Wang / Xiao Intelligent Microgrid Management and EV Control Under Uncertainties in Smart Grid

1. Auflage 2018
ISBN: 978-981-10-4250-8
Verlag: Springer Nature Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 150 Seiten

This book, discusses the latest research on the intelligent control of two important components in smart grids, namely microgrids (MGs) and electric vehicles (EVs). It focuses on developing theoretical frameworks and proposing corresponding algorithms, to optimally schedule virtualized elements under different uncertainties so that the total cost of operating the microgrid or the EV charging system can be minimized and the systems maintain stabilized. With random factors in the problem formulation and corresponding designed algorithms, it provides insights into how to handle uncertainties and develop rational strategies in the operation of smart grid systems. Written by leading experts, it is a valuable resource for researchers, scientists and engineers in the field of intelligent management of future power grids. 


Dr. Ran Wang is currently an assistant professor at College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics (NUAA), P.R. China and Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, P.R. China. He received his B.E. in Electronic and Information Engineering from Honors School, Harbin Institute of Technology (HIT), P.R. China in July 2011 and Ph.D. in Computer Science and Engineering from Nanyang Technological University (NTU), Singapore in April 2016. He was a research fellow in the School of Electrical and Electronic Engineering, Nanyang Technological University (NTU), Singapore from October 2015 to August 2016. His current research interests include intelligent management and control in Smart Grid, network performance analysis and evolution of complex networks, etc.
Dr. Gaoxi Xiao received the Ph.D. degree in computing from the Hong Kong Polytechnic University in 1998. He was a Postdoctoral Research Fellow in Polytechnic University, Brooklyn, New York in 1999; and a Visiting Scientist in the University of Texas at Dallas in 1999-2001. He joined the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, in 2001, where he is now an Associate Professor. His research interests include complex systems and networks, optical and wireless networking, smart grid, system resilience and Internet technologies. Dr. Xiao serves as an Academic Editor for PLOS ONE.
Dr. Ping Wang received the PhD degree in electrical engineering from University of Waterloo, Canada, in 2008. Currently she is an Associate Professor in the School of Computer Science and Engineering, Nanyang Technological University, Singapore. Her current research interests include resource allocation in multimedia wireless networks, cloud computing, and smart grid. She was a corecipient of the Best Paper Award from IEEE Wireless Communications and Networking Conference (WCNC) 2012 and IEEE International Conference on Communications (ICC) 2007.  

Autoren/Hrsg.

