Foreword
Preface
Contents
Smart Grid 3.0: Grid with Proactive Intelligence
1 Introduction
2 Evolution of Power Grid to Smart Grid 3.0
2.1 Smart Grid 1.0
2.2 Smart Grid 2.0
2.3 Smart Grid 3.0
3 Smart Grid 3.0: Communication and Computational Technologies
3.1 AI for Smart Grid
3.2 Blockchain for Smart Grid
3.3 Bigdata Analytics for Smart Grid
3.4 Cloud, Fog, and Edge Computing for Smart Grid
3.5 5G for Smart Grid
3.6 IoT for Smart Grid
4 Challenges and Possible Solutions
4.1 Challenges for Implementing AI in Smart Grid
4.2 Challenges for Blockchain in Smart Grid
4.3 Challenges for Bigdata Analytics in Smart Grid
4.4 Challenges for Edge, Fog, and Cloud Computing in Smart Grid
4.5 Challenges for 5G and IoT in Smart Grid
5 Conclusions
References
Blockchain for Energy Management: Smart Meters, Home Automation, and Electric Vehicles
1 Introduction
2 Blockchain TechnologyâDefinition, Evolution, and Operation
2.1 Definition and Structure
2.2 Blockchain Technology Evolution
2.3 Smart Contracts
2.4 The Token Concept
3 Architecture of an Association of Producers/Energy Distributors
3.1 Peer-to-PeerâDSO Networks
3.2 Peer-to-PeerâMicrogrid Networks
3.3 Association of Renewable Energy Producers with Surplus Energy Injected into the National System that Distributes Energy to Consumers
3.4 Renewable Energy Producer/consumer of an Association Using a Batteries Stack
4 Application of Blockchain Technology for Proposed Energy Architectures
4.1 Smart Contract for the Login Application
4.2 ResultsâTesting and Validating the Behavior of the Smart Contract and the Web Application
5 Discussions and Future Perspectives
6 Conclusions
References
Engineering Applications of Blockchain Based Crowdsourcing Concept in Active Distribution Grids
1 Introduction
2 Crowdsourcing Energy System
3 Blockchain Technology
4 Enhanced Prosumers Trading Approach
4.1 Problem Formulation
4.2 The Blockchain-Based Crowdsourcing Algorithm Design for P2P Energy Transactions
5 Case Study
6 Conclusions
References
Machine Learning-Based Approaches for Transmission Line Fault Detection Using Synchrophasor Measurements in a Smart Grid
1 Introduction
2 LabVIEW Based Synchrophasor Measurements
2.1 Phasor Measurement Unit (PMU)
2.2 Phasor Estimation
2.3 LabVIEW Based SPM
2.4 Fault Detection from SPMs
3 Machine Learning Algorithms
3.1 KNN Algorithm
3.2 Support Vector Machine
3.3 Logistic Regression
4 Experimental Setup and Results Discussion
5 Conclusion
References
Data Mining-Based Approaches in the Power Quality Analysis
1 Introduction
2 Performance Indicators for the Electricity Distribution Service
3 Data Mining-Based Analysis of the Power Quality
4 Testing the Methodology
4.1 Voltage Quality Analysis
4.2 Analysis of Continuity in the Electricity Supply
5 Conclusions
References
Machine Learning and Deep Learning Approaches for Energy Management in Smart Grid 3.0
1 Introduction
1.1 Smart Grid 3.0
1.2 Energy Management System
1.3 Role of Machine Learning and Deep Learning in EMS
2 Energy Management System: Architecture, Levels, and Applications
2.1 Architecture of EMS
2.2 Levels of EMS in SG 3.0
2.3 Applications of EMS
3 Key Technologies for EMS in SG 3.0
3.1 Key Technologies Used in EMS Architecture
4 Communication Technologies for EMS in SG 3.0
4.1 Wireless Communication Technologies
4.2 Wired Communication Technologies
5 Machine Learning and Deep Learning Approaches for EMS in SG 3.0
5.1 ML for EMS in SG 3.0
5.2 DL for EMS in SG 3.0
6 Future Research Directions and Challenges
6.1 Future Research Directions
6.2 Major Challenges in Communication Technologies
6.3 Major Challenges in ML Techniques for EMS
7 Conclusion
References
Evolutionary Algorithms for Load Frequency Control of Renewable Microgrid
1 Introduction
2 Mathematical Model of Multi-microgrid
2.1 Diesel Generators
2.2 Battery Energy Storage System (BESS)
2.3 Wind Turbine Generator (WTG) Model
2.4 Wind Turbine Generator (WTG) Model
3 Evolutionary Algorithms for Load Frequency Control
3.1 Grey Wolf Optimization
3.2 Particle Swarm Optimization
3.3 Teaching Learning Based Optimization
3.4 Gravitational Search Algorithm
4 Results and Discussions
4.1 Tuning of the Controllers and Time Response Analysis
4.2 Effects of Parameter Variation
5 Conclusion
References
Agents-Based Energy Scheduling of EVs and Smart Homes in Smart Grid
1 Introduction
1.1 Introducing SGs and SHs
1.2 Literature Review
1.3 Motivation and Scope
1.4 Novelty and Contributions
1.5 Chapter Organization
2 EM Scheduling Strategies of EVs and SHs in SGs
2.1 SG Technology and Its Role in EM
2.2 Optimized Energy Scheduling Strategies and Their Implementation in SGs
2.3 Optimization Algorithms and Their Approaches
2.4 SHs and EVs in SGs
2.5 Associated Key Benefits and Challenges with EVs and SHs in SG
3 SG Structure and EV Charging Infrastructure
3.1 Structure of an SG
