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Smart Energy for Transportation and Health in a Smart City de CS Lai

Smart Energy for Transportation and Health in a Smart City: IEEE Press Series on Power and Energy Systems

Autor CS Lai
en Limba Engleză Hardback – 15 noi 2022
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 authors--noted experts on the topic--include the theoretical underpinnings, the practical information, and the 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 the advanced metering infrastructure for electric vehicle charging, power system dispatching with plug-in hybrid electric vehicles, and the best practices for low power wide area network technologies. In addition, the book explores smart health that is based on the Internet of Things and smart devices that can help improve patient care processes and decrease costs while maintaining quality. This important resource:
  • Examines the challenges and opportunities that arise with the development of smart cities
  • Presents a state-of-the-art financial models of smart energy storage
  • Clearly explores the elements of a smart city based on the advancement of information and communication technology
  • Contains a review of advances in smart health for smart cities
  • Includes a variety of real-life case studies that illustrate the various components of a smart city
Written for practicing engineers and engineering students, Smart Energy for Transportation and Health in Smart Cities offers a practical guide to the various aspects that create a sustainable smart city.
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Specificații

ISBN-13: 9781119790334
ISBN-10: 1119790336
Pagini: 576
Dimensiuni: 178 x 254 x 34 mm
Greutate: 1.2 kg
Editura: Wiley
Seria IEEE Press Series on Power and Energy Systems

Locul publicării:Hoboken, United States

Cuprins

Foreword xv Preface xvii Authors' Biography xxi Acknowledgments xxiii 1 What Is Smart City? 1 1.1 Introduction 1 1.2 Characteristics, Functions, and Applications 4 1.2.1 Sensors and Intelligent Electronic Devices 4 1.2.2 Information Technology, Communication Networks, and Cyber Security 5 1.2.3 Systems Integration 6 1.2.4 Intelligence and Data Analytics 6 1.2.5 Management and Control Platforms 7 1.3 Smart Energy 7 1.4 Smart Transportation 11 1.4.1 Data Processing 11 1.5 Smart Health 12 1.6 Impact of COVID-19 Pandemic 12 1.7 Standards 14 1.7.1 International Standards for Smart City 14 1.7.2 Smart City Pilot Projects 19 1.8 Challenges and Opportunities 26 1.9 Conclusions 29 Acknowledgements 29 References 29 2 Lithium-Ion Storage Financial Model 37 2.1 Introduction 37 2.2 Literature Review 38 2.2.1 Techno-economic Studies of Biogas, PV, and EES Hybrid Energy Systems 38 2.2.2 EES Degradation 39 2.2.3 Techno-Economic Analysis for EES 41 2.2.4 Financing for Renewable Energy Systems and EES 42 2.3 Research Background: Hybrid Energy System in Kenya 46 2.3.1 Hybrid System Sizing and Operation 46 2.3.2 Solar and Retail Electricity Price Data 47 v ftoc.3d 5 8/10/2022 8:29:08 PM 2.4 A Case Study on the Degradation Effect on LCOE 49 2.4.1 Sensitivity Analysis on the SOCThreshold 49 2.4.2 Sensitivity Analysis on PV and EES Rated Capacities 50 2.5 Financial Modeling for EES 52 2.5.1 Model Description 53 2.5.2 Case Studies Context 55 2.6 Case Studies on Financing EES in Kenya 57 2.6.1 Influence of WACC on Equity NPV and LCOS 57 2.6.2 Equity and Firm Cash Flows 58 2.6.2.1 Cash Flows for EES Capital Cost at 1500 $/kWh 58 2.6.2.2 Cash Flows for EES Capital Cost at 200 $/kWh 58 2.6.3 LCOS and Project Lifecycle Cost Composition 61 2.6.4 EES Finance Under Different Electricity