Wang, Ran

Wang, Ping

Xiao, Gaoxi

Inhaltsverzeichnis

1;Acknowledgements;5
2;Contents;6
3;List of Figures;10
4;List of Tables;13
5;Abstract;14
6;1 Introduction;16
6.1;1.1 Background;16
6.1.1;1.1.1 Electrical Power Systems;16
6.1.2;1.1.2 Transition to a Smart Grid;17
6.1.3;1.1.3 Microgrids (MGs) and Electric Vehicles (EVs);20
6.2;1.2 Research Focus;21
6.3;1.3 Organization of the Chapters;22
6.4;References;23
7;2 Literature Review;24
7.1;2.1 Energy Management in Microgrid;24
7.1.1;2.1.1 Supply and Demand Management;24
7.1.2;2.1.2 Energy Generation Scheduling;25
7.2;2.2 Electric Vehicle Charging Control;29
7.3;References;31
8;3 Demand and Supply Management in Microgrids;35
8.1;3.1 Introduction;35
8.2;3.2 Formulation of the Microgrid Demand and Supply Management Problem;36
8.2.1;3.2.1 Energy Demand Side;37
8.2.2;3.2.2 Energy Supply Side;39
8.2.3;3.2.3 Problem Formulation;40
8.2.4;3.2.4 Probability Distribution Measure of Renewable Energy;40
8.3;3.3 Optimization Algorithms;42
8.3.1;3.3.1 Robust Approach for the Load Balance Constraint;42
8.3.2;3.3.2 Sub-Problem: Determine the Robust REU Decision Threshold;43
8.3.3;3.3.3 Main Problem: Determine the Optimal Energy Consumption and Generation Scheduling;47
8.3.4;3.3.4 Extensions of the Proposed Algorithm: A Brief Discussion;48
8.4;3.4 Simulation Results and Discussions;49
8.4.1;3.4.1 The Impacts of Distribution Uncertainty Set;50
8.4.2;3.4.2 Effects of Fault Tolerant Limit ?;52
8.4.3;3.4.3 The Impacts of Uninterruptible Loads;53
8.4.4;3.4.4 The Price of User Elasticity;54
8.5;3.5 Conclusion;56
8.6;References;57
9;4 Energy Generation Scheduling in Microgrids;59
9.1;4.1 Introduction;59
9.2;4.2 System Model;60
9.2.1;4.2.1 CHP Generators;61
9.2.2;4.2.2 Electricity from External Utility Grid;62
9.2.3;4.2.3 Fluctuant Electricity and Heat Demand;63
9.3;4.3 Problem Formulation;63
9.3.1;4.3.1 Cost Minimization Formulation;63
9.3.2;4.3.2 Probability Distribution Measure of Uncertainties;64
9.4;4.4 Optimization Algorithms;66
9.4.1;4.4.1 Robust Approach for Constraints (4.3) and (4.4);66
9.4.2;4.4.2 Sub-Problem: Determine the Robust ES Decision Threshold;67
9.4.3;4.4.3 Main Problem: Robust Approach for the Uncertain Electricity Prices;70
9.5;4.5 Possible Extensions of the Proposed Algorithm;71
9.6;4.6 Simulation Results and Discussions;73
9.6.1;4.6.1 Parameters and Settings;73
9.6.2;4.6.2 Results and Discussions;74
9.7;4.7 Conclusions;80
9.8;References;81
10;5 Energy Generation Scheduling in Microgrids Involving Temporal-Correlated Renewable Energy;82
10.1;5.1 Introduction;82
10.2;5.2 System Model;83
10.3;5.3 Problem Formulation;85
10.3.1;5.3.1 Cost Minimization Formulation;85
10.3.2;5.3.2 Moment Statistic Model;86
10.4;5.4 Optimization Algorithm;87
10.4.1;5.4.1 Robust Approach for Constraint (5.4);87
10.4.2;5.4.2 Determine the Robust EA Decision Threshold;88
10.5;5.5 Performance Evaluation and Analysis;89
10.5.1;5.5.1 Parameters and Settings;89
10.5.2;5.5.2 Results and Discussion;90
10.6;5.6 Conclusion;93
10.7;References;94
11;6 Massive Electric Vehicle Charging Involving Renewable Energy;95
11.1;6.1 Introduction;95
11.2;6.2 Two-Stage Decision-Making Model and Problem Formulation;97
11.2.1;6.2.1 Two-Stage Decision-Making Model;97
11.2.2;6.2.2 Modeling System Uncertainties;97
11.2.3;6.2.3 Day-Ahead Energy Acquisition Scheduling;100
11.2.4;6.2.4 Real-Time Power Regulation and Elastic EV Charging;100
11.3;6.3 The Charging Rate Compression Algorithm;103
11.4;6.4 Simulation Results and Discussions;105
11.4.1;6.4.1 Parameters and Settings;105
11.4.2;6.4.2 Results and Discussions;106
11.5;6.5 Extensions;110
11.5.1;6.5.1 Tracking a Given Load Profile;110
11.5.2;6.5.2 Discrete Charging Rates;111
11.6;6.6 Conclusion;113
11.7;References;114
12;7 Hybrid Charging Control of Electric Vehicles;115
12.1;7.1 Introduction;115
12.2;7.2 System Model;117
12.2.1;7.2.1 Centralized Charging Control Model;117
12.2.2;7.2.2 Decentralized Charging Control Model;119
12.3;7.3 Centralized Charging Scheme;120
12.3.1;7.3.1 Global Optimal Scheduling;120
12.3.2;7.3.2 A Dynamic Scheduling Approach;121
12.4;7.4 Decentralized Charging Scheme;122
12.4.1;7.4.1 Game Formulation;122
12.4.2;7.4.2 Existence of GSE;125
12.4.3;7.4.3 Solution and Algorithm;127
12.4.4;7.4.4 Algorithm to Determine a Proper Emh;131
12.5;7.5 Experimental Evaluation;132
12.5.1;7.5.1 Simulation Setting;132
12.5.2;7.5.2 Results and Discussion;132
12.6;7.6 Conclusion;137
12.7;References;138
13;8 Summary and Future Work;139
13.1;8.1 Summary of Contributions;139
13.2;8.2 Future Work;141
13.2.1;8.2.1 Energy Storage Integration into the Microgrid;141
13.2.2;8.2.2 Design of a Vehicle to Grid (V2G) Aggregator;142
13.2.3;8.2.3 More Detailed Statistical Properties of Renewable Energy Generation;142
14;Appendix A Energy Generation Scheduling in Microgrids;143
15;A.1 Proof of Proposition 4.1;143
16;A.2 Reformulation of Problem (4.6);144
17;Appendix B Massive Electric Vehicle Charging Involving Renewable Energy;146
18;B.1 Proof for Lemma 5.1;146
19;B.2 Proof for Theorem 5.1;147
20;Appendix C Hybrid Charging Control of Electric Vehicles;149
21;C.1 Proof of Theorem 6.3;149
22;C.2 Proof of Theorem 6.4;149