Smart energy for transportation and health in a smart city / / Chun Sing Lai, Loi Lei Lai, Qi Hong Lai

• Autore: Lai Chun Sing
• Pubblicazione: Hoboken, New Jersey : , : Wiley, , [2023]; ©2023
• Descrizione fisica: 1 online resource (571 pages)
• Disciplina: 307.1160286
• Soggetto topico: Smart power grids; Smart cities; Transportation engineering
• Persona (resp. second.): LaiQi Hong; LaiLoi Lei
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Cover -- Title Page -- Copyright Page -- Contents -- Authors' Biography -- Foreword -- Preface -- Acknowledgments -- Chapter 1 What Is Smart City? -- 1.1 Introduction -- 1.2 Characteristics, Functions, and Applications -- 1.2.1 Sensors and Intelligent Electronic Devices -- 1.2.2 Information Technology, Communication Networks, and Cyber Security -- 1.2.3 Systems Integration -- 1.2.4 Intelligence and Data Analytics -- 1.2.5 Management and Control Platforms -- 1.3 Smart Energy -- 1.4 Smart Transportation -- 1.4.1 Data Processing -- 1.5 Smart Health -- 1.6 Impact of COVID-19 Pandemic -- 1.7 Standards -- 1.7.1 International Standards for Smart City -- 1.7.2 Smart City Pilot Projects -- 1.8 Challenges and Opportunities -- 1.9 Conclusions -- Acknowledgements -- References -- Chapter 2 Lithium-Ion Storage Financial Model -- 2.1 Introduction -- 2.2 Literature Review -- 2.2.1 Techno-economic Studies of Biogas, PV, and EES Hybrid Energy Systems -- 2.2.2 EES Degradation -- 2.2.3 Techno-Economic Analysis for EES -- 2.2.4 Financing for Renewable Energy Systems and EES -- 2.3 Research Background: Hybrid Energy System in Kenya -- 2.3.1 Hybrid System Sizing and Operation -- 2.3.2 Solar and Retail Electricity Price Data -- 2.4 A Case Study on the Degradation Effect on LCOE -- 2.4.1 Sensitivity Analysis on the SOCThreshold -- 2.4.2 Sensitivity Analysis on PV and EES Rated Capacities -- 2.5 Financial Modeling for EES -- 2.5.1 Model Description -- 2.5.2 Case Studies Context -- 2.6 Case Studies on Financing EES in Kenya -- 2.6.1 Influence of WACC on Equity NPV and LCOS -- 2.6.2 Equity and Firm Cash Flows -- 2.6.2.1 Cash Flows for EES Capital Cost at 1500 /kWh -- 2.6.2.2 Cash Flows for EES Capital Cost at 200 /kWh -- 2.6.3 LCOS and Project Lifecycle Cost Composition -- 2.6.4 EES Finance Under Different Electricity Prices. ; 2.6.4.1 Study on the Retail Electricity Price -- 2.7 Sensitivity Analysis of Technical and Economic Parameters -- 2.8 Discussion and Future Work -- 2.9 Conclusions -- Acknowledgments -- References -- Chapter 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 -- 3.3.1 Solar and Load Data Analysis -- 3.3.2 Problem Context -- 3.3.3 Operating Regime -- 3.3.4 Case Study -- 3.4 Economic Analysis -- 3.4.1 AD Operational Cost Model -- 3.4.2 LiCoO2 Degradation Cost Model and Number of Replacements -- 3.4.3 Levelized Cost of Electricity Derivation -- 3.4.3.1 LCOE for PV -- 3.4.3.2 LCOE for AD -- 3.4.3.3 Levelized Cost of Storage (LCOS) -- 3.4.3.4 Levelized Cost of Delivery (LCOD) -- 3.4.3.5 LCOE for System -- 3.4.4 LCOE Analyses and Discussion -- 3.5 Conclusions -- Acknowledgment -- References -- Chapter 4 Electricity Plan Recommender System -- Nomenclature -- 4.1 Introduction -- 4.2 Proposed Matrix Recovery Methods -- 4.2.1 Previous Matrix Recovery Methods -- 4.2.2 Matrix Recovery Methods with Electrical Instructions -- 4.2.3 