Smart Energy for Transportation and Health in a Smart City E-Book

Chun Sing Lai

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: IEEE Press Series on Power Engineering
• Sprache: Englisch

Seitenzahl: 1094

Veröffentlichungsjahr: 2022

Beschreibung

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.

Leseprobe

Table of Contents

Cover

Series Page

Title Page

Copyright Page

Authors’ Biography

Foreword

Preface

Acknowledgments

1 What Is Smart City?

1.1 Introduction

1.2 Characteristics, Functions, and Applications

1.3 Smart Energy

1.4 Smart Transportation

1.5 Smart Health

1.6 Impact of COVID‐19 Pandemic

1.7 Standards

1.8 Challenges and Opportunities

1.9 Conclusions

Acknowledgements

References

2 Lithium‐Ion Storage Financial Model

2.1 Introduction

2.2 Literature Review

2.3 Research Background: Hybrid Energy System in Kenya

2.4 A Case Study on the Degradation Effect on LCOE

2.5 Financial Modeling for EES

2.6 Case Studies on Financing EES in Kenya

2.7 Sensitivity Analysis of Technical and Economic Parameters

2.8 Discussion and Future Work

2.9 Conclusions

Acknowledgments

References

3 Levelized Cost of Electricity for Photovoltaic with Energy Storage

Nomenclature

3.1 Introduction

3.2 Literature Review

3.3 Data Analysis and Operating Regime

3.4 Economic Analysis

3.5 Conclusions

Acknowledgment

References

4 Electricity Plan Recommender System

Nomenclature

4.1 Introduction

4.2 Proposed Matrix Recovery Methods

4.3 Proposed Electricity Plan Recommender System

4.4 Simulations and Discussions

4.5 Conclusion and Future Work

Acknowledgments

References

5 Classifier Economics of Semi‐intrusive Load Monitoring

5.1 Introduction

5.2 Typical Feature Space of SILM

5.3 Modeling of SILM Classifier Network

5.4 Classifier Locating Optimization with Ensuring on Accuracy and Classifier Economics

5.5 Numerical Study

5.6 Conclusion

Acknowledgements

References

6 Residential Demand Response Shifting Boundary

6.1 Introduction

6.2 Residential Customer Behavior Modeling

6.3 Residential Customer Shifting Boundary

6.4 Case Study

6.5 Case Study on Residential Customer TOU Time Zone Planning

6.6 Case Study on Smart Meter Installation Scale Analysis

6.7 Conclusions and Future Work

Acknowledgements

References

7 Residential PV Panels Planning‐Based Game‐Theoretic Method

Nomenclature

7.1 Introduction

7.2 System Modeling

7.3 Bi‐level Energy Sharing Model for Determining Optimal PV Panels Installation Capacity

7.4 Stochastic Optimal PV Panels Allocation in the Coalition of Prosumer Agents

7.5 Numerical Results

7.6 Conclusion

Acknowledgements

References

8 Networked Microgrids Energy Management Under High Renewable Penetration

Nomenclature

8.1 Introduction

8.2 Problem Description

8.3 Components Modeling

8.4 Proposed Two‐Stage Operation Model

8.5 Case Studies

8.6 Conclusions

Acknowledgements

References

9 A Multi‐agent Reinforcement Learning for Home Energy Management

Nomenclature

9.1 Introduction

9.2 Problem Modeling

9.3 Proposed Data‐Driven‐Based Solution Method

9.4 Test Results

9.5 Conclusion

Acknowledgements

References

10 Virtual Energy Storage Systems Smart Coordination

10.1 Introduction

10.2 VESS Modeling, Aggregation, and Coordination Strategy

10.3 Proposed Approach for Network Loading and Voltage Management by VESSs

10.4 Case Studies

10.5 Conclusions and Future Work

Acknowledgements

References

11 Reliability Modeling and Assessment of Cyber‐Physical Power Systems

Nomenclature

11.1 Introduction

11.2 Composite Markov Model

11.3 Linear Programming Model for Maximum Flow

11.4 Reliability Analysis Method

11.5 Case Analysis

11.6 Conclusion

Acknowledgements

References

12 A Vehicle‐To‐Grid Voltage Support Co‐simulation Platform

12.1 Introduction

12.2 Related Works

12.3 Direct‐Execution Simulation

12.4 Co‐Simulation Platform for Agent‐Based Smart Grid Applications

12.5 Agent‐Based FLISR Case Study

12.6 Simulation Results

12.7 Case Study on V2G for Voltage Support

12.8 Conclusions

Acknowledgements

References

13 Advanced Metering Infrastructure for Electric Vehicle Charging

13.1 Introduction

13.2 EVAMI Overview

13.3 System Architecture, Protocol Design, and Implementation

13.4 Performance Evaluation

13.5 Conclusion

Acknowledgements

References

14 Power System Dispatching with Plug‐In Hybrid Electric Vehicles

Nomenclature

14.1 Introduction

14.2 Framework of PHEVs Dispatching

14.3 Framework for the Two‐Stage Model

14.4 The Charging and Discharging Mode

14.5 The Optimal Dispatching Model with PHEVs

14.6 Numerical Examples

14.7 Practical Application – The Impact of Electric Vehicles on Distribution Network

14.8 Conclusions

Acknowledgements

References

Appendix 14.A

15 Machine Learning for Electric Bus Fast‐Charging Stations Deployment

Nomenclature

15.1 Introduction

15.2 Problem Description and Assumptions

15.3 Model Formulation

15.4 Results and Discussion

15.5 Conclusions

Acknowledgements

References

16 Best Practice for Parking Vehicles with Low‐power Wide‐Area Network

16.1 Introduction

16.2 Related Work

16.3 LP‐INDEX for Best Practices of LPWAN Technologies

16.4 Case Study

16.5 Conclusion and Future Work

Acknowledgements

References

17 Smart Health Based on Internet of Things (IoT) and Smart Devices

17.1 Introduction

17.2 Technology Used in Healthcare

17.3 Case Study

17.4 Conclusions

References

18 Criteria Decision Analysis Based Cardiovascular Diseases Classifier for Drunk Driver Detection

18.1 Introduction

18.2 Cardiovascular Diseases Classifier

18.3 Multiple Criteria Decision Analysis of the Optimal CDC

18.4 Analytic Hierarchy Process Scores and Analysis

18.5 Development of EDG‐Based Drunk Driver Detection

18.6 ECG‐Based Drunk Driver Detection Scheme Design

18.7 Result Comparisons

18.8 Conclusions

Acknowledgements

References

19 Bioinformatics and Telemedicine for Healthcare

19.1 Introduction

19.2 Bioinformatics

19.3 Top‐Level Design for Integration of Bioinformatics to Smart Health

19.4 Artificial Intelligence Roadmap

19.5 Intelligence Techniques for Data Analysis Examples

19.6 Decision Support System

19.7 Conclusions

References

20 Concluding Remark and the Future

20.1 The Relationship

20.2 Roadmap

20.3 The Future

References

Index

bibliography

End User License Agreement

List of Tables

Chapter 1

Table 1.1 Recent Institute of Electrical and Electronics Engineers (IEEE) St...

