{"product_id":"smart-energy-for-transportation-and-health-in-a-smart-city-9781119790334","title":"Smart Energy for Transportation and Health in a","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eSmart Energy for Transportation and Health in a Smart City A comprehensive review of the advances of smart cities' smart energy, transportation, infrastructure, and health  Smart Energy for Transportation and Health in a Smart City offers an essential guide to the functions, characteristics, and domains of smart cities and the energy technology necessary to sustain them. The authorsnoted experts on the topicinclude theoretical underpinnings, practical information, and potential benefits for the development of smart cities.  The book includes information on various financial models of energy storage, the management of networked micro-grids, coordination of virtual energy storage systems, reliability modeling and assessment of cyber space, and the development of a vehicle-to-grid voltage support. The authors review smart transportation elements such as advanced metering infrastructure for electric vehicle charging, power system dispatching with plug-in hybrid electric vehicles, and best \u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eForeword xv\u003c\/p\u003e \u003cp\u003ePreface xvii\u003c\/p\u003e \u003cp\u003eAuthors’ Biography xxi\u003c\/p\u003e \u003cp\u003eAcknowledgments xxiii\u003c\/p\u003e \u003cp\u003e1 What Is Smart City? 1\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 Characteristics, Functions, and Applications 4\u003c\/p\u003e \u003cp\u003e1.2.1 Sensors and Intelligent Electronic Devices 4\u003c\/p\u003e \u003cp\u003e1.2.2 Information Technology, Communication Networks, and Cyber Security 5\u003c\/p\u003e \u003cp\u003e1.2.3 Systems Integration 6\u003c\/p\u003e \u003cp\u003e1.2.4 Intelligence and Data Analytics 6\u003c\/p\u003e \u003cp\u003e1.2.5 Management and Control Platforms 7\u003c\/p\u003e \u003cp\u003e1.3 Smart Energy 7\u003c\/p\u003e \u003cp\u003e1.4 Smart Transportation 11\u003c\/p\u003e \u003cp\u003e1.4.1 Data Processing 11\u003c\/p\u003e \u003cp\u003e1.5 Smart Health 12\u003c\/p\u003e \u003cp\u003e1.6 Impact of COVID-19 Pandemic 12\u003c\/p\u003e \u003cp\u003e1.7 Standards 14\u003c\/p\u003e \u003cp\u003e1.7.1 International Standards for Smart City 14\u003c\/p\u003e \u003cp\u003e1.7.2 Smart City Pilot Projects 19\u003c\/p\u003e \u003cp\u003e1.8 Challenges and Opportunities 26\u003c\/p\u003e \u003cp\u003e1.9 Conclusions 29\u003c\/p\u003e \u003cp\u003eAcknowledgements 29\u003c\/p\u003e \u003cp\u003eReferences 29\u003c\/p\u003e \u003cp\u003e2 Lithium-Ion Storage Financial Model 37\u003c\/p\u003e \u003cp\u003e2.1 Introduction 37\u003c\/p\u003e \u003cp\u003e2.2 Literature Review 38\u003c\/p\u003e \u003cp\u003e2.2.1 Techno-economic Studies of Biogas, PV, and EES Hybrid Energy Systems 38\u003c\/p\u003e \u003cp\u003e2.2.2 EES Degradation 39\u003c\/p\u003e \u003cp\u003e2.2.3 Techno-Economic Analysis for EES 41\u003c\/p\u003e \u003cp\u003e2.2.4 Financing for Renewable Energy Systems and EES 42\u003c\/p\u003e \u003cp\u003e2.3 Research Background: Hybrid Energy System in Kenya 46\u003c\/p\u003e \u003cp\u003e2.3.1 Hybrid System Sizing and Operation 46\u003c\/p\u003e \u003cp\u003e2.3.2 Solar and Retail Electricity Price Data 47\u003c\/p\u003e \u003cp\u003ev\u003c\/p\u003e \u003cp\u003eftoc.3d 5 8\/10\/2022 8:29:08 PM\u003c\/p\u003e \u003cp\u003e2.4 A Case Study on the Degradation Effect on LCOE 49\u003c\/p\u003e \u003cp\u003e2.4.1 Sensitivity Analysis on the SOCThreshold 49\u003c\/p\u003e \u003cp\u003e2.4.2 Sensitivity Analysis on PV and EES Rated Capacities 50\u003c\/p\u003e \u003cp\u003e2.5 Financial Modeling for EES 52\u003c\/p\u003e \u003cp\u003e2.5.1 Model Description 53\u003c\/p\u003e \u003cp\u003e2.5.2 Case