3.2 Problem Formulation
3.3 Deterministic-Based Energy Scheduling of EV Aggregator
3.4 Agents-Based Energy Scheduling Strategy for EVs and SHs in Domestic Area
3.5 Importance of RERs in SGs
3.6 EV Technology and Charging Options
3.7 EV Charging Infrastructure and Associated Challenges to Widespread Adoption
4 Results and Performance Evaluation of the Energy Scheduling Strategy
4.1 Input Data and System Setup
4.2 Simulation Results and Discussion
4.3 Validation of Optimal Energy Scheduling Strategy
5 Conclusions
References
Advanced Control Functionalities of Smart Grids from Communication and Computational Perspectives
1 Introduction
2 Hierarchical Control of Power Systems
3 Advanced Control Schemes for the Smart Grid Scenario
3.1 Advanced Control and Energy Management Systems in Bulk Power Systems
3.2 Hierarchical Control of Microgrids
3.3 Distributed Energy Resources Equipped with Smart Inverters
3.4 Smart Home and Smart Buildings
4 Discussion
5 Conclusions
References
Multistage PD-(1+PI) Controller Design for Frequency Control of a Microgrid Considering Demand Response Program
1 Introduction
2 Case of the Study
3 Power Plants of the MG and DRP Configuration
3.1 Solar Thermal Power (STP) Plant
3.2 Micro-hydro Power (MHP) Plant
3.3 Biogas Turbine Generator (BGTG) Unit
3.4 Biodiesel Engine Generator (BDEG) Unit
3.5 Wind Turbine Generator (WTG) Unit
3.6 Load-Generator Dynamic Model
3.7 Demand Response Program (DRP)
4 Structure of the Proposed Controller
5 Optimization Problem-Solving Procedure
5.1 Standard PSO Algorithm
5.2 PSO with Non-linear Time-Varying Acceleration Coefficients Algorithm
5.3 Objective Function (OF)
6 Simulation and Performance Review
7 Conclusion
References
Solid State Transformer: Topologies, Design and Its Applications in a Smart Grid
1 Introduction
2 Solid State Transformer
2.1 An Overview of Soft Magnetic Materials
2.2 Isolated DC/DC Power Converters
2.3 Application of Machine Learning in SST Design and Optimisation
3 New Trends in SSTs: Multi-Port SSTs
4 SST Applications in Smart Grids
4.1 Energy Internet Concept
4.2 Use of the SST as an Energy Router
4.3 MPSST as an Energy Router
4.4 A Case StudyâFour-Port MPSST as an Energy Router
5 Discussion
References
Emerging Communication Technologies for V2X: Standards and Protocols
1 Introduction
2 V2X Protocol Stacks, Use Cases, and Requirements
2.1 ETSI Cooperative-ITS (C-ITS) Reference Architecture
2.2 DSRC and IEEE 1609 Protocol Family
2.3 LTE V2X and NR V2X Protocol Stack
2.4 EV to Smart Grid Protocols
3 DSRCâIEEE 802.11p and IEEE 802.11bd
3.1 IEEE 802.11p
3.2 IEEE 802.11bd
4 Cellular V2XâLTE V2X and NR V2X
4.1 LTE-V2X
4.2 NR V2X
5 Security in V2X
6 Conclusions
References
Internet of Things for Smart Homes and Smart Cities
1 Introduction
2 Internet of Things (IoT)
2.1 IoT Architecture
2.2 5G and Beyond Technologies for IoT
3 Smart Homes
3.1 Architectures, Communication Medium and Protocols
3.2 IoT Enabled Smart Home Services
4 Smart Cities
4.1 Concepts and Architecture
4.2 IoT Based Smart Cities
5 Future Perspectives
References
Advancements in DC Microgrids: Integrating Machine Learning and Communication Technologies for a Decentralized Future
1 Introduction
2 Components of DC Mıcrogrids
2.1 Distributed Generation and Renewable Energy Sources
2.2 Loads
2.3 Energy Storage System
2.4 Point of Common Coupling (PCC)
2.5 Communication System and Controller
3 Architecture of DC Microgrids
3.1 Single-Bus DC Microgrid Structure
3.2 Multi-bus DC Microgrid Structure
3.3 Ring-Bus DC Microgrid Structure
3.4 Zonal DC Microgrid Structure
4 DC Microgrid Control Strategies
4.1 Hierarchical Control Strategy
4.2 Centralized Control
4.3 Decentralized Control
4.4 Distributed Control
5 Emerging Communication Technologies for DC Microgrids
5.1 Consumerâs Premises Area Networks
5.2 Neighborhood Area Networks (NAN)
6 Wide Area Networks (WAN)
6.1 DC Microgrid Communication Applications
6.2 Challenges in DC Microgrid Communication Infrastructures
7 Machine Learning Techniques in DC Microgrids
7.1 Support Vector Classifier (SVC)
7.2 Bernoulli Naive Bayes (NB)
7.3 Decision Trees (DT)
7.4 Nearest Centroid (NC)
7.5 Multi-layer Perceptron (MLP)
7.6 Challenges Machine Learning Techniques in DC Microgrids
8 Conclusion
References
Advanced Communication and Computational Technologies in a Sustainable Urban Context: Smart Grids, Smart Cities and Smart Health
1 Introduction
1.1 Global Environmental Problems
1.2 Urban Environmental Problems
2 The Potential for Advanced CCT Approaches in Cities
2.1 Smart Grids (SGs)
2.2 Smart Cities: General Considerations
2.3 Smart Transport
2.4 Urban Equality, Health and Wellbeing
3 Potential Problems with Advanced New CCT Approaches
3.1 General Considerations
3.2 Smart Grid Challenges
3.3 Smart City Challenges
3.4 Smart Health Challenges
4 Discussion and Conclusions
References