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Biblioteca

MARC Lista (tabellare)
Electrification of Smart Cities
Electrification of Smart Cities
Autore Lai Chun Sing
Pubbl/distr/stampa Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022
Descrizione fisica 1 online resource (114 p.)
Soggetto topico History of engineering & technology
Technology: general issues
Soggetto non controllato adaptive optimization
blockchain
detour
double auction
edge enhancement
electric vehicles
electricity trading
energy conservation
energy consumption monitoring
energy usage behaviour
generative adversarial networks
intelligent transportation system
multi-exposure image fusion
n/a
occupancy monitoring
out-of-hours consumption
route computation
smart cities
smart match mechanism
video super-resolution
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910566461903321
Lai Chun Sing  
Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Smart energy for transportation and health in a smart city / / Chun Sing Lai, Loi Lei Lai, Qi Hong Lai
Smart energy for transportation and health in a smart city / / Chun Sing Lai, Loi Lei Lai, Qi Hong Lai
Autore Lai Chun Sing
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley, , [2023]
Descrizione fisica 1 online resource (571 pages)
Disciplina 307.1160286
Collana IEEE Press series on power and energy systems
Soggetto topico Smart power grids
Smart cities
Transportation engineering
ISBN 1-119-79040-9
1-119-79036-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
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.
Record Nr. UNINA-9910830582003321
Lai Chun Sing  
Hoboken, New Jersey : , : Wiley, , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui