Suzuki / Inagaki / Susuki Design and Analysis of Distributed Energy Management Systems

Integration of EMS, EV, and ICT
1. Auflage 2020
ISBN: 978-3-030-33672-1
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 209 Seiten

Reihe: Power Electronics and Power Systems

This book provides key ideas for the design and analysis of complex energy management systems (EMS) for distributed power networks. Future distributed power networks will have strong coupling with (electrified) mobility and information-communication technology (ICT) and this book addresses recent challenges for electric vehicles in the EMS, and how to synthesize the distributed power network using ICT. This book not only describes theoretical developments but also shows many applications using test beds and provides an overview of cutting edge technologies by leading researchers in their corresponding fields. Describes design and analysis of energy management systems;Illustrates the synthesis of distributed energy management systems based on aggregation of local agents;Discusses dependability issues of the distributed EMS with emphasis on the verification scheme based on remote-operational hardware-in-the-loop (HIL) simulation and cybersecurity.

Tatsuya Suzuki is a Professor at Nagoya University's Graduate School of Engineering.    Shinkichi Inagaki is an Associate Professor at Nagoya University's Graduate School of Engineering.    Yoshihiko Susuki is an Associate Professor at Osaka Prefecture University's Graduate School of Engineering.   Anh Tuan Tran is a Post Doctoral researcher at Nagoya University's Graduate School of Engineering. ng. 

Autoren/Hrsg.