Studies Context 55\u003c\/p\u003e \u003cp\u003e2.6 Case Studies on Financing EES in Kenya 57\u003c\/p\u003e \u003cp\u003e2.6.1 Influence of WACC on Equity NPV and LCOS 57\u003c\/p\u003e \u003cp\u003e2.6.2 Equity and Firm Cash Flows 58\u003c\/p\u003e \u003cp\u003e2.6.2.1 Cash Flows for EES Capital Cost at 1500 $\/kWh 58\u003c\/p\u003e \u003cp\u003e2.6.2.2 Cash Flows for EES Capital Cost at 200 $\/kWh 58\u003c\/p\u003e \u003cp\u003e2.6.3 LCOS and Project Lifecycle Cost Composition 61\u003c\/p\u003e \u003cp\u003e2.6.4 EES Finance Under Different Electricity Prices 63\u003c\/p\u003e \u003cp\u003e2.6.4.1 Study on the Retail Electricity Price 63\u003c\/p\u003e \u003cp\u003e2.7 Sensitivity Analysis of Technical and Economic Parameters 64\u003c\/p\u003e \u003cp\u003e2.8 Discussion and Future Work 66\u003c\/p\u003e \u003cp\u003e2.9 Conclusions 68\u003c\/p\u003e \u003cp\u003eAcknowledgments 68\u003c\/p\u003e \u003cp\u003eReferences 68\u003c\/p\u003e \u003cp\u003e3 Levelized Cost of Electricity for Photovoltaic with Energy Storage 73\u003c\/p\u003e \u003cp\u003eNomenclature 73\u003c\/p\u003e \u003cp\u003e3.1 Introduction 75\u003c\/p\u003e \u003cp\u003e3.2 Literature Review 76\u003c\/p\u003e \u003cp\u003e3.3 Data Analysis and Operating Regime 78\u003c\/p\u003e \u003cp\u003e3.3.1 Solar and Load Data Analysis 78\u003c\/p\u003e \u003cp\u003e3.3.2 Problem Context 79\u003c\/p\u003e \u003cp\u003e3.3.3 Operating Regime 81\u003c\/p\u003e \u003cp\u003e3.3.4 Case Study 84\u003c\/p\u003e \u003cp\u003e3.4 Economic Analysis 86\u003c\/p\u003e \u003cp\u003e3.4.1 AD Operational Cost Model 86\u003c\/p\u003e \u003cp\u003e3.4.2 LiCoO2 Degradation Cost Model and Number of Replacements 86\u003c\/p\u003e \u003cp\u003e3.4.3 Levelized Cost of Electricity Derivation 90\u003c\/p\u003e \u003cp\u003e3.4.3.1 LCOE for PV 91\u003c\/p\u003e \u003cp\u003e3.4.3.2 LCOE for AD 92\u003c\/p\u003e \u003cp\u003e3.4.3.3 Levelized Cost of Storage (LCOS) 92\u003c\/p\u003e \u003cp\u003e3.4.3.4 Levelized Cost of Delivery (LCOD) 93\u003c\/p\u003e \u003cp\u003e3.4.3.5 LCOE for System 94\u003c\/p\u003e \u003cp\u003e3.4.4 LCOE Analyses and Discussion 94\u003c\/p\u003e \u003cp\u003e3.5 Conclusions 96\u003c\/p\u003e \u003cp\u003eAcknowledgment 97\u003c\/p\u003e \u003cp\u003eReferences 97\u003c\/p\u003e \u003cp\u003e4 Electricity Plan Recommender System 101\u003c\/p\u003e \u003cp\u003eNomenclature 101\u003c\/p\u003e \u003cp\u003e4.1 Introduction 102\u003c\/p\u003e \u003cp\u003e4.2 Proposed Matrix Recovery Methods 105\u003c\/p\u003e \u003cp\u003e4.2.1 Previous Matrix Recovery Methods 105\u003c\/p\u003e \u003cp\u003evi Contents\u003c\/p\u003e \u003cp\u003eftoc.3d 6 8\/10\/2022 8:29:09 PM\u003c\/p\u003e \u003cp\u003e4.2.2 Matrix Recovery Methods with Electrical Instructions 106\u003c\/p\u003e \u003cp\u003e4.2.3 Solution 107\u003c\/p\u003e \u003cp\u003e4.2.4 Convergence Analysis and Complexity Analysis 111\u003c\/p\u003e \u003cp\u003e4.3 Proposed Electricity Plan Recommender System 112\u003c\/p\u003e \u003cp\u003e4.3.1 Feature Formulation Stage 112\u003c\/p\u003e \u003cp\u003e4.3.2 Recommender Stage 112\u003c\/p\u003e \u003cp\u003e4.3.3 Algorithm and Complexity Analysis 113\u003c\/p\u003e \u003cp\u003e4.4 Simulations and Discussions 115\u003c\/p\u003e \u003cp\u003e4.4.1 Recovery Simulation 115\u003c\/p\u003e \u003cp\u003e4.4.2 Recovery Result Discussions 119\u003c\/p\u003e \u003cp\u003e4.4.3 Application Study 121\u003c\/p\u003e \u003cp\u003e4.4.4 Application Result Discussions 125\u003c\/p\u003e \u003cp\u003e4.5 Conclusion and Future Work 126\u003c\/p\u003e \u003cp\u003eAcknowledgments 127\u003c\/p\u003e \u003cp\u003eReferences 127\u003c\/p\u003e \u003cp\u003e5 Classifier Economics of Semi-intrusive Load Monitoring 131\u003c\/p\u003e \u003cp\u003e5.1 Introduction 131\u003c\/p\u003e \u003cp\u003e5.1.1 