconnected actors that exchange information and power at different levels (building, microgrid, district, city, distribution network, and transmission grid), delivering a variety of services, based on their expertise, for

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an efficient, balanced, and secure operation of the energy system of the future (Figure 12.4).

References [1] https://www.mckinsey.com/industries/electric-power-and-natural-gas/our-insights/ the-digital-utility-new-opportunities-and-challenges. [2]

https://www.i-scoop.eu/digital-transformation/digital-transformation-focus-on-the-utilities-industry.

[3] Sharifi, R., et al., “Economic Demand Response Model in Liberalised Electricity Markets with Respect to Flexibility of Consumers,” IET Generation, Transmission & Distribution, Vol. 11, No. 17, November 2017, pp. 4291–4298. [4] Lamprinos, I., et al., “Making Demand Response a Reality in Europe: Policy, Regulations, and Deployment Status,” IEEE Communications Magazine, December 2016, pp. 108–113. [5] The Nordic Council of Minister, “Flexible Demand for Electricity and Power – Barriers and Opportunities,” Nordic Publications, 2017. [6] https://www.grandviewresearch.com/press-release/global-microgrid-market. [7] Vandoorn, T. L., et al., “Smart Microgrids and Virtual Power Plants in a Hierarchical Control Structure,” 2nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies (ISGT Europe), Manchester, U.K., December 5–7, 2011. [8] http://www.newcastlesciencecentral.com. [9] http://www.clydewaterfront.com/projects/glasgow-city-centre. [10] http://vancouver.ca/home-property-development/major-planning-projects.aspx. [11] Saboori, H., M. Mohammadi, and R. Taghe, “Virtual Power Plant (VPP), Definition, Concept, Components and Types,” Asia-Pacific Power and Energy Engineering Conference (APPEEC), Wuhan, China, March 25–28, 2011. [12] Pudjianto, D., C. Ramsay, and G. Strbac, “Virtual Power Plant and System Integration of Distributed Energy Resources,” IET Renewable Power Generation, Vol. 1, No. 1, March 2007, pp. 10–16. [13] Othman, M., Y. G. Hegazy, and A. Abdelaziz, “A Review of Virtual Power Plant Definitions, Components, Framework and Optimization,” International Electrical Engineering Journal, Vol. 6, No. 9, November 2015, pp. 2010–2024. [14] Henriquez Auba, R., et al., “Participation of Demand Response Aggregators in Electricity Markets: Optimal Portfolio Management,” IEEE Transactions on Smart Grid, Vol. 99, February 2017. [15] MacDougall, P., et al., “Value Assessment of Aggregated Energy Flexibility When Traded on Multiple Markets,” 14th International Conference on the European Energy Market (EEM), Dresden, 2017, pp. 1–6.



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[16] Explicit Demand Response in Europe - Mapping the Market 2017, SEDC, Smart Energy Demand Coalition report, April 2017, wwwsmartenergydemand.eu. [17] Steiniger, H., “Virtual Power Plants: Bringing the Flexibility of Decentralized Loads and Generation to Power Markets,” Ch. 17 in F. P. Sioshansi, (ed.), Innovation and Disruption at the Grid’s Edge, New York: Elsevier Academic Press, 2017, pp. 331–362. [18] Demand Side Flexibility: The Potential Benefits and State of Play in the European Union, Cambridge Economic Policy Associates Ltd, TPA Solutions & Imperial College London, final report, September 2014, http://www.acer.europa.eu. [19] Demand Response Deconstructed. Get Paid for Your Flexibility, EnerNOC, an Enel Group Company, https://www.enernoc.com/demand-response-deconstructed, 2018. [20] Lamprinos, I., et al., “Making Demand Response a Reality in Europe: Policy, Regulations, and Deployment Status,” IEEE Communications Magazine, Vol. 54, No. 12, December 2016, pp. 108–113.

