Smart Grid and Enabling Technologies E-Book

Shady S Refaat

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: Wiley - IEEE
• Sprache: Englisch

Seitenzahl: 877

Veröffentlichungsjahr: 2021

Beschreibung

SMART GRID AND ENABLING TECHNOLOGIES Discover foundational topics in smart grid technology as well as an exploration of the current and future state of the industry As the relationship between fossil fuel use and climate change becomes ever clearer, the search is on for reliable, renewable and less harmful sources of energy. Sometimes called the "electronet" or the "energy Internet," smart grids promise to integrate renewable energy, information, and communication technologies with the existing electrical grid and deliver electricity more efficiently and reliably. Smart Grid and Enabling Technologies delivers a complete vision of smart grid technology and applications, including foundational and fundamental technologies, the technology that enables smart grids, the current state of the industry, and future trends in smart energy. The book offers readers thorough discussions of modern smart grid technology, including advanced metering infrastructure, net zero energy buildings, and communication, data management, and networks in smart grids. The accomplished authors also discuss critical challenges and barriers facing the smart grid industry as well as trends likely to be of importance in its future development. Readers will also benefit from the inclusion of: * A thorough introduction to smart grid architecture, including traditional grids, the fundamentals of electric power, definitions and classifications of smart grids, and the components of smart grid technology * An exploration of the opportunities and challenges posed by renewable energy integration * Practical discussions of power electronics in the smart grid, including power electronics converters for distributed generation, flexible alternating current transmission systems, and high voltage direct current transmission systems * An analysis of distributed generation Perfect for scientists, researchers, engineers, graduate students, and senior undergraduate students studying and working with electrical power systems and communication systems. Smart Grid and Enabling Technologies will also earn a place in the libraries of economists, government planners and regulators, policy makers, and energy stakeholders working in the smart grid field.