Solution -- 4.2.4 Convergence Analysis and Complexity Analysis -- 4.3 Proposed Electricity Plan Recommender System -- 4.3.1 Feature Formulation Stage -- 4.3.2 Recommender Stage -- 4.3.3 Algorithm and Complexity Analysis -- 4.4 Simulations and Discussions -- 4.4.1 Recovery Simulation -- 4.4.2 Recovery Result Discussions -- 4.4.3 Application Study -- 4.4.4 Application Result Discussions -- 4.5 Conclusion and Future Work -- Acknowledgments -- References -- Chapter 5 Classifier Economics of Semi-intrusive Load Monitoring -- 5.1 Introduction -- 5.1.1 Technical Background -- 5.1.2 Original Contribution -- 5.2 Typical Feature Space of SILM -- 5.3 Modeling of SILM Classifier Network -- 5.3.1 Problem Definition. ; 5.3.2 SILM Classifier Network Construction -- 5.4 Classifier Locating Optimization with Ensuring on Accuracy and Classifier Economics -- 5.4.1 Objective of SILM Construction -- 5.4.2 Constraint of Devices Covering Completeness and Over Covering -- 5.4.3 Constraint of Bottom Accuracy and Accuracy Measurement -- 5.4.4 Constraint of Sampling Computation Requirements -- 5.4.5 Optimization Algorithm -- 5.5 Numerical Study -- 5.5.1 Devices Operational Datasets for Numerical Study -- 5.5.2 Feature Space Set for Numerical Study -- 5.5.3 Numerical Study 1: Classifier Economics via Different Meter Price and Different Accuracy Constraints -- 5.5.3.1 Result Analysis via Row Variation in Table 5.5 -- 5.5.3.2 Result Analysis via Column Variation in Table 5.5 -- 5.5.3.3 Result Converging via Price Variation -- 5.5.4 Numerical Study 2: Classifier Economics via different Classifiers Models -- 5.6 Conclusion -- Acknowledgements -- References -- Chapter 6 Residential Demand Response Shifting Boundary -- 6.1 Introduction -- 6.2 Residential Customer Behavior Modeling -- 6.2.1 Multi-Agent System Modeling -- 6.2.2 Multi-agent System Structure for PBP Demand Response -- 6.2.3 Agent of Residential Consumer -- 6.3 Residential Customer Shifting Boundary -- 6.3.1 Consumer Behavior Decision-Making -- 6.3.2 Shifting Boundary -- 6.3.3 Target Function and Constraints -- 6.4 Case Study -- 6.4.1 Case Study Description -- 6.4.2 Residential Shifting Boundary Simulation under TOU -- 6.4.3 Residential Shifting Boundary Simulation Under RTP -- 6.5 Case Study on Residential Customer TOU Time Zone Planning -- 6.5.1 Case Study Description -- 6.5.2 Result and Analysis -- 6.6 Case Study on Smart Meter Installation Scale Analysis -- 6.6.1 Case Study Description -- 6.6.2 Analysis on Multiple Smart Meter Installation Scale under TOU and RTP -- 6.7 Conclusions and Future Work -- Acknowledgements. ; References -- Chapter 7 Residential PV Panels Planning-Based Game-Theoretic Method -- Nomenclature -- 7.1 Introduction -- 7.2 System Modeling -- 7.2.1 Network Branch Flow Model -- 7.2.2 Energy Sharing Agent Model -- 7.3 Bi-level Energy Sharing Model for Determining Optimal PV Panels Installation Capacity -- 7.3.1 Uncertainty Characterization -- 7.3.2 Stackelberg Game Model -- 7.3.3 Bi-level Energy Sharing Model -- 7.3.4 Linearization of Bi-level Energy Sharing Model -- 7.3.5 Descend Search-Based Solution Algorithm -- 7.4 Stochastic Optimal PV Panels Allocation in the Coalition of Prosumer Agents -- 7.5 Numerical Results -- 7.5.1 Implementation on IEEE 33-Node Distribution System -- 7.5.2 Implementation on IEEE 