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Smart Energy for Transportation and Health in a Smart City. Edition No. 1. IEEE Press Series on Power and Energy Systems

  • Book

  • 576 Pages
  • November 2022
  • John Wiley and Sons Ltd
  • ID: 5839933
Smart 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 authors - noted experts on the topic - include 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 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 challenges and opportunities that arise with the development of smart cities - Presents state-of-the-art financial models of smart energy storage - Clearly explores 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 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.

Table of Contents

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

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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

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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

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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

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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

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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

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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

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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

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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

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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

Authors

Chun Sing Lai Loi Lei Lai City University, London, UK. Qi Hong Lai