Prices 63 2.6.4.1 Study on the Retail Electricity Price 63 2.7 Sensitivity Analysis of Technical and Economic Parameters 64 2.8 Discussion and Future Work 66 2.9 Conclusions 68 Acknowledgments 68 References 68 3 Levelized Cost of Electricity for Photovoltaic with Energy Storage 73 Nomenclature 73 3.1 Introduction 75 3.2 Literature Review 76 3.3 Data Analysis and Operating Regime 78 3.3.1 Solar and Load Data Analysis 78 3.3.2 Problem Context 79 3.3.3 Operating Regime 81 3.3.4 Case Study 84 3.4 Economic Analysis 86 3.4.1 AD Operational Cost Model 86 3.4.2 LiCoO2 Degradation Cost Model and Number of Replacements 86 3.4.3 Levelized Cost of Electricity Derivation 90 3.4.3.1 LCOE for PV 91 3.4.3.2 LCOE for AD 92 3.4.3.3 Levelized Cost of Storage (LCOS) 92 3.4.3.4 Levelized Cost of Delivery (LCOD) 93 3.4.3.5 LCOE for System 94 3.4.4 LCOE Analyses and Discussion 94 3.5 Conclusions 96 Acknowledgment 97 References 97 4 Electricity Plan Recommender System 101 Nomenclature 101 4.1 Introduction 102 4.2 Proposed Matrix Recovery Methods 105 4.2.1 Previous Matrix Recovery Methods 105 vi Contents ftoc.3d 6 8/10/2022 8:29:09 PM 4.2.2 Matrix Recovery Methods with Electrical Instructions 106 4.2.3 Solution 107 4.2.4 Convergence Analysis and Complexity Analysis 111 4.3 Proposed Electricity Plan Recommender System 112 4.3.1 Feature Formulation Stage 112 4.3.2 Recommender Stage 112 4.3.3 Algorithm and Complexity Analysis 113 4.4 Simulations and Discussions 115 4.4.1 Recovery Simulation 115 4.4.2 Recovery Result Discussions 119 4.4.3 Application Study 121 4.4.4 Application Result Discussions 125 4.5 Conclusion and Future Work 126 Acknowledgments 127 References 127 5 Classifier Economics of Semi-intrusive Load Monitoring 131 5.1 Introduction 131 5.1.1 Technical Background 131 5.1.2 Original Contribution 132 5.2 Typical Feature Space of SILM 132 5.3 Modeling of SILM Classifier Network 134 5.3.1 Problem Definition 134 5.3.2 SILM Classifier Network Construction 135 5.4 Classifier Locating Optimization with Ensuring on Accuracy and Classifier Economics 137 5.4.1 Objective of SILM Construction 137 5.4.2 Constraint of Devices Covering Completeness and Over Covering 137 5.4.3 Constraint of Bottom Accuracy and Accuracy Measurement 138 5.4.4 Constraint of Sampling Computation Requirements 138 5.4.5 Optimization Algorithm 139 5.5 Numerical Study 140 5.5.1 Devices Operational Datasets for Numerical Study 140 5.5.2 Feature Space Set for Numerical Study 140 5.5.3 Numerical Study 1: Classifier Economics via Different Meter Price and Different Accuracy Constraints 141 5.5.3.1 Result Analysis via Row Variation in Table 5.5 143 5.5.3.2 Result Analysis via Column Variation in Table 5.5 143 5.5.3.3 Result Converging via Price Variation 144 5.5.4 Numerical Study 2: Classifier Economics via different Classifiers Models 146 5.6 Conclusion 147 Acknowledgements 147 References 147 6 Residential Demand Response Shifting Boundary 151 6.1 Introduction 151 6.2 Residential Customer Behavior Modeling 153 6.2.1 Multi-Agent System Modeling 153 Contents vii ftoc.3d 7 8/10/2022 8:29:09 PM 6.2.2 Multi-agent System Structure for PBP Demand Response 153 6.2.3 Agent of Residential Consumer 155 6.3 Residential Customer Shifting Boundary 157 6.3.1 Consumer Behavior Decision-Making 157 6.3.2 Shifting Boundary 157 6.3.3 Target Function and Constraints 158 6.4 Case Study 160 6.4.1 Case Study Description 160 6.4.2 Residential Shifting Boundary Simulation under TOU 164 6.4.3 Residential Shifting Boundary Simulation Under RTP 169 6.5 Case Study on Residential Customer TOU Time Zone Planning 173 6.5.1 Case Study Description 173 6.5.2 Result and Analysis 173 6.6 Case