Table 1.2

Recent IEEE Standards in development for smart health.

Table 1.3

Recent IEEE Standards in development for smart mobility and transp...

Table 1.4

Recent IEEE Standards in development for smart education.

Table 1.5 Recent IEEE Standards in development for smart governance.

Table 1.6 Smart cities pilot projects in Africa.

Table 1.7 Smart cities pilot projects in Asia.

Table 1.8 Smart cities pilot projects in Europe.

Table 1.9 Smart cities pilot projects in North America and South America....

Chapter 2

Table 2.1 Comparison of recent techno‐economic and financing studies for EES...

Table 2.2 Technical and economic specifications.

Table 2.3 Financial modeling results for the six scenarios.

Table 2.4 Equity NPV (M$) under different EES’s capital costs and retail ele...

Chapter 3

Table 3.1 Kenya normalized national load demand [1]

Table 3.2 Cost and components size.

Chapter 4

Table 4.1 Some typical indirect EPRS methods.

Table 4.2 Some typical matrix recovery methods.

Table 4.3 The selected appliances.

Table 4.4 The RMSE performance for 2014 data.

Table 4.5

The RMSE performance for 2015 data.

Table 4.6 The RMSE performance for 2016 data.

Table 4.7

The RMSE performance for 2017 data.

Table 4.8 The RMSE performance for 2018 data.

Table 4.9

t

‐test result between proposed models and other methods for 2014 d...

Table 4.10

t

‐test result between proposed models and other methods for 2015 ...

Table 4.11

t

‐test result between proposed models and other methods for 2016 ...

Table 4.12

t

‐test result between proposed models and other methods for 2017 ...

Table 4.13

t

‐test result between proposed models and other methods for 2018 ...

Table 4.14 The precision performance for 2015 data.

Table 4.15 The precision performance for 2016 data.

Table 4.16 The precision performance for 2017 data.

Table 4.17 The precision performance for 2018 data.

Table 4.18

t

‐test result between proposed models and other methods for 2015...

Table 4.19

t

‐test result between proposed models and other methods for 2016 ...

Table 20

t

‐test result between proposed models and other methods for 2017 da...

Table 4.21

t

‐test result between proposed models and other methods for 2018...

Chapter 5

Table 5.1 Price sample of meters in Figure 5.2.

Table 5.2 Device selections in Figure 5.7.

Table 5.3 Device selections in Figure 5.8.

Table 5.4 Parameters of random forest and genetic algorithm.

Table 5.5 Classifier economics optimization result on REDD dataset and fact...

Table 5.6 Classifier economics optimization result on REDD dataset with dif...

Chapter 6

Table 6.1 Parameters for Eq. (6.4).

Table 6.2 Parameters for Eq. (6.5)

.

Table 6.3 Parameters for Eq. (6.7)

.

Table 6.4 Typical transformation range of daily meals

.

Table 6.5 Details of home appliances in case study

.

Table 6.6 Distribution of family membership.

Table 6.7 Load variation between TOU and static price

.

Table 6.8 some TOU tariffs selections of time zone planning.

Table 6.9 Morning peak variation.

Table 6.10 Relation between smart meter scale and consumer participation ra...

Chapter 7

Table 7.1 Related parameters in the 33‐node case.

Table 7.2 Optimal residential PV panels planning result in 33‐node system....

Table 7.3 Residential PV panels planning size and cost and daily revenue wi...

Table 7.4 Computation performance on solving proposed bi‐level energy shari...

Table 7.5 Optimal residential PV panels planning result in 123‐node system....

Chapter 8

Table 8.1 Parameters of CDGs in each microgrid and MGC [34].

Table 8.2 Parameters of BESSs in each microgrid and MGC [35].

Table 8.3 Operation cost for microgrids components in Case 1.

Table 8.4 Operation cost for microgrids components in Case 2.

Chapter 9

Table 9.1 State set, action set, and reward function of each agent.

Table 9.2 Parameters of each house appliance and EV battery.

Table 9.3 MAPE of prediction result in cases 1–3.

Table 9.4 Computational efficiency performance with different number of stat...

Table 9.5 Comparison of electricity cost with and without.

Table 9.6 Comparison on computation efficiency by GA optimization method and...

Chapter 10

Table 10.1 VESS parameters sampling ranges for Monte‐Carlo simulation.

Table 10.2 Rated capacity in each node of the system.

Table 10.3 Calculation speed and system performance for different network to...

Chapter 11

Table 11.1 Parameters of composite markov model of acquisition nodes.

Table 11.2

Parameters of binary‐state markov model of routing nodes.

Table 11.3 Parameters of binary‐state markov model of central nodes.

Table 11.4 Reliability indices by sequential Monte‐Carlo simulations with di...

Table 11.5 Availability in different scenarios.

Table 11.6 Reliability indices of the physical network.

Chapter 12

Table 12.1 Characteristics of related works in smart grid co‐simulation.

Table 12.2 Characteristics of simulated network.

Table 12.3 Simulation event trace for NA1 and NA6.

Table 12.4 Percentage change of reconfiguration time.

Table 12.5 Amount of EV and total (dis)charging power at each bus.

Table 12.6 Electrical characteristics of EV.

Table 12.7 RTT delay of each link.

Table 12.8 Duration of disruption.

Chapter 13

Table 13.1 Specification of PLC module.

Table 13.2 Definition of charging level [24].

Chapter 14

Table 14.1 Parameters of generators of the 6‐bus system.

Table 14.2

Hourly load data of the 6‐bus system.

Table 14.3

Periods and prices of charging and discharging.

Table 14.4

Bus data of the 6‐bus system.

Table 14.5 Branch data of the 6‐bus system.

Table 14.6 The optimal dispatching schemes with and without PHEVs.

Table 14.7 The optimal charging and discharging schedules for PHEVs of the 6...

Table 14.8 The line flow of the 6‐bus system.

Table 14.9 Optimal dispatching schemes under different cost coefficients of ...

Table 14.10 Electric private vehicle plug‐in time.

Table 14.11 Electric private vehicle charging quantity.

Table 14.12 Plug‐in‐time and charging quantity of private vehicles and elect...

Table 14.13 Electric buses plug‐in time.

Table 14.14 Electric buses charging quantity.