About the Authors Federico Delfino is a full professor of power systems engineering at the University of Genoa, Italy, where he teaches circuit theory and applications and power systems management in the B.Sc. program in mechanical engineering and in the M.Sc. program in energy engineering, respectively. His research activities include modeling, control and operation of smart power grids and sustainable microgrids, distributed generation and energy networks for smart city applications, and energy management strategies and decision support systems for sustainable urban planning. Professor Delfino is the head of the University of Genoa Savona Campus research and teaching facilities, where he acts as the scientific manager of several international and national innovation projects dealing with the real demonstration of the concepts of sustainable energy and smart city, in partnership with industry and institutional stakeholders. Professor Delfino has been a member of the expert committees of Energy Efficiency at the Italian Ministry of Economic Development and of Smart Grids at the Italian Regulatory Authority for Electricity and Gas. He is presently the scientific coordinator of the Living Lab Microgrid, jointly operated on a global scale by the Italian DSO ENEL S.p.A. and the University of Genoa. He is also the scientific coordinator of the demonstration project Living Grid within the Italian Technological Cluster on Energy (CTN Energia) and is the president of the Scientific Research Committee of the Italian Association of Bank Foundations (ACRI). Renato Procopio is an associate professor of power systems engineering at the University of Genoa, Italy. He is the author or coauthor of more than 100 papers published in international journals or presented at international conferences. He is a member of the IEEE Technical Committee 5: High Power Electromagnetics and a member of the editorial boards of the Journal of 297

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Electrical and Computer Engineering (power system division) and of The Open Atmospheric Science Journal. Dr. Procopio is a memeber of the Ph.D. board of electrical engineering. He is the 2003 recipient of the Emerald Literati Awards’ Outstanding Paper accolade for the best article published in the electrical engineering journal COMPEL. He teaches in three Bachelor’s degree and Master’s degree courses at the University of Genoa and is responsible for the connection between the Savona Campus (1,700 students) and the secondary school institutions of the Liguria region and for the counseling service for students at the Savona Campus. Mansueto Rossi is a researcher at the University of Genoa, Italy, where he teaches circuit theory in the B.Sc. program in mechatronic engineering. His research interests are focused on methods for the integration of renewable resources in power networks, microgrids, lightning modeling, transmission line analysis, and evaluation of lightning-induced overvoltages. He is involved in the scientific activities performed at the Savona Campus, collaborating on national and international projects on the topics of smart grids and smart cities, developed in cooperation with industrial and institutional partners. Stefano Bracco is a researcher of power systems engineering at the University of Genoa, Italy, where he teaches in courses on power systems simulation and optimization, electrical installations, and critical energy infrastructures modeling and simulation. His research activities include modeling, control and operation of smart grids and microgrids, distributed generation and cogeneration, energy management for sustainable urban planning in smart cities, dynamic simulation and optimization of energy systems, electrical storage systems, and smart electric mobility. He has been involved in many research projects in the energy sector at the national and international levels, both funded by public authorities or private companies. He has been the technical officer of the University of Genoa for the Smart Energy Building project at the Italian Ministry for the Environment and Protection of Land and Sea, and he has been a member of the scientific committee of ITS Foundation–High Technical Institute of Energy Efficiency. Dr. Bracco is currently the author of papers in international journals, books, and proceedings of international conferences; moreover, he serves as an editor and reviewer for international conferences and journals in the areas of power and energy systems, sustainable energy, and electric mobility. Massimo Brignone is a researcher at the University of Genoa, where he teaches electrical engineering and strategies for energy. He is the coauthor of more than 100 scientific contributions published in international journals or presented at international conferences. His research interests include microgrid, optimization process for energy flows, lightning modeling and its effects on the electric infrastructures, as well as direct and indirect electromagnetic problems. He is member of the editorial



About the Authors

299

boards of several scientific journals. Since 2017, he has been the head of the start-up WiGaar S.r.L., whose main activities are related to project, control, management, and optimization of electrical and thermal energy flows inside smart microgrid. Michela Robba is a researcher at the University of Genoa, Italy, in the field of optimization and control of smart grids, renewable energy resources, and natural resources management. She is responsible at the EU ESFRI (European Strategy Forum on Research Infrastructures) for the energy area, and she is a member of the scientific board of the Regional Energy Cluster Energia e Ambiente. Since 2014, she has been the editor for the Journal of Control Science and Engineering, and she has joined several international program committees at conferences in the fields of control and optimization. She is a lecturer for the courses “Simulation of Energy and Environmental Systems” and “Models and Methods for Energy Engineering” at Savona Campus Polytechnic School, University of Genoa. She is the author of more than 120 publications in international journals, books, and proceedings of international conferences.

