CHUN SING LAI, DPhil (Oxon), is Lecturer at Brunel University London, UK, Working Group Chair for the IEEE Standards Association P2814 and P3166 standards, Vice-chair of the IEEE Smart Cities Publications Committee, and a Technical Program Chair of IEEE International Smart Cities Conference 2022. LOI LEI LAI, DSc, is University Distinguished Professor at the Guangdong University of Technology, China, Chair of the IEEE Smart Cities Publications Committee, Technical Program Chair of the IEEE International Smart Cities Conference 2020, and the Editor-in-Chief of IEEE Smart Cities eNewsletter. QI HONG LAI, BSc (1st Hons), is a DPhil Candidate in Molecular Cell Biology in Health and Disease at the Sir William Dunn School of Pathology, University of Oxford, UK and Secretary of IEEE P3166 Working Group.

• Foreword xvPreface xviiAuthors’ Biography xxiAcknowledgments xxiii1 What Is Smart City? 11.1 Introduction 11.2 Characteristics, Functions, and Applications 41.2.1 Sensors and Intelligent Electronic Devices 41.2.2 Information Technology, Communication Networks, and Cyber Security 51.2.3 Systems Integration 61.2.4 Intelligence and Data Analytics 61.2.5 Management and Control Platforms 71.3 Smart Energy 71.4 Smart Transportation 111.4.1 Data Processing 111.5 Smart Health 121.6 Impact of COVID-19 Pandemic 121.7 Standards 141.7.1 International Standards for Smart City 141.7.2 Smart City Pilot Projects 191.8 Challenges and Opportunities 261.9 Conclusions 29Acknowledgements 29References 292 Lithium-Ion Storage Financial Model 372.1 Introduction 372.2 Literature Review 382.2.1 Techno-economic Studies of Biogas, PV, and EES Hybrid Energy Systems 382.2.2 EES Degradation 392.2.3 Techno-Economic Analysis for EES 412.2.4 Financing for Renewable Energy Systems and EES 422.3 Research Background: Hybrid Energy System in Kenya 462.3.1 Hybrid System Sizing and Operation 462.3.2 Solar and Retail Electricity Price Data 47vftoc.3d 5 8/10/2022 8:29:08 PM2.4 A Case Study on the Degradation Effect on LCOE 492.4.1 Sensitivity Analysis on the SOCThreshold 492.4.2 Sensitivity Analysis on PV and EES Rated Capacities 502.5 Financial Modeling for EES 522.5.1 Model Description 532.5.2 Case Studies Context 552.6 Case Studies on Financing EES in Kenya 572.6.1 Influence of WACC on Equity NPV and LCOS 572.6.2 Equity and Firm Cash Flows 582.6.2.1 Cash Flows for EES Capital Cost at 1500 $/kWh 582.6.2.2 Cash Flows for EES Capital Cost at 200 $/kWh 582.6.3 LCOS and Project Lifecycle Cost Composition 612.6.4 EES Finance Under Different Electricity Prices 632.6.4.1 Study on the Retail Electricity Price 632.7 Sensitivity Analysis of Technical and Economic Parameters 642.8 Discussion and Future Work 662.9 Conclusions 68Acknowledgments 68References 683 Levelized Cost of Electricity for Photovoltaic with Energy Storage 73Nomenclature 733.1 Introduction 753.2 Literature Review 763.3 Data Analysis and Operating Regime 783.3.1 Solar and Load Data Analysis 783.3.2 Problem Context 793.3.3 Operating Regime 813.3.4 Case Study 843.4 Economic Analysis 863.4.1 AD Operational Cost Model 863.4.2 LiCoO2 Degradation Cost Model and Number of Replacements 863.4.3 Levelized Cost of Electricity Derivation 903.4.3.1 LCOE for PV 913.4.3.2 LCOE for AD 923.4.3.3 Levelized Cost of Storage (LCOS) 923.4.3.4 Levelized Cost of Delivery (LCOD) 933.4.3.5 LCOE for System 943.4.4 LCOE Analyses and Discussion 943.5 Conclusions 96Acknowledgment 97References 974 Electricity Plan Recommender System 101Nomenclature 1014.1 Introduction 1024.2 Proposed Matrix Recovery Methods 1054.2.1 Previous Matrix Recovery Methods 105vi Contentsftoc.3d 6 8/10/2022 8:29:09 PM4.2.2 Matrix Recovery Methods with Electrical Instructions 1064.2.3 Solution 1074.2.4 Convergence Analysis and Complexity Analysis 1114.3 Proposed Electricity Plan Recommender System 1124.3.1 Feature Formulation Stage 1124.3.2 Recommender Stage 1124.3.3 Algorithm and Complexity Analysis 1134.4 Simulations and Discussions 1154.4.1 Recovery Simulation 1154.4.2 Recovery Result Discussions 1194.4.3 Application Study 1214.4.4 Application Result Discussions 1254.5 Conclusion and Future Work 126Acknowledgments 127References 1275 Classifier Economics of Semi-intrusive Load Monitoring 1315.1 Introduction 1315.1.1 Technical Background 1315.1.2 Original Contribution 1325.2 Typical Feature Space of SILM 1325.3 Modeling of SILM Classifier Network 1345.3.1 Problem Definition 1345.3.2 SILM Classifier Network Construction 1355.4 Classifier Locating Optimization with Ensuring on Accuracy and ClassifierEconomics 1375.4.1 Objective of SILM Construction 1375.4.2 Constraint of Devices Covering Completeness and Over Covering 1375.4.3 Constraint of Bottom Accuracy and Accuracy Measurement 1385.4.4 Constraint of Sampling Computation Requirements 1385.4.5 Optimization Algorithm 1395.5 Numerical Study 1405.5.1 Devices Operational Datasets for Numerical Study 1405.5.2 Feature Space Set for Numerical Study 1405.5.3 Numerical Study 1: Classifier Economics via Different Meter Price and Different AccuracyConstraints 1415.5.3.1 Result Analysis via Row Variation in Table 5.5 1435.5.3.2 Result Analysis via Column Variation in Table 5.5 1435.5.3.3 Result Converging via Price Variation 1445.5.4 Numerical Study 2: Classifier Economics via different Classifiers Models 1465.6 Conclusion 147Acknowledgements 147References 1476 Residential Demand Response Shifting Boundary 1516.1 Introduction 1516.2 Residential Customer Behavior Modeling 1536.2.1 Multi-Agent System Modeling 153Contents viiftoc.3d 7 8/10/2022 8:29:09 PM6.2.2 Multi-agent System Structure for PBP Demand Response 1536.2.3 Agent of Residential Consumer 1556.3 Residential Customer Shifting Boundary 1576.3.1 Consumer Behavior Decision-Making 1576.3.2 Shifting Boundary 1576.3.3 Target Function and Constraints 1586.4 Case Study 1606.4.1 Case Study