Table 14.15 Prediction of vehicle PARC in Yangjiang.

Table 14.16 Prediction of EV PARC in Yangjiang (From [B]).

Chapter 15

Table 15.1 Research on e‐bus charging stations deployment.

Table 15.2 Bus dispatching schedule.

Table 15.3 Planning scheme of fast‐charging stations.

Table 15.4 Cost of fast‐charging stations.

Table 15.5 Parameter values of the battery sizes

.

Chapter 16

Table 16.1 Comparison between LPWAN and other types of wireless technologies...

Table 16.2 The weightings used for the case study to calculate LP‐INDEX.

Chapter 17

Table 17.1 Number of IoT connected devices worldwide from 2020 to 2030.

Chapter 18

Table 18.1 Database specification of ECG data for CDC.

Table 18.2 CDC of each configuration.

Table 18.3 Pairwise comparison 7 × 7 matrix

A

m

.

Table 18.4 Performance of NC versus TC.

Table 18.5 Energy spectral of ECG signal.

Chapter 19

Table 19.1 Number of electricity, gas, and water smart meters between 2014 a...

List of Illustrations

Chapter 2

Figure 2.1 Diagram of the hybrid energy system.

Figure 2.2 Kenya solar irradiance data.

Figure 2.3 Retail electricity price in Kenya for CI3 customers [62].

Figure 2.4 System LCOE studies with various

SOC

Threshold

.

Figure 2.5 System LCOE with the degradation cost considered.

Figure 2.6 System LCOE with the degradation cost not considered.

Figure 2.7 Exemplification of the financial model for EES.

Figure 2.8 Technical, financial, and economic inputs for EES financial asses...

Figure 2.9 LCOS with respect to various WACC. The vertical dashed lines are ...

Figure 2.10 Equity NPV with respect to various WACC. The vertical dashed lin...

Figure 2.11 Cumulated cash flow to the firm and cumulated cash to the equity...

Figure 2.12 Cumulated cash flow to the firm and cumulated cash to equity for...

Figure 2.13 Percentage of the costs for three operating scenarios with EES c...

Figure 2.14 Relationship between LCOS and EES capital cost under the three o...

Figure 2.15 The cumulated cash flow to the equity under different retail ele...

Figure 2.16 Sensitivity analysis on parameters for “High‐AD” scenario.

Figure 2.17 Sensitivity analysis on parameters for “Balanced” scenario.

Figure 2.18 Sensitivity analysis on parameters for “High‐PV” scenario.

Chapter 3

Figure 3.1 Solar insolation and capacity factor for solar farm

Figure 3.2 Schematic diagram of the hybrid system.

Figure 3.3 Proposed hybrid system operating regime.

Figure 3.4 System’s power output for summer case.

Figure 3.5 System’s power output for spring case.

Figure 3.6 EES SOC for 0 and 100%

SOC

Threshold

in summer and spring cases....

Figure 3.7 Fuel consumption curve for AD [30].

Figure 3.8 LiCoO

2

cycle life function for discharge from 100% SOC.

Figure 3.9 Comparison of Li‐ion EES degradation costs and cycle life for dis...

Figure 3.10 Number of lifetime EES replacements for different

SOC

Threshold

....

Figure 3.11 System’s lifetime SOCs and SOC differences for

SOC

Threshold

at 0...

Figure 3.12 LCOE for AD, EES and system at different

SOC

Threshold

values....

Figure 3.13 System LCOE with different EES capital costs.

Chapter 4

Figure 4.1 The dual‐stage framework of EPRS.

Figure 4.2 The recovery example of LRR and RPCA.

Figure 4.3 The framework of EPRS‐EI.

Figure 4.4 RMSE for different databases with different parameters. (only sho...

Figure 4.5 Learning curve with different training data size (only show 2015–...

Figure 4.6 Precision for different databases with different parameters (only...

Chapter 5

Figure 5.1 SILM/NILM monitoring structure.

Figure 5.2 Meters. (a) For active power only. (b) For both active power and ...

Figure 5.3 Preliminary experiment on classifier performances by increasing d...

Figure 5.4 Grid connection of each consumer.

Figure 5.5 Sample section of candidate SILM network.

Figure 5.6 Modified GA optimization algorithm.

Figure 5.7 Typical residential network topology for numerical study.

Figure 5.8 Factory topology for numerical study.

Figure 5.9 Average meter number of each row in Table 5.5.

Figure 5.10 Average meter number of each row in Table 5.5.

Figure 5.11 Average number of advanced meter of each column in Table 5.5....

Figure 5.12 SILM solution comparison between 130 and 150$ with 90% accuracy ...

Figure 5.13 Cost variation of SILM solution via price of advanced meter and ...

Figure 5.14 Typical performance comparison among different classifier models...

Chapter 6

Figure 6.1 Loop interaction structure of PBPs.

Figure 6.2 Multi‐agent system model for loop interaction structure.

Figure 6.3 Agent structure of consumers DR participated.

Figure 6.4 Inner structure of decision‐making.

Figure 6.5 Typical time varying at home rate and awake rate for residential ...

Figure 6.6 Agents’ time‐varying at home status and sleeping status.

Figure 6.7 Initial consumer behavior on home appliances before optimization....

Figure 6.8 Normalized load comparison between the load constructed from surv...

Figure 6.9 A typical 2‐period TOU tariff for Chinese Residents.

Figure 6.10 Daily load curve comparison between static price and TOU shiftin...

Figure 6.11 Load curve comparison between static price and TOU shifting boun...

Figure 6.12 Load curve comparison between static price and TOU shifting boun...

Figure 6.13 Load curve comparison between static price and TOU shifting boun...

Figure 6.14 Daily load curve and price comparison among 3 PBPs.

Figure 6.15 Daily load curve of appliances under different PBPs.

Figure 6.16 Five‐day (weekdays) price curve variation comparison between RTP...

Figure 6.17 Boundary comparison between static price, TOU, and RTP.

Figure 6.18 Variation of daily bill and daily consumption under different pr...

Figure 6.19 Daily morning peak variation.

Figure 6.20 Daily morning peak compare among different peak period start tim...

Figure 6.21 Variation of original daily evening peak and new peak generated ...

Figure 6.22 Daily morning peak comparison among different peak period end ti...

Figure 6.23 Daily load curve comparison among multiple smart meter installat...

Figure 6.24 Comparison between morning peak, daily peak, and daily valley am...

Figure 6.25 Daily load curve comparison among multiple smart meter installat...

Figure 6.26 Comparison between morning peak, daily peak, and daily valley am...

Chapter 7

Figure 7.1 The proposed energy sharing framework between the coalition and c...