Study on Smart Meter Installation Scale Analysis 178 6.6.1 Case Study Description 178 6.6.2 Analysis on Multiple Smart Meter Installation Scale under TOU and RTP 179 6.7 Conclusions and Future Work 181 Acknowledgements 181 References 182 7 Residential PV Panels Planning-Based Game-Theoretic Method 185 Nomenclature 185 7.1 Introduction 186 7.2 System Modeling 188 7.2.1 Network Branch Flow Model 188 7.2.2 Energy Sharing Agent Model 189 7.3 Bi-level Energy Sharing Model for Determining Optimal PV Panels Installation Capacity 191 7.3.1 Uncertainty Characterization 191 7.3.2 Stackelberg Game Model 191 7.3.3 Bi-level Energy Sharing Model 192 7.3.4 Linearization of Bi-level Energy Sharing Model 194 7.3.5 Descend Search-Based Solution Algorithm 195 7.4 Stochastic Optimal PV Panels Allocation in the Coalition of Prosumer Agents 197 7.5 Numerical Results 199 7.5.1 Implementation on IEEE 33-Node Distribution System 199 7.5.2 Implementation on IEEE 123-Node Distribution System 205 7.6 Conclusion 206 Acknowledgements 207 References 207 8 Networked Microgrids Energy Management Under High Renewable Penetration 211 Nomenclature 211 8.1 Introduction 212 8.2 Problem Description 215 8.2.1 Components and Configuration of Networked MGs 215 8.2.2 Proposed Strategy 216 8.3 Components Modeling 216 viii Contents ftoc.3d 8 8/10/2022 8:29:09 PM 8.3.1 CDGs 216 8.3.2 BESSs 217 8.3.3 Controllable Load 218 8.3.4 Uncertain Sets of RESs, Load, and Electricity Prices 218 8.3.5 Market Model 218 8.4 Proposed Two-Stage Operation Model 219 8.4.1 Hourly Day-Ahead Optimal Scheduling Model 219 8.4.1.1 Lower Level EMS 219 8.4.1.2 Upper Level EMS 220 8.4.2 5-Minute Real-Time Dispatch Model 221 8.5 Case Studies 222 8.5.1 Set Up 222 8.5.2 Results and Discussion 222 8.6 Conclusions 230 Acknowledgements 231 References 231 9 A Multi-agent Reinforcement Learning for Home Energy Management 233 Nomenclature 233 9.1 Introduction 233 9.2 Problem Modeling 236 9.2.1 State 238 9.2.2 Action 238 9.2.3 Reward 239 9.2.4 Total Reward of HEM System 239 9.2.5 Action-value Function 240 9.3 Proposed Data-Driven-Based Solution Method 240 9.3.1 ELM-Based Feedforward NN for Uncertainty Prediction 241 9.3.2 Multi-Agent Q-Learning Algorithm for Decision-Making 241 9.3.3 Implementation Process of Proposed Solution Method 241 9.4 Test Results 244 9.4.1 Case Study Setup 244 9.4.2 Performance of the Proposed Feedforward NN 244 9.4.3 Performance of Multi-Agent Q-Learning Algorithm 246 9.4.4 Numerical Comparison with Genetic Algorithm 249 9.5 Conclusion 251 Acknowledgements 251 References 251 10 Virtual Energy Storage Systems Smart Coordination 255 10.1 Introduction 255 10.1.1 Related Work 255 10.1.2 Main Contributions 257 10.2 VESS Modeling, Aggregation, and Coordination Strategy 257 10.2.1 VESS Modeling 257 10.2.2 VESS Aggregation 259 10.2.3 VESS Coordination Strategies 260 10.3 Proposed Approach for Network Loading and Voltage Management by VESSs 261 Contents ix ftoc.3d 9 8/10/2022 8:29:09 PM 10.3.1 Network Loading Management Strategy 261 10.3.2 Voltage Regulation Strategy 264 10.4 Case Studies 267 10.4.1 Case 1 269 10.4.2 Case 2 269 10.5 Conclusions and Future Work 276 Acknowledgements 276 References 276 11 Reliability Modeling and Assessment of Cyber-Physical Power Systems 279 Nomenclature 279 11.1 Introduction 279 11.2 Composite Markov Model 282 11.2.1 Multistate Markov Chain of Information Layer 282 11.2.2 Two-state Markov Chain of Physical Layer 284 11.2.3 Coupling Model of Physical and Information Layers 285 11.3 Linear Programming Model for Maximum Flow 286 11.3.1 Node Classification and Flow Constraint Model 286 11.3.2 Programming Model for Network Flow 