Index Autoregressive moving averrage (ARMA), 187, 188, 194, 197 Auxiliary binary variable, 144

ABB (Microgrid Plus), 250–51 Absorption chillers coefficient of performance, 35 defined, 34 direct-fired, 35 indirect-fired, 34–35 Smart Polygeneration Microgrid (SPM), 264–66 See also Cooling energy production Active power droop controller, 216 Active power priority mode, 176 Admittance matrix, 241 AET100 microturbine, 38 Africa, microgrid installations in, 73–75 Aggregators advantages of joining, 291–92 defined, 290 interactions, 291 Aichi microgrid project, 67–68 Albuquerque microgrid test bed, 58 America, microgrid installations in, 56–60 Am Steinweg microgrid, 61 Analog ensemble (AnEn), 191 Artificial neural networks, 187, 191 Asia, microgrid installations in, 67–72 Australia, microgrid installations in, 73 Automatic voltage regulator (AVR), 4 Autoregressive integrated moving average (ARIMA), 187, 191, 194, 197 Autoregressive moving average with exogenous input (ARMAX), 188

BACnet, 86 Battery energy storage, 45–46 Bella Coola microgrid, 60 Berkeley Lab (DER-CAM), 249–50 Biomass production plants decision variables, 128 model description and constraints, 135 operating costs, 141 Blending algorithm, 194 Boilers efficiency of, 31–32 model description and constraints, 134–35 operating costs, 141 Smart Polygeneration Microgrid (SPM), 264 thermal power, 31 use of, 30 See also Thermal energy production Box-Jenkins methods, 194 British Columbia microgrid, 58–59 Bronsbergen Holiday Park microgrid, 61, 62 Building management systems (BMS) communication between SCADA and, 92 interoperability, 91–95 interoperability solutions, 95

301

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Microgrid Design and Operation: Toward Smart Energy in Cities

Building management systems (continued) reasons for bridging domains, 93–95 SCADA differences, 91 Canadian microgrids, 58–60 Case A, 233–34, 235 Case B, 234–35, 236 Case C, 235–38 Case study conclusions, 284–85 introduction to, 255–58 overview, 255 Smart Energy Building, 274 Smart Polygeneration Microgrid, 259–74 timelines and challenges, 283–84 See also Savona Campus Cauchy problem, 243 CESI Ricerca DER test bed, 61 Chile microgrid project, 60 Chillers absorption, 34–35, 264–66 compression, 33–34 decision variables, 128 model description and constraints, 136–37 China, microgrid projects, 70–73 Cogeneration microturbine accumulation of mass and kinetic energy, 110–11 combustion chamber, 111 compressor, 110 control system, 114 heat exchangers, 112–13 illustrated, 109 information diagram, 113–14 as multicomponent system, 109–14 overview, 109–10 shaft, 112 turbine, 111–12 Cogeneration/trigeneration technologies concentrating solar power systems, 43–44 design of, 36–37 electricity and heat, 36 fuel cells, 44–45 gas turbines, 37–41 small reciprocating internal combustion engines (SRICEs), 42–43 Combined heat and power (CHP) plants

Capstone models, 146 decision variables, 127 model description and constraints, 134 operating costs, 140 Combustion chamber, 111 Communication-based control average current sharing control scheme, 213 direct and quadrature axis control loops, 209, 211 equivalent circuit of three parallel inverters, 207 Kirchhoff voltage law (KVL), 208, 209 in managing PWM inverters, 206 master circuit, 207 master controller, 210 reference voltage, 212 slave controller, 211 Communication systems master-slave scheme, 81 overview, 79–81 protocols, 81–90 stability requirement, 79 Complete load-flow model constraints, 166 defined, 163–64 Compression chillers, 33–34 Compressor, 110 Concentrating solar power systems defined, 43 Dish Stirling, 43–44 Smart Polygeneration Microgrid (SPM), 266–67 Consortium for Electrical Reliability Technology Solutions (CERTS) project, 56–57 Continuity equation, 103–4 Control system, 114 Conventional energy sources (CESs), 6 Cooling energy production absorption chillers, 34–35 compression chillers, 33–34 technologies for, 33–35 Cost parameters, 130–31 Data-driven models photovoltaic production forecasting, 190–91 wind power production forecasting, 194–95