Figure 7.2 Flowchart of our proposed two‐stage game‐theoretic residential PV...

Figure 7.3 IEEE 33‐node distribution grid with candidate nodes for PV panels...

Figure 7.4 Uncertainty scenarios of (a) load factor

, (b) PV output factor

Figure 7.5 Hourly revenue of the coalition in representative (a) scenario 6 ...

Figure 7.6 PV generation sold to the consumer agents and sold to the utility...

Figure 7.7 Optimal uniform price of each time period in representative Scena...

Figure 7.8 Tradeoff curve between the total PV panel size and the daily coal...

Figure 7.9 IEEE 123‐node distribution grid with candidate nodes for PV panel...

Figure 7.10 Tradeoff curve between the total PV panel size and the daily coa...

Chapter 8

Figure 8.1 The structure of networked microgrids.

Figure 8.2 Flowchart of proposed multi‐time scale energy management strategy...

Figure 8.3 Day‐ahead forecasted profiles (a) Forecasted electricity load and...

Figure 8.4 Exchanged power results in microgrids in Case 1 and Case 2. (a) W...

Figure 8.5 Distributions of system operation cost in day‐ahead market in Cas...

Figure 8.6 (a) Summation of power scheduling of BESS, CDG, and controllable ...

Figure 8.7 (a) Summation of exchanged power in individual microgrids in day‐...

Chapter 9

Figure 9.1 Structure of our proposed HEM system (REFG, refrigerator; AS, ala...

Figure 9.2 Schematic of the reinforcement learning‐based data‐driven HEM sys...

Figure 9.3 Flowchart of implementing our proposed solution method for each a...

Figure 9.4 Comparison of the actual and predicted electricity prices on 1–4 ...

Figure 9.5 Comparison of the actual and predicted solar generations on 1–4 J...

Figure 9.6 Comparison of operation cost with different prediction accuracy....

Figure 9.7 The convergence of Q‐value for power‐shiftable agents on 1 Januar...

Figure 9.8 Energy consumption of five power‐shiftable appliances in each tim...

Figure 9.9 Energy consumption of AC1 with changing dissatisfaction coefficie...

Figure 9.10 Energy consumption of all appliances on 1 January 2019, (a) with...

Figure 9.11 Optimization performance comparison of the three methods for sch...

Chapter 10

Figure 10.1 Schematic of VESS models: (a). one‐parameter model (b). two‐para...

Figure 10.2 Proposed hierarchical coordination strategy of aggregated VESSs ...

Figure 10.3 Operating conditions for network loading management and voltage ...

Figure 10.4 Flowchart of proposed control scheme for network loading managem...

Figure 10.5 One‐line diagram of nine‐node test feeder.

Figure 10.6 VESSs aggregator maximum controllable capacity in one day.

Figure 10.7 Voltage profile in one day (a) Voltage profile before control. (...

Figure 10.8 Network loading change in one day without and with control.

Figure 10.9 VESSs aggregator active power contribution.

Figure 10.10 Voltage performance under Star‐shape topology (a) Star‐shape to...

Figure 10.11 Voltage regulation performance under Complete‐shape topology (a...

Figure 10.12 Voltage regulation performance and network loading management p...

Chapter 11

Figure 11.1 Multistate transition in the information layer.

Figure 11.2 Two‐state transition of the physical layer.

Figure 11.3 Coupling model in both information and physical layers.

Figure 11.4 Node models in the power information system.

Figure 11.5 Reliability calculating flow chart.

Figure 11.6 Network topography of the power information system based on IEEE...

Figure 11.7 The proposed flowchart using a linear programming model.

Figure 11.8 Information load rate of intermediate nodes.

Figure 11.9 Flow distribution in a certain scenario.

Figure 11.10 Information load rate under different scenarios.

Figure 11.11 Impact of the overall bandwidth on system reliability.

Chapter 12

Figure 12.1 Converting JADE and agent code into direct‐execution simulators ...

Figure 12.2 Co‐simulation architecture.

Figure 12.3 (a) A modified IEEE 34‐bus distribution network, and feeder buse...

Figure 12.4 Two‐tiered MAS hierarchy.

Figure 12.5 Operation flow of a node agent.

Figure 12.6 New network configuration, with two restoration trees rooted at ...

Figure 12.7 Simulation of electrical transients during fault and restoration...

Figure 12.8 Reconfiguration solution time under different levels of backgrou...

Figure 12.9 Relationship between reconfiguration solution time and link fail...

Figure 12.10 Reconfiguration solution time affected by the network location ...

Figure 12.11 IEEE 14‐bus system topology and EV locations.

Figure 12.12 Connection of PMUs, DMS, aggregators, and EVs.

Figure 12.13 Voltage waveform of bus 9–14 (Ideal).

Figure 12.14 Voltage waveform of bus 9–14 (No V2G).

Figure 12.15 Voltage waveform of bus 12 (after fault).

Figure 12.16 Voltage waveform of bus 12 (after reclosing).

Figure 12.17 Frequency waveform of bus 12 (after fault).

Figure 12.18 Frequency waveform of bus 12 (after reclosing).

Chapter 13

Figure 13.1 EVAMI overview.

Figure 13.2 Operation flow of the EVCS.

Figure 13.3 System architecture.

Figure 13.4 Interaction between EVCS and DCU during Charging Process.

Figure 13.5 Data flow during service initialization process.

Figure 13.6 Data flow during service termination process.

Figure 13.7 EVCS failure report procedure.

Figure 13.8 Energy price update procedure. (a) DCU exchanges setting report ...

Figure 13.9 Charging session schedule update.

Figure 13.10 User interface for service manager.

Figure 13.11 User interface for EV owner. (a) Charging profile and billing i...

Figure 13.12 Network performance of OCS.

Figure 13.13 Impact of EV charging level on load profile of a residential bu...

Figure 13.14 Impact of EV AMI on load profile of a residential building.

Chapter 14

Figure 14.1 EV aggregator illustration.

Figure 14.2 The flowchart of the computational procedure.

Figure 14.3 Topology structure of the 6‐bus system.

Figure 14.4 The plug‐in time and charging quantity of electric private vehic...

Figure 14.5 The plug‐in time of buses. (a) Plug-in time 00.00–06:00. (b) Plu...

Figure 14.6 The charging quantity of electric buses.

Figure 14.7 Charging load of private vehicles.

Figure 14.8 Charging load of buses.

Figure 14.9 Load curve of the region considering the uncontrolled charging l...

Figure 14.A.1 Summer time load patterns with PHEV charging.

Figure 14.A.2 Winter time load patterns with PHEV charging.

Chapter 15

Figure 15.1 AP‐BPSO algorithm flowchart.

Figure 15.2 Schematic diagram of the planning area.