Suzuki, Tatsuya

Inagaki, Shinkichi

Susuki, Yoshihiko

Tran, Anh Tuan

Inhaltsverzeichnis

1;Preface;6
2;Contents;10
3;Part I Design and Analysis of Energy Management Systems Considering Consumer Demand and Use of Electric Vehicles;12
3.1;1 Activity-Based Modeling for Integration of Energy Systemsfor House and Electric Vehicle;13
3.1.1;1.1 Introduction;13
3.1.2;1.2 Energy Demand Modeling for the Residential Sector;14
3.1.2.1;1.2.1 Occupancy-Based Approach;14
3.1.2.2;1.2.2 Activity-Based Approach;15
3.1.2.3;1.2.3 Time-Based Household Energy Demand Model;16
3.1.2.4;1.2.4 Important Factors in Energy Demand Modeling;17
3.1.3;1.3 Energy Demand Modeling for Transportation;17
3.1.3.1;1.3.1 Trip-Based Modeling;18
3.1.3.2;1.3.2 Activity-Based Modeling;18
3.1.4;1.4 Case Study;20
3.1.4.1;1.4.1 In-Home Activity Model;22
3.1.4.2;1.4.2 Out-of-Home Activity Model;24
3.1.4.3;1.4.3 Results;27
3.1.4.3.1;1.4.3.1 Activity;27
3.1.4.3.2;1.4.3.2 Electricity Demand;28
3.1.4.3.3;1.4.3.3 EV Integration Potential;29
3.1.4.3.4;1.4.3.4 Influence of Geographical Location;31
3.1.5;1.5 Conclusion;32
3.1.6;References;32
3.2;2 Probabilistic Model and Prediction of Vehicle Daily Use;36
3.2.1;2.1 How to Use a Vehicle at Home;36
3.2.2;2.2 Related Works;38
3.2.3;2.3 Statistical Data of Daily Car Use;39
3.2.4;2.4 Preparation for Problem Formulation;41
3.2.4.1;2.4.1 Description of Time;41
3.2.4.2;2.4.2 Expression of Future Profile of Vehicle Use;42
3.2.5;2.5 Problem Formulation of Predicting a PDTT;42
3.2.6;2.6 Markov Model Representing Production Process of a PDTT;44
3.2.6.1;2.6.1 A PDTT and a Markov Model;44
3.2.6.2;2.6.2 Calculation of Probabilities;45
3.2.7;2.7 Solving Method for Predicting a PDTT by Dynamic Programming;47
3.2.8;2.8 Simulation Result of the PDTT Estimation;50
3.2.9;2.9 Conclusion;53
3.2.10;References;53
3.3;3 Design of a Home Energy Management System Integratedwith an Electric Vehicle (V2H+HPWH EMS);55
3.3.1;3.1 Introduction;55
3.3.2;3.2 HPWH Model;57
3.3.2.1;3.2.1 Overview of the HPWH Model;57
3.3.2.2;3.2.2 Nomenclature;57
3.3.2.3;3.2.3 Mathematical Model of the HPWH;58
3.3.2.4;3.2.4 Piecewise Linearization of the HPWH Model;59
3.3.3;3.3 Model Predictive HEMS Using an In-Vehicle Storage Battery and a HPWH;60
3.3.3.1;3.3.1 Formulation of the Optimization Problem;61
3.3.3.2;3.3.2 Cost Function;64
3.3.3.3;3.3.3 Constraints on Household Electric Power Consumption;65
3.3.3.4;3.3.4 Constraints on In-Vehicle Storage Batteries;65
3.3.3.5;3.3.5 Constraints on the HPWH;65
3.3.4;3.4 Numerical Simulations;67
3.3.4.1;3.4.1 Simulation Settings;67
3.3.4.2;3.4.2 Simulation Results;68
3.3.4.3;3.4.3 Discussion;71
3.3.4.3.1;3.4.3.1 Computation Time;71
3.3.4.3.2;3.4.3.2 Surplus Electric Power;71
3.3.4.3.3;3.4.3.3 Electricity Charges;72
3.3.5;3.5 Conclusion;72
3.3.6;References;73
3.4;4 Range Extension Autonomous Driving for Electric Vehicles Based on Optimization of Velocity Profile Considering Traffic Signal Information;75
3.4.1;4.1 Introduction;75
3.4.2;4.2 Experimental Vehicle and Its Mathematical Model;76
3.4.2.1;4.2.1 Experimental Vehicle;76
3.4.2.2;4.2.2 Vehicle Model;77
3.4.2.3;4.2.3 Power Flow Model Harada2;79
3.4.3;4.3 Optimization of Velocity Profile Considering Traffic Signal Information;81
3.4.3.1;4.3.1 Signal Information Model;81
3.4.3.2;4.3.2 Evaluation Function and Constraint Condition;81
3.4.3.3;4.3.3 Comparison Conditions;82
3.4.3.3.1;4.3.3.1 Conventional Profile 1: Constant Acceleration and Deceleration with Signal Information;82
3.4.3.3.2;4.3.3.2 Conventional Profile 2: Optimized Velocity Profile Without Signal Information;83
3.4.3.3.3;4.3.3.3 Proposed Profile: Optimized Velocity Profile with Signal Information;83
3.4.4;4.4 Simulation Results;83
3.4.5;4.5 Experimental Results;87
3.4.5.1;4.5.1 Control System;87
3.4.5.2;4.5.2 Experimental Environment;87
3.4.5.3;4.5.3 Experiment Results of RC-S;89
3.4.5.4;4.5.4 Experimental Results of the Driving Test;90
3.4.6;4.6 Conclusion;91
3.4.7;References;91
4;Part II Synthesis of Distributed Energy Management Systems Based on Aggregation of Local EMSs and Vehicles;93
4.1;5 Real-Time Pricing and Decentralized Optimization Strategyfor Power Flow Balancing in EV/PHV Storage Management;94
4.1.1;5.1 Introduction;94
4.1.2;5.2 Real-Time Pricing and Decentralized Optimization Leading to Optimal Operation;95
4.1.2.1;5.2.1 Dynamics of Agent;95
4.1.2.2;5.2.2 Decentralized Determination of Optimal Set-Point;96
4.1.2.3;5.2.3 Real-Time Pricing Strategy;98
4.1.2.3.1;5.2.3.1 Pricing Strategy in Steady-State;98
4.1.2.3.2;5.2.3.2 Gradient Based Real-Time Pricing Strategy;99
4.1.2.4;5.2.4 Stability Analysis;100
4.1.2.4.1;5.2.4.1 Local Behavior of Decentralized Decision-Making;101
4.1.2.4.2;5.2.4.2 Uniqueness of the Equilibrium Point in Local Dynamics;102
4.1.2.4.3;5.2.4.3 Local Stability of the Closed-Loop System;103