Leseprobe

Table of Contents

Cover

Title Page

Copyright Page

About the Author

Acknowledgments

Preface

List of Abbreviations

About the Companion Website

1 Smart Grid Architecture Overview

1.1 Introduction

1.2 Fundamentals of a Current Electric Power System

1.3 Limitations of the Traditional Power Grid

1.4 Smart Grid Definition

1.5 Smart Grid Elements

1.6 Smart Grid Control

1.7 Smart Grid Characteristics

1.8 Transformation from Traditional Grid to Smart Grid

1.9 Smart Grid Enabling Technologies

1.10 Actions for Shifting toward Smart Grid Paradigm

1.11 Highlights on Smart Grid Benefits

1.12 Smart Grid Challenges

1.13 Smart Grid Cost

1.14 Organization of the Book

References

2 Renewable Energy

2.1 Introduction

2.2 Description of Renewable Energy Sources

2.3 Renewable Energy: Growth, Investment, Benefits and Deployment

2.4 Smart Grid Enable Renewables

2.5 Conclusion

References

3 Power Electronics Converters for Distributed Generation

3.1 An Overview of Distributed Generation Systems with Power Electronics

3.2 Power Electronics for Grid‐Connected AC Smart Grid

3.3 Power Electronics Enabled Autonomous AC Power Systems

3.4 Power Electronics Enabled Autonomous DC Power Systems

3.5 Conclusion

References

4 Energy Storage Systems as an Enabling Technology for the Smart Grid

4.1 Introduction

4.2 Structure of Energy Storage System

4.3 Energy Storage Systems Classification and Description

4.4 Current State of Energy Storage Technologies

4.5 Techno‐Economic Characteristics of Energy Storage Systems

4.6 Selection of Energy Storage Technology for Certain Application

4.7 Energy Storage Applications

4.8 Barriers to the Deployment of Energy Storage

4.9 Energy Storage Roadmap

4.10 Conclusion

References

5 Microgrids

5.1 Introduction

5.2 DC Versus AC Microgrid

5.3 Microgrid Design

5.4 Microgrid Control

5.5 Microgrid Economics

5.6 Operation of Multi‐Microgrids

5.7 Microgrid Benefits

5.8 Challenges

5.9 Conclusion

References

6 Smart Transportation

6.1 Introduction

6.2 Electric Vehicle Topologies

6.3 Powertrain Architectures

6.4 Battery Technology

6.5 Battery Charger Technology

6.6 Vehicle to Grid (V2G) Concept

6.7 Barriers to EV Adoption

6.8 Trends and Future Developments

6.9 Conclusion

References

7 Net Zero Energy Buildings

7.1 Introduction

7.2 Net Zero Energy Building Definition

7.3 Net Zero Energy Building Design

7.4 Net Zero Energy Building: Modeling, Controlling and Optimization

7.5 Net Zero Energy Community

7.6 Net Zero Energy Building: Trends, Benefits, Barriers and Efficiency Investments

7.7 Conclusion

References

8 Smart Grid Communication Infrastructures

8.1 Introduction

8.2 Advanced Metering Infrastructure

8.3 Smart Grid Communications

8.4 Conclusion

References

9 Smart Grid Information Security

9.1 Introduction

9.2 Smart Grid Layers

9.3 Attacking Smart Grid Network Communication

9.4 Design of Cyber Secure and Resilient Industrial Control Systems

9.5 Cyber Security Challenges in Smart Grid

9.6 Adopting an Smart Grid Security Architecture Methodology

9.7 Validating Your Smart Grid

9.8 Threats and Impacts: Consumers and Utility Companies

9.9 Governmental Effort to Secure Smart Grids

9.10 Conclusion

References

10 Data Management in Smart Grid

10.1 Introduction

10.2 Sources of Data in Smart Grid

10.3 Big Data Era

10.4 Tools to Manage Big Data

10.5 Big Data Integration, Frameworks, and Data Bases

10.6 Building the Foundation for Big Data Processing

10.7 Transforming Big Data for High Value Action

10.8 Privacy Information Impacts on Smart Grid

10.9 Meter Data Management for Smart Grid

10.10 Summary

References

11 Demand‐Management

11.1 Introduction

11.2 Demand Response

11.3 Demand Response Programs

11.4 End‐User Engagement

11.5 Challenges of DR within Smart Grid

11.6 Demand‐Side Management

11.7 DSM Techniques

11.8 DSM Evaluation

11.9 Demand Response Applications

11.10 Summary

References

12 Business Models for the Smart Grid

12.1 The Business Model Concept

12.2 The Electricity Value Chain

12.3 Electricity Markets

12.4 Review of the Previous Proposed Smart Grid Business Models

12.5 Blockchain‐Based Electricity Market

12.6 Conclusion

References

13 Smart Grid Customers' Acceptance and Engagement

13.1 Introduction

13.2 Customer as One of the Smart Grid Domains

13.3 Understanding the Smart Grid Customer

13.4 Smart Grid Customer Acceptance

13.5 Customer Engagement in the Smart Grid

13.6 Challenges for Consumer Engagement, Policy Recommendation and Research Agenda

13.7 Conclusion

References

14 Cloud Computing for Smart Grid

14.1 Introduction

14.2 Overview of Cloud Computing for Smart Grid

14.3 Cloud Computing Service Models

14.4 Cloud Computing Architecture

14.5 Cloud Computing Applications

14.6 Cloud Computing Characteristics in Improving Smart Grid

14.7 Opportunities and Challenges of Cloud Computing in Smart Grid

14.8 Multiple Perspectives for Cloud Implementation

14.9 Conclusion

References

15 On the Pivotal Role of Artificial Intelligence Toward the Evolution of Smart Grids

15.1 Introduction

15.2 Research Methodology and Systematic Review Protocol

15.3 Century‐Old Grid and Smart Grid Transition

15.4 Review of AI Methods

15.5 Major Applications of AI in Smart Grid

15.6 Challenges and Future Scope

15.7 Conclusion

References

16 Simulation Tools for Validation of Smart Grid

16.1 Introduction

16.2 Simulation Approaches

16.3 Review of Smart Grid Planning and Analysis Tools

References

17 Smart Grid Standards and Interoperability

17.1 Introduction

17.2 Organizations for Smart Grid Standardization

17.3 Smart Grid Policies for Standard Developments

17.4 Smart Grid Standards

17.5 Conclusion

References

18 Smart Grid Challenges and Barriers

18.1 Introduction

18.2 Structure of Modern Smart Grids

18.3 Concept of Reliability in Power Systems

18.4 Smart Grid Challenges and Barriers

18.5 New Reliability Paradigm in Smart Grids

18.6 Summary

References

Index

End User License Agreement

List of Tables

Chapter 1

Table 1.1 A detailed comparison between conventional power grids and smart gr...

Table 1.2 Investment costs of a fully functioning SG ($ M) [90]. Reproduced w...

Chapter 2

Table 2.1 Potential benefits and technical limitations of biomass energy. Ada...

Table 2.2 Types of geothermal resources, temperatures and their applications....

Table 2.3 Advantages and disadvantages of different renewable energy resource...

Table 2.4 Some negative environmental impacts of different renewable energy r...

Chapter 3

Table 3.1 Characteristics of various energy storage systems.

Chapter 4

Table 4.1 Commonly used energy storage technologies. Adapted from Ref Num [11...

Table 4.2 Technical and economic characteristics of energy storage technologi...

Table 4.3 Major energy storage applications.

Table 4.4 Benefits of energy storage systems by users.

Table 4.5 Energy storage applications with suitable technology.

Chapter 5

Table 5.1 Some examples of AC microgrid systems (http://microgridprojects.com...

Table 5.2 Typical examples of DC microgrid systems.

Table 5.3 Comparison between AC distribution lines and DC distribution lines....

Table 5.4 Comparison of centralized and decentralized control approaches. Ada...

Chapter 6

Table 6.1 Overview of battery electric vehicles.

Table 6.2 Comparison of different EV topologies. Ref Num [3].

Table 6.3 The comparison of series and parallel HEV configurations. Ref Num [...

Table 6.4 Existing battery technology, energy density, specific density. Ref ...

Table 6.5 Battery capacity and technologies by various EV manufacturers. Ref ...

Table 6.6 Charge methods electrical ratings.

Table 6.7 US electric vehicle charging sites and stations. Adapted from Ref N...

Table 6.8 Europe electric vehicle charging sites and stations. Adapted from R...

Table 6.9 Comparison of V2G technologies.

Chapter 7

Table 7.1 ZEB definitions summary.

Table 7.2 Classifying nZEBs by renewable energy supply.

Table 7.3 Energy savings potential using smart technology. Ref Num [13]. Repr...

Table 7.4 Renewable energy technologies applications in nZEB.

Table 7.5 Classification of algorithms for building performance optimization....

Table 7.6 Summary of the three optimization algorithms' performance under six...

Table 7.7 A building design framework using the optimization methods as a dec...

Table 7.8 Community efficiency and renewable supply hierarchy.

Chapter 8

Table 8.1 Benefits of advanced metering infrastructure. Adapted from Ref [4].

Table 8.2 Usual QoS conditions of few representative kinds of traffic in SG c...

Table 8.3 Comparison between wired communication technologies. Ref [17]. Repr...

Table 8.4 Comparison between wireless communication technologies. Ref [17]. R...

Chapter 9

Table 9.1 Time latency for SG applications [26]. Reproduced with permission f...

Table 9.2 Main cyber security requirements for the SG. Adapted from Ref Num [...

Chapter 10

Table 10.1 Typical big data sources [7]. Reproduced with permission from IEEE...

Chapter 11

Table 11.1 The properties of load‐response programs [8]. Reproduced with perm...

Table 11.2 Price response program overview [8]. Reproduced with permission fr...

Table 11.3 A brief description of each technique [18, 19].

Chapter 12

Table 12.1 The core influence of SGs on electricity firms' business model inn...

Table 12.2 Traditional and the smart utilities. Ref [7]. Reproduced with perm...

Table 12.3 Utility‐side vs. customer‐side business model. Ref [2]. Reproduces...

Table 12.4 Different prosumer‐oriented business model characteristics. Adapte...

Chapter 13

Table 13.1 Possible enablers and barriers of end‐user engagement in SG projec...

Chapter 14

Table 14.1 The criteria of cloud computing prospective.

Chapter 15

Table 15.1 List of the ML‐based AI review papers for 2020.

Table 15.2 Mainstream DL architectures with their advantages, limitations, an...

Table 15.3 List of hybrid models for smart energy applications.

Table 15.4 Comparative study of the proposed methods.

Table 15.5 Comparative study of metaheuristic algorithms for SG applications.

Table 15.6 Most popular error measures for regression tasks.

Table 15.7 Classes of score metrics.

Table 15.8 Short‐term SG‐based AI applications.

Table 15.9 Medium‐term SG‐based AI applications.

Table 15.10 Long‐term SG‐based AI applications.

Chapter 17

Table 17.1 Overview of standards.

List of Illustrations

Chapter 1

Figure 1.1 The fundamentals of electric power system. Adapted from Ref Num [...

Figure 1.2 Selection of rated voltage for three‐phase AC transmission line. ...

Figure 1.3 Main types used in electric power distribution, (a) Redial feeder...

Figure 1.4 Traditional power grid.

Figure 1.5 The conceptual model of SG framework. Ref [18]. Reproduced with p...

Figure 1.6 SG components.

Figure 1.7 Main key technology areas of smart grid.

Figure 1.8 Distributed energy resources paradigm in smart grid. Ref [20]. Re...

Figure 1.9 The distributed energy storage system.

Figure 1.10 Schematic diagram communication infrastructure for the SG.

Figure 1.11 Customer engagement demand side management spending by region, 2...

Figure 1.12 Distributed operation architecture with two levels.

Figure 1.13 Decentralized operation architecture.

Figure 1.14 Local operation architecture.

Figure 1.15 Central operation architecture.

Figure 1.16 Classification of DR.

Figure 1.17 The difference between the conventional power grid and smart gri...

Figure 1.18 Three trends of the grid edge transformation.

Figure 1.19 Technologies for the evolution of the SG.

Figure 1.20 Fishbone diagram showing gaps.

Figure 1.21 The main stages for achieving grid modernization.

Figure 1.22 SG role in the electricity power sector.

Figure 1.23 SG investment. Adapted from [89].

Figure 1.24 SG costs Ref [90]. Reproduced with permission from EPRI (Electri...

Chapter 2

Figure 2.1 Flowchart of the common renewable energy sources.

Figure 2.2 Renewable energy resources theoretical potential.

Figure 2.3 Total renewable power installed capacity (GW), including its annu...

Figure 2.4 Main features of the bioenergy energy technology. Adapted from [1...

Figure 2.5 Bioenergy conversion processes for different end products.

Figure 2.6 Global biomass cumulative installed capacity, 2000–2013. Ref Num ...

Figure 2.7 Biomass installed capacity for energy systems (2010–2025). Ref Nu...

Figure 2.8 Cumulative installed geothermal generating capacity by top 10 cou...

Figure 2.9 Global geothermal installed capacity from 1950 up to 2019 and its...

Figure 2.10 Hydropower generation by top 10 countries in 2019. Adapted from ...

Figure 2.11 The evolution of world hydropower generation since 1980.

Figure 2.12 Global ocean power capacity forecasting.

Figure 2.13 Global integrated solar PV capacity from 2000 to 2019.

Figure 2.14 Solar PV global capacity by top 10 countries in 2019.

Figure 2.15 PV shares of grid‐connected (distributed and centralized) and of...

Figure 2.16 Global installed concentrating solar power capacity, 2000–2019....

Figure 2.17 Concentrating solar power capacity in the top 10 countries in 20...

Figure 2.18 Solar water heating collectors’ global capacity, 2000–2019.

Figure 2.19 Solar water heating collector capacity by top 10 countries in 20...

Figure 2.20 Growth in capacity and rotor diameter of wind turbines, 1985–201...

Figure 2.21 Total installed global wind power capacity, 2000–2019.

Figure 2.22 Installed wind power capacity, top 10 countries in 2019.

Figure 2.23 Wind power market forecast for 2017–2021. Ref [55]. Reproduced w...

Figure 2.24 Predicted world total installed renewable generating capacity, 2...

Figure 2.25 Predicted world renewable electricity generation and their globa...

Figure 2.26 Potential features of renewable energy sources integration.

Figure 2.27 Financial investments in renewable energy by technology, 2004–20...

Figure 2.28 Research and development costs on renewable energy, 2004–2019, A...

Figure 2.29 Global employment in renewable energy 2011–2018, Adapted from [5...

Figure 2.30 Renewable energy market development process, [9]. Reproduced wit...

Figure 2.31 Barriers to renewable energy technology deployment.

Figure 2.32 Maturity of selected renewable energy technologies, Adapted from...

Figure 2.33 Common challenges in bulk implementation of RES into the SG [9]....

Figure 2.34 Modern power system flexibility measures.

Figure 2.35 Overview of technical solutions for renewables integration into ...

Chapter 3

Figure 3.1 The world net electricity generation from 2012 to 2040 (trillion ...

Figure 3.2 A general grid connected PV power system.

Figure 3.3 A general wind power system. Adapted from Ref Num [12].

Figure 3.4 Voltage source converter with synchronous reference frame control...

Figure 3.5 Voltage source converter with stationary reference frame control ...

Figure 3.6 A standard PLL structure for grid synchronization.

Figure 3.7 A grid connected virtual synchronous generator system.

Figure 3.8 Classical multilevel converter topologies: (a) three‐level neutra...

Figure 3.9 A typical configuration of a three‐phase MMC to be applied in sma...

Figure 3.10 General block diagram of a classical control method for MMC. Ada...

Figure 3.11 Typical structure of an AC microgrid having power electronic int...

Figure 3.12 Power flow on a transmission line.

Figure 3.13

f

‐

P

and

V

‐

Q

droops applied in power electronic based systems.

Figure 3.14

f

‐

Q

and

V

‐

P

droops applied in power electronic based systems.

Figure 3.15 Schematic of virtual impedance in a voltage source inverter to b...

Figure 3.16 Grid structure control. (a) Centralized control scheme. (b) Dist...

Figure 3.17 Typical structure of a DC microgrid connected to a utility grid ...

Figure 3.18 DC‐DC power converters and the double loop PI control scheme.

Figure 3.19 Control of the AC/DC rectifier shown in Figure 3.17.

Figure 3.20 Equivalent circuit for V‐I droop where V is the voltage referenc...

Figure 3.21 Equivalent circuit of the extended droop for HESS applications....

Figure 3.22 Coordination between V‐I droop and the extended droop for the HE...

Figure 3.23 The current sharing pattern in the HESS by using the extended dr...

Figure 3.24 Mode adaptive droop control [55].

Chapter 4

Figure 4.1 An electrical energy storage (EES) system structure (a) and energ...

Figure 4.2 Classification of electrical energy storage technologies accordin...

Figure 4.3 Electrical energy storage technologies classification according t...

Figure 4.4 Maturity of electrical energy storage technologies.

Figure 4.5 Global Grid‐Connected Energy Storage Capacity, by Technology, 201...

Figure 4.6 Electricity storage technologies comparison – discharge time vs p...

Figure 4.7 Self‐discharge and suggested storage period of energy storage sys...

Figure 4.8 Comparison of power density and energy density of energy storage ...

Figure 4.9 Energy storage technologies capital cost, 2018.

Figure 4.10 Levelized cost of storage (LCOS) for different technologies, 201...

Figure 4.11 Typical cycle efficiency (max. and min.) of energy storage syste...

Figure 4.12 Energy Stored on Energy Invested (ESOI) ratios of different ener...

Figure 4.13 Cost calculation for energy storage system. Ref Num [39]. Reprod...

Figure 4.14 An example for optimal allocation procedure of ESS in distributi...

Figure 4.15 Selected services of energy storage systems with the correspondi...

Figure 4.16 Typical grid energy storage applications at different voltage le...

Figure 4.17 Energy Storage main deployment barriers.

Chapter 5

Figure 5.1 The simplified single‐line diagram of a microgrid.

Figure 5.2 Typical configuration of the DG units with (a) LVAC network; (b) ...

Figure 5.3 AC microgrid structure with DG units and mixed types of loads.

Figure 5.4 Concept of a DC microgrid system with the DG units and mixed type...

Figure 5.5 PV solar power modules price learning curve for different technol...

Figure 5.6 The hierarchical control structure of the microgrid. Adapted from...

Figure 5.7 The simplified single‐line diagram and DG structure of microgrid ...

Figure 5.8 P/ω and Q/E droop characteristics [8]. Reproduced with permission...

Figure 5.9 The simplified control scheme of traditional droop control for DG...

Figure 5.10 Comparison of traditional and opposite droop characteristics.

Figure 5.11 General structure of centralized and decentralized control appro...

Figure 5.12 Control and management architecture of a multi‐microgrid system....

Figure 5.13 An overview of micro grid benefits Ref [67]. Reproduced with per...

Chapter 6

Figure 6.1 Global sales of electric cars (BEV and PHEV) by year.

Figure 6.2 The typical powertrain configuration of series HEV.

Figure 6.3 The typical powertrain configuration of parallel HEV.

Figure 6.4 The typical powertrain configuration of series–parallel HEV.

Figure 6.5 The simplified block diagram of battery charger.

Figure 6.6 EV charging configuration at (a) AC Level 1 and 2 setups; (b) DC ...

Figure 6.7 The general structure of wireless power transfer technology for E...

Figure 6.8 Concepts of (a) static WPT system; and (b) dynamic WPT system.

Figure 6.9 The general structure of V2G, V2H, and V2V concepts in power syst...

Figure 6.10 The typical bidirectional power converter topology for bidirecti...

Figure 6.11 Technological, social, and economic problems of EVs.

Chapter 7

Figure 7.1 The net‐Zero Energy Concept (nZEC). Adapted from Ref Num [2].

Figure 7.2 Net Zero Energy Building (nZEB) overview and relevant terminology...

Figure 7.3 The nZEB balance concept. Ref Num [4]. Reproduced with permission...

Figure 7.4 nZEB main design elements.

Figure 7.5 Main design elements for the nZEB.

Figure 7.6 Fully integrated nZEB energy management system. Adapted from Ref ...

Figure 7.7 Utilization share of major simulation programs in building optimi...

Figure 7.8 Renewable energy system size Multi‐objective design optimization....

Figure 7.9 Net Zero Energy Community: general overview.

Figure 7.10 Global building efficiency revenue, 2011–2018.

Chapter 8

Figure 8.1 Evolution of electricity metering.

Figure 8.2 Illustration of typical smart meter systems.

Figure 8.3 Architectural model of smart meter system.

Figure 8.4 Smart meter security objectives. Ref Num [6]. Reproduced with per...

Figure 8.5 SG communication: a hierarchical structure with three major netwo...

Chapter 9

Figure 9.1 Basic information layer of SG.

Figure 9.2 Basic communication layer. Adapted from Ref Num [5].

Figure 9.3 Resilient control system framework.

Figure 9.4 Percentage of attacks on grid components. Ref Num [15]. Reproduce...

Figure 9.5 Main security objectives in SG. Adapted from Ref [24].

Figure 9.6 Relation between cyber threats and SG cyber requirements. Adapted...

Figure 9.7 Common sources of cyber‐attacks. Adapted from Ref [34].

Figure 9.8 Impact of attacks on the power grid.

Figure 9.9 Cyber incidents percentages in 2017.

Chapter 10

Figure 10.1 Data sources used by SG.

Figure 10.2 Pattern of big data volume in electric utilities. Adapted from R...

Figure 10.3 Big Data architecture and patterns.

Figure 10.4 Big data process.

Figure 10.5 An approach to develop a platform‐oriented analytical architectu...

Figure 10.6 Big Data V's.

Figure 10.7 The path of SG. Adapted from Ref Num [33].

Figure 10.8 Integrated analytics model to generate value.

Figure 10.9 Meter data management.

Chapter 11

Figure 11.1 Benefits of demand side management. Adapted from Ref [3].

Figure 11.2 Demand response.

Figure 11.3 Major features load response programs.

Figure 11.4 Major features of price response programs.

Figure 11.5 DR programs. Adapted from Ref [8].

Figure 11.6 Clarification of the procedure of end‐user‐interaction defining ...

Figure 11.7 A stylized interpretation.

Figure 11.8 Major areas of dDSM.

Figure 11.9 Relationships between DSM Table 11.1.1 Programs. Adapted from Re...

Figure 11.10 Process evaluation of DSM.

Figure 11.11 Major Research Studies worldwide. Ref [22]. Reproduced with per...

Chapter 12

Figure 12.1 Conceptualization of the business model canvas.

Figure 12.2 The business model framework used to understand the impact of SG...

Figure 12.3 The traditional electricity value chain.

Figure 12.4 The emerging electricity value chain with both power and informa...

Figure 12.5 Structure of electricity trading for different time horizons.

Figure 12.6 Market entities and their interactions in smart grid arena.

Figure 12.7 The timing‐based business model and its created value for the SG...

Figure 12.8 The business intelligence framework which can be applied to the ...

Figure 12.9 Benefits of integrated energy services.

Figure 12.10 Future business model levers for the SG. Ref [24]. Used with pe...

Figure 12.11 Blockchain based electricity market [25].

Chapter 13

Figure 13.1 Traditional electric power system structure.

Figure 13.2 Concept of the SG – indicating physical and communication interc...

Figure 13.3 Concept of prosumer on SG.

Figure 13.4 Building blocks of the SG [22]. Reproduced with permission from ...

Figure 13.5 The three pillars of the SG: smart marker; smart utility and sma...

Figure 13.6 Different SG domains.

Figure 13.7 The energy cultures conceptual framework.

Figure 13.8 Theory of reasoned action.

Figure 13.9 Theory of planned behavior.

Figure 13.10 Theory of planned behavior model with resistance to change vari...

Figure 13.11 Technology acceptance model.

Figure 13.12 Technology acceptance model integrated with perceived risk.

Figure 13.13 Technology acceptance model for SG with external variables set....

Figure 13.14 Value‐based adoption model of technology.

Figure 13.15 The three dimensions of the SG social acceptance, which are: ma...

Figure 13.16 The innovation decision process in the SG arena.

Figure 13.17 Factors affecting the rate of innovation adoption toward SG.

Figure 13.18 Transtheoretical model – the process of change toward SG.

Figure 13.19 The Fogg behavior model has three factors: motivation, ability,...

Figure 13.20 SG expectation cycle [88]. Reproduces with permission from Pric...

Figure 13.21 Energy providers‐consumer new relationships focus areas in SG....

Chapter 14

Figure 14.1 The relationship between SG, cloud computing, and big data.

Figure 14.2 Cloud computing model essential characteristics.

Figure 14.3 Cloud computing layers.

Figure 14.4 Three forms of the clouds; Public, Private, or Hybrid clouds.

Figure 14.5 Workload distribution architecture.

Figure 14.6 Cloud bursting architecture.

Figure 14.7 Dynamic scalable architecture.

Figure 14.8 Elastic resource capacity architecture.

Figure 14.9 Cloud computing platform coupled with SG.

Figure 14.10 Various SG applications supported by cloud.

Figure 14.11 SG with and without cloud computing.

Figure 14.12 The cloud computing characteristics.

Figure 14.13 Cloud computing opportunities and challenges for smart grid.

Figure 14.14 Major categories of data security challenges.

Chapter 15

Figure 15.1 Frequency of use of terms AI, SG, DL, and smart cities in books ...

Figure 15.2 Search methodology based on keyword combinations.

Figure 15.3 Machine learning classes and usage.

Figure 15.4 Machine learning models with their open‐source libraries.

Figure 15.5 Pictorial representation of the AI Models applied to SG in 2019–...

Figure 15.6 Timescale evolution of Artificial Neural Networks with Operation...

Figure 15.7 Fuzzy inference system structure.

Figure 15.8 Flow diagram of expert system.

Figure 15.9 Representation of hybrid models applied to SG.

Figure 15.10 Representation of hybrid models applied to SG.

Figure 15.11 Commonly used score metrics for regression.

Figure 15.12 Forecasting procedure.

Figure 15.13 Flowchart of advanced MPPT algorithms.

Figure 15.14 Classification of faults in power systems.

Figure 15.15 Structure of the cyber‐physical system.

Figure 15.16 Flowchart of electricity pricing factors.