Figure 15.3 Deployment of fast‐charging stations.

Figure 15.4 Changes in SOC of bus line 8.

Figure 15.5 Cost and convergence comparison of different optimization planni...

Figure 15.6 Deployment comparison under different time headways.

Figure 15.7 Cost comparison under different time headways.

Figure 15.8 Deployment comparison under different battery size. (a) Number o...

Figure 15.9 Deployment comparison under different charging power. (a) Number...

Chapter 16

Figure 16.1 Number of IoT devices from 2012 to 2020.

Figure 16.2 A beautiful city affected by air pollution for (a) summer and (b...

Figure 16.3 Number of connections by applications and technologies used.

Figure 16.4 The setup of parking detection sensors.

Chapter 17

Figure 17.1 Percentage of the world population over 65 from 2020 to 2050....

Figure 17.2 Conventional blood glucose measuring components.

Figure 17.3 Conventional blood glucose meter.

Figure 17.4 A continuous blood glucose meters [76].

Figure 17.5 The pug – A lovely pet or an ugly dog?

Figure 17.6 A collar‐wearing pet cat, living with her owner.

Figure 17.7 Pets are dressed as family members.

Figure 17.8 People live with pets as family members.

Chapter 18

Figure 18.1 Block diagram of the new method.

Figure 18.2 Conventional drunk driving test.

Figure 18.3 Devices for electrocardiogram (ECG) measurement.

Figure 18.4 Flowchart of proposed ECG‐based drunk driver detection scheme.

Figure 18.5 Preprocessing of ECG signal.

Figure 18.6 Comparing DDD with other methods [50].

Chapter 19

Figure 19.1 A top‐level smart health with smart meter system design [29].

Figure 19.2 A component and module diagram for smart health with a smart met...

Figure 19.3 A deep learning approach in decision‐making for health status.

Figure 19.4 Multi‐view deep forecasting (MvDF) framework.

Figure 19.5 A residual block of TCN with the black lines as kernels and the ...

Figure 19.6 BLSTMattn architecture [43].

Figure 19.7 C_GRU schematic diagram [43].

Figure 19.8 Fine‐tuning of MvDF [43].

Figure 19.9 DSS architecture.

Chapter 20

Figure 20.1 Pet owner with her dog.

Figure 20.2 Owner exercising with the pet.

Guide

Cover Page

Smart Energy for Transportation and Health in a Smart City

Title Page

Copyright Page

Authors’ Biography

Foreword

Preface

Acknowledgments

Table of Contents

Begin Reading

Index

IEEE Press Series on Power and Energy Systems

WILEY END USER LICENSE AGREEMENT

Pages

ii

iii

iv

xv

xvi

xvii

xviii

xix

xx

xxi

xxii

xxiii

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

65

66

67

68

69

70

71

72

73

74

75

76

77

78

79

80

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

185

186

187

188

189

190

191

192

193

194

195

196

197

198

199

200

201

202

203

204

205

206

207

208

209

211

212

213

214

215

216

217

218

219

220

221

222

223

224

225

226

227

228

229

230

231

232

233

234

235

236

237

238

239

240

241

242

243

244

245

246

247

248

249

250

251

252

253

254

255

256

257

258

259

260

261

262

263

264

265

266

267

268

269

270

271

272

273

274

275

276

277

278

279

280

281

282

283

284

285

286

287

288

289

290

291

292

293

294

295

296

297

298

299

300

301

302

303

304

305

306

307

308

309

310

311

312

313

314

315

316

317

318

319

320

321

322

323

324

325

326

327

328

329

330

331

332

333

335

336

337

338

339

340

341

342

343

344

345

346

347

348

349

350

351

352

353

355

356

357

358

359

360

361

362

363

364

365

366

367

368

369

370

371

372

373

374

375

376

377

378

379

380

381

382

383

384

385

386

387

388

389

390

391

392

393

394

395

396

397

398

399

400

401

402

403

404

405

406

407

408

409

410

411

412

413

414

415

416

417

418

419

420

421

422

423

424

425

426

427

428

429

430

431

432

433

434

435

436

437

438

439

440

441

442

443

444

445

446

447

448

449

450

451

452

453

454

455

456

457

458

459

460

461

462

463

464

465

466

467

468

469

470

471

472

473

474

475

476

477

478

479

481

482

483

484

485

486

487

488

489

490

491

492

493

494

495

496

497

498

499

500

501

502

503

504

505

507

508

509

510

511

512

513

514

515

516

517

518

519

520

521

522

523

524

525

526

527

528

529

530

531

532

533

534

535

536

537

538

539

540

541

542

543

544

545

IEEE Press 445 Hoes Lane Piscataway, NJ 08854

IEEE Press Editorial Board Sarah Spurgeon, Editor in Chief

Jón Atli Benediktsson

Andreas Molisch

Diomidis Spinellis

Anjan Bose

Saeid Nahavandi

Ahmet Murat Tekalp

Adam Drobot

Jeffrey Reed

Peter (Yong) Lian

Thomas Robertazzi

Smart Energy for Transportation and Health in a Smart City

Chun Sing Lai

Guangdong University of Technology

China

Brunel University London

UK

Loi Lei Lai

Guangdong University of Technology

China

Qi Hong Lai

University of Oxford

UK

Copyright © 2023 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.

IEEE Standards designations are trademarks of The Institute of Electrical and Electronics Engineers, Incorporated (http://www.ieee.org/).

Non‐IEEE trademarks are the respective property of their owners.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.Published simultaneously in Canada.

No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per‐copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750‐8400, fax (978) 750‐4470, or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748‐6011, fax (201) 748‐6008, or online at http://www.wiley.com/go/permission.

Trademarks: Wiley and the Wiley logo are trademarks or registered trademarks of John Wiley & Sons, Inc. and/or its affiliates in the United States and other countries and may not be used without written permission. All other trademarks are the property of their respective owners. John Wiley & Sons, Inc. is not associated with any product or vendor mentioned in this book.

Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.

For general information on our other products and services or for technical support, please contact our Customer Care Department within the United States at (800) 762‐2974, outside the United States at (317) 572‐3993 or fax (317) 572‐4002.

Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic formats. For more information about Wiley products, visit our web site at www.wiley.com.

Library of Congress Cataloging‐in‐Publication Data applied for:Hardback ISBN: 9781119790334

Cover Design: WileyCover Images: © girafchik/Shutterstock

Authors’ Biography

Dr. Chun Sing Lai received the B.Eng. (First Class Hons.) in electrical and electronic engineering from Brunel University London, London, UK, in 2013, and the D.Phil. degree in engineering science from the University of Oxford, Oxford, UK, in 2019.