Description 1606.4.2 Residential Shifting Boundary Simulation under TOU 1646.4.3 Residential Shifting Boundary Simulation Under RTP 1696.5 Case Study on Residential Customer TOU Time Zone Planning 1736.5.1 Case Study Description 1736.5.2 Result and Analysis 1736.6 Case Study on Smart Meter Installation Scale Analysis 1786.6.1 Case Study Description 1786.6.2 Analysis on Multiple Smart Meter Installation Scale under TOU and RTP 1796.7 Conclusions and Future Work 181Acknowledgements 181References 1827 Residential PV Panels Planning-Based Game-Theoretic Method 185Nomenclature 1857.1 Introduction 1867.2 System Modeling 1887.2.1 Network Branch Flow Model 1887.2.2 Energy Sharing Agent Model 1897.3 Bi-level Energy Sharing Model for Determining Optimal PV Panels InstallationCapacity 1917.3.1 Uncertainty Characterization 1917.3.2 Stackelberg Game Model 1917.3.3 Bi-level Energy Sharing Model 1927.3.4 Linearization of Bi-level Energy Sharing Model 1947.3.5 Descend Search-Based Solution Algorithm 1957.4 Stochastic Optimal PV Panels Allocation in the Coalition of Prosumer Agents 1977.5 Numerical Results 1997.5.1 Implementation on IEEE 33-Node Distribution System 1997.5.2 Implementation on IEEE 123-Node Distribution System 2057.6 Conclusion 206Acknowledgements 207References 2078 Networked Microgrids Energy Management Under High Renewable Penetration 211Nomenclature 2118.1 Introduction 2128.2 Problem Description 2158.2.1 Components and Configuration of Networked MGs 2158.2.2 Proposed Strategy 2168.3 Components Modeling 216viii Contentsftoc.3d 8 8/10/2022 8:29:09 PM8.3.1 CDGs 2168.3.2 BESSs 2178.3.3 Controllable Load 2188.3.4 Uncertain Sets of RESs, Load, and Electricity Prices 2188.3.5 Market Model 2188.4 Proposed Two-Stage Operation Model 2198.4.1 Hourly Day-Ahead Optimal Scheduling Model 2198.4.1.1 Lower Level EMS 2198.4.1.2 Upper Level EMS 2208.4.2 5-Minute Real-Time Dispatch Model 2218.5 Case Studies 2228.5.1 Set Up 2228.5.2 Results and Discussion 2228.6 Conclusions 230Acknowledgements 231References 2319 A Multi-agent Reinforcement Learning for Home Energy Management 233Nomenclature 2339.1 Introduction 2339.2 Problem Modeling 2369.2.1 State 2389.2.2 Action 2389.2.3 Reward 2399.2.4 Total Reward of HEM System 2399.2.5 Action-value Function 2409.3 Proposed Data-Driven-Based Solution Method 2409.3.1 ELM-Based Feedforward NN for Uncertainty Prediction 2419.3.2 Multi-Agent Q-Learning Algorithm for Decision-Making 2419.3.3 Implementation Process of Proposed Solution Method 2419.4 Test Results 2449.4.1 Case Study Setup 2449.4.2 Performance of the Proposed Feedforward NN 2449.4.3 Performance of Multi-Agent Q-Learning Algorithm 2469.4.4 Numerical Comparison with Genetic Algorithm 2499.5 Conclusion 251Acknowledgements 251References 25110 Virtual Energy Storage Systems Smart Coordination 25510.1 Introduction 25510.1.1 Related Work 25510.1.2 Main Contributions 25710.2 VESS Modeling, Aggregation, and Coordination Strategy 25710.2.1 VESS Modeling 25710.2.2 VESS Aggregation 25910.2.3 VESS Coordination Strategies 26010.3 Proposed Approach for Network Loading and Voltage Management by VESSs 261Contents ixftoc.3d 9 8/10/2022 8:29:09 PM10.3.1 Network Loading Management Strategy 26110.3.2 Voltage Regulation Strategy 26410.4 Case Studies 26710.4.1 Case 1 26910.4.2 Case 2 26910.5 Conclusions and Future Work 276Acknowledgements 276References 27611 Reliability Modeling and Assessment of Cyber-Physical Power Systems 279Nomenclature 27911.1 Introduction 27911.2 Composite Markov Model 28211.2.1 Multistate Markov Chain of Information Layer 28211.2.2 Two-state Markov Chain of Physical Layer 28411.2.3 Coupling Model of Physical and Information Layers 28511.3 Linear Programming Model for Maximum Flow 28611.3.1 Node Classification and Flow Constraint Model 28611.3.2 Programming Model for Network Flow 28811.4 Reliability Analysis Method 28911.4.1 Definition and Measures of System Reliability 28911.4.2 Sequential Monte-Carlo Simulation 28911.4.2.1 System State Sampling 28911.4.2.2 Reliability Computing Procedure 29011.5 Case Analysis 29111.5.1 Case Description 29111.5.2 Calculation Results and Analysis 29311.5.2.1 Effect of Demand Flow on Reliability 29311.5.2.2 Effect of Node Capacity on Reliability 29511.5.2.3 Effect of the Information Flow Level on Reliability 29711.6 Conclusion 298Acknowledgements 299References 29912 A Vehicle-To-Grid Voltage Support Co-simulation Platform 30112.1 Introduction 30112.2 Related Works 30312.2.1 Simulation of Power Systems 30312.2.2 Simulation of Communication Network 30412.2.3 Simulation of Distributed Software 30512.2.4 Time Synchronization 30512.2.5 Co-Simulation Interface 30612.3 Direct-Execution Simulation 30612.3.1 Operation of a Direct-Execution Simulation 30712.3.1.1 Simulation Metadata 30712.3.1.2 Enforcing Simulated Thread Scheduling 30812.3.1.3 Tracking Action Timestamps 308x Contentsftoc.3d 10 8/10/2022 8:29:09 PM12.3.1.4 Enforcing Timestamp Order 30812.3.1.5 Handling External Events 30812.3.2 DecompositionJ Framework 30912.4 Co-Simulation Platform for Agent-Based Smart Grid Applications 31012.4.1 Co-Simulation Message Exchange 31112.4.2 Co-Simulation Time Synchronization 31212.5 Agent-Based FLISR Case Study 31212.5.1 The Restoration Problem 31212.5.2 Reconfiguration Algorithm 31412.5.3 Restoration Agents 31512.5.4 Communication Network Configurations 31612.6 Simulation Results 31612.6.1 Agent Actions and Events 31712.6.1.1 Phase 1 – Fault Detection 31712.6.1.2 Phase 2 – Fault Location 31712.6.1.3 Phase 3 – Enquire DERs 31712.6.1.4 Phase 4 – Reconfiguration 32012.6.1.5 Phase 5 – Transient 32012.6.2 Effects of Background Traffics and Link Failure 32112.6.3 Effects of Link Failure Time 32212.6.4 Effects of Main-Container Location Configuration 32312.6.5 Summary on Simulation Results 32412.7 Case Study on V2G for Voltage Support 