288 11.4 Reliability Analysis Method 289 11.4.1 Definition and Measures of System Reliability 289 11.4.2 Sequential Monte-Carlo Simulation 289 11.4.2.1 System State Sampling 289 11.4.2.2 Reliability Computing Procedure 290 11.5 Case Analysis 291 11.5.1 Case Description 291 11.5.2 Calculation Results and Analysis 293 11.5.2.1 Effect of Demand Flow on Reliability 293 11.5.2.2 Effect of Node Capacity on Reliability 295 11.5.2.3 Effect of the Information Flow Level on Reliability 297 11.6 Conclusion 298 Acknowledgements 299 References 299 12 A Vehicle-To-Grid Voltage Support Co-simulation Platform 301 12.1 Introduction 301 12.2 Related Works 303 12.2.1 Simulation of Power Systems 303 12.2.2 Simulation of Communication Network 304 12.2.3 Simulation of Distributed Software 305 12.2.4 Time Synchronization 305 12.2.5 Co-Simulation Interface 306 12.3 Direct-Execution Simulation 306 12.3.1 Operation of a Direct-Execution Simulation 307 12.3.1.1 Simulation Metadata 307 12.3.1.2 Enforcing Simulated Thread Scheduling 308 12.3.1.3 Tracking Action Timestamps 308 x Contents ftoc.3d 10 8/10/2022 8:29:09 PM 12.3.1.4 Enforcing Timestamp Order 308 12.3.1.5 Handling External Events 308 12.3.2 DecompositionJ Framework 309 12.4 Co-Simulation Platform for Agent-Based Smart Grid Applications 310 12.4.1 Co-Simulation Message Exchange 311 12.4.2 Co-Simulation Time Synchronization 312 12.5 Agent-Based FLISR Case Study 312 12.5.1 The Restoration Problem 312 12.5.2 Reconfiguration Algorithm 314 12.5.3 Restoration Agents 315 12.5.4 Communication Network Configurations 316 12.6 Simulation Results 316 12.6.1 Agent Actions and Events 317 12.6.1.1 Phase 1 - Fault Detection 317 12.6.1.2 Phase 2 - Fault Location 317 12.6.1.3 Phase 3 - Enquire DERs 317 12.6.1.4 Phase 4 - Reconfiguration 320 12.6.1.5 Phase 5 - Transient 320 12.6.2 Effects of Background Traffics and Link Failure 321 12.6.3 Effects of Link Failure Time 322 12.6.4 Effects of Main-Container Location Configuration 323 12.6.5 Summary on Simulation Results 324 12.7 Case Study on V2G for Voltage Support 324 12.7.1 Modeling of Electrical Grid and EVs 324 12.7.2 Modeling of Communication Network 326 12.7.3 Simulation Events 327 12.7.4 Co-simulation Results 327 12.8 Conclusions 330 Acknowledgements 331 References 331 13 Advanced Metering Infrastructure for Electric Vehicle Charging 335 13.1 Introduction 335 13.2 EVAMI Overview 338 13.2.1 Advantage of Adopting EVAMI 338 13.2.2 Choice of Signal Transmission Platform 338 13.2.3 Onsite Charging System 340 13.2.4 EV Charging Station 340 13.2.5 Utility Information Management System 340 13.2.6 Third Party Customer Service Platform 341 13.3 System Architecture, Protocol Design, and Implementation 341 13.3.1 Communication Protocol 342 13.3.1.1 Charging Service Session Management 343 13.3.1.2 Device Management 344 13.3.1.3 Demand Response Management 346 13.3.2 Web Portal 347 13.4 Performance Evaluation 348 Contents xi ftoc.3d 11 8/10/2022 8:29:09 PM 13.4.1 Network Performance of OCS 348 13.4.2 Effectiveness of EVAMI on Demand Response 348 13.5 Conclusion 351 Acknowledgements 352 References 352 14 Power System Dispatching with Plug-In Hybrid Electric Vehicles 355 Nomenclature 355 14.1 Introduction 357 14.1.1 Model Decoupling 357 14.1.2 Security Reinforcement 358 14.1.3 Potential for Practical Application 358 14.2 Framework of PHEVs Dispatching 358 14.3 Framework for the Two-Stage Model 359 14.4 The Charging and Discharging Mode 360 14.4.1 PHEV Charging Mode 360 14.4.2 PHEV Discharging Mode 360 14.4.3 PHEV Charging and Discharging Power 361 14.5 The Optimal Dispatching Model with PHEVs 361 14.5.1 Sub-Model 1 361 14.5.2 Sub-Model 2 363 14.6 Numerical Examples 364 14.7 Practical Application - The Impact of Electric Vehicles on Distribution Network 370 14.7.1 Modeling of Electric Vehicles 370 14.7.2 Uncontrolled Charging 374 14.7.3 Results 376 14.8 Conclusions 376 Acknowledgements 377 References 377 15 Machine Learning for Electric Bus Fast-Charging Stations Deployment 381 Nomenclature 381 15.1 Introduction 383 15.2 Problem Description and Assumptions 387 15.2.1 Operating Characteristics of Electric Buses 388 15.2.2 Affinity Propagation Algorithm 388 15.3 Model Formulation 389 15.3.1 Capacity Model of Electric Bus Fast-Charging Station 389 15.3.2 Deployment Model of Electric Bus Fast-Charging Station 392 15.3.3 Constraints 393 15.4 Results and Discussion 394 15.4.1 Spatio-temporal Distribution of Buses 394 15.4.2 Optimized Deployment of EB Fast-Charging Stations 394 15.4.3 Comparison of Different Planning Methods 395 xii Contents ftoc.3d 12 8/10/2022 8:29:09 PM 15.4.4 Comparison Under Different Time Headways 399 15.4.5 Comparison Under Different Battery Size and Charging Power 399 15.4.6 Policy and Business Model Implications 402 15.5 Conclusions 403 Acknowledgements 403 References 404 16 Best Practice for Parking Vehicles with Low-power Wide-Area Network 407 16.1 Introduction 407 16.2 Related Work 413 16.2.1 LoRaWAN 414 16.2.2 NB-IoT 415 16.2.3 Sigfox 416 16.3 LP-INDEX for Best Practices of LPWAN Technologies 416 16.3.1 Latency 417 16.3.2 Data Capacity 417 16.3.3 Power and Cost 418 16.3.4 Coverage 418 16.3.5 Scalability 419 16.3.6 Security 419 16.4 Case Study 419 16.4.1 Experimental Setup 419 16.4.2 Depolyment of Car Park Sensors 419 16.4.3 Evaluation Matrices and Results 419 16.5 Conclusion and Future Work 421 Acknowledgements 421 References 421 17 Smart Health Based on Internet of Things (IoT) and Smart Devices 425 17.1 Introduction 425 17.2 Technology Used in Healthcare 430 17.2.1 Internet of Things 434 17.2.2 Smart Meters 438 17.3 Case Study 443 17.3.1 Continuous Glucose Monitoring 443 17.3.2 Smart Pet 445 17.3.3 Smart Meters for Healthcare 448 17.3.4 Other Case Studies 453 17.3.4.1 Cancer Treatment 453 17.3.4.2 Connected Inhalers 454 17.3.4.3 Ingestible Sensors 454 17.3.4.4 Elderly People 454 17.4 Conclusions 455 References 456 Contents xiii ftoc.3d 13 8/10/2022 8:29:09 PM 18 Criteria Decision Analysis Based Cardiovascular Diseases Classifier for Drunk Driver Detection 463 18.1 Introduction 463 18.2 Cardiovascular Diseases Classifier 465 18.2.1 Design of the Optimal CDC 466 18.2.2 Data Pre-Processing and Features Construction 466 18.2.3 Cardiovascular Diseases Classifier Construction 467 18.3 Multiple Criteria Decision Analysis of the Optimal CDC 468 18.4 Analytic Hierarchy Process Scores and Analysis 470 18.5 Development of EDG-Based Drunk Driver Detection 471 18.5.1 ECG Sensors Implementations 472 18.5.2 Drunk Driving Detection Algorithm 473 18.6 ECG-Based Drunk Driver Detection Scheme Design 473 18.7 Result Comparisons 475 18.8 Conclusions 476 Acknowledgements 477 References 477 19 Bioinformatics and Telemedicine for Healthcare 481 19.1 Introduction 481 19.2 Bioinformatics 483 19.3 Top-Level Design for Integration of Bioinformatics to Smart Health 486 19.4 Artificial Intelligence Roadmap 488 19.5 Intelligence Techniques for Data Analysis Examples 492 19.6 Decision Support System 497 19.7 Conclusions 501 References 501 20 Concluding Remark and the Future 507 20.1 The Relationship 507 20.2 Roadmap 508 20.3 The Future 509 20.3.1 Smart Energy 509 20.3.2 Healthcare 513 20.3.3 Smart Transportation 516 20.3.4 Smart Buildings 517 References 518 Index 000

Notă biografică

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.