He is currently an Honorary Visiting Fellow of the School of Automation, Guangdong University of Technology, China, and a Lecturer in Circuits & Devices; he is also the Course Director, MSc Electric Vehicle Systems, with the Department of Electronic and Electrical Engineering, Brunel University London, UK. From 2018 to 2020, he was an UK Engineering and Physical Sciences Research Council Research Fellow with the School of Civil Engineering, University of Leeds, Leeds, UK. His current research interests are in power system optimization and data analytics.

Dr. Lai was the Publications Co‐Chair for both 2020 and 2021 IEEE International Smart Cities Conferences. He is the Vice‐Chair of the IEEE Smart Cities Publications Committee and Associate Editor for IET Energy Conversion and Economics. He is the Working Group Chair for IEEE P2814 Standard; Associate Vice President, Systems Science and Engineering of the IEEE Systems, Man, and Cybernetics Society (IEEE/SMCS); and Chair of the IEEE SMC Intelligent Power and Energy Systems Technical Committee. He received a Best Paper Award from the IEEE International Smart Cities Conference in October 2020. He was awarded the 2022 Meritorious Service Award by the IEEE/SMCS. Award citation is for meritorious and significant service to IEEE SMC Society technical activities and standards development. Dr. Lai has contributed to four journal articles that appeared in Web of Science as Highly Cited Papers, out of which he is the lead author for three of them. He is an IEEE Senior Member, an IET Member, and a Chartered Engineer.

Professor Loi Lei Lai received the B.Sc. (First Class Hons.), Ph.D., and D.Sc. degrees in electrical and electronic engineering from the University of Aston, Birmingham, UK, and City, University of London, London, UK, in 1980, 1984, and 2005, respectively.

Professor Lai is currently a University Distinguished Professor with Guangdong University of Technology, Guangzhou, China. He was a Pao Yue Kong Chair Professor with Zhejiang University, Hangzhou, China, and the Professor and Chair of Electrical Engineering with City, University of London. His current research areas are in smart cities and smart grid. Professor Lai was awarded an IEEE Third Millennium Medal, the IEEE Power and Energy Society (IEEE/PES) UKRI Power Chapter Outstanding Engineer Award in 2000, the IEEE/PES Energy Development and Power Generation Committee Prize Paper in 2006 and 2009, the IEEE/SMCS Outstanding Contribution Award in 2013 and 2014, and the Most Active Technical Committee Award in 2016.

Professor Lai is an Associate Editor of the IEEE Transactions on Systems, Man, and Cybernetics: Systems, Editor‐in‐Chief of the IEEE Smart Cities Newsletter, a member of the IEEE Smart Cities Steering Committee, and the Chair of the IEEE Systems, Man, and Cybernetics Society (IEEE/SMCS) Standards Committee. He is Conference General Chair of the 12th International Conference on Power and Energy Systems 2022 (ICPES 2022), IEEE. He was a member of the IEEE Smart Grid Steering Committee; the Director of Research and Development Center, State Grid Energy Research Institute, China; a Vice President for Membership and Student Activities with IEEE/SMCS; and a Fellow Committee Evaluator for the IEEE Industrial Electronics Society and IEEE/PES Lifetime Achievement Award Assessment Committee Member. He is an IEEE Life Fellow and IET Fellow.

Ms. Qi Hong Lai studied at Harrow International School Beijing, China, where she was awarded funding under the IEEE Systems, Man, and Cybernetics Society (IEEE/SMC) Pre‐College Activities initiative to set up a program on Brain Computer Interface. She went on to gain her Bachelor of Science in Biomedical Science with First Class Honors from King’s College London, UK, in 2019.

At present, she is working toward her Doctor of Philosophy in Molecular Cell Biology in Health and Disease at the Sir William Dunn School of Pathology, University of Oxford, UK.

She is the Working Group Secretary of the IEEE P3166 Standard on Smart Cities Terminology. Her current research interests are in transcription, bioinformatics, biotechnology, and smart health. She is an IEEE Student Member.

Foreword

As the world population continues to rise, the optimal management of major cities will play a key role in orchestrating the global responses to challenges posed by rapid urbanization. The notion of smart city is driven by stakeholders’ intention to meet increasing societal demands as large city populations grow in all corners of the world. A prosperous smart city would manage a collection of large and critical infrastructures that support socioeconomic initiatives as it celebrates cultural and ethnic diversities. Smart cities manifest a safer, more secure, more economical, and more sustainable environment that promotes optimal resource allocation and utilization, industrial ecology, and energy conservation. However, a smart city is not all about decarbonization and energy sustainability. It also focuses on public safety, clean water utilization and conservation, public waste management, traffic control and congestion management, telemedicine and public health, and cyber‐resilient communication for the automation of personal and social services that can improve the quality of life.

Smart cities rely on widely distributed smart devices to monitor and collect the pertinent data in real‐time for intelligent decision‐making. To accomplish the task, a distributed network of smart sensor nodes and data centers that stores and shares sensor data will make up the multiple levels of hierarchy in smart city infrastructures. Smart cities are operated in affordable and sustainable manners with more sophisticated control and management systems to ensure that social objectives can be attained in a fair and equitable style. The implementation of new technologies is also accelerated in smart cities as decision‐makers and city planners seek to improve their effectiveness to manage limited resources in a more resilient fashion.

This book, which is on smart energy management for optimizing the transportation and healthcare infrastructures in a smart city, brings forth the importance of sustaining a secure, clean, and economical energy network in a smart city. In particular, the availability of a reliable, sustainable, and affordable supply of clean energy is critical for the electrification of smart city infrastructures. The respective authors provide a detailed coverage of these forthcoming topics and their roles in building smart cities.

The book is the product of major contributions of well‐known experts and technical investigators with the goal of covering all levels of understanding to optimize the delivery of the concept to various interest levels. It explains in depth the compelling reasons for erecting smart cities and touches on analytical models that are deemed critical for analyzing the essence of establishing smart cities. Various practical examples and pertinent technologies are discussed to highlight the nucleus and promote the curtailed subject areas of energy, transportation, and healthcare in smart cities. The book provides various smart city stakeholders including operating managers, planners, practitioners, and research investigators with valuable insights on many levels of practical and academic landscapes as individuals embark on establishing smart cities for better serving their concerned citizens.