Index DC/DC converter circuital layout, 228 DC/DC converter model, 228 Decentralized Energy Management System (DEMS) defined, 188, 247 forecasting update logic, 174 functions, 247–48 Generation Forecast tool, 188 load forecast function, 198–99, 248 online optimization and coordination function, 249 Unit Commitment function, 248–49 Weather Forecast tool, 188 Decision problem, 127–28 Decision variables biomass production plants, 128 chillers, 128 fossil fuel plants, 128 heat pumps, 127–28 list of, 131–32 photovoltaic fields, 127 solar thermal systems, 128 storage system, 144 thermal power plants, 128 wind turbines, 127 Decoupled load flow model constraints, 169 defined, 164–65 Demand-side management, 292–94 DEMS. See Decentralized Energy Management System DER-CAM (Berkeley Lab), 246, 249–50 Design choices, 123 decision problem, 127–28 optimal, 125 optimization algorithms in, 124 See also Microgrid planning Digital utility transformation, 288–90 Digitization, 288 Direct and quadrature axis control loops, 209, 211 Dispatchable units, 159–60 Distributed energy resources (DERs) categories of, 6 conventional energy sources (CESs), 6 defined, 6 examples, 7 renewable energy resources (RESs), 6–7 Distributed generation (DG)

303 concept, 15 defined, 14 features, 14–15 microgrids and, 14–17 Distribution system operator (DSO) control by, 288 digital, 289 requirements, 175–81 DNP3 (Distributed Network Protocol 3), 82–83 Droop-based control active power balance, 215 active power droop controller, 216 conditions for reaching same angular frequency with, 240–44 defined, 213 drawbacks, 216–17 droop logics, 216 effectiveness, 242 equivalent circuit of inverter, 214 implementation, 216 line impedance, 214 reactive power droop controller, 216 for resistive microgrids, 217–18 scheme illustration, 216 virtual impedance method, 218, 219 VPD-FQB method, 217–18 Droop control law, 242 Dynamic Harmonic Regression, 191 Economic dispatch, 176 EcoStruxure Microgrid Advisor (Schneider), 251–52 Electrical and thermal power balance, 137–39 Electrical energy production hydro power plants, 22–24 photovoltaic systems, 17–22 technologies for, 17–22 wind power plants, 24–29 Electrical Energy Systems (EES) laboratory, 65 Electrical storage systems battery, 45–46 importance of, 45 use of, 45 Electric loads, 162 Electric network models complete load-flow model, 163–64 decoupled load flow model, 164–65

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Electric network models (continued) linear load flow model, 164 single busbar model, 162–63 Electric networks distributed energy resources, 5–7 first revolution, 4–5 infrastructure, 2 main characteristics of, 4 power electronics and, 4–5 second revolution, 5–7 structure illustration, 3 subsystems, 2 third revolution, 7–10 as three-phase balanced system, 2–3 traditional, 1–4 transmission and distribution topology, 3 See also Microgrids; Smart grids Electric storage model, 224–27 Energia 2020 project, 257 Energy balance equation, 104 Energy management system (EMS) architecture flowchart, 170–71 component models, 159–62 costs, 157 dispatchable units, 159–60 electrical network, 155–56 electric loads, 162 electric network models, 162–65 forecasted quantities, 156 forecasting update logic, 169–71 forecasting update logic application, 173–75 general data, 155–56 heat generators, 160 introduction to, 153–54 list of symbols, 154–57 optimal power production, 158 optimization for, 153–81 optimization problem, 165–69 overview, 157–58 renewable energy sources, 161–62 Smart Polygeneration Microgrid (SPM), 271–74 storage system, 161 technical data, 155 thermal loads, 162 validation of, 172–81 variables involved in optimization procedure, 156



verification of distributed system operator requirements, 175–81 Energy Management System Application Program Interface (EMS-API), 86 EnergyPlus, 246 Entropy balance, 105–6 Europe, microgrid installations, 61–67 Flat-plate collectors, 29 Flexible AC Transmission Systems (FACTS), 4–5 Fluid-dynamic capacitance, 106–7, 111 Fluid-dynamic inductance component illustration, 111 control volume, 107 in dynamic simulators, 108 friction and, 108 momentum equation, 107–8 Fluid-dynamic resistance, 109 Forecasted parameters, 186 Forecasted power, 170 Forecasting energy demand, 187 load, 195–99 solar and photovoltaic production, 189–92 wind power production, 192–95 Forecasting tools EMS interaction, 186 introduction to, 185–89 load forecasting, 195–99 solar and photovoltaic production forecasting, 189–92 wind power production forecasting, 192–95 Forecasting update logic application, 173–75 flowchart, 170–71 logic, 170 strategy effects, 174, 175 Fort Collins Demonstration Project, 58 Fossil fuel plants decision variables, 128 model description and constraints, 137 operating costs, 141 Fuel cells compatibility, 44–45 defined, 44 fuel source, 44



Index Function-based optimization technique, 220 Fuzzy regression models, 197 Gas turbines fuels, 37 market availability, 37 scheme, 38 See also Cogeneration/trigeneration technologies; Microturbines GE (Grid IQ Microgrid Control System), 252–53 General Packet Radio Service (GPRS), 292 Generic Object Oriented Substration Event (GOOSE), 84 Genoa Smart Polygeneration Microgrid. See Smart Polygeneration Microgrid (SPM) Geothermal heat pump, 277, 280 Grid-connected photovoltaic systems, 19–21 Grid IQ Microgrid Control System (GE), 252–53 Griffith University microgrid test bed, 73, 74 Hachinohe microgrid project, 68, 69 Heat exchangers, 112–13 Heat generators, 160 Heat pumps decision variable, 127–28 efficiency of, 33 geothermal, 277, 280 in heating mode, 32 model description and constraints, 135–36 operating costs, 141–42 use of, 32 HOMER, 124, 246 Honeywell (VERA), 246–47 Human-machine interface (HMI), 65 Hydro power plants characteristics of, 22–23 global efficiency, 23 golden age of, 23 impulse turbines, 25 reaction turbines, 26 reliability and efficiency, 22–23 turbines, 23–24 See also Electrical energy production IEC 60870-5, 82–83

305 IEC 61850 applications, 84 benefits of, 85 defined, 83 devices, 85–86 logical nodes, 84 SCL, 84–85 See also Protocols IEC 61968, 86 IEC 61970, 86 India, microgrid installations in, 73 Information and communications technology (ICT) infrastructure defined, 10 role of, 287 Smart Energy Building, 281–83 Smart Polygeneration Microgrid (SPM), 269–71 Information diagram, pressure mass-flow rate, 113–14 Installation costs, 143 Institute of Nuclear Energy Research (INER) microgrid, 73 Interconnection active/reactive power relationship at point of, 180 power factor, point of, 179 reactive power, 177, 178 voltage at point of, 179 Interoperability, 91–95 Inverter model, 227–28 Islanded microgrids active power frequency control, 204 communication-based control, 206–13 droop-based control, 213–18 hierarchical structure and, 205 overview, 203–5 primary control, 205–18 problems, 203 Smart Polygeneration Microgrid (SPM), 222–38 voltage control, 204 Islanded microgrid simulations battery, 118–19 capacitor, 117 electrical devices modeling for, 114–20 first harmonic model, 115–16 no-inertia, 115 photovoltaic units, 117–18

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Islanded microgrid simulations (continued) simplified model (SM), 116–17 storage unit, 118, 119 Japan, microgrid projects, 67–70 Jeju Smart Grid Demonstration Project, 70 Kasabonika microgrid, 60 Kirchhoff voltage law (KVL), 208, 209 KNX, 88 Korea, microgrid projects, 70 Kyotango microgrid project, 68 Kythnos Island microgrid, 64 Lagrange multipliers, 158 Linear load flow model constraints, 166–69 defined, 164 linearization of capability constraint, 166 linearization of component constraints, 166 linearization of cost function and current constraint, 168–69 linearization of load flow equations, 167 Lingo 9.0 optimization package, 147 Load flow equations, linearization of, 167 Load flow models complete, 163–64, 166 decoupled, 164–65, 169 linear, 164, 166–69 Load forecasting defined, 195 DEMS energy management system, 198–99 flow diagram, 196 for local systems, 197–98 models, 195–96 stakeholders, 195 studies, 196–97 techniques, 197 tools illustration, 196 See also Forecasting; Forecasting tools Longmeadow microgrid, 73–75 LonWorks characteristics, 87 control network, 87 defined, 86–87