32412.7.1 Modeling of Electrical Grid and EVs 32412.7.2 Modeling of Communication Network 32612.7.3 Simulation Events 32712.7.4 Co-simulation Results 32712.8 Conclusions 330Acknowledgements 331References 33113 Advanced Metering Infrastructure for Electric Vehicle Charging 33513.1 Introduction 33513.2 EVAMI Overview 33813.2.1 Advantage of Adopting EVAMI 33813.2.2 Choice of Signal Transmission Platform 33813.2.3 Onsite Charging System 34013.2.4 EV Charging Station 34013.2.5 Utility Information Management System 34013.2.6 Third Party Customer Service Platform 34113.3 System Architecture, Protocol Design, and Implementation 34113.3.1 Communication Protocol 34213.3.1.1 Charging Service Session Management 34313.3.1.2 Device Management 34413.3.1.3 Demand Response Management 34613.3.2 Web Portal 34713.4 Performance Evaluation 348Contents xiftoc.3d 11 8/10/2022 8:29:09 PM13.4.1 Network Performance of OCS 34813.4.2 Effectiveness of EVAMI on Demand Response 34813.5 Conclusion 351Acknowledgements 352References 35214 Power System Dispatching with Plug-In Hybrid Electric Vehicles 355Nomenclature 35514.1 Introduction 35714.1.1 Model Decoupling 35714.1.2 Security Reinforcement 35814.1.3 Potential for Practical Application 35814.2 Framework of PHEVs Dispatching 35814.3 Framework for the Two-Stage Model 35914.4 The Charging and Discharging Mode 36014.4.1 PHEV Charging Mode 36014.4.2 PHEV Discharging Mode 36014.4.3 PHEV Charging and Discharging Power 36114.5 The Optimal Dispatching Model with PHEVs 36114.5.1 Sub-Model 1 36114.5.2 Sub-Model 2 36314.6 Numerical Examples 36414.7 Practical Application – The Impact of Electric Vehicles on Distribution Network 37014.7.1 Modeling of Electric Vehicles 37014.7.2 Uncontrolled Charging 37414.7.3 Results 37614.8 Conclusions 376Acknowledgements 377References 37715 Machine Learning for Electric Bus Fast-Charging Stations Deployment 381Nomenclature 38115.1 Introduction 38315.2 Problem Description and Assumptions 38715.2.1 Operating Characteristics of Electric Buses 38815.2.2 Affinity Propagation Algorithm 38815.3 Model Formulation 38915.3.1 Capacity Model of Electric Bus Fast-Charging Station 38915.3.2 Deployment Model of Electric Bus Fast-Charging Station 39215.3.3 Constraints 39315.4 Results and Discussion 39415.4.1 Spatio-temporal Distribution of Buses 39415.4.2 Optimized Deployment of EB Fast-Charging Stations 39415.4.3 Comparison of Different Planning Methods 395xii Contentsftoc.3d 12 8/10/2022 8:29:09 PM15.4.4 Comparison Under Different Time Headways 39915.4.5 Comparison Under Different Battery Size and Charging Power 39915.4.6 Policy and Business Model Implications 40215.5 Conclusions 403Acknowledgements 403References 40416 Best Practice for Parking Vehicles with Low-power Wide-Area Network 40716.1 Introduction 40716.2 Related Work 41316.2.1 LoRaWAN 41416.2.2 NB-IoT 41516.2.3 Sigfox 41616.3 LP-INDEX for Best Practices of LPWAN Technologies 41616.3.1 Latency 41716.3.2 Data Capacity 41716.3.3 Power and Cost 41816.3.4 Coverage 41816.3.5 Scalability 41916.3.6 Security 41916.4 Case Study 41916.4.1 Experimental Setup 41916.4.2 Depolyment of Car Park Sensors 41916.4.3 Evaluation Matrices and Results 41916.5 Conclusion and Future Work 421Acknowledgements 421References 42117 Smart Health Based on Internet of Things (IoT) and Smart Devices 42517.1 Introduction 42517.2 Technology Used in Healthcare 43017.2.1 Internet of Things 43417.2.2 Smart Meters 43817.3 Case Study 44317.3.1 Continuous Glucose Monitoring 44317.3.2 Smart Pet 44517.3.3 Smart Meters for Healthcare 44817.3.4 Other Case Studies 45317.3.4.1 Cancer Treatment 45317.3.4.2 Connected Inhalers 45417.3.4.3 Ingestible Sensors 45417.3.4.4 Elderly People 45417.4 Conclusions 455References 456Contents xiiiftoc.3d 13 8/10/2022 8:29:09 PM18 Criteria Decision Analysis Based Cardiovascular Diseases Classifier for Drunk DriverDetection 46318.1 Introduction 46318.2 Cardiovascular Diseases Classifier 46518.2.1 Design of the Optimal CDC 46618.2.2 Data Pre-Processing and Features Construction 46618.2.3 Cardiovascular Diseases Classifier Construction 46718.3 Multiple Criteria Decision Analysis of the Optimal CDC 46818.4 Analytic Hierarchy Process Scores and Analysis 47018.5 Development of EDG-Based Drunk Driver Detection 47118.5.1 ECG Sensors Implementations 47218.5.2 Drunk Driving Detection Algorithm 47318.6 ECG-Based Drunk Driver Detection Scheme Design 47318.7 Result Comparisons 47518.8 Conclusions 476Acknowledgements 477References 47719 Bioinformatics and Telemedicine for Healthcare 48119.1 Introduction 48119.2 Bioinformatics 48319.3 Top-Level Design for Integration of Bioinformatics to Smart Health 48619.4 Artificial Intelligence Roadmap 48819.5 Intelligence Techniques for Data Analysis Examples 49219.6 Decision Support System 49719.7 Conclusions 501References 50120 Concluding Remark and the Future 50720.1 The Relationship 50720.2 Roadmap 50820.3 The Future 50920.3.1 Smart Energy 50920.3.2 Healthcare 51320.3.3 Smart Transportation 51620.3.4 Smart Buildings 517References 518Index 000

CHUN SING LAI, DPhil (Oxon), is Lecturer at Brunel University London, UK, Working Group Chair for the IEEE Standards Association P2814 and P3166 standards, Vice-chair of the IEEE Smart Cities Publications Committee, and a Technical Program Chair of IEEE International Smart Cities Conference 2022. LOI LEI LAI, DSc, is University Distinguished Professor at the Guangdong University of Technology, China, Chair of the IEEE Smart Cities Publications Committee, Technical Program Chair of the IEEE International Smart Cities Conference 2020, and the Editor-in-Chief of IEEE Smart Cities eNewsletter. QI HONG LAI, BSc (1st Hons), is a DPhil Candidate in Molecular Cell Biology in Health and Disease at the Sir William Dunn School of Pathology, University of Oxford, UK and Secretary of IEEE P3166 Working Group.