Technical Background 131\u003c\/p\u003e \u003cp\u003e5.1.2 Original Contribution 132\u003c\/p\u003e \u003cp\u003e5.2 Typical Feature Space of SILM 132\u003c\/p\u003e \u003cp\u003e5.3 Modeling of SILM Classifier Network 134\u003c\/p\u003e \u003cp\u003e5.3.1 Problem Definition 134\u003c\/p\u003e \u003cp\u003e5.3.2 SILM Classifier Network Construction 135\u003c\/p\u003e \u003cp\u003e5.4 Classifier Locating Optimization with Ensuring on Accuracy and Classifier\u003c\/p\u003e \u003cp\u003eEconomics 137\u003c\/p\u003e \u003cp\u003e5.4.1 Objective of SILM Construction 137\u003c\/p\u003e \u003cp\u003e5.4.2 Constraint of Devices Covering Completeness and Over Covering 137\u003c\/p\u003e \u003cp\u003e5.4.3 Constraint of Bottom Accuracy and Accuracy Measurement 138\u003c\/p\u003e \u003cp\u003e5.4.4 Constraint of Sampling Computation Requirements 138\u003c\/p\u003e \u003cp\u003e5.4.5 Optimization Algorithm 139\u003c\/p\u003e \u003cp\u003e5.5 Numerical Study 140\u003c\/p\u003e \u003cp\u003e5.5.1 Devices Operational Datasets for Numerical Study 140\u003c\/p\u003e \u003cp\u003e5.5.2 Feature Space Set for Numerical Study 140\u003c\/p\u003e \u003cp\u003e5.5.3 Numerical Study 1: Classifier Economics via Different Meter Price and Different Accuracy\u003c\/p\u003e \u003cp\u003eConstraints 141\u003c\/p\u003e \u003cp\u003e5.5.3.1 Result Analysis via Row Variation in Table 5.5 143\u003c\/p\u003e \u003cp\u003e5.5.3.2 Result Analysis via Column Variation in Table 5.5 143\u003c\/p\u003e \u003cp\u003e5.5.3.3 Result Converging via Price Variation 144\u003c\/p\u003e \u003cp\u003e5.5.4 Numerical Study 2: Classifier Economics via different Classifiers Models 146\u003c\/p\u003e \u003cp\u003e5.6 Conclusion 147\u003c\/p\u003e \u003cp\u003eAcknowledgements 147\u003c\/p\u003e \u003cp\u003eReferences 147\u003c\/p\u003e \u003cp\u003e6 Residential Demand Response Shifting Boundary 151\u003c\/p\u003e \u003cp\u003e6.1 Introduction 151\u003c\/p\u003e \u003cp\u003e6.2 Residential Customer Behavior Modeling 153\u003c\/p\u003e \u003cp\u003e6.2.1 Multi-Agent System Modeling 153\u003c\/p\u003e \u003cp\u003eContents vii\u003c\/p\u003e \u003cp\u003eftoc.3d 7 8\/10\/2022 8:29:09 PM\u003c\/p\u003e \u003cp\u003e6.2.2 Multi-agent System Structure for PBP Demand Response 153\u003c\/p\u003e \u003cp\u003e6.2.3 Agent of Residential Consumer 155\u003c\/p\u003e \u003cp\u003e6.3 Residential Customer Shifting Boundary 157\u003c\/p\u003e \u003cp\u003e6.3.1 Consumer Behavior Decision-Making 157\u003c\/p\u003e \u003cp\u003e6.3.2 Shifting Boundary 157\u003c\/p\u003e \u003cp\u003e6.3.3 Target Function and Constraints 158\u003c\/p\u003e \u003cp\u003e6.4 Case Study 160\u003c\/p\u003e \u003cp\u003e6.4.1 Case Study Description 160\u003c\/p\u003e \u003cp\u003e6.4.2 Residential Shifting Boundary Simulation under TOU 164\u003c\/p\u003e \u003cp\u003e6.4.3 Residential Shifting Boundary Simulation Under RTP 169\u003c\/p\u003e \u003cp\u003e6.5 Case Study on Residential Customer TOU Time Zone Planning 173\u003c\/p\u003e \u003cp\u003e6.5.1 Case Study Description 173\u003c\/p\u003e \u003cp\u003e6.5.2 Result and Analysis 173\u003c\/p\u003e \u003cp\u003e6.6 Case Study on Smart Meter Installation Scale Analysis 178\u003c\/p\u003e \u003cp\u003e6.6.1 Case Study Description 178\u003c\/p\u003e \u003cp\u003e6.6.2 Analysis on Multiple Smart Meter Installation Scale under TOU and RTP 179\u003c\/p\u003e \u003cp\u003e6.7 Conclusions and Future Work 181\u003c\/p\u003e \u003cp\u003eAcknowledgements 181\u003c\/p\u003e \u003cp\u003eReferences 182\u003c\/p\u003e \u003cp\u003e7 Residential PV Panels Planning-Based Game-Theoretic Method 185\u003c\/p\u003e \u003cp\u003eNomenclature 185\u003c\/p\u003e \u003cp\u003e7.1 Introduction 186\u003c\/p\u003e \u003cp\u003e7.2 System Modeling 188\u003c\/p\u003e \u003cp\u003e7.2.1 Network Branch