See also Protocols LoRaWan, 89 Low-power WAN (LPWAN), 89 Low-size cogeneration microturbine, 39 Luxi microgrid, 71, 72 Management tools DEMS (Siemens), 247–49 DER-CAM (Berkeley Lab), 249–50 EcoStruxure Microgrid Advisor (Schneider), 251–52 Grid IQ Microgrid Control System (GE), 252–53 large-scale applications, 246 Micro Energy Management System (TOSHIBA), 252 Microgrid Plus (ABB), 250–51 overview, 245–46 VERA (Honeywell), 246–47 Markov chain models, 188 Master circuit, 207 Master controller, 210 Master-slave communication scheme, 81 MGC600 series of controllers, 250 Micro Energy Management System (TOSHIBA), 252 Microgrid cloud, 16 Microgrid installations in Africa, 73–75 in America, 56–60 in Asia, 67–72 in Australia, 73 in Europe, 61–67 in India, 73 overview, 55–56 Microgrid planning decision problem, 127–28 decision variables and parameters, 128–32 examples, 146–50 HOMER and, 124 introduction to, 123–24 optimal, including storage systems, 143–45 optimization for, 123–50 optimization problem, 139–43 state of the art approaches, 124 system model description/constraints, 132–39 See also Design



Index Microgrid Plus (ABB), 250–51 Microgrids benefits of, 55 communication and monitoring systems, 79–95 comparison issues, 56 defined, 9 distributed generation and, 14–17 as electrical and thermal grids, 16 experimentation, 55–56 future smart cities and, 293 illustrated, 8 islanded, 203–38 modeling and simulation for, 99 no-inertia, 115 power plants for, 17 Microturbines accumulation of mass and kinetic energy within, 110–11 AET100, 38 cogeneration, 38–39 cogeneration, as multicomponent system, 109–14 in distributed generation applications, 41 external ambient conditions and, 40–41 first-principle global thermodynamic efficiency, 41 low-size, 39 performance parameters, 39 rated operating conditions, 40 Smart Polygeneration Microgrid (SPM), 263–64 temperature and flow rate, 40 use of, 41 Modbus defined, 81 drawbacks, 82 features, 81–82 See also Protocols Modeling defined, 99 introduction to, 99–101 for islanded microgrid, 114–20 of multicomponent energy systems, 101–14 Momentum equation, 105, 107–8 Monitoring systems, 90–91 MOTIE microgrid and smart grid test bed, 70

307 Multicomponent energy systems characterized by, 102–3 continuity equation, 103–4 dynamic simulation of cogeneration microturbine, 109–14 electrical analogy, 106–9 energy balance equation, 104 entropy balance, 105–6 equations governing behavior, 103–6 fluid-dynamic capacitance, 106–7, 111 fluid-dynamic inductance, 107–8, 111 fluid-dynamic resistance, 109 momentum equation, 105 overview, 102–3 schematic representation, 103 thermo-fluid dynamic components, 102 Multilayer Perceptron (MLP) model, 191 Nanijing University of Astronautics and Aeronautics (NUAA) microgrid, 70–71 National Technical University of Athens (NTUA) multi-microgrid, 65, 66 Network model, 229–31 Never-Failing Perfect Power Prototype, 57 New Energy and Industrial Technology Development Organization (NEDO) Aichi microgrid project, 67–68 Hachinohe microgrid project, 68, 69 Kyotango microgrid project, 68 Sendai microgrid project, 68–70 No-inertia microgrids, 115 Nowcasting, 190–91 Numerical Weather Predictions models, 190 Objective functions, 143, 145, 175, 178 Ohm’s law, 109 On-load tap charger (OLTC) transformers, 4 OPC, 90 OpenModelica environment, 251 Operational management costs, 139–42 Optimax PowerFit tool, 251 Optimization algorithms, 124 constraints, 126 decision problem, 127–28 decision variables, 126

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Microgrid Design and Operation: Toward Smart Energy in Cities