Mohammad ShahidehpourElected Member, US National Academy of EngineeringLife Fellow, IEEEFellow, American Association for the Advancement of ScienceFellow, National Academy of InventorsUniversity Distinguished Professor, Illinois Institute of Technology, Chicago, United States

Preface

To make city safer, more secure, and environmentally sustainable, environmental governance, public safety, city planning, industrial promotion, resource utilization, energy conservation, traffic control, telemedicine, interpersonal communication, education, social activities, and entertainment are focused upon. Smart cities have been driven by the desire of citizens to meet increasing demands and allow the choice on the basis of price and service provided. The dramatic changes in the organization of city management bring new challenges and opportunities, by a new competitive and marketable framework. This book was written in response to the growing interest in green smart city technology and its deployment on a global scale. People firmly believe that the technology will produce win‐win solutions in terms of environmental, social, and economic impacts.

To achieve net‐zero emissions by 2050, preserve biodiversity, and mitigate global warming, people are committing to building a better and more sustainable world. Smart energy will play a key role in a carbon‐neutral society. Major environmental, economic, and technological challenges such as climate change, economic restructuring, pressure on public finances, digitalization of the retail and entertainment industries, and growth of urban and ageing populations have generated huge interest for cities to be run differently and smartly.

Smart health will enable medical practitioners to manage patient health using digital means in a secure and private environment whenever and wherever care is required. Road traffic accidents are one of the major causes of injury‐related deaths. Safety is the highest priority for transportation. The application of the Internet of Things (IoT) is of special interest to support the aim of efficiently transforming cities to acquire more substantial and sustainable development, as well as a higher quality of life by using data for decision‐making to control resources and assets more efficiently.

Smart city is now a hot topic, but its definition and specifics remain unclear. This has led to different interpretations of a smart city. A smart city may be described by six basic pillars: (i) smart economy that improves competitiveness, (ii) smart people relating to social and human capital, (iii) smart governance handling social operation decisions, (iv) smart mobility integrating ICT with transportation to minimize fatality and maximize comfortability, (v) smart environment aiming to achieve net‐zero emission through the utilization of natural resources, and (vi) smart living seeking to improve quality of life and life expectancy.

This book focuses on delivering a comprehensive and detailed analysis of smart energy, smart transportation, smart infrastructures, and smart health. The purpose is to first inform readers through a more general but comprehensive coverage of the smart city concept, and then go deep into more specific areas, rather than over‐specialization, as to avoid only presenting qualitative data and numerical techniques, and where feasible, provide actual case studies and project discussions.

The book is composed of five parts, namely, Part 1 is the Introduction as presented in Chapter 1; Part 2 is related to smart energy for smart cities and is presented in Chapters 2–11 on power systems, battery, PVs economy and cost, planning, demand response, network microgrids, home energy management, virtual energy storage, reliability modeling of CPS, and vehicle‐to‐grid; Part 3 is related to smart transportation to related to fast‐charging station, electric vehicles, and parking vehicles based on machine learning and wireless communication and is presented in Chapters 12–16; Part 4 is related to smart health and is presented in Chapters 17–19; and Part 5 is the concluding remarks and proposed future directions for smart cities and this is given in Chapter 20. The details for each chapter are given as follows.

The first chapter discusses the definition of a smart city and explains its functions, characteristics, and domains. It will go through some case studies and the established standards of smart cities worldwide.

Chapter 2 introduces a state‐of‐the‐art financial model that has achieved novel and meaningful financial and economic results when applied to lithium‐ion (Li‐ion) electrical energy storage (EES). Real solar irradiance and load and retail electricity price data from Kenya were used to develop a set of case studies. EES is combined with photovoltaic and anaerobic digestion biogas power plants.

Due to the diurnal and intermittent nature of solar irradiance, photovoltaic (PV) power plants will introduce power generation and load power imbalance issues. Anaerobic digestion (AD) biogas power plants also have a partial load operation constraint that needs to be met. To overcome these limitations, EES is needed to provide power generation flexibility. Chapter 3 reports on the optimal operating mechanism designed for the PV‐AD‐EES hybrid system, followed by the study of the levelized cost of electricity (LCOE). The degradation cost per kilowatt‐hour and the degradation cost per cycle of EES are considered. The study used the 22 years (1994–2015) irradiance data of Kenya's Tkwell Canyon Dam (1.90 °N, 35.34 °E) and Kenya's national load.

With demand‐side management (DSM), several electricity prices have emerged, and residential customers are faced with the challenge of choosing a plan that meets their individual needs. The Electricity Plan Recommender System (EPRS) can alleviate this problem. Chapter 4 proposes a new EPRS model integrated with electrical instruction‐based recovery (EPRS‐EI) to restore electrical appliance usage and set the recovered data as features that represent the customer’s life pattern. With these functions, a personal electricity plan is recommended.

Chapter 5 proposes a new classifier network construction method: non‐intrusive load monitoring (NILM) and semi‐intrusive load monitoring (SILM). This method is not to create a classifier for NILM or SILM but to help decision‐makers choose different types of classifiers and optimize the location of the classifiers. In this method, the economy of each classifier is considered to ensure that the cost of decision‐makers is reduced. A combinatorial optimization problem is established on the tree‐type model for the optimized classifier network. Numerical studies on public data sets and industrial operation data have demonstrated the benefits obtained.

Demand response (DR) is one of the typical methods to optimize the load characteristics of the power system. Chapter 6 introduces the boundary model framework for the construction and transformation of consumer behavior of household appliances. Electricity tariffs are analyzed by this model for their load variation potentials.

Case studies are also included to reflect the implementation potential of the model framework in terms of pricing and smart meter deployment.

Chapter 7 proposes a novel two‐stage game‐theoretic residential PV panels planning framework for distribution grids with potential PV prosumers. A residential PV panels location‐allocation model is integrated with the energy sharing mechanism to increase economic benefits to PV prosumers and meanwhile facilitate the reasonable installation of residential PV panels. Simulations on IEEE 33‐node and 123‐node test systems prove the effectiveness of the proposed method.

Chapter 8 proposes a two‐stage energy management strategy for networked microgrids in the presence of a large number of renewable resources. It decomposes the microgrids energy management into two stages to offset the intra‐day stochastic variations of renewable energy resources, electricity load, and electricity prices. According to the simulation results, the proposed method can identify optimal scheduling results, reduce operation costs of risk‐aversion, and mitigate the impact of uncertainties.

Chapter 9 proposes a novel framework for home energy management (HEM) based on reinforcement learning in achieving efficient household DR. The Extreme Learning Machine (ELM) processes real data on electricity prices and solar PV power generation promptly in a rolling time window to make uncertain predictions. The simulation was performed at the residential level, which included multiple household appliances, an electric car, and multiple PV panels. The test results prove the effectiveness of the proposed data‐driven HEM.

Chapter 10 proposes a two‐level consensus‐driven distributed control strategy to coordinate virtual energy storage systems (VESSs), i.e. residential households with air conditioners, to avoid the violation of voltage and loading, which are regarded as part of the main power quality issues in future distribution networks. Changes in dynamic communication network topology are studied to prove their impacts on system performance. Simulation results based on an actual system in NSW, Australia, are used to demonstrate the proposed control scheme that can effectively manage voltage and loading and is scalable and robust.