Chapter 16

Figure 16.1 A conceptual structure of co‐simulation [12]. Reproduces with pe...

Figure 16.2 Distinction between co‐simulation and other simulation types [1]...

Figure 16.3 Specific and generic co‐simulation structures.

Figure 16.4 The structure of Controller‐HIL and Power‐HIL platforms.

Figure 16.5 Summary of basic analysis fields in each tool.

Chapter 17

Figure 17.1 US Smart Meter Installations.

Figure 17.2 General overview of the active committees in the SG environment....

Figure 17.3 SG technical standards architecture developed by SGCC.

Chapter 18

Figure 18.1 Structure of future power networks with hybrid AC and DC sub‐gri...

Figure 18.2 Typical structure of smart distribution grids.

Figure 18.3 Reliability concepts in electric power systems.

Figure 18.4 Reliability concepts in modern SGs.

Guide

Cover Page

Title Page

Copyright Page

About the Author

Acknowledgments

Preface

List of Abbreviations

About the Companion Website

Table of Contents

Begin Reading

Index

Wiley End User License Agreement

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Smart Grid and Enabling Technologies

Shady S. Refaat

Texas A&M University at Qatar, Doha, Qatar

Omar Ellabban

CSA Catapult Innovation Centre, Newport, UK

Sertac Bayhan

Qatar Environment and Energy Research Institute, Hamad bin Khalifa University, Doha, Qatar

Haitham Abu-Rub

Texas A&M University at Qatar, Doha, Qatar

Frede Blaabjerg

Aalborg University, Aalborg, Denmark

Miroslav M. Begovic

Texas A&M University, College Station, USA

This edition first published 2021© 2021 John Wiley & Sons Ltd

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The right of Shady S. Refaat, Omar Ellabban, Sertac Bayhan, Haitham Abu‐Rub, Frede Blaabjerg, and Miroslav M. Begovic to be identified as the authors of this work has been asserted in accordance with law.

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Library of Congress Cataloging‐in‐Publication Data

Names: Refaat, Shady S., author. | Ellabban, Omar, author. | Bayhan, Sertac, author. | Abu-Rub, Haithem, author. | Blaabjerg, Frede, author. | Begovic, Miroslav M., 1956- author.Title: Smart grid and enabling technologies / Shady S. Refaat, Texas A&M University at Qatar, Doha, Qatar, Omar Ellabban, CSA Catapult Innovation Centre, Newport, UK, Sertac Bayhan, Qatar Environment and Energy Research Institute, Hamad bin Khalifa University, Doha, Qatar, Haitham Abu-Rub, Texas A&M University at Qatar, Doha, Qatar, Frede Blaabjerg, Aalborg University, Aalborg, Denmark, Miroslav M. Begovic, Texas A&M University, College Station, USA.Description: First edition. | Hoboken, NJ : Wiley, 2021. | Includes bibliographical references and index.Identifiers: LCCN 2021012116 (print) | LCCN 2021012117 (ebook) | ISBN 9781119422310 (hardback) | ISBN 9781119422433 (adobe pdf) | ISBN 9781119422457 (epub)Subjects: LCSH: Smart power grids.Classification: LCC TK3105 .R44 2021 (print) | LCC TK3105 (ebook) | DDC 621.31–dc23LC record available at https://lccn.loc.gov/2021012116LC ebook record available at https://lccn.loc.gov/2021012117

Cover Design: WileyCover Image: © NicoElNino/Getty Images

About the Author

Shady S. Refaat received the BASc, MASc, and PhD degrees in Electrical Engineering in 2002, 2007, and 2013, respectively, all from Cairo University, Giza, Egypt. He has worked in the industry for more than 12 years as Engineering Team Leader, Senior Electrical Engineer, and Electrical Design Engineer on various electrical engineering projects. Currently, he is an associate research scientist in the Department of Electrical and Computer Engineering, Texas A&M University at Qatar. He is a senior member of the Institute of Electrical and Electronics Engineers (IEEE), a member of The Institution of Engineering and Technology (IET), a member of the Smart Grid Center – Extension in Qatar (SGC‐Q). He has published more than 105 journal and conference articles. His principal work area focuses on electrical machines, power systems, smart grid, Big Data, energy management systems, reliability of power grids and electric machinery, fault detection, and condition monitoring and development of fault‐tolerant systems. Also, he has participated and led several scientific projects over the last eight years. He has successfully realized many potential research projects.

Omar Ellabban (S’10–M’12–SM’13) is a senior researcher and creative manager with more than 20 years of combined experiences (teaching, research, industrial experience, consulting services and project management) between academia, research institutes, industry and power utility companies in various fields.

Dr. Ellabban is conducting and leading many research projects in different areas, such as: power electronics, electric vehicles, automatic control, motor drive, energy management, grid control, renewable energy, energy storage devices, distributed energy systems and their integration into the smart grid. Dr. Ellabban received his BS (Hons) degree in electrical machines and power engineering from Helwan University, Egypt; his MS degree in electrical machines and power engineering from Cairo University, Egypt; and his PhD (Hons.) degree in electrical engineering from Free University of Brussels, Belgium, in 1998, 2005, and 2011, respectively. He joined the Research and Development Department, Punch Powertrain, Sint‐Truiden, Belgium, in 2011, where he and his team developed a next‐generation, high‐performance hybrid powertrain. In 2012, he joined Texas A&M University in Qatar as a postdoctoral research associate and became an assistant research scientist in 2013, where he is involved in different renewable energy integration projects. In 2016, he joined Iberdrola Innovation Middle East as the Research and Development Director to lead a number of research, development and innovation projects under various topics focusing on transforming the current electric grid into a smart grid and integrating renewable energies and energy storage systems interfaced by power electronics converters as microgrids penetrating the distribution networks. In 2020, he joined CSA Catapult as Principal Power Electronics Engineer to lead different projects focusing on Compound Semiconductors applications across different sectors.

Dr. Ellabban has authored more than 70 journal and conference papers, one book chapter, two books entitled, “Impedance Source Power Electronic Converters, 2016” and “Smart Grid Enabling Technologies, 2020” and many international conference tutorials. His current research interests include renewable energies, grid control, smart grid, automatic control, motor drives, power electronics, and electric vehicles. He is a Senior Member of the IEEE, IET member and currently serves as an Associate Editor of the IEEE Transactions on Industrial Electronics.

Sertac Bayhan received the MS and PhD degrees in electrical engineering from Gazi University, Ankara, Turkey, in 2008 and 2012, respectively. In 2008, he joined the Electronics and Automation Department, Gazi University, as a Lecturer, where he was promoted to Associate Professor in 2017. From 2014 to 2018, he worked at Texas A&M University at Qatar as a Postdoctoral Fellow and Research Scientist. Dr. Bayhan is currently working in the Qatar Environment and Energy Research Institute (QEERI) as a Senior Scientist and he is a faculty member with the rank of Associate Professor in the Sustainable Division of the College of Science and Engineering at Hamad Bin Khalifa University.