• Foreword xvPreface xviiAuthors’ Biography xxiAcknowledgments xxiii1 What Is Smart City? 11.1 Introduction 11.2 Characteristics, Functions, and Applications 41.2.1 Sensors and Intelligent Electronic Devices 41.2.2 Information Technology, Communication Networks, and Cyber Security 51.2.3 Systems Integration 61.2.4 Intelligence and Data Analytics 61.2.5 Management and Control Platforms 71.3 Smart Energy 71.4 Smart Transportation 111.4.1 Data Processing 111.5 Smart Health 121.6 Impact of COVID-19 Pandemic 121.7 Standards 141.7.1 International Standards for Smart City 141.7.2 Smart City Pilot Projects 191.8 Challenges and Opportunities 261.9 Conclusions 29Acknowledgements 29References 292 Lithium-Ion Storage Financial Model 372.1 Introduction 372.2 Literature Review 382.2.1 Techno-economic Studies of Biogas, PV, and EES Hybrid Energy Systems 382.2.2 EES Degradation 392.2.3 Techno-Economic Analysis for EES 412.2.4 Financing for Renewable Energy Systems and EES 422.3 Research Background: Hybrid Energy System in Kenya 462.3.1 Hybrid System Sizing and Operation 462.3.2 Solar and Retail Electricity Price Data 47vftoc.3d 5 8/10/2022 8:29:08 PM2.4 A Case Study on the Degradation Effect on LCOE 492.4.1 Sensitivity Analysis on the SOCThreshold 492.4.2 Sensitivity Analysis on PV and EES Rated Capacities 502.5 Financial Modeling for EES 522.5.1 Model Description 532.5.2 Case Studies Context 552.6 Case Studies on Financing EES in Kenya 572.6.1 Influence of WACC on Equity NPV and LCOS 572.6.2 Equity and Firm Cash Flows 582.6.2.1 Cash Flows for EES Capital Cost at 1500 $/kWh 582.6.2.2 Cash Flows for EES Capital Cost at 200 $/kWh 582.6.3 LCOS and Project Lifecycle Cost Composition 612.6.4 EES Finance Under Different Electricity Prices 632.6.4.1 Study on the Retail Electricity Price 632.7 Sensitivity Analysis of Technical and Economic Parameters 642.8 Discussion and Future Work 662.9 Conclusions 68Acknowledgments 68References 683 Levelized Cost of Electricity for Photovoltaic with Energy Storage 73Nomenclature 733.1 Introduction 753.2 Literature Review 763.3 Data Analysis and Operating Regime 783.3.1 Solar and Load Data Analysis 783.3.2 Problem Context 793.3.3 Operating Regime 813.3.4 Case Study 843.4 Economic Analysis 863.4.1 AD Operational Cost Model 863.4.2 LiCoO2 Degradation Cost Model and Number of Replacements 863.4.3 Levelized Cost of Electricity Derivation 903.4.3.1 LCOE for PV 913.4.3.2 LCOE for AD 923.4.3.3 Levelized Cost of Storage (LCOS) 923.4.3.4 Levelized Cost of Delivery (LCOD) 933.4.3.5 LCOE for System 943.4.4 LCOE Analyses and Discussion 943.5 Conclusions 96Acknowledgment 97References 974 Electricity Plan Recommender System 101Nomenclature 1014.1 Introduction 1024.2 Proposed Matrix Recovery Methods 1054.2.1 Previous Matrix Recovery Methods 105vi Contentsftoc.3d 6 8/10/2022 8:29:09 PM4.2.2 Matrix Recovery Methods with Electrical Instructions 1064.2.3 Solution 1074.2.4 Convergence Analysis and Complexity Analysis 1114.3 Proposed Electricity Plan Recommender System 1124.3.1 Feature Formulation Stage 1124.3.2 Recommender Stage 1124.3.3 Algorithm and Complexity Analysis 1134.4 Simulations and Discussions 1154.4.1 Recovery Simulation 1154.4.2 Recovery Result Discussions 1194.4.3 Application Study 1214.4.4 Application Result Discussions 1254.5 Conclusion and Future Work 126Acknowledgments 127References 1275 Classifier Economics of Semi-intrusive Load Monitoring 1315.1 Introduction 1315.1.1 Technical Background 1315.1.2 Original Contribution 1325.2 Typical Feature Space of SILM 1325.3 Modeling of SILM Classifier Network 1345.3.1 Problem Definition 1345.3.2 SILM Classifier Network Construction 1355.4 Classifier Locating Optimization with Ensuring on Accuracy and ClassifierEconomics 1375.4.1 Objective of SILM Construction 1375.4.2 Constraint of Devices Covering Completeness and Over Covering 1375.4.3 Constraint of Bottom Accuracy and Accuracy Measurement 1385.4.4 Constraint of Sampling Computation Requirements 1385.4.5 Optimization Algorithm 1395.5 Numerical Study 1405.5.1 Devices Operational Datasets for Numerical Study 1405.5.2 Feature Space Set for Numerical Study 1405.5.3 Numerical Study 1: Classifier Economics via Different Meter Price and Different AccuracyConstraints 1415.5.3.1 Result Analysis via Row Variation in Table 5.5 1435.5.3.2 Result Analysis via Column Variation in Table 5.5 1435.5.3.3 Result Converging via Price Variation 1445.5.4 Numerical Study 2: Classifier Economics via different Classifiers Models 1465.6 Conclusion 147Acknowledgements 147References 1476 Residential Demand Response Shifting Boundary 1516.1 Introduction 1516.2 Residential Customer Behavior Modeling 1536.2.1 Multi-Agent System Modeling 153Contents viiftoc.3d 7 8/10/2022 8:29:09 PM6.2.2 Multi-agent System Structure for PBP Demand Response 1536.2.3 Agent of Residential Consumer 1556.3 Residential Customer Shifting Boundary 1576.3.1 Consumer Behavior Decision-Making 1576.3.2 Shifting Boundary 1576.3.3 Target Function and Constraints 1586.4 Case Study 1606.4.1 Case Study Description 1606.4.2 Residential Shifting Boundary Simulation under TOU 1646.4.3 Residential Shifting Boundary Simulation Under RTP 1696.5 Case Study on Residential Customer TOU Time Zone Planning 1736.5.1 Case Study Description 1736.5.2 Result and Analysis 1736.6 Case Study on Smart Meter Installation Scale Analysis 1786.6.1 Case Study Description 1786.6.2 Analysis on Multiple Smart Meter Installation Scale under TOU and RTP 1796.7 Conclusions and Future Work 181Acknowledgements 181References 1827 Residential PV Panels Planning-Based Game-Theoretic Method 185Nomenclature 1857.1 Introduction 1867.2 System Modeling 1887.2.1 Network Branch Flow Model 1887.2.2 Energy Sharing Agent Model 1897.3 Bi-level Energy Sharing Model for Determining Optimal PV Panels InstallationCapacity 1917.3.1 Uncertainty Characterization 1917.3.2 Stackelberg Game Model 1917.3.3 Bi-level Energy Sharing Model 1927.3.4 Linearization of Bi-level Energy Sharing Model 1947.3.5 Descend Search-Based Solution Algorithm 1957.4 Stochastic Optimal PV Panels Allocation in the Coalition of Prosumer Agents 1977.5 Numerical Results 1997.5.1 Implementation on IEEE 33-Node Distribution System 1997.5.2 Implementation on IEEE 123-Node Distribution System 2057.6 Conclusion 206Acknowledgements 207References 2078 Networked Microgrids Energy Management Under High Renewable Penetration 211Nomenclature 2118.1 Introduction 2128.2 Problem Description 2158.2.1 Components and Configuration of Networked MGs 2158.2.2 Proposed