Flow Model 188\u003c\/p\u003e \u003cp\u003e7.2.2 Energy Sharing Agent Model 189\u003c\/p\u003e \u003cp\u003e7.3 Bi-level Energy Sharing Model for Determining Optimal PV Panels Installation\u003c\/p\u003e \u003cp\u003eCapacity 191\u003c\/p\u003e \u003cp\u003e7.3.1 Uncertainty Characterization 191\u003c\/p\u003e \u003cp\u003e7.3.2 Stackelberg Game Model 191\u003c\/p\u003e \u003cp\u003e7.3.3 Bi-level Energy Sharing Model 192\u003c\/p\u003e \u003cp\u003e7.3.4 Linearization of Bi-level Energy Sharing Model 194\u003c\/p\u003e \u003cp\u003e7.3.5 Descend Search-Based Solution Algorithm 195\u003c\/p\u003e \u003cp\u003e7.4 Stochastic Optimal PV Panels Allocation in the Coalition of Prosumer Agents 197\u003c\/p\u003e \u003cp\u003e7.5 Numerical Results 199\u003c\/p\u003e \u003cp\u003e7.5.1 Implementation on IEEE 33-Node Distribution System 199\u003c\/p\u003e \u003cp\u003e7.5.2 Implementation on IEEE 123-Node Distribution System 205\u003c\/p\u003e \u003cp\u003e7.6 Conclusion 206\u003c\/p\u003e \u003cp\u003eAcknowledgements 207\u003c\/p\u003e \u003cp\u003eReferences 207\u003c\/p\u003e \u003cp\u003e8 Networked Microgrids Energy Management Under High Renewable Penetration 211\u003c\/p\u003e \u003cp\u003eNomenclature 211\u003c\/p\u003e \u003cp\u003e8.1 Introduction 212\u003c\/p\u003e \u003cp\u003e8.2 Problem Description 215\u003c\/p\u003e \u003cp\u003e8.2.1 Components and Configuration of Networked MGs 215\u003c\/p\u003e \u003cp\u003e8.2.2 Proposed Strategy 216\u003c\/p\u003e \u003cp\u003e8.3 Components Modeling 216\u003c\/p\u003e \u003cp\u003eviii Contents\u003c\/p\u003e \u003cp\u003eftoc.3d 8 8\/10\/2022 8:29:09 PM\u003c\/p\u003e \u003cp\u003e8.3.1 CDGs 216\u003c\/p\u003e \u003cp\u003e8.3.2 BESSs 217\u003c\/p\u003e \u003cp\u003e8.3.3 Controllable Load 218\u003c\/p\u003e \u003cp\u003e8.3.4 Uncertain Sets of RESs, Load, and Electricity Prices 218\u003c\/p\u003e \u003cp\u003e8.3.5 Market Model 218\u003c\/p\u003e \u003cp\u003e8.4 Proposed Two-Stage Operation Model 219\u003c\/p\u003e \u003cp\u003e8.4.1 Hourly Day-Ahead Optimal Scheduling Model 219\u003c\/p\u003e \u003cp\u003e8.4.1.1 Lower Level EMS 219\u003c\/p\u003e \u003cp\u003e8.4.1.2 Upper Level EMS 220\u003c\/p\u003e \u003cp\u003e8.4.2 5-Minute Real-Time Dispatch Model 221\u003c\/p\u003e \u003cp\u003e8.5 Case Studies 222\u003c\/p\u003e \u003cp\u003e8.5.1 Set Up 222\u003c\/p\u003e \u003cp\u003e8.5.2 Results and Discussion 222\u003c\/p\u003e \u003cp\u003e8.6 Conclusions 230\u003c\/p\u003e \u003cp\u003eAcknowledgements 231\u003c\/p\u003e \u003cp\u003eReferences 231\u003c\/p\u003e \u003cp\u003e9 A Multi-agent Reinforcement Learning for Home Energy Management 233\u003c\/p\u003e \u003cp\u003eNomenclature 233\u003c\/p\u003e \u003cp\u003e9.1 Introduction 233\u003c\/p\u003e \u003cp\u003e9.2 Problem Modeling 236\u003c\/p\u003e \u003cp\u003e9.2.1 State 238\u003c\/p\u003e \u003cp\u003e9.2.2 Action 238\u003c\/p\u003e \u003cp\u003e9.2.3 Reward 239\u003c\/p\u003e \u003cp\u003e9.2.4 Total Reward of HEM System 239\u003c\/p\u003e \u003cp\u003e9.2.5 Action-value Function 240\u003c\/p\u003e \u003cp\u003e9.3 Proposed Data-Driven-Based Solution Method 240\u003c\/p\u003e \u003cp\u003e9.3.1 ELM-Based Feedforward NN for Uncertainty Prediction 241\u003c\/p\u003e \u003cp\u003e9.3.2 Multi-Agent Q-Learning Algorithm for Decision-Making 241\u003c\/p\u003e \u003cp\u003e9.3.3 Implementation Process of Proposed Solution Method 241\u003c\/p\u003e \u003cp\u003e9.4 Test Results 244\u003c\/p\u003e \u003cp\u003e9.4.1 Case Study Setup 244\u003c\/p\u003e \u003cp\u003e9.4.2 Performance of the Proposed Feedforward NN 244\u003c\/p\u003e \u003cp\u003e9.4.3 Performance of Multi-Agent Q-Learning Algorithm 246\u003c\/p\u003e \u003cp\u003e9.4.4 Numerical Comparison with Genetic Algorithm 249\u003c\/p\u003e \u003cp\u003e9.5 Conclusion 251\u003c\/p\u003e \u003cp\u003eAcknowledgements 251\u003c\/p\u003e \u003cp\u003eReferences 251\u003c\/p\u003e \u003cp\u003e10 Virtual Energy Storage Systems Smart Coordination 255\u003c\/p\u003e \u003cp\u003e10.1 Introduction 255\u003c\/p\u003e \u003cp\u003e10.1.1 Related Work 255\u003c\/p\u003e \u003cp\u003e10.1.2 Main Contributions 257\u003c\/p\u003e \u003cp\u003e10.2 VESS Modeling, Aggregation, and Coordination Strategy 257\u003c\/p\u003e \u003cp\u003e10.2.1 VESS Modeling 257\u003c\/p\u003e \u003cp\u003e10.2.2 VESS Aggregation 259\u003c\/p\u003e \u003cp\u003e10.2.3 VESS Coordination Strategies 260\u003c\/p\u003e \u003cp\u003e10.3 Proposed Approach for Network Loading and Voltage Management by VESSs 261\u003c\/p\u003e \u003cp\u003eContents ix\u003c\/p\u003e \u003cp\u003eftoc.3d 9 8\/10\/2022 8:29:09 PM\u003c\/p\u003e \u003cp\u003e10.3.1 Network Loading Management Strategy 261\u003c\/p\u003e \u003cp\u003e10.3.2 Voltage Regulation Strategy 264\u003c\/p\u003e \u003cp\u003e10.4 Case Studies 267\u003c\/p\u003e \u003cp\u003e10.4.1 Case 1 269\u003c\/p\u003e \u003cp\u003e10.4.2 Case 2 269\u003c\/p\u003e \u003cp\u003e10.5 Conclusions and Future Work 276\u003c\/p\u003e \u003cp\u003eAcknowledgements 276\u003c\/p\u003e \u003cp\u003eReferences 276\u003c\/p\u003e \u003cp\u003e11 Reliability Modeling and Assessment of Cyber-Physical Power Systems 279\u003c\/p\u003e \u003cp\u003eNomenclature 279\u003c\/p\u003e \u003cp\u003e11.1 Introduction 279\u003c\/p\u003e \u003cp\u003e11.2 Composite Markov Model 282\u003c\/p\u003e \u003cp\u003e11.2.1 Multistate Markov Chain of Information Layer 282\u003c\/p\u003e \u003cp\u003e11.2.2 Two-state Markov Chain of Physical Layer 284\u003c\/p\u003e \u003cp\u003e11.2.3 Coupling Model of Physical and Information Layers 285\u003c\/p\u003e \u003cp\u003e11.3 Linear Programming Model for Maximum Flow 286\u003c\/p\u003e \u003cp\u003e11.3.1 Node Classification and Flow Constraint Model 286\u003c\/p\u003e \u003cp\u003e11.3.2 Programming Model for Network Flow 288\u003c\/p\u003e \u003cp\u003e11.4 Reliability Analysis Method 289\u003c\/p\u003e \u003cp\u003e11.4.1 Definition and Measures of System Reliability 289\u003c\/p\u003e \u003cp\u003e11.4.2 Sequential Monte-Carlo Simulation 289\u003c\/p\u003e \u003cp\u003e11.4.2.1 System State Sampling 289\u003c\/p\u003e \u003cp\u003e11.4.2.2 Reliability Computing Procedure 290\u003c\/p\u003e \u003cp\u003e11.5 Case Analysis 291\u003c\/p\u003e \u003cp\u003e11.5.1 Case Description 291\u003c\/p\u003e \u003cp\u003e11.5.2 Calculation Results and Analysis 293\u003c\/p\u003e \u003cp\u003e11.5.2.1 Effect of Demand Flow on Reliability 293\u003c\/p\u003e \u003cp\u003e11.5.2.2 Effect of Node Capacity on Reliability 295\u003c\/p\u003e \u003cp\u003e11.5.2.3 Effect of the Information Flow Level on Reliability 297\u003c\/p\u003e \u003cp\u003e11.6 Conclusion 298\u003c\/p\u003e \u003cp\u003eAcknowledgements 299\u003c\/p\u003e \u003cp\u003eReferences 299\u003c\/p\u003e \u003cp\u003e12 A Vehicle-To-Grid Voltage Support Co-simulation Platform 301\u003c\/p\u003e \u003cp\u003e12.1 Introduction 301\u003c\/p\u003e \u003cp\u003e12.2 Related Works 303\u003c\/p\u003e \u003cp\u003e12.2.1 Simulation of Power Systems 303\u003c\/p\u003e \u003cp\u003e12.2.2 Simulation of Communication Network 304\u003c\/p\u003e \u003cp\u003e12.2.3 Simulation of Distributed Software 305\u003c\/p\u003e \u003cp\u003e12.2.4 Time Synchronization 305\u003c\/p\u003e \u003cp\u003e12.2.5 Co-Simulation Interface 306\u003c\/p\u003e \u003cp\u003e12.3 Direct-Execution Simulation 306\u003c\/p\u003e \u003cp\u003e12.3.1 Operation of a Direct-Execution Simulation 307\u003c\/p\u003e \u003cp\u003e12.3.1.1 Simulation Metadata 307\u003c\/p\u003e \u003cp\u003e12.3.1.2 Enforcing Simulated Thread Scheduling 308\u003c\/p\u003e \u003cp\u003e12.3.1.3 Tracking Action Timestamps 