Optimization (continued) examples, 146–50 for microgrid management, 153–81 in microgrid planning, 123–50 objective function, 126 parameters, 126 results, 148, 149 two-step procedure, 144 Optimization problem entities, 125–26 installation costs, 143 objective function, 143 operational management costs, 139–42 Ordinary differential equation (ODE), 242–43 Overhead lines (OHL), 2 Parameters forecasted, 186 microturbine performance, 39 optimization, 126 related to costs, 130–31 related to power flows, 128–30 Park phasors, 240 Park reference frame, 206, 240 Park transformation, 242 Photovoltaic production forecasting challenge of, 190 conversion technologies and, 190 environmental parameters and, 190 nowcasting and, 190–91 overview, 189–90 Photovoltaic systems cell basis, 18 field composition, 21–22 global efficiency, 18–19 grid-connected, 19–21 hourly solar radiation and, 20 installation illustrations, 21 market, 17–18 microgrid connection, 47 model description and constraints, 132–33 operating costs, 141–42 performance indicators, 18–19 rooftop, 19 Smart Polygeneration Microgrid (SPM), 266 stand-alone, 19 yield, 18

See also Electrical energy production Photovoltaic unit model, 223–24 Piecewise continuous function, 180 Power distribution center (PDC) algorithm, 206 Power electronic converters photovoltaic microgrid connection, 47 reasons for use, 46–47 types of, 46 wind turbine network connection, 47 Power electronics, 4–5 Power flow parameters, 128–30 Power plants categories of, 16 hydro, 22–24 for microgrids, 17 wind, 24–29 See also specific types of power plants PREMIO project, 61 Primary control communication-based control, 206–13 droop-based control, 213–17 droop-based control for resistive microgrids, 217–18 requirements, 205 See also Islanded microgrids Probability density functions, 194 Protocols BACnet, 86 DNP3, 82–83 IEC 60870-5, 82–83 IEC 61850, 83–86 KNX, 88 LonWorks, 86–87 LoRaWAN, 89 Modbus, 81–82 OPC, 90 REST, 90 SOAP, 90 ZigBee, 88–90 PSCAD, 232 Pulse width modulator (PWM) inverters, 206 Rainwater harvesting system, 276 Reactive power droop controller, 216 Reference voltage, 212 Renewable energy resources (RESs), 6–7, 161–62 Representational State Transfer (REST), 90



Index Rooftop photovoltaic systems, 19 Sampled Measured Values (SMV), 84 Savona Campus electrical energy profiles, 273 history, 258 location, 256 single-line wiring diagram, 261 thermal energy profiles, 274 See also Case study Schneider (EcoStruxure Microgrid Advisor), 251–52 Science Central, 65–67 Secondary control block diagram, 220, 221 function-based optimization technique, 220 parameters, 220 realization, 218 See also Islanded microgrids Sendai microgrid project, 68–70 Shaft, cogeneration microturbine, 112 Short-term forecasts, 186 Siemens. See Decentralized Energy Management System Simple Object Access Protocol (SOAP), 90 Simplified model (SM), 116–17 Simulation case studies, 100 of cogeneration microturbine, 109–14 defined, 99 introduction to, 99–101 islanded microgrid, 114–20 of multicomponent energy systems, 101–14 project schemes, 101 steady-state versus dynamic, 100 Simulators, 100, 108 Single busbar model constraints, 165–66 defined, 162–63 Slave controller, 211 Small reciprocating internal combustion engines (SRICEs), 42–43 Smart cities, microgrids and, 293 Smart City MV distribution network, 289 Smart Energy Building air-handling unit plant, 280 background, 257 building characterization, 274–75

309 challenges, 283–84 construction timeline, 283 defined, 274 domestic hot water pump, 279 electrical system, 279–81 EMS interaction, 282 energy infrastructure, 275 geothermal heat pump, 277, 280 gym, 281 ICT infrastructure, 281–83 illustrated, 275 overview, 259 power plants, 259–69 rainwater harvesting system, 276 single-line wiring diagram, 260 solar thermal collectors, 280 technologies, 275–76 thermal system, 276–79 thermal system illustration, 278 vacuum collectors, 279 See also Case study Smart grids characteristics, 8–9 defined, 7 evolution of electric distribution system according to, 289 operational and energy measures, 7 typical structure, 9 See also Microgrids Smart Polygeneration Microgrid (SPM) absorption chillers, 264–66 AC filter parameters, 228 background, 257 boilers, 264 Case A, 233–34, 235 Case B, 234–35, 236 Case C, 235–38 challenges, 283–84 completed model built in PSCAD, 232 concentrating solar power systems, 266–67 construction timeline, 283 DC/DC converter model, 228 electrical storage systems, 267, 268 electric storage model, 224–27 e-mobility, 267–69 energy infrastructure, 263 energy management system, 271–74 energy management system uses as input, 271–72