Strategy 2168.3 Components Modeling 216viii Contentsftoc.3d 8 8/10/2022 8:29:09 PM8.3.1 CDGs 2168.3.2 BESSs 2178.3.3 Controllable Load 2188.3.4 Uncertain Sets of RESs, Load, and Electricity Prices 2188.3.5 Market Model 2188.4 Proposed Two-Stage Operation Model 2198.4.1 Hourly Day-Ahead Optimal Scheduling Model 2198.4.1.1 Lower Level EMS 2198.4.1.2 Upper Level EMS 2208.4.2 5-Minute Real-Time Dispatch Model 2218.5 Case Studies 2228.5.1 Set Up 2228.5.2 Results and Discussion 2228.6 Conclusions 230Acknowledgements 231References 2319 A Multi-agent Reinforcement Learning for Home Energy Management 233Nomenclature 2339.1 Introduction 2339.2 Problem Modeling 2369.2.1 State 2389.2.2 Action 2389.2.3 Reward 2399.2.4 Total Reward of HEM System 2399.2.5 Action-value Function 2409.3 Proposed Data-Driven-Based Solution Method 2409.3.1 ELM-Based Feedforward NN for Uncertainty Prediction 2419.3.2 Multi-Agent Q-Learning Algorithm for Decision-Making 2419.3.3 Implementation Process of Proposed Solution Method 2419.4 Test Results 2449.4.1 Case Study Setup 2449.4.2 Performance of the Proposed Feedforward NN 2449.4.3 Performance of Multi-Agent Q-Learning Algorithm 2469.4.4 Numerical Comparison with Genetic Algorithm 2499.5 Conclusion 251Acknowledgements 251References 25110 Virtual Energy Storage Systems Smart Coordination 25510.1 Introduction 25510.1.1 Related Work 25510.1.2 Main Contributions 25710.2 VESS Modeling, Aggregation, and Coordination Strategy 25710.2.1 VESS Modeling 25710.2.2 VESS Aggregation 25910.2.3 VESS Coordination Strategies 26010.3 Proposed Approach for Network Loading and Voltage Management by VESSs 261Contents ixftoc.3d 9 8/10/2022 8:29:09 PM10.3.1 Network Loading Management Strategy 26110.3.2 Voltage Regulation Strategy 26410.4 Case Studies 26710.4.1 Case 1 26910.4.2 Case 2 26910.5 Conclusions and Future Work 276Acknowledgements 276References 27611 Reliability Modeling and Assessment of Cyber-Physical Power Systems 279Nomenclature 27911.1 Introduction 27911.2 Composite Markov Model 28211.2.1 Multistate Markov Chain of Information Layer 28211.2.2 Two-state Markov Chain of Physical Layer 28411.2.3 Coupling Model of Physical and Information Layers 28511.3 Linear Programming Model for Maximum Flow 28611.3.1 Node Classification and Flow Constraint Model 28611.3.2 Programming Model for Network Flow 28811.4 Reliability Analysis Method 28911.4.1 Definition and Measures of System Reliability 28911.4.2 Sequential Monte-Carlo Simulation 28911.4.2.1 System State Sampling 28911.4.2.2 Reliability Computing Procedure 29011.5 Case Analysis 29111.5.1 Case Description 29111.5.2 Calculation Results and Analysis 29311.5.2.1 Effect of Demand Flow on Reliability 29311.5.2.2 Effect of Node Capacity on Reliability 29511.5.2.3 Effect of the Information Flow Level on Reliability 29711.6 Conclusion 298Acknowledgements 299References 29912 A Vehicle-To-Grid Voltage Support Co-simulation Platform 30112.1 Introduction 30112.2 Related Works 30312.2.1 Simulation of Power Systems 30312.2.2 Simulation of Communication Network 30412.2.3 Simulation of Distributed Software 30512.2.4 Time Synchronization 30512.2.5 Co-Simulation Interface 30612.3 Direct-Execution Simulation 30612.3.1 Operation of a Direct-Execution Simulation 30712.3.1.1 Simulation Metadata 30712.3.1.2 Enforcing Simulated Thread Scheduling 30812.3.1.3 Tracking Action Timestamps 308x Contentsftoc.3d 10 8/10/2022 8:29:09 PM12.3.1.4 Enforcing Timestamp Order 30812.3.1.5 Handling External Events 30812.3.2 DecompositionJ Framework 30912.4 Co-Simulation Platform for Agent-Based Smart Grid Applications 31012.4.1 Co-Simulation Message Exchange 31112.4.2 Co-Simulation Time Synchronization 31212.5 Agent-Based FLISR Case Study 31212.5.1 The Restoration Problem 31212.5.2 Reconfiguration Algorithm 31412.5.3 Restoration Agents 31512.5.4 Communication Network Configurations 31612.6 Simulation Results 31612.6.1 Agent Actions and Events 31712.6.1.1 Phase 1 – Fault Detection 31712.6.1.2 Phase 2 – Fault Location 31712.6.1.3 Phase 3 – Enquire DERs 31712.6.1.4 Phase 4 – Reconfiguration 32012.6.1.5 Phase 5 – Transient 32012.6.2 Effects of Background Traffics and Link Failure 32112.6.3 Effects of Link Failure Time 32212.6.4 Effects of Main-Container Location Configuration 32312.6.5 Summary on Simulation Results 32412.7 Case Study on V2G for Voltage Support 32412.7.1 Modeling of Electrical Grid and EVs 32412.7.2 Modeling of Communication Network 32612.7.3 Simulation Events 32712.7.4 Co-simulation Results 32712.8 Conclusions 330Acknowledgements 331References 33113 Advanced Metering Infrastructure for Electric Vehicle Charging 33513.1 Introduction 33513.2 EVAMI Overview 33813.2.1 Advantage of Adopting EVAMI 33813.2.2 Choice of Signal Transmission Platform 33813.2.3 Onsite Charging System 34013.2.4 EV Charging Station 34013.2.5 Utility Information Management System 34013.2.6 Third Party Customer Service Platform 34113.3 System Architecture, Protocol Design, and Implementation 34113.3.1 Communication Protocol 34213.3.1.1 Charging Service Session Management 34313.3.1.2 Device Management 34413.3.1.3 Demand Response Management 34613.3.2 Web Portal 34713.4 Performance Evaluation 348Contents xiftoc.3d 11 8/10/2022 8:29:09 PM13.4.1 Network Performance of OCS 34813.4.2 Effectiveness of EVAMI on Demand Response 34813.5 Conclusion 351Acknowledgements 352References 35214 Power System Dispatching with Plug-In Hybrid Electric Vehicles 355Nomenclature 35514.1 Introduction 35714.1.1 Model Decoupling 35714.1.2 Security Reinforcement 35814.1.3 Potential for Practical Application 35814.2 Framework of PHEVs Dispatching 35814.3 Framework for the Two-Stage Model 35914.4 The Charging and Discharging Mode 36014.4.1 PHEV Charging Mode 36014.4.2 PHEV Discharging Mode 36014.4.3 PHEV Charging and Discharging Power 36114.5 The Optimal Dispatching Model with PHEVs 36114.5.1 Sub-Model 1 36114.5.2 Sub-Model 2 36314.6 Numerical Examples 36414.7 Practical Application – The Impact of Electric Vehicles on Distribution Network 37014.7.1 Modeling of Electric Vehicles 37014.7.2 Uncontrolled Charging 37414.7.3 Results 37614.8 Conclusions 376Acknowledgements 377References 37715 Machine Learning for Electric Bus Fast-Charging Stations Deployment 381Nomenclature 38115.1 Introduction 38315.2 Problem Description and Assumptions 38715.2.1 Operating Characteristics of Electric Buses 38815.2.2 Affinity Propagation Algorithm 38815.3 Model Formulation 38915.3.1 Capacity Model of Electric Bus Fast-Charging Station 38915.3.2 Deployment Model of Electric Bus Fast-Charging Station 39215.3.3 Constraints 39315.4 Results and Discussion 39415.4.1 Spatio-temporal Distribution of Buses 39415.4.2 Optimized Deployment of EB Fast-Charging Stations 39415.4.3 Comparison of Different Planning Methods 395xii Contentsftoc.3d 12 8/10/2022 8:29:09 PM15.4.4 Comparison Under Different Time Headways 39915.4.5 Comparison Under Different Battery Size and Charging Power 39915.4.6 Policy and Business Model Implications 40215.5 Conclusions 403Acknowledgements 403References 40416 Best Practice for Parking Vehicles with Low-power Wide-Area Network 40716.1 Introduction 40716.2 Related Work 41316.2.1 LoRaWAN 41416.2.2 NB-IoT 41516.2.3 Sigfox 41616.3 LP-INDEX for Best Practices of LPWAN Technologies 41616.3.1 Latency 41716.3.2 Data Capacity 41716.3.3 Power and Cost 41816.3.4 Coverage 41816.3.5 Scalability 41916.3.6 Security 41916.4 Case Study 41916.4.1 Experimental Setup 41916.4.2 Depolyment of Car Park Sensors 41916.4.3 Evaluation Matrices and Results 41916.5 Conclusion and Future Work 421Acknowledgements 421References 42117 Smart Health Based on Internet of Things (IoT) and Smart Devices 42517.1 Introduction 42517.2 Technology Used in Healthcare 43017.2.1 Internet of Things 43417.2.2 Smart Meters 43817.3 Case Study 44317.3.1 Continuous Glucose Monitoring 44317.3.2 Smart Pet 44517.3.3 Smart Meters for Healthcare 44817.3.4 Other Case Studies 45317.3.4.1 Cancer Treatment 45317.3.4.2 Connected Inhalers 45417.3.4.3 Ingestible Sensors 45417.3.4.4 Elderly People 45417.4 Conclusions 455References 456Contents xiiiftoc.3d 13 8/10/2022 8:29:09 PM18 Criteria Decision Analysis Based Cardiovascular Diseases Classifier for Drunk DriverDetection 46318.1 Introduction 46318.2 Cardiovascular Diseases Classifier 46518.2.1 Design of the Optimal CDC 46618.2.2 Data Pre-Processing and Features Construction 46618.2.3 Cardiovascular Diseases Classifier Construction 46718.3 Multiple Criteria Decision Analysis of the Optimal CDC 46818.4 Analytic Hierarchy Process Scores and Analysis 47018.5 Development of EDG-Based Drunk Driver Detection 47118.5.1 ECG Sensors Implementations 47218.5.2 Drunk Driving Detection Algorithm 47318.6 ECG-Based Drunk Driver Detection Scheme Design 47318.7 Result Comparisons 47518.8 Conclusions 476Acknowledgements 477References 47719 Bioinformatics and Telemedicine for Healthcare 48119.1 Introduction 48119.2 Bioinformatics 48319.3 Top-Level Design for Integration of Bioinformatics to Smart Health 48619.4 Artificial Intelligence Roadmap 48819.5 Intelligence Techniques for Data Analysis Examples 49219.6 Decision Support System 49719.7 Conclusions 501References 50120 Concluding Remark and the Future 50720.1 The Relationship 50720.2 Roadmap 50820.3 The Future 50920.3.1 Smart Energy 50920.3.2 Healthcare 51320.3.3 Smart Transportation 51620.3.4 Smart Buildings 517References 518Index 000

CHUN SING LAI, DPhil (Oxon), is Lecturer at Brunel University London, UK, Working Group Chair for the IEEE Standards Association P2814 and P3166 standards, Vice-chair of the IEEE Smart Cities Publications Committee, and a Technical Program Chair of IEEE International Smart Cities Conference 2022. LOI LEI LAI, DSc, is University Distinguished Professor at the Guangdong University of Technology, China, Chair of the IEEE Smart Cities Publications Committee, Technical Program Chair of the IEEE International Smart Cities Conference 2020, and the Editor-in-Chief of IEEE Smart Cities eNewsletter. QI HONG LAI, BSc (1st Hons), is a DPhil Candidate in Molecular Cell Biology in Health and Disease at the Sir William Dunn School of Pathology, University of Oxford, UK and Secretary of IEEE P3166 Working Group.

• Foreword xvPreface xviiAuthors’ Biography xxiAcknowledgments xxiii1 What Is Smart City? 11.1 Introduction 11.2 Characteristics, Functions, and Applications 41.2.1 Sensors and Intelligent Electronic Devices 41.2.2 Information Technology, Communication Networks, and Cyber Security 51.2.3 Systems Integration 61.2.4 Intelligence and Data Analytics 61.2.5 Management and Control Platforms 71.3 Smart Energy 71.4 Smart Transportation 111.4.1 Data Processing 111.5 Smart Health 121.6 Impact of COVID-19 Pandemic 121.7 Standards 141.7.1 International Standards for Smart City 141.7.2 Smart City Pilot Projects 191.8 Challenges and Opportunities 261.9 Conclusions 29Acknowledgements 29References 292 Lithium-Ion Storage Financial Model 372.1 Introduction 372.2 Literature Review 382.2.1 Techno-economic Studies of Biogas, PV, and EES Hybrid Energy Systems 382.2.2 EES Degradation 392.2.3 Techno-Economic Analysis for EES 412.2.4 Financing for Renewable Energy Systems and EES 422.3 Research Background: Hybrid Energy System in Kenya 462.3.1 Hybrid System Sizing and Operation 462.3.2 Solar and Retail Electricity Price Data 47vftoc.3d 5 8/10/2022 8:29:08 PM2.4 A Case Study on the Degradation Effect on LCOE 492.4.1 Sensitivity Analysis on the SOCThreshold 492.4.2 Sensitivity Analysis on PV and EES Rated Capacities 502.5 Financial Modeling for EES 522.5.1 Model Description 532.5.2 Case Studies Context 552.6 Case Studies on Financing EES in Kenya 572.6.1 Influence of WACC on Equity NPV and LCOS 572.6.2 Equity and Firm Cash Flows 582.6.2.1 Cash Flows for EES Capital Cost at 1500 $/kWh 582.6.2.2 Cash Flows for EES Capital Cost at 200 $/kWh 582.6.3 LCOS and Project Lifecycle Cost Composition 612.6.4 EES Finance Under Different Electricity Prices 632.6.4.1 Study on the Retail Electricity Price 632.7 Sensitivity Analysis of Technical and Economic Parameters 642.8 Discussion and Future Work 662.9 Conclusions 68Acknowledgments 68References 683 Levelized Cost of Electricity for Photovoltaic with Energy Storage 73Nomenclature 733.1 Introduction 753.2 Literature Review 763.3 Data Analysis and Operating Regime 783.3.1 Solar and Load Data Analysis 783.3.2 Problem Context 793.3.3 Operating Regime 813.3.4 Case Study 843.4 Economic Analysis 863.4.1 AD Operational Cost Model 863.4.2 LiCoO2 Degradation Cost Model and Number of Replacements 863.4.3 Levelized Cost of Electricity Derivation 903.4.3.1 LCOE for PV 913.4.3.2 LCOE for AD 923.4.3.3 Levelized Cost of Storage (LCOS) 923.4.3.4 Levelized Cost of Delivery (LCOD) 933.4.3.5 LCOE for System 943.4.4 LCOE Analyses and Discussion 943.5 Conclusions 96Acknowledgment 97References 974 Electricity Plan Recommender System 101Nomenclature 1014.1 Introduction 1024.2 Proposed Matrix Recovery Methods 1054.2.1 Previous Matrix Recovery Methods 105vi Contentsftoc.3d 6 8/10/2022 8:29:09 PM4.2.2 Matrix Recovery