Chapter 11 proposes a reliability modeling and evaluation method for the power information system, i.e. cyberspace in power system. The proposed composite Markov model will couple physical characteristics and information flow performances in a two‐layer model. The proposed reliability method combines sequential Monte Carlo simulation with a linear programming model to obtain the maximum flow that can meet the power demand.

Chapter 12 introduces the co‐simulation integration of the direct‐execution simulator, which provides special support for distributed smart grid software. A case study of agent‐based smart grid restoration using this new type of co‐simulation platform is conducted. The results show that the proposed direct‐execution simulation framework can promote the understanding, evaluation, and debugging of distributed smart grid software. A case study on vehicle‐to‐grid voltage support application is given.

Chapter 13 reports on the development of Advanced Metering Infrastructure (AMI), which is an effective tool to reshape the electric vehicle (EV) charging load curve by adopting appropriate DSM strategies. An overall solution for an electric vehicle charging service platform (EVAMI) based on power line and Internet communication is proposed. EV owners understand their energy usage, so they can effectively carry out energy‐saving activities.

Since plug‐in hybrid electric vehicles (PHEVs) are expected to be widely used in the near future, in Chapter 14 a mathematical model is developed based on the traditional security‐constrained unit commitment (SCUC) formulation to address the power system dispatching problem with PHEVs taken into account. A real system in China is used to study the impact of PHEV charging on the distribution system. It is proved that charging brings peak load to the grid, and control is essential to reduce the risk of instability.

As more and more electric buses (EBs) are put into use, the reasonable location of charging stations plays an important role in the process of bus electrification. Chapter 15 proposes a location planning model for EB fast‐charging stations that considers the bus operation network and the distribution network. The goal of the model is to minimize the sum of the construction cost of charging stations, the operation and maintenance costs, the cost to go to charging stations, and the distribution network losses. The model is applied to simulate and analyze the bus public transportation of a coastal city in South China. The case study shows that the model can effectively optimize the layout of a city’s bus charging stations.

Infrastructure and applications based on the IoT are essential for smart cities deployment. The low power wide area network (LPWAN) plays a key role in IoT techniques due to its wide coverage and low power consumption. However, it is hard to decide which one of the LPWAN techniques to be implemented in a specific application to obtain best practice. Therefore in Chapter 16, the main characteristics of the three popular LPWAN technologies, namely, LoRaWAN, NB‐IoT, and Sigfox are discussed, and LP‐INDEX is proposed to weigh performance factors according to application requirements. To further distinguish the differences, a comparative test based on parking detection sensors using the three different technologies was carried out as a case study.

It is foreseen that the trends for the next decade in healthcare will include more patients requiring care, increased use of technology, the need for greater information storage capacity, development of new healthcare delivery models, error reduction, more emphasis on preventative healthcare, and faster disease diagnosis and innovation‐driven by competition. It is important not only to improve patient care processes but also to decrease costs while maintaining quality. Chapter 17 reviews the benefits and challenges of innovations in healthcare, with emphasis on the IoT and smart devices. In Chapter 18, an electrocardiogram (ECG) scheme based on multiple criteria decision‐making approach and analytic hierarchy process is proposed to detect drunk status for drivers. Chapter 19 explains the use of bioinformatics and telemedicine for healthcare. Some models, designs, and frameworks for potential applications will be illustrated.

In addition to the smart energy, transportation, and health mentioned in the first19 chapters, there are more elements in a green smart city, such as water and waste; biology, food, and agriculture; education, safety and well‐being; government engagement with society and citizens; social entrepreneurship, digital finance, and legal and economic development; sustainable flexible buildings and infrastructure; and open data, privacy, and security for research. In the final Chapter 20, the authors formulate the roadmap and the interrelationships between certain elements. Based on current work and existing information, some suggestions are made, and an overall view of the development and deployment of green smart cities in the next ten years or so has been put forward, and the progress of smart energy, health, transportation, and construction has also been critically evaluated.

This book addresses the latest problems and solutions of smart cities in a coherent manner. It is the product of the contributions of world‐class experts, educators, and students, so it covers all levels of understanding to optimize its delivery. Therefore, we believe it will provide decision‐makers, engineers, doctors, educators, system operators, managers, planners, practitioners, and researchers with valuable insights on all levels of professional and academic progress.

Chun Sing LaiLoi Lei LaiQi Hong LaiFrom Guangzhou, China, and London and Oxford, UK

Acknowledgments

First of all, the authors wish to thank the late Professor Mohamed E. El‐Hawary, a great professor and real gentleman, in inviting them to write the so‐called the most authoritative and definitive book on Smart Cities. At the time, Professor El‐Hawary was Editor of the series of books entitled Advances in Electric Power and Energy, which is part of the Power Engineering Series of Wiley/IEEE Press books. The authors would also like to thank Mary Hatcher, Editor for the Wiley‐IEEE Press book program, and Victoria Bradshaw, Senior Editorial Assistant for Electrical Engineering in supporting the project management.

The authors wish also to thank friends, colleagues, and students, without their support this book could not have been completed. In particular, the authors thank Dr. Kim Fung Tsang, Dr. Xiaomei Wu, Professor Alfredo Vaccaro, Dr. Dongxiao Wang, and Zhanlian Li. The permission to reproduce copyright materials by the IEEE and Elsevier for a number of papers mentioned in some of the chapters is most helpful.

Last but not least, we thank Teresa Netzler, Senior Managing Editor, Academic and Professional Learning; Ms. Priyadharshini Arumugam, Permissions Specialist and Jeevaghan Devapal, Content Refinement Specialist, Wiley for supporting the production of this book and for the extremely pleasant co‐operation. Special help from Li Rong Li and Qi Ling Lai in selecting the book cover is very much appreciated as well.

1What Is Smart City?

1.1 Introduction

One of the reasons behind the lack of unified definitions of a smart city is because of the various entities involved and the functions the smart city provides. Hence, existing definitions can vary greatly. There are several definitions for a smart city which are defined by various organizations and stakeholders.

The most common consensus is that the smart city employs various kinds of digital and electronic technologies to transform the living environments with Information and Communications Technologies (ICTs) [1, 2]. Deakin [3] labeled the smart city as a city that employs ICT to meet the market (the citizens’) needs. There is a need for larger community involvement to achieve a smart city. A smart city does not simply contain ICT technology but has also developed the technology to achieve positive impacts to the local community. Some definitions for a smart city from major professional organizations and government agencies are given as follows:

Association of Southeast Asian Nations [4]