123-Node Distribution System -- 7.6 Conclusion -- Acknowledgements -- References -- Chapter 8 Networked Microgrids Energy Management Under High Renewable Penetration -- Nomenclature -- 8.1 Introduction -- 8.2 Problem Description -- 8.2.1 Components and Configuration of Networked MGs -- 8.2.2 Proposed Strategy -- 8.3 Components Modeling -- 8.3.1 CDGs -- 8.3.2 BESSs -- 8.3.3 Controllable Load -- 8.3.4 Uncertain Sets of RESs, Load, and Electricity Prices -- 8.3.5 Market Model -- 8.4 Proposed Two-Stage Operation Model -- 8.4.1 Hourly Day-Ahead Optimal Scheduling Model -- 8.4.1.1 Lower Level EMS -- 8.4.1.2 Upper Level EMS -- 8.4.2 5-Minute Real-Time Dispatch Model -- 8.5 Case Studies -- 8.5.1 Set Up -- 8.5.2 Results and Discussion -- 8.6 Conclusions -- Acknowledgements -- References -- Chapter 9 A Multi-agent Reinforcement Learning for Home Energy Management -- Nomenclature -- 9.1 Introduction -- 9.2 Problem Modeling -- 9.2.1 State -- 9.2.2 Action -- 9.2.3 Reward -- 9.2.4 Total Reward of HEM System -- 9.2.5 Action-value Function -- 9.3 Proposed Data-Driven-Based Solution Method -- 9.3.1 ELM-Based Feedforward NN for Uncertainty Prediction. ; 9.3.2 Multi-Agent Q-Learning Algorithm for Decision-Making -- 9.3.3 Implementation Process of Proposed Solution Method -- 9.4 Test Results -- 9.4.1 Case Study Setup -- 9.4.2 Performance of the Proposed Feedforward NN -- 9.4.3 Performance of Multi-Agent Q-Learning Algorithm -- 9.4.4 Numerical Comparison with Genetic Algorithm -- 9.5 Conclusion -- Acknowledgements -- References -- Chapter 10 Virtual Energy Storage Systems Smart Coordination -- 10.1 Introduction -- 10.1.1 Related Work -- 10.1.2 Main Contributions -- 10.2 VESS Modeling, Aggregation, and Coordination Strategy -- 10.2.1 VESS Modeling -- 10.2.2 VESS Aggregation -- 10.2.3 VESS Coordination Strategies -- 10.3 Proposed Approach for Network Loading and Voltage Management by VESSs -- 10.3.1 Network Loading Management Strategy -- 10.3.2 Voltage Regulation Strategy -- 10.4 Case Studies -- 10.4.1 Case 1 -- 10.4.2 Case 2 -- 10.5 Conclusions and Future Work -- Acknowledgements -- References -- Chapter 11 Reliability Modeling and Assessment of Cyber-Physical Power Systems -- Nomenclature -- 11.1 Introduction -- 11.2 Composite Markov Model -- 11.2.1 Multistate Markov Chain of Information Layer -- 11.2.2 Two-state Markov Chain of Physical Layer -- 11.2.3 Coupling Model of Physical and Information Layers -- 11.3 Linear Programming Model for Maximum Flow -- 11.3.1 Node Classification and Flow Constraint Model -- 11.3.2 Programming Model for Network Flow -- 11.4 Reliability Analysis Method -- 11.4.1 Definition and Measures of System Reliability -- 11.4.2 Sequential Monte-Carlo Simulation -- 11.4.2.1 System State Sampling -- 11.4.2.2 Reliability Computing Procedure -- 11.5 Case Analysis -- 11.5.1 Case Description -- 11.5.2 Calculation Results and Analysis -- 11.5.2.1 Effect of Demand Flow on Reliability -- 11.5.2.2 Effect of Node Capacity on Reliability. ; 11.5.2.3 Effect of the Information Flow Level on Reliability.
• Titolo autorizzato: Smart energy for transportation and health in a smart city
• ISBN: 1-119-79040-9; 1-119-79036-0
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910830582003321
• Lo trovi qui: Univ. Federico II
• Serie: IEEE Press series on power and energy systems.