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Microgrid Design and Operation: Toward Smart Energy in Cities

Smart Polygeneration Microgrid (continued) E(SOC) cell characteristic, 227 experimental campaign and measurement setup, 232 ICT infrastructure, 269–71 ICT infrastructure illustration, 270 inverter model, 227–28 in islanded configuration, 222–38 islanded portion illustration, 222 microturbines, 263–64 network model, 229–31 off-grid portion, 222–23 photovoltaic fields, 266 photovoltaic module parameters, 224 photovoltaic unit model, 223–24 power plant management, 272–74 power plants illustration, 262 SCADA system, 269 simplified model, 223–31 sourced, 223–31 subsystems, 259–62 validation cases, 233–38 V/SOC cell characteristic, 225 Solar Dish Stirling systems, 43–44 Solar production forecasting, 189–92 Solar thermal systems components of, 30 decision variables, 128 flat-plate collectors, 29 illustrated, 31 model description and constraints, 133 operating costs, 141–42 solar collectors, 29–30 See also Thermal energy production Stand-alone photovoltaic systems, 19 Static synchronous series compensation (SSSC), 5 Storage systems decision variables, 144 energy management system (EMS), 161 optimal planning and, 143–45 presence of, 144 Smart Polygeneration Microgrid (SPM), 267, 268 Storage variables, 145 Substration Configuration Language (SCL), 84–85 Sugar Island hybrid distributed generation projects, 73

Supervisory control and data acquisition (SCADA) communication between BMS and, 92 control room, 271 functionalities, 91 interoperability, 91–95 interoperability model, 94 interoperability solutions, 95 NTUA facilities, 65 power meters, 271 reasons for bridging domains, 93–95 Smart Polygeneration Microgrid (SPM), 269 Support vector machines, 188 System model description and constraints biomass plants, 135 chillers, 136–37 combined heat and power (CHP) microturbine plants, 134 electrical and thermal power balance, 137–39 fossil fuel plants, 137 heat pumps, 135–36 PV power plant, 132–33 solar thermal power plant, 133 thermal boilers, 134–35 wind turbine power plant, 133–34 Tertiary control block diagram, 221 function of, 220 See also Islanded microgrids Thermal energy production boilers, 30–32 decision variables, 128 heat pumps, 32–33 solar thermal systems, 29–30 technologies for, 29–33 Thermal loads, 162 TOSHIBA (Micro Energy Management System), 252 Traditional electric networks, 1–4 Transmission system operator (TSO), 289 Trigeneration. See Cogeneration/ trigeneration technologies Turbines cogeneration microturbine, 111–12 hydro power plant, 23–24 impulse hydro, 25 reaction hydro, 26



Index

wind power plants, 24–29 See also Microturbines

Unified power flow controller (UPFC), 5 Universidade do Porto microgrid test bed, 63–64 University of Seville microgrid, 62–63 UTA microgrid test bed, 58, 59 UT Austin microgrid, 58 UTC microgrid, 62 Utsira Island microgrid, 64–65 VERA (Honeywell), 246–47 Virtual impedance method, 218, 219 Virtual power plants (VPPs) advantages of joining, 291–92 concept, 290 decentralized resources in, 291 focus, 290–91 Voltage control, 204 VPD-FQB method, 217–18 Web Service Description Language (WSDL), 90 West Virginia Supercircuit Project, 58 Wind Atlas Analysis and Application Program (WasP), 193–94

311 Wind power plants capacity, 24 decision variable, 127 growing interest in, 24 horizontal access turbines, 28 model description and constraints, 133–34 network connection, 47 operating costs, 141–42 turbine illustrations, 28 turbine power curves, 29 turbines, 24–29 vertical access turbines, 27, 28 See also Electrical energy production Wind power production forecasting data-driven models, 194–95 overview, 192 PDFs, 194 physical model, 193–94 power dependency, 192 statistical models, 194 tools illustration, 193 WasP, 193 ZigBee, 88–90