Intelligent Microgrid Management and EV Control Under Uncertainties in Smart Grid (eBook)

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.  

Acknowledgements 5
Contents 6
List of Figures 10
List of Tables 13
Abstract 14
1 Introduction 16
1.1 Background 16
1.1.1 Electrical Power Systems 16
1.1.2 Transition to a Smart Grid 17
1.1.3 Microgrids (MGs) and Electric Vehicles (EVs) 20
1.2 Research Focus 21
1.3 Organization of the Chapters 22
References 23
2 Literature Review 24
2.1 Energy Management in Microgrid 24
2.1.1 Supply and Demand Management 24
2.1.2 Energy Generation Scheduling 25
2.2 Electric Vehicle Charging Control 29
References 31
3 Demand and Supply Management in Microgrids 35
3.1 Introduction 35
3.2 Formulation of the Microgrid Demand and Supply Management Problem 36
3.2.1 Energy Demand Side 37
3.2.2 Energy Supply Side 39
3.2.3 Problem Formulation 40
3.2.4 Probability Distribution Measure of Renewable Energy 40
3.3 Optimization Algorithms 42
3.3.1 Robust Approach for the Load Balance Constraint 42
3.3.2 Sub-Problem: Determine the Robust REU Decision Threshold 43
3.3.3 Main Problem: Determine the Optimal Energy Consumption and Generation Scheduling 47
3.3.4 Extensions of the Proposed Algorithm: A Brief Discussion 48
3.4 Simulation Results and Discussions 49
3.4.1 The Impacts of Distribution Uncertainty Set 50
3.4.2 Effects of Fault Tolerant Limit ? 52
3.4.3 The Impacts of Uninterruptible Loads 53
3.4.4 The Price of User Elasticity 54
3.5 Conclusion 56
References 57
4 Energy Generation Scheduling in Microgrids 59
4.1 Introduction 59
4.2 System Model 60
4.2.1 CHP Generators 61
4.2.2 Electricity from External Utility Grid 62
4.2.3 Fluctuant Electricity and Heat Demand 63
4.3 Problem Formulation 63
4.3.1 Cost Minimization Formulation 63
4.3.2 Probability Distribution Measure of Uncertainties 64
4.4 Optimization Algorithms 66
4.4.1 Robust Approach for Constraints (4.3) and (4.4) 66
4.4.2 Sub-Problem: Determine the Robust ES Decision Threshold 67
4.4.3 Main Problem: Robust Approach for the Uncertain Electricity Prices 70
4.5 Possible Extensions of the Proposed Algorithm 71
4.6 Simulation Results and Discussions 73
4.6.1 Parameters and Settings 73
4.6.2 Results and Discussions 74
4.7 Conclusions 80
References 81
5 Energy Generation Scheduling in Microgrids Involving Temporal-Correlated Renewable Energy 82
5.1 Introduction 82
5.2 System Model 83
5.3 Problem Formulation 85
5.3.1 Cost Minimization Formulation 85
5.3.2 Moment Statistic Model 86
5.4 Optimization Algorithm 87
5.4.1 Robust Approach for Constraint (5.4) 87
5.4.2 Determine the Robust EA Decision Threshold 88
5.5 Performance Evaluation and Analysis 89
5.5.1 Parameters and Settings 89
5.5.2 Results and Discussion 90
5.6 Conclusion 93
References 94
6 Massive Electric Vehicle Charging Involving Renewable Energy 95
6.1 Introduction 95
6.2 Two-Stage Decision-Making Model and Problem Formulation 97
6.2.1 Two-Stage Decision-Making Model 97
6.2.2 Modeling System Uncertainties 97
6.2.3 Day-Ahead Energy Acquisition Scheduling 100
6.2.4 Real-Time Power Regulation and Elastic EV Charging 100
6.3 The Charging Rate Compression Algorithm 103
6.4 Simulation Results and Discussions 105
6.4.1 Parameters and Settings 105
6.4.2 Results and Discussions 106
6.5 Extensions 110
6.5.1 Tracking a Given Load Profile 110
6.5.2 Discrete Charging Rates 111
6.6 Conclusion 113
References 114
7 Hybrid Charging Control of Electric Vehicles 115
7.1 Introduction 115
7.2 System Model 117
7.2.1 Centralized Charging Control Model 117
7.2.2 Decentralized Charging Control Model 119
7.3 Centralized Charging Scheme 120
7.3.1 Global Optimal Scheduling 120
7.3.2 A Dynamic Scheduling Approach 121
7.4 Decentralized Charging Scheme 122
7.4.1 Game Formulation 122
7.4.2 Existence of GSE 125
7.4.3 Solution and Algorithm 127
7.4.4 Algorithm to Determine a Proper Emh 131
7.5 Experimental Evaluation 132
7.5.1 Simulation Setting 132
7.5.2 Results and Discussion 132
7.6 Conclusion 137
References 138
8 Summary and Future Work 139
8.1 Summary of Contributions 139
8.2 Future Work 141
8.2.1 Energy Storage Integration into the Microgrid 141
8.2.2 Design of a Vehicle to Grid (V2G) Aggregator 142
8.2.3 More Detailed Statistical Properties of Renewable Energy Generation 142
Appendix A Energy Generation Scheduling in Microgrids 143
A.1 Proof of Proposition 4.1 143
A.2 Reformulation of Problem (4.6) 144
Appendix B Massive Electric Vehicle Charging Involving Renewable Energy 146
B.1 Proof for Lemma 5.1 146
B.2 Proof for Theorem 5.1 147
Appendix C Hybrid Charging Control of Electric Vehicles 149
C.1 Proof of Theorem 6.3 149
C.2 Proof of Theorem 6.4 149