Dr. Bayhan is the recipient of many prestigious international awards, such as the Research Fellow Excellence Award in recognition of his research achievements and exceptional contributions to the Texas A&M University at Qatar in 2018, the Best Paper Presentation Recognition at the 41st and 42nd Annual Conference of the IEEE Industrial Electronics Society in 2015 and 2016, Research Excellence Travel Awards in 2014 and 2015 (Texas A&M University at Qatar), and Researcher Support Awards from the Scientific and Technological Research Council of Turkey (TUBITAK). He has acquired $13 M in research funding and published more than 150 papers in mostly prestigious IEEE journals and conferences. He is also the coauthor of two books and four book chapters.

Dr. Bayhan has been an active Senior Member of IEEE. Because of the visibility of his research, he has recently been elected as Chair of IES Power Electronics Technical Committee and selected as a Co‐Chair of IEEE‐IES Student and Young Professional Activity Program. He currently serves as Associate Editor for IEEE Transactions on Industrial Electronics, IEEE Journal of Emerging and Selected Topics in Industrial Electronics, and IEEE Industrial Electronics Technology News, and Guest Editor for the IEEE Transactions on Industrial Informatics.

Haitham Abu‐Rub is a full professor holding two PhDs from Gdansk University of Technology (1995) and from Gdansk University (2004). Dr. Abu Rub has much teaching and research experience at many universities in a number of countries including Qatar, Poland, Palestine, the USA, and Germany.

Since 2006, Dr. Abu‐Rub has been associated with Texas A&M University at Qatar, where he has served for five years as chair of Electrical and Computer Engineering Program and has been serving as the Managing Director of the Smart Grid Center at the same university.

His main research interests are energy conversion systems, smart grid, renewable energy systems, electric drives, and power electronic converters.

Dr. Abu‐Rub is the recipient of many prestigious international awards and recognitions, such as the American Fulbright Scholarship and the German Alexander von Humboldt Fellowship. He has co‐authored around 400 journal and conference papers, five books, and five book chapters. Dr. Abu‐Rub is an IEEE Fellow and Co‐Editor in Chief of the IEEE Transactions on Industrial Electronics.

Frede Blaabjerg (S’86–M’88–SM’97–F’03) was with ABB‐Scandia, Randers, Denmark, from 1987 to 1988. He became an Assistant Professor in 1992, an Associate Professor in 1996, and a Full Professor of power electronics and drives in 1998. From 2017, he became a Villum Investigator. He is honoris causa at University Politehnica Timisoara (UPT), Romania and Tallinn Technical University (TTU) in Estonia. His current research interests include power electronics and its applications such as in wind turbines, PV systems, reliability, harmonics and adjustable speed drives. He has published more than 600 journal papers in the fields of power electronics and its applications. He is the co‐author of four monographs and editor of 10 books in power electronics and its applications. He has received 32 IEEE Prize Paper Awards, the IEEE PELS Distinguished Service Award in 2009, the EPE‐PEMC Council Award in 2010, the IEEE William E. Newell Power Electronics Award 2014, the Villum Kann Rasmussen Research Award 2014, the Global Energy Prize in 2019, and the 2020 IEEE Edison Medal. He was the Editor‐in‐Chief of the IEEE Transactions on Power Electronics from 2006 to 2012. He has been Distinguished Lecturer for the IEEE Power Electronics Society from 2005 to 2007 and for the IEEE Industry Applications Society from 2010 to 2011 as well as 2017 to 2018. In 2019–2020 he serves as President of IEEE Power Electronics Society. He is also Vice‐President of the Danish Academy of Technical Sciences. He was nominated in 2014–2019 by Thomson Reuters to be among the most 250 cited researchers in Engineering across the world.

Miroslav M. Begovic (FIEEE’04) is Department Head of Electrical and Computer Engineering and Carolyn S. & Tommie E. Lohman ‘59 Professor at Texas A&M University. Prior to that, he was Professor and Chair of the Electric Energy Research Group in the School of Electrical and Computer Engineering, and an affiliated faculty member of the Brook Byers Institute for Sustainable Systems and University Center of Excellence in Photovoltaic Research at Georgia Tech. Dr. Begovic obtained his PhD from Virginia Tech University. His research interests are in monitoring, analysis, and control of power systems, as well as development and applications of renewable and sustainable energy systems. For the Centennial Olympic Games in 1996 in Atlanta, he designed with Professor Ajeet Rohatgi, a 340 kW photovoltaic system on the roof of Aquatic Center at Georgia Tech, which at that time was the largest roof‐mounted PV system in the world. He has been a member of the IEEE PES Power System Relaying Committee for two decades and chaired a number of its working groups. Professor Begovic was Editor of the section on Transmission Systems and Smart Grids in the Springer Encyclopedia on Sustainability (published in 2012), coordinated by an Editorial Board consisting of five Nobel Prize Laureats, has also served as guest editor of the IET Generation, Transmission & Distribution Special Issue on Wide Area Monitoring and Control in 2010, authored one section of a book, nearly 200 journal and conference papers, two IEEE special publications, and delivered more than 100 keynote and invited presentations. He authored invited papers in three Special issues of IEEE Proceedings: on Future Energy Systems (2010), on Critical Infrastructures (2005) and on Renewable Energy (2001).

Dr. Begovic is a Fellow of IEEE and member of Sigma Xi, Tau Beta Pi, Phi Kappa Phi and Eta Kappa Nu. Dr. Begovic is a former Chair of the Emerging Technologies Coordinating Committee of IEEE PES, IEEE PES Treasurer (2010–2011), IEEE PES Distinguished Lecturer, and serves as President of the IEEE Power and Energy Society.

Acknowledgments

We would like to take this opportunity to express our sincere appreciation to all the people who were directly or indirectly helpful in making this book a reality.

We are grateful to the Qatar National Research Fund (a member of Qatar Foundation) for funding many of the research projects, whose outcomes helped us in preparing a major part of this book chapters. Chapters 1, 8, 9, and 17 for NPRP grant [NPRP12S‐0226‐190 158], chapter five for NPRP grant [NPRP9‐310‐2‐134], Chapters 10, 14 and 15 for NPRP grant [NPRP10‐0101‐170 082], and Chapter 6 for NPRP grant [NPRP12S‐0214‐190 083]. The statements made herein are solely the responsibility of the authors.

Also, we appreciate the help from many colleagues and students for providing constructive feedback on the material and for help with the editing. Particular appreciation goes to Mohammad Saleh, Amira Mohammed, and Mohamed Massoudi.

We are indebted to our family members for their continuous support, patience, and encouragement without which this book would not have been completed.

Preface

Smart grid (SG) is an emerging area of engineering and technology which integrates electricity, communication, and information infrastructures to ensure an efficient, clean, and reliable electric energy supply. This is an extremely complex field with different disciplines and engineering areas pooled together. This book aims to cover SG technologies and their applications in a systematic and comprehensive way. Different areas of SGs have been included in this book, such as architectural aspects of the SG, renewable energy integration, power electronics domination in the SG, energy storage technologies for SG applications, smart transportation, communication and security aspects, the pivotal role of artificial intelligence toward the evolution of SGs, SG challenges and barriers, standardization, and future vision. For this reason, the book has been written by experienced individuals who specialize in various areas of SGs.