Methods with Electrical Instructions 1064.2.3 Solution 1074.2.4 Convergence Analysis and Complexity Analysis 1114.3 Proposed Electricity Plan Recommender System 1124.3.1 Feature Formulation Stage 1124.3.2 Recommender Stage 1124.3.3 Algorithm and Complexity Analysis 1134.4 Simulations and Discussions 1154.4.1 Recovery Simulation 1154.4.2 Recovery Result Discussions 1194.4.3 Application Study 1214.4.4 Application Result Discussions 1254.5 Conclusion and Future Work 126Acknowledgments 127References 1275 Classifier Economics of Semi-intrusive Load Monitoring 1315.1 Introduction 1315.1.1 Technical Background 1315.1.2 Original Contribution 1325.2 Typical Feature Space of SILM 1325.3 Modeling of SILM Classifier Network 1345.3.1 Problem Definition 1345.3.2 SILM Classifier Network Construction 1355.4 Classifier Locating Optimization with Ensuring on Accuracy and ClassifierEconomics 1375.4.1 Objective of SILM Construction 1375.4.2 Constraint of Devices Covering Completeness and Over Covering 1375.4.3 Constraint of Bottom Accuracy and Accuracy Measurement 1385.4.4 Constraint of Sampling Computation Requirements 1385.4.5 Optimization Algorithm 1395.5 Numerical Study 1405.5.1 Devices Operational Datasets for Numerical Study 1405.5.2 Feature Space Set for Numerical Study 1405.5.3 Numerical Study 1: Classifier Economics via Different Meter Price and Different AccuracyConstraints 1415.5.3.1 Result Analysis via Row Variation in Table 5.5 1435.5.3.2 Result Analysis via Column Variation in Table 5.5 1435.5.3.3 Result Converging via Price Variation 1445.5.4 Numerical Study 2: Classifier Economics via different Classifiers Models 1465.6 Conclusion 147Acknowledgements 147References 1476 Residential Demand Response Shifting Boundary 1516.1 Introduction 1516.2 Residential Customer Behavior Modeling 1536.2.1 Multi-Agent System Modeling 153Contents viiftoc.3d 7 8/10/2022 8:29:09 PM6.2.2 Multi-agent System Structure for PBP Demand Response 1536.2.3 Agent of Residential Consumer 1556.3 Residential Customer Shifting Boundary 1576.3.1 Consumer Behavior Decision-Making 1576.3.2 Shifting Boundary 1576.3.3 Target Function and Constraints 1586.4 Case Study 1606.4.1 Case Study Description 1606.4.2 Residential Shifting Boundary Simulation under TOU 1646.4.3 Residential Shifting Boundary Simulation Under RTP 1696.5 Case Study on Residential Customer TOU Time Zone Planning 1736.5.1 Case Study Description 1736.5.2 Result and Analysis 1736.6 Case Study on Smart Meter Installation Scale Analysis 1786.6.1 Case Study Description 1786.6.2 Analysis on Multiple Smart Meter Installation Scale under TOU and RTP 1796.7 Conclusions and Future Work 181Acknowledgements 181References 1827 Residential PV Panels Planning-Based Game-Theoretic Method 185Nomenclature 1857.1 Introduction 1867.2 System Modeling 1887.2.1 Network Branch Flow Model 1887.2.2 Energy Sharing Agent Model 1897.3 Bi-level Energy Sharing Model for Determining Optimal PV Panels InstallationCapacity 1917.3.1 Uncertainty Characterization 1917.3.2 Stackelberg Game Model 1917.3.3 Bi-level Energy Sharing Model 1927.3.4 Linearization of Bi-level Energy Sharing Model 1947.3.5 Descend Search-Based Solution Algorithm 1957.4 Stochastic Optimal PV Panels Allocation in the Coalition of Prosumer Agents 1977.5 Numerical Results 1997.5.1 Implementation on IEEE 33-Node Distribution System 1997.5.2 Implementation on IEEE 123-Node Distribution System 2057.6 Conclusion 206Acknowledgements 207References 2078 Networked Microgrids Energy Management Under High Renewable Penetration 211Nomenclature 2118.1 Introduction 2128.2 Problem Description 2158.2.1 Components and Configuration of Networked MGs 2158.2.2 Proposed Strategy 2168.3 Components Modeling 216viii Contentsftoc.3d 8 8/10/2022 8:29:09 PM8.3.1 CDGs 2168.3.2 BESSs 2178.3.3 Controllable Load 2188.3.4 Uncertain Sets of RESs, Load, and Electricity Prices 2188.3.5 Market Model 2188.4 Proposed Two-Stage Operation Model 2198.4.1 Hourly Day-Ahead Optimal Scheduling Model 2198.4.1.1 Lower Level EMS 2198.4.1.2 Upper Level EMS 2208.4.2 5-Minute Real-Time Dispatch Model 2218.5 Case Studies 2228.5.1 Set Up 2228.5.2 Results and Discussion 2228.6 Conclusions 230Acknowledgements 231References 2319 A Multi-agent Reinforcement Learning for Home Energy Management 233Nomenclature 2339.1 Introduction 2339.2 Problem Modeling 2369.2.1 State 2389.2.2 Action 2389.2.3 Reward 2399.2.4 Total Reward of HEM System 2399.2.5 Action-value Function 2409.3 Proposed Data-Driven-Based Solution Method 2409.3.1 ELM-Based Feedforward NN for Uncertainty Prediction 2419.3.2 Multi-Agent Q-Learning Algorithm for Decision-Making 2419.3.3 Implementation Process of Proposed Solution Method 2419.4 Test Results 2449.4.1 Case Study Setup 2449.4.2 Performance of the Proposed Feedforward NN 2449.4.3 Performance of Multi-Agent Q-Learning Algorithm 2469.4.4 Numerical Comparison with Genetic Algorithm 2499.5 Conclusion 251Acknowledgements 251References 25110 Virtual Energy Storage Systems Smart Coordination 25510.1 Introduction 25510.1.1 Related Work 25510.1.2 Main Contributions 25710.2 VESS Modeling, Aggregation, and Coordination Strategy 25710.2.1 VESS Modeling 25710.2.2 VESS Aggregation 25910.2.3 VESS Coordination Strategies 26010.3 Proposed Approach for Network Loading and Voltage Management by VESSs 261Contents ixftoc.3d 9 8/10/2022 8:29:09 PM10.3.1 Network Loading Management Strategy 26110.3.2 Voltage Regulation Strategy 26410.4 Case Studies 26710.4.1 Case 1 26910.4.2 Case 2 26910.5 Conclusions and Future Work 276Acknowledgements 276References 27611 Reliability Modeling and Assessment of Cyber-Physical Power Systems 279Nomenclature 27911.1 Introduction 27911.2 Composite Markov Model 28211.2.1 Multistate Markov Chain of Information Layer 28211.2.2 Two-state Markov Chain of Physical Layer 28411.2.3 Coupling Model of Physical and Information Layers 28511.3 Linear Programming Model for Maximum Flow 28611.3.1 Node Classification and Flow Constraint Model 28611.3.2 Programming Model for Network Flow 28811.4 Reliability Analysis Method 28911.4.1 Definition and Measures of System Reliability 28911.4.2 Sequential Monte-Carlo Simulation 28911.4.2.1 System State Sampling 28911.4.2.2 Reliability Computing Procedure 29011.5 Case Analysis 29111.5.1 Case Description 29111.5.2 Calculation Results and Analysis 29311.5.2.1 Effect of Demand Flow on Reliability 29311.5.2.2 Effect of Node Capacity on Reliability 29511.5.2.3 Effect of the Information Flow Level on Reliability 29711.6 Conclusion 298Acknowledgements 299References 