308\u003c\/p\u003e \u003cp\u003ex Contents\u003c\/p\u003e \u003cp\u003eftoc.3d 10 8\/10\/2022 8:29:09 PM\u003c\/p\u003e \u003cp\u003e12.3.1.4 Enforcing Timestamp Order 308\u003c\/p\u003e \u003cp\u003e12.3.1.5 Handling External Events 308\u003c\/p\u003e \u003cp\u003e12.3.2 DecompositionJ Framework 309\u003c\/p\u003e \u003cp\u003e12.4 Co-Simulation Platform for Agent-Based Smart Grid Applications 310\u003c\/p\u003e \u003cp\u003e12.4.1 Co-Simulation Message Exchange 311\u003c\/p\u003e \u003cp\u003e12.4.2 Co-Simulation Time Synchronization 312\u003c\/p\u003e \u003cp\u003e12.5 Agent-Based FLISR Case Study 312\u003c\/p\u003e \u003cp\u003e12.5.1 The Restoration Problem 312\u003c\/p\u003e \u003cp\u003e12.5.2 Reconfiguration Algorithm 314\u003c\/p\u003e \u003cp\u003e12.5.3 Restoration Agents 315\u003c\/p\u003e \u003cp\u003e12.5.4 Communication Network Configurations 316\u003c\/p\u003e \u003cp\u003e12.6 Simulation Results 316\u003c\/p\u003e \u003cp\u003e12.6.1 Agent Actions and Events 317\u003c\/p\u003e \u003cp\u003e12.6.1.1 Phase 1 – Fault Detection 317\u003c\/p\u003e \u003cp\u003e12.6.1.2 Phase 2 – Fault Location 317\u003c\/p\u003e \u003cp\u003e12.6.1.3 Phase 3 – Enquire DERs 317\u003c\/p\u003e \u003cp\u003e12.6.1.4 Phase 4 – Reconfiguration 320\u003c\/p\u003e \u003cp\u003e12.6.1.5 Phase 5 – Transient 320\u003c\/p\u003e \u003cp\u003e12.6.2 Effects of Background Traffics and Link Failure 321\u003c\/p\u003e \u003cp\u003e12.6.3 Effects of Link Failure Time 322\u003c\/p\u003e \u003cp\u003e12.6.4 Effects of Main-Container Location Configuration 323\u003c\/p\u003e \u003cp\u003e12.6.5 Summary on Simulation Results 324\u003c\/p\u003e \u003cp\u003e12.7 Case Study on V2G for Voltage Support 324\u003c\/p\u003e \u003cp\u003e12.7.1 Modeling of Electrical Grid and EVs 324\u003c\/p\u003e \u003cp\u003e12.7.2 Modeling of Communication Network 326\u003c\/p\u003e \u003cp\u003e12.7.3 Simulation Events 327\u003c\/p\u003e \u003cp\u003e12.7.4 Co-simulation Results 327\u003c\/p\u003e \u003cp\u003e12.8 Conclusions 330\u003c\/p\u003e \u003cp\u003eAcknowledgements 331\u003c\/p\u003e \u003cp\u003eReferences 331\u003c\/p\u003e \u003cp\u003e13 Advanced Metering Infrastructure for Electric Vehicle Charging 335\u003c\/p\u003e \u003cp\u003e13.1 Introduction 335\u003c\/p\u003e \u003cp\u003e13.2 EVAMI Overview 338\u003c\/p\u003e \u003cp\u003e13.2.1 Advantage of Adopting EVAMI 338\u003c\/p\u003e \u003cp\u003e13.2.2 Choice of Signal Transmission Platform 338\u003c\/p\u003e \u003cp\u003e13.2.3 Onsite Charging System 340\u003c\/p\u003e \u003cp\u003e13.2.4 EV Charging Station 340\u003c\/p\u003e \u003cp\u003e13.2.5 Utility Information Management System 340\u003c\/p\u003e \u003cp\u003e13.2.6 Third Party Customer Service Platform 341\u003c\/p\u003e \u003cp\u003e13.3 System Architecture, Protocol Design, and Implementation 341\u003c\/p\u003e \u003cp\u003e13.3.1 Communication Protocol 342\u003c\/p\u003e \u003cp\u003e13.3.1.1 Charging Service Session Management 343\u003c\/p\u003e \u003cp\u003e13.3.1.2 Device Management 344\u003c\/p\u003e \u003cp\u003e13.3.1.3 Demand Response Management 346\u003c\/p\u003e \u003cp\u003e13.3.2 Web Portal 347\u003c\/p\u003e \u003cp\u003e13.4 Performance Evaluation 348\u003c\/p\u003e \u003cp\u003eContents xi\u003c\/p\u003e \u003cp\u003eftoc.3d 11 8\/10\/2022 8:29:09 PM\u003c\/p\u003e \u003cp\u003e13.4.1 Network Performance of OCS 348\u003c\/p\u003e \u003cp\u003e13.4.2 Effectiveness of EVAMI on Demand Response 348\u003c\/p\u003e \u003cp\u003e13.5 Conclusion 351\u003c\/p\u003e \u003cp\u003eAcknowledgements 352\u003c\/p\u003e \u003cp\u003eReferences 352\u003c\/p\u003e \u003cp\u003e14 Power System Dispatching with Plug-In Hybrid Electric Vehicles 355\u003c\/p\u003e \u003cp\u003eNomenclature 355\u003c\/p\u003e \u003cp\u003e14.1 Introduction 357\u003c\/p\u003e \u003cp\u003e14.1.1 Model Decoupling 357\u003c\/p\u003e \u003cp\u003e14.1.2 Security Reinforcement 358\u003c\/p\u003e \u003cp\u003e14.1.3 Potential for Practical Application 358\u003c\/p\u003e \u003cp\u003e14.2 Framework of PHEVs Dispatching 358\u003c\/p\u003e \u003cp\u003e14.3 Framework for the Two-Stage Model 359\u003c\/p\u003e \u003cp\u003e14.4 The Charging and Discharging Mode 360\u003c\/p\u003e \u003cp\u003e14.4.1 PHEV Charging Mode 360\u003c\/p\u003e \u003cp\u003e14.4.2 PHEV Discharging Mode 360\u003c\/p\u003e \u003cp\u003e14.4.3 PHEV Charging and Discharging Power 361\u003c\/p\u003e \u003cp\u003e14.5 The Optimal Dispatching Model with PHEVs 361\u003c\/p\u003e \u003cp\u003e14.5.1 Sub-Model 1 361\u003c\/p\u003e \u003cp\u003e14.5.2 Sub-Model 2 363\u003c\/p\u003e \u003cp\u003e14.6 Numerical Examples 364\u003c\/p\u003e \u003cp\u003e14.7 Practical Application – The Impact of Electric Vehicles on Distribution Network 370\u003c\/p\u003e \u003cp\u003e14.7.1 Modeling of Electric Vehicles 370\u003c\/p\u003e \u003cp\u003e14.7.2 Uncontrolled Charging 374\u003c\/p\u003e \u003cp\u003e14.7.3 Results 376\u003c\/p\u003e \u003cp\u003e14.8 Conclusions 376\u003c\/p\u003e \u003cp\u003eAcknowledgements 377\u003c\/p\u003e \u003cp\u003eReferences 377\u003c\/p\u003e \u003cp\u003e15 Machine Learning for Electric Bus Fast-Charging Stations Deployment 381\u003c\/p\u003e \u003cp\u003eNomenclature 381\u003c\/p\u003e \u003cp\u003e15.1 Introduction 383\u003c\/p\u003e \u003cp\u003e15.2 Problem Description and Assumptions 387\u003c\/p\u003e \u003cp\u003e15.2.1 Operating Characteristics of Electric Buses 388\u003c\/p\u003e \u003cp\u003e15.2.2 Affinity Propagation Algorithm 388\u003c\/p\u003e \u003cp\u003e15.3 Model Formulation 389\u003c\/p\u003e \u003cp\u003e15.3.1 Capacity Model of Electric Bus Fast-Charging Station 389\u003c\/p\u003e \u003cp\u003e15.3.2 Deployment Model of Electric Bus Fast-Charging Station 392\u003c\/p\u003e \u003cp\u003e15.3.3 Constraints 393\u003c\/p\u003e \u003cp\u003e15.4 Results and Discussion 394\u003c\/p\u003e \u003cp\u003e15.4.1 Spatio-temporal Distribution of Buses 394\u003c\/p\u003e \u003cp\u003e15.4.2 Optimized Deployment of EB Fast-Charging Stations 394\u003c\/p\u003e \u003cp\u003e15.4.3 Comparison of Different Planning Methods 395\u003c\/p\u003e \u003cp\u003exii Contents\u003c\/p\u003e \u003cp\u003eftoc.3d 12 8\/10\/2022 8:29:09 PM\u003c\/p\u003e \u003cp\u003e15.4.4 Comparison Under Different Time Headways 399\u003c\/p\u003e \u003cp\u003e15.4.5 Comparison Under Different Battery Size and Charging Power 399\u003c\/p\u003e \u003cp\u003e15.4.6 Policy and Business Model Implications 402\u003c\/p\u003e \u003cp\u003e15.5 Conclusions 403\u003c\/p\u003e \u003cp\u003eAcknowledgements 403\u003c\/p\u003e \u003cp\u003eReferences 404\u003c\/p\u003e \u003cp\u003e16 Best Practice for Parking Vehicles with Low-power Wide-Area Network 407\u003c\/p\u003e \u003cp\u003e16.1 Introduction 407\u003c\/p\u003e \u003cp\u003e16.2 Related Work 413\u003c\/p\u003e \u003cp\u003e16.2.1 LoRaWAN 414\u003c\/p\u003e \u003cp\u003e16.2.2 NB-IoT 415\u003c\/p\u003e \u003cp\u003e16.2.3 Sigfox 416\u003c\/p\u003e \u003cp\u003e16.3 LP-INDEX for Best Practices of LPWAN Technologies 416\u003c\/p\u003e \u003cp\u003e16.3.1 Latency 417\u003c\/p\u003e \u003cp\u003e16.3.2 Data Capacity 417\u003c\/p\u003e \u003cp\u003e16.3.3 Power and Cost 418\u003c\/p\u003e \u003cp\u003e16.3.4 Coverage 418\u003c\/p\u003e \u003cp\u003e16.3.5 Scalability 419\u003c\/p\u003e \u003cp\u003e16.3.6 Security 419\u003c\/p\u003e \u003cp\u003e16.4 Case Study 419\u003c\/p\u003e \u003cp\u003e16.4.1 Experimental Setup 419\u003c\/p\u003e \u003cp\u003e16.4.2 Depolyment of Car Park Sensors 419\u003c\/p\u003e \u003cp\u003e16.4.3 Evaluation Matrices and Results 419\u003c\/p\u003e \u003cp\u003e16.5 Conclusion and Future Work 421\u003c\/p\u003e \u003cp\u003eAcknowledgements 421\u003c\/p\u003e \u003cp\u003eReferences 421\u003c\/p\u003e \u003cp\u003e17 Smart Health Based on Internet of Things (IoT) and Smart Devices 425\u003c\/p\u003e \u003cp\u003e17.1 Introduction 425\u003c\/p\u003e \u003cp\u003e17.2 Technology Used in Healthcare 430\u003c\/p\u003e \u003cp\u003e17.2.1 Internet of Things 434\u003c\/p\u003e \u003cp\u003e17.2.2 Smart Meters 438\u003c\/p\u003e \u003cp\u003e17.3 Case Study 443\u003c\/p\u003e \u003cp\u003e17.3.1 Continuous Glucose Monitoring 443\u003c\/p\u003e \u003cp\u003e17.3.2 Smart Pet 445\u003c\/p\u003e \u003cp\u003e17.3.3 Smart Meters for Healthcare 448\u003c\/p\u003e \u003cp\u003e17.3.4 Other Case Studies 453\u003c\/p\u003e \u003cp\u003e17.3.4.1 Cancer Treatment 453\u003c\/p\u003e \u003cp\u003e17.3.4.2 Connected Inhalers 454\u003c\/p\u003e \u003cp\u003e17.3.4.3 Ingestible Sensors 454\u003c\/p\u003e \u003cp\u003e17.3.4.4 Elderly People 454\u003c\/p\u003e \u003cp\u003e17.4 Conclusions 455\u003c\/p\u003e \u003cp\u003eReferences 456\u003c\/p\u003e \u003cp\u003eContents xiii\u003c\/p\u003e \u003cp\u003eftoc.3d 13 8\/10\/2022 8:29:09 PM\u003c\/p\u003e \u003cp\u003e18 Criteria Decision Analysis Based Cardiovascular Diseases Classifier for Drunk Driver\u003c\/p\u003e \u003cp\u003eDetection 463\u003c\/p\u003e \u003cp\u003e18.1 Introduction 463\u003c\/p\u003e \u003cp\u003e18.2 Cardiovascular Diseases Classifier 465\u003c\/p\u003e \u003cp\u003e18.2.1 Design of the Optimal CDC 466\u003c\/p\u003e \u003cp\u003e18.2.2 Data Pre-Processing and Features Construction 466\u003c\/p\u003e \u003cp\u003e18.2.3 Cardiovascular Diseases Classifier Construction 467\u003c\/p\u003e \u003cp\u003e18.3 Multiple Criteria Decision Analysis of the Optimal CDC 468\u003c\/p\u003e \u003cp\u003e18.4 Analytic Hierarchy Process Scores and Analysis 470\u003c\/p\u003e \u003cp\u003e18.5 Development of EDG-Based Drunk Driver Detection 471\u003c\/p\u003e \u003cp\u003e18.5.1 ECG Sensors Implementations 472\u003c\/p\u003e \u003cp\u003e18.5.2 Drunk Driving Detection Algorithm 473\u003c\/p\u003e \u003cp\u003e18.6 ECG-Based Drunk Driver Detection Scheme Design 473\u003c\/p\u003e \u003cp\u003e18.7 Result Comparisons 475\u003c\/p\u003e \u003cp\u003e18.8 Conclusions 476\u003c\/p\u003e \u003cp\u003eAcknowledgements 477\u003c\/p\u003e \u003cp\u003eReferences 477\u003c\/p\u003e \u003cp\u003e19 Bioinformatics and Telemedicine for Healthcare 481\u003c\/p\u003e \u003cp\u003e19.1 Introduction 481\u003c\/p\u003e \u003cp\u003e19.2 Bioinformatics 483\u003c\/p\u003e \u003cp\u003e19.3 Top-Level Design for Integration of Bioinformatics to Smart Health 486\u003c\/p\u003e \u003cp\u003e19.4 Artificial Intelligence Roadmap 488\u003c\/p\u003e \u003cp\u003e19.5 Intelligence Techniques for Data Analysis Examples 492\u003c\/p\u003e \u003cp\u003e19.6 Decision Support System 497\u003c\/p\u003e \u003cp\u003e19.7 Conclusions 501\u003c\/p\u003e \u003cp\u003eReferences 501\u003c\/p\u003e \u003cp\u003e20 Concluding Remark and the Future 507\u003c\/p\u003e \u003cp\u003e20.1 The Relationship 507\u003c\/p\u003e \u003cp\u003e20.2 Roadmap 508\u003c\/p\u003e \u003cp\u003e20.3 The Future 509\u003c\/p\u003e \u003cp\u003e20.3.1 Smart Energy 509\u003c\/p\u003e \u003cp\u003e20.3.2 Healthcare 513\u003c\/p\u003e \u003cp\u003e20.3.3 Smart Transportation 516\u003c\/p\u003e \u003cp\u003e20.3.4 Smart Buildings 517\u003c\/p\u003e \u003cp\u003eReferences 518\u003c\/p\u003e \u003cp\u003eIndex 000\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":51039268864343,"sku":"9781119790334","price":92.7,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119790334.jpg?v=1750943112","url":"https:\/\/bookcurl.com\/products\/smart-energy-for-transportation-and-health-in-a-smart-city-9781119790334","provider":"Book Curl","version":"1.0","type":"link"}