4.1.3;5.3 EV Storage Management for Power Flow Balancing;104
4.1.4;5.4 Numerical Case Studies;107
4.1.4.1;5.4.1 Numerical Case Study 1: Charging and Discharging;108
4.1.4.2;5.4.2 Numerical Case Study 2: Plug-and-Play Type Operation;108
4.1.4.3;5.4.3 Numerical Case Study 3: Community Consists of 50 Vehicles;111
4.1.5;5.5 Conclusions;111
4.1.6;References;113
4.2;6 A Scalable Control Approach for Providing Regulation Services with Grid-Integrated Electric Vehicles;114
4.2.1;6.1 Introduction;114
4.2.2;6.2 Related Work;115
4.2.2.1;6.2.1 Regulation Services;115
4.2.2.2;6.2.2 Heuristic Scheduling for DR;116
4.2.3;6.3 Problem Formulation;116
4.2.3.1;6.3.1 Decision Phase I;117
4.2.3.2;6.3.2 Decision Phase II;118
4.2.3.3;6.3.3 Challenges in a Large-Scale Setting;120
4.2.4;6.4 Bin-Based GIV Control Approach;121
4.2.4.1;6.4.1 EV Charging Behavior;122
4.2.4.2;6.4.2 Binning Mechanism;124
4.2.4.3;6.4.3 Scheduling Mechanism;128
4.2.5;6.5 Evaluation;129
4.2.5.1;6.5.1 Alternative GIV Approaches;129
4.2.5.2;6.5.2 Simulation Results: Scheduling Quality;130
4.2.5.3;6.5.3 Simulation Results: Scheduling Scalability;131
4.2.6;6.6 Conclusions and Future Work;133
4.2.7;References;134
4.3;7 A Continuum Approach to Assessing the Impact of Spatiotemporal EV Charging to Distribution Grids;136
4.3.1;7.1 Introduction;136
4.3.2;7.2 ODE Modeling of Distribution Voltage Profile;138
4.3.3;7.3 Numerical Demonstration;140
4.3.3.1;7.3.1 Setting of Feeder and Charging Stations;140
4.3.3.2;7.3.2 Construction of Power Density Function;141
4.3.3.3;7.3.3 Results;142
4.3.4;7.4 Discussions;144
4.3.4.1;7.4.1 Comparison with Power-Flow Equation;144
4.3.4.2;7.4.2 An Analytical Treatment;145
4.3.5;7.5 Conclusion;146
4.3.6;References;147
5;Part III Toward Dependable Distributed Energy Management System Using ICT;149
5.1;8 Cyber Security for Voltage Control of Distribution Systems Under Data Falsification Attacks;150
5.1.1;8.1 Introduction;150
5.1.2;8.2 Voltage Regulation Based on Centralized Control;152
5.1.2.1;8.2.1 Centralized Voltage Control;152
5.1.2.2;8.2.2 Simulation Settings for Voltage Regulation;154
5.1.3;8.3 Cyberattacks and Security Measures in Voltage Regulation;156
5.1.3.1;8.3.1 False Data Injection (FDI) Attacks;157
5.1.3.2;8.3.2 Detection Algorithm for FDI Attacks;158
5.1.3.3;8.3.3 Stealthy Cyberattack Strategies;158
5.1.3.4;8.3.4 Further Measures for Resilient Control;160
5.1.4;8.4 Simulation Settings for Cyber Security;162
5.1.4.1;8.4.1 Attacker Strategy;162
5.1.4.2;8.4.2 Resilient Control;163
5.1.5;8.5 Verification Via Simulations;164
5.1.6;8.6 Discussions and Further Studies;167
5.1.7;8.7 Conclusion;167
5.1.8;References;169
5.2;9 Machine Learning Based Intrusion Detection in Control System Communication;171
5.2.1;9.1 Current Status for Intrusion Detection in Control System;171
5.2.2;9.2 Various Approaches;172
5.2.3;9.3 Intrusion Detection for Control System Communication Without Sequence Patterns;173
5.2.3.1;9.3.1 The Feature Representation for Intrusion Detection in Control System Communication;174
5.2.3.2;9.3.2 Binary Classification Methods;175
5.2.3.2.1;9.3.2.1 C4.5;175
5.2.3.2.2;9.3.2.2 Support Vector Machine (SVM);176
5.2.3.3;9.3.3 Anomaly Detection Methods;178
5.2.3.3.1;9.3.3.1 Local Outlier Factor (LOF);178
5.2.3.3.2;9.3.3.2 One-Class Support Vector Machine (OCSVM);180
5.2.3.4;9.3.4 Support Vector Data Description (SVDD);182
5.2.4;9.4 Experiments for Control System Communication Without Control Sequence Patterns;184
5.2.4.1;9.4.1 Experimental Setup;184
5.2.4.1.1;9.4.1.1 Water Storage Tank Control System Communication Data;184
5.2.4.1.2;9.4.1.2 Gas Pipeline Control System Communication Data;185
5.2.4.1.3;9.4.1.3 Cyberattacks in Experiments;185
5.2.4.2;9.4.2 Control System Communication Data;186
5.2.4.2.1;9.4.2.1 Difference Between Measured Values d;186
5.2.4.2.2;9.4.2.2 Alarm e;186
5.2.4.2.3;9.4.2.3 Data Normalization;187
5.2.4.2.4;9.4.2.4 ID Frequency;187
5.2.4.2.5;9.4.2.5 Training Data and Test Data;187
5.2.4.3;9.4.3 Evaluation Criteria;188
5.2.4.4;9.4.4 Experimental Results;188
5.2.5;9.5 Intrusion Detection Considering Sequences;192
5.2.5.1;9.5.1 Control Sequences for Intrusion Detection;192
5.2.5.2;9.5.2 Hidden Markov Model (HMM);193
5.2.5.3;9.5.3 Conditional Random Field (CRF);194
5.2.6;9.6 Experiments for Control System Communication with Control Sequence Patterns;196
5.2.6.1;9.6.1 Experimental Setup;197
5.2.6.1.1;9.6.1.1 Benchmark Data (DARPA Data);197
5.2.6.1.2;9.6.1.2 Control System Communication Data by Simulator;198
5.2.6.2;9.6.2 Evaluation Criteria;200
5.2.6.3;9.6.3 Experimental Results;201
5.2.6.3.1;9.6.3.1 Experimental Results for Benchmark Data (DARPA Data);201
5.2.6.3.2;9.6.3.2 Experimental Results for Simulated Control System Communication Data;203
5.2.7;9.7 Conclusion;204
5.2.8;References;205
6;Index;207