29912 A Vehicle-To-Grid Voltage Support Co-simulation Platform 30112.1 Introduction 30112.2 Related Works 30312.2.1 Simulation of Power Systems 30312.2.2 Simulation of Communication Network 30412.2.3 Simulation of Distributed Software 30512.2.4 Time Synchronization 30512.2.5 Co-Simulation Interface 30612.3 Direct-Execution Simulation 30612.3.1 Operation of a Direct-Execution Simulation 30712.3.1.1 Simulation Metadata 30712.3.1.2 Enforcing Simulated Thread Scheduling 30812.3.1.3 Tracking Action Timestamps 308x Contentsftoc.3d 10 8/10/2022 8:29:09 PM12.3.1.4 Enforcing Timestamp Order 30812.3.1.5 Handling External Events 30812.3.2 DecompositionJ Framework 30912.4 Co-Simulation Platform for Agent-Based Smart Grid Applications 31012.4.1 Co-Simulation Message Exchange 31112.4.2 Co-Simulation Time Synchronization 31212.5 Agent-Based FLISR Case Study 31212.5.1 The Restoration Problem 31212.5.2 Reconfiguration Algorithm 31412.5.3 Restoration Agents 31512.5.4 Communication Network Configurations 31612.6 Simulation Results 31612.6.1 Agent Actions and Events 31712.6.1.1 Phase 1 – Fault Detection 31712.6.1.2 Phase 2 – Fault Location 31712.6.1.3 Phase 3 – Enquire DERs 31712.6.1.4 Phase 4 – Reconfiguration 32012.6.1.5 Phase 5 – Transient 32012.6.2 Effects of Background Traffics and Link Failure 32112.6.3 Effects of Link Failure Time 32212.6.4 Effects of Main-Container Location Configuration 32312.6.5 Summary on Simulation Results 32412.7 Case Study on V2G for Voltage Support 32412.7.1 Modeling of Electrical Grid and EVs 32412.7.2 Modeling of Communication Network 32612.7.3 Simulation Events 32712.7.4 Co-simulation Results 32712.8 Conclusions 330Acknowledgements 331References 33113 Advanced Metering Infrastructure for Electric Vehicle Charging 33513.1 Introduction 33513.2 EVAMI Overview 33813.2.1 Advantage of Adopting EVAMI 33813.2.2 Choice of Signal Transmission Platform 33813.2.3 Onsite Charging System 34013.2.4 EV Charging Station 34013.2.5 Utility Information Management System 34013.2.6 Third Party Customer Service Platform 34113.3 System Architecture, Protocol Design, and Implementation 34113.3.1 Communication Protocol 34213.3.1.1 Charging Service Session Management 34313.3.1.2 Device Management 34413.3.1.3 Demand Response Management 34613.3.2 Web Portal 34713.4 Performance Evaluation 348Contents xiftoc.3d 11 8/10/2022 8:29:09 PM13.4.1 Network Performance of OCS 34813.4.2 Effectiveness of EVAMI on Demand Response 34813.5 Conclusion 351Acknowledgements 352References 35214 Power System Dispatching with Plug-In Hybrid Electric Vehicles 355Nomenclature 35514.1 Introduction 35714.1.1 Model Decoupling 35714.1.2 Security Reinforcement 35814.1.3 Potential for Practical Application 35814.2 Framework of PHEVs Dispatching 35814.3 Framework for the Two-Stage Model 35914.4 The Charging and Discharging Mode 36014.4.1 PHEV Charging Mode 36014.4.2 PHEV Discharging Mode 36014.4.3 PHEV Charging and Discharging Power 36114.5 The Optimal Dispatching Model with PHEVs 36114.5.1 Sub-Model 1 36114.5.2 Sub-Model 2 36314.6 Numerical Examples 36414.7 Practical Application – The Impact of Electric Vehicles on Distribution Network 37014.7.1 Modeling of Electric Vehicles 37014.7.2 Uncontrolled Charging 37414.7.3 Results 37614.8 Conclusions 376Acknowledgements 377References 37715 Machine Learning for Electric Bus Fast-Charging Stations Deployment 381Nomenclature 38115.1 Introduction 38315.2 Problem Description and Assumptions 38715.2.1 Operating Characteristics of Electric Buses 38815.2.2 Affinity Propagation Algorithm 38815.3 Model Formulation 38915.3.1 Capacity Model of Electric Bus Fast-Charging Station 38915.3.2 Deployment Model of Electric Bus Fast-Charging Station 39215.3.3 Constraints 39315.4 Results and Discussion 39415.4.1 Spatio-temporal Distribution of Buses 39415.4.2 Optimized Deployment of EB Fast-Charging Stations 39415.4.3 Comparison of Different Planning Methods 395xii Contentsftoc.3d 12 8/10/2022 8:29:09 PM15.4.4 Comparison Under Different Time Headways 39915.4.5 Comparison Under Different Battery Size and Charging Power 39915.4.6 Policy and Business Model Implications 40215.5 Conclusions 403Acknowledgements 403References 40416 Best Practice for Parking Vehicles with Low-power Wide-Area Network 40716.1 Introduction 40716.2 Related Work 41316.2.1 LoRaWAN 41416.2.2 NB-IoT 41516.2.3 Sigfox 41616.3 LP-INDEX for Best Practices of LPWAN Technologies 41616.3.1 Latency 41716.3.2 Data Capacity 41716.3.3 Power and Cost 41816.3.4 Coverage 41816.3.5 Scalability 41916.3.6 Security 41916.4 Case Study 41916.4.1 Experimental Setup 41916.4.2 Depolyment of Car Park Sensors 41916.4.3 Evaluation Matrices and Results 41916.5 Conclusion and Future Work 421Acknowledgements 421References 42117 Smart Health Based on Internet of Things (IoT) and Smart Devices 42517.1 Introduction 42517.2 Technology Used in Healthcare 43017.2.1 Internet of Things 43417.2.2 Smart Meters 43817.3 Case Study 44317.3.1 Continuous Glucose Monitoring 44317.3.2 Smart Pet 44517.3.3 Smart Meters for Healthcare 44817.3.4 Other Case Studies 45317.3.4.1 Cancer Treatment 45317.3.4.2 Connected Inhalers 45417.3.4.3 Ingestible Sensors 45417.3.4.4 Elderly People 45417.4 Conclusions 455References 456Contents xiiiftoc.3d 13 8/10/2022 8:29:09 PM18 Criteria Decision Analysis Based Cardiovascular Diseases Classifier for Drunk DriverDetection 46318.1 Introduction 46318.2 Cardiovascular Diseases Classifier 46518.2.1 Design of the Optimal CDC 46618.2.2 Data Pre-Processing and Features Construction 46618.2.3 Cardiovascular Diseases Classifier Construction 46718.3 Multiple Criteria Decision Analysis of the Optimal CDC 46818.4 Analytic Hierarchy Process Scores and Analysis 47018.5 Development of EDG-Based Drunk Driver Detection 47118.5.1 ECG Sensors Implementations 47218.5.2 Drunk Driving Detection Algorithm 47318.6 ECG-Based Drunk Driver Detection Scheme Design 47318.7 Result Comparisons 47518.8 Conclusions 476Acknowledgements 477References 47719 Bioinformatics and Telemedicine for Healthcare 48119.1 Introduction 48119.2 Bioinformatics 48319.3 Top-Level Design for Integration of Bioinformatics to Smart Health 48619.4 Artificial Intelligence Roadmap 48819.5 Intelligence Techniques for Data Analysis Examples 49219.6 Decision Support System 49719.7 Conclusions 501References 50120 Concluding Remark and the Future 50720.1 The Relationship 50720.2 Roadmap 50820.3 The Future 50920.3.1 Smart Energy 50920.3.2 Healthcare 51320.3.3 Smart Transportation 51620.3.4 Smart Buildings 517References 518Index 000