The objective of this book is to equip readers with up‐to‐date knowledge of the fundamentals, emerging grid structure, current research status, and future vision in the development and deployment of SGs. The concepts presented in this book include four main areas of SGs and its applications: Advanced SG Architecture which includes smart power systems, communication systems, information technology, security, and the advancement of microgrids. Renewables energies, entail technologies of both energy storages, and power electronics suitable for renewable energy systems and SG applications. SG applications are divided into fundamental and emerging applications. The fundamental applications refer to energy management strategies, reliability models, security, and privacy, in addition to promoting demand‐side management (DSM). Emerging applications include the deployment of electric vehicles (EVs) and mobile charging stations. SG tools are divided into crucial tools for distribution grids such as Big Data management and analytics, cloud management and monitoring tools, consumer engagement, and artificial intelligence for the SG, the requirements for the simulation tools and the recently adopted standards, in addition to the challenges and future business models of SGs.

The book builds its foundation by introducing the SG architecture and integrating renewable energy sources and energy storage systems in the next generation power grid. The first chapter provides a basic discussion on the infrastructure of SGs followed by the technologies used in the SG. An overview of different renewable energy resources is discussed in Chapter 2 showing their current status, future opportunities, and the challenges of integrating them with the grid. Energy storage systems and power electronics converters as grid integration units are presented in Chapters 3–4. A comprehensive review of microgrids, including their characteristics, challenges, design, control, and operation either in grid‐connected or islanded modes are introduced in Chapter 5. Chapter 6 is devoted to one of the most emerging applications of SGs, which is smart transportation. This chapter presents an overview of EVs; their current status and future opportunities, in addition to the challenges of integrating them into the SG. The impact of EVs on SG operation and modeling EV mobility in energy service networks are also exemplified in this chapter. The net‐zero energy cost building uses energy efficiency and renewable energy strategies as part of the business model. Chapter 7 describes the zero energy buildings (ZEBs) definition, design, modeling, control, and optimization. This chapter discusses the benefits and barriers of the current state and the future trends of ZEBs as a step to reduce energy consumption in the building sector. The goal of Chapter 8 is focused on the SG features utilizing multi‐way communication among energy production, transmission, distribution, and usage facilities. The multi‐way communication among energy generation, transmission, distribution, and usage facilities is discussed in Chapter 8. The reliable, efficient, and intelligent management of complex power systems necessitates an employment of high‐speed, reliable, and secure data information and communication technology into the SG to manage and control power production and usage; this topic is described in Chapter 9. The electric energy sector is sitting on a data goldmine, the so‐called Big Data. The real value of Big Data utilization resides in a good understanding of its analytics technologies, promise, and potential applications in SGs, which is discussed in Chapter 10. Driven by concerns regarding electric sustainability, energy security, and economic growth, it is essential to have a coordination mechanism based on heuristic rules to manage the energy demand and enhance the survivability of the system when failures occur or at peak periods, which is achieved by the principle of DSM systems defined in Chapter 11. It is essential to know the business model concepts, their main components, and how they can be used to analyze the impact of SG technology to create, deliver, and capture values for the utility business. The value chain for both traditional and smart energy industries are needed. The different electricity markets have been described and presented in Chapter 12. Chapter 13 aims to provide energy systems researchers and decision‐makers with proper insight into the underlying drivers of consumer acceptance of the SG and the logical steps for their engagement to promote SG technology and make it feasible promptly. The fundamental relationship between SG and cloud computing services is also covered. The architectural principles, characteristics of cloud computing services, and the examination of the advantages and disadvantages of those characteristics for SG are defined. Furthermore, the opportunities and challenges of using cloud computing in SGs, and the major categories of data security challenges of cloud computing are described in Chapter 14.

In Chapter 15, the latest taxonomy of Artificial Intelligence (AI) applications in SGs is discussed, including load and renewable energy forecasting, power optimization, electricity price forecasting, fault diagnosis, and cyber and physical layers security. Chapter 16 discusses the current state of simulation‐based approaches including multi‐domain simulation, co‐simulation, and real‐time simulation and hardware‐in‐the‐loop for SGs. Furthermore, some SG planning and analysis software are summarized with their advantages and disadvantages. Chapter 17 presents an overview of SG standards; new standardization studies, SG policies of some countries, and some important standards for the smart grid. Chapter 18 depicts the concepts of distributed generation, micro‐grid, SG, and distributed operation, which all pose more complexity and challenges to the modern power systems. This chapter presents the challenges and barriers that modern SGs face from different perspectives.

This book has the typical attributes of a contemporary book and discusses several aspects that will appeal to students, researchers, professionals, and engineers from various disciplines looking to increase their knowledge, insights, and ideas for the future development of SG as the next energy paradigm. This work perfectly fills the current gap and contributes to the realization and a better understanding of SG and its enabling technologies.

List of Abbreviations

3 GPP

Third Generation Partnership Project

ABC

Ant bee colony

Adaboost–MLP

Adaptive boosting-Multilayer perceptron

AER

all-electric-range

AMI

Advanced metering infrastructure

AMR

Automated Meter Reading

AMS

Automatic metering services

AM

analytical methods

ANSI

American National Standards Institute

AC

Alternative Current

AVR

Automatic Voltage Regulator

AWS

Amazon Web Services

API

Application programming interface

ANN

artificial neural net-work

ARIMA-XGBoost

Autoregressive integrated moving Average-Extreme gradient boosting

ARMA-TDNN

Autoregressive and moving average-Time delay neural network

ARMA

Autoregressive and Moving Average

ARIMA

Autoregressive integrated moving average

Adaboost–MLP

Adaptive boosting-Multilayer perceptron

ARIMA-ANFIS

Autoregressive integrated moving average-

adaptive neuro-fuzzy inference system

ANFIS

Adaptive neuro-Fuzzy inference system

AODE

Aggregating One-Dependence Estimators classifier

ARMA

Autoregressive moving average

ACO

Ant colony optimization

Ant colony

Ant colony

ABC

Ant bee colony

ARIMA Neurofuzzy-

Artificial neural networks-fuzzy logic-Autoregressive integrated moving average

ARIMA Mixed

Mixed autoregressive integrate moving average

Improved ARIMAX

Improved Autoregressive integrated moving average process with exogenous inputs

BAC

Building Automation and Control

BM

business model

BPE

Building Produced Energy

BANs

Building/Business Area Network

BPL

Broadband over Power Line

Bayesian

Wavelet-Extreme learning machine

Boosting additive quantile regression

Boosting additive quantile regression

BNN

Bayesian neural network

BAC

Bayesian actor-Critic algorithms

BBN

Bayesian belief network

Bio

Biological swarm chasing algorithm

bioenergy

Biomass energy

biofuels

liquid fuels

BEVs

battery electric vehicles

CC

Cloud Computing

CIM

Common Information Model

CPP

critical peak pricing

CHIL

controller HIL

CHP

Combined heat and power systems

CIS

Customer Information System

CEPRI

China Electric Power Research Institute

COAG

Council of Australian Governments

CAES

compressed air energy storage

CHB

cascaded H-bridge converter

CMC

central management controller

Faster R-CNN

Faster Region-based Convolutional neural network

CNN-WT

Convolutional neural network-Wavelet transform

CVAELM

Complementary ensemble empirical mode decomposition with adaptive noise- Variational mode decomposition – Adaptive boosting-Extreme learning machine

CRfs

Conditional random fields

CRO-SL

Coral reefs optimization algorithm

CSP

concentrating solar power

c-Si

silicon

CO

2

carbon dioxide

CAES

Compressed Air Energy Storage

CICEVs

conventional internal-combustion-engine vehicles

DG

Distributed Generation

DR

Demand Response

DMS

Distribution Management System

DSI

Demand-Side Integration

DSM

Demand Side Management

DSL

Digital Subscriber Line

DSO

distribution network operator

DER

Distributed Energy Resources

DRMS

Demand Response Management System

DFIG

doubly fed induction generator

DC

Direct Current

DPR

digital protective relay

DFR

digital fault recorder

DGs

distributed generators

DES

Distributed Energy Storage

DBN

Deep belief networks

DCNN

Deep convolutional neural network

DRN-DWWC

Deep residual networks - Dynamically weighted wavelet coefficients

DQL

Deep Q-learning

DNI

direct component

DoD

Depth of Discharge

E2E

End-to-End

EMS

Energy management system

EU

European Union

EPBD

Energy Performance of Building Directive

ETL

extract, transform and load

ETAP

Electrical Transient Analyzer Program

EPRI

Electric Power Research Institute

EEGI

European Electricity Grid Initiative

ESSs

energy storage systems

EMI

electromagnetic interference

ERP

Enterprise Resource Planning

EV

Electric Vehicles

ESN

Echo state networks

EKF-based NN

Extended Kalman filter method Neural Network

Extra tree

Extra tree

ETC

evacuated tube solar collectors

ESOI

energy stored on energy invested

EVSE

Electric Vehicle Supply Equipment

EPS

Electric power system

FACTS

Flexible AC transmission systems

FERC

Federal Energy Regulatory Commission

FMI

Functional Mockup Interface

FMU

Functional Mock-up Units

FAN

Field Area Network

FC

flying capacitor converter

FL

Fuzzy logic

FCRBM

Factored conditional restricted Boltzmann Machine

Faster R-CNN

Faster Region-based Convolutional neural network

FIS-LSE

Fuzzy inference system-least-squares estimation

FLC-FA

Fuzzy logic controller-Firefly algorithm

FR

Fresnel Reflector

FPC

flat-plate solar collectors

FES

Flywheel Energy Storage

FCEVs

fuel-cell electric vehicles

GOOSE

generic object-oriented system event

GSM

Global System for Mobile Communications

GIS

Geographic Information System

GPS

Global Positioning System

GWAC

Grid Wise Architecture Council

GASVM

Genetic functionality support vector machine

GRU NN

Gated recurrent unit neural network

GP

Gaussian process

GANs

Generative adversarial networks

GBM

Gradient boosting machine

GARCH

Generalized Autoregressive Conditional Heteroskedastic

GA-NN

Genetic algorithm-Neural network

GBRT

Gradient boosted regression tree

Glow-worm

Glow-worm optimization

GWO

grey wolf optimization

GBTD

Gradient boosting theft detector

GHP

Geothermal Heat Pump

GWEC

The Global Wind Energy Council

GHG

greenhouse gases

HANs

Home area networks

HEMs

home energy management

HIL

Hardware-in-the-Loop

HPCC

HomePlug Command and Control

HVDC

High-Voltage Direct Current

HCS

hill climbing searching

HESSs

hybrid energy storage systems

HPF

high pass filter

HDFS

Hadoop Distributed File System

HNN

Hybrid neural network

HMM

Hidden Markov models

HPPs

Hydropower plants

HEVs

hybrid electric vehicles

ICT

Information and Communication Technologies

IEDs

Intelligent Electronic Devices

IHD

In-Home Display

IANs

Industrial Area Networks and

IEC

International Electro Technical Commission

IEEE

Institute of Electrical and Electronics Engineers

ITL

Information Technology Laboratory

ISO

International Organization for Standardization

ISA

International Society of Automation

IC

internal combustion

IaaS

Infrastructure as a Service

IGCC

Integrated Gasification Combined Cycle

ITU

International Telecommunication Union

ISACA

Information Systems Audit and Control Association

Ipso-ANN

Improved article Swarm Optimization Algorithm-Artificial neural network

Improved ARIMAX

Improved Autoregressive integrated moving average process with exogenous inputs

IRENA

International Renewable Energy Agency

IEA

International Energy Agency

KPIs

Key Performance Indicators

KATS

Korean Agency for Technology and Standards

kbps

kilobits per second

KNN

K-nearest neighbors

KNN-ANN

K-nearest neighbor-Artificial neural network

KAIST

Korea Advanced Institute of Science and Technology

LPF

low pass filter

LAN

local area network

LVDC

low-voltage direct current

LVAC

low-voltage alternating current

LSTM NN

Long short-term memory Neural network

LES

Linear exponential smoothing

LightGBM

Light gradient boosting method

LCOE

levelized cost of electricity

LCOS

levelized cost of storage

Li-ion

Lithium-Ion

Li-Po

Lithium-Polymer

MG

Microgrid

MDMS

Meter Data Management System

MPPT

maximum power point tracking

MMC

modular multilevel converter

MVDC

Medium Voltage DC

MPC

Model Predictive Control

MOPSO

Multi-Objective Particle Swarm Optimization

MOGA

Multi-Objective Genetic

MDMS

Meter Data Management System

MVAC

medium voltage AC

MAS

multi agent-based control system

MV

medium voltage

MSS-Ada

MSS-Adaptive boosting

Adaboost–MLP

Adaptive boosting-Multilayer perceptron

Mixed ARIMA

Mixed autoregressive integrate moving average

MNB

Multinomial naïve bayes

MS

Molten Salt

MP

mathematical programming

NANs

neighbored area networks

NIST

National Institute of Standards and Technology

NPC

neutral-point clamped converter

nZEB

net Zero Energy Buildings

nZEC

net Zero Energy Community

NAN

Neighborhood Area Network

NN

Neural network

Neuro-Fuzzy

Artificial neural networks-Fuzzy logic

Neurofuzzy-ARIMA

Artificial neural networks-fuzzy logic-Autoregressive integrated moving average

NARX

Nonlinear autoregressive exogenous

NB

Naïve bayes

Ni-Cd

Nickel-Cadmium

Ni-MH

Nickel-Metal Hydride

OMS

Outage Management System

OPFR

Optimal Power Flow Reserves

OpenADR

Open Automated Demand Response

OPF

Optimal Power Flow Tool

OTEC

Ocean thermal energy conversion

OTEGs

ocean thermo-electric generators

OLEV

on-line electric vehicle

ORNL

Oak Ridge National Laboratory

PHIL

power HIL

PHEV

Plug-in Hybrid Electric Vehicle

PSCAD

Power Systems Computer Aided Design

PLC

Power Line Communication

PV

photovoltaic

P2P

peer-to-peer

PLL

Phase Locked Loop

PVQV

Voltage Adequacy and Stability Tool

PES

Power and Energy Society

PSF

power signal feedback

PI

proportional integral

PID

proportional–integral–derivative

PMU

phasor measurement units

PaaS

Platform as a Service

PSO