Decentralized Electric Vehicle Charging Strategies for Reduced Load Variation and Guaranteed Charge Completion in Regional Distribution Grids

A novel, fully decentralized strategy to coordinate charge operation of electric vehicles is proposed in this paper. Based on stochastic switching control of on-board chargers, this strategy ensures high-efficiency charging, reduces load variations to the grid during charging periods, achieves charge completion with high probability, and accomplishes approximate “valley-filling”. Further improvements on the core strategy, including individualized power management, adaptive strategies, and battery support systems, are introduced to further reduce power fluctuation variances and to guarantee charge completion. Stochastic analysis is performed to establish the main properties of the strategies and to quantitatively show the performance improvements. Compared with the existing decentralized charging strategies, the strategies proposed in this paper can be implemented without any information exchange between grid operators and electric vehicles (EVs), resulting in a communications cost reduction. Additionally, it is shown that by using stochastic charging rules, a grid-supporting battery system with a very small energy capacity can achieve substantial reduction of EV load fluctuations with high confidence. An extensive set of simulations and case studies with real-world data are used to demonstrate the benefits of the proposed strategies.

[1]  Delphine Riu,et al.  A review on lithium-ion battery ageing mechanisms and estimations for automotive applications , 2013 .

[2]  Qi Zhang,et al.  A methodology for economic and environmental analysis of electric vehicles with different operational conditions , 2013 .

[3]  Joeri Van Mierlo,et al.  Lithium-ion batteries: Evaluation study of different charging methodologies based on aging process , 2015 .

[4]  P. Kohl,et al.  The effects of pulse charging on cycling characteristics of commercial lithium-ion batteries , 2001 .

[5]  Stanton W. Hadley,et al.  Potential Impacts of Plug-in Hybrid Electric Vehicles on Regional Power Generation , 2009 .

[6]  J. Driesen,et al.  The Impact of Charging Plug-In Hybrid Electric Vehicles on a Residential Distribution Grid , 2010, IEEE Transactions on Power Systems.

[7]  Lide M. Rodriguez-Martinez,et al.  Cycle ageing analysis of a LiFePO4/graphite cell with dynamic model validations: Towards realistic lifetime predictions , 2014 .

[8]  Amit Kumar Tamang Coordinated Charging of Plug-in Hybrid Electric Vehicles to Minimize Distribution System Losses , 2013 .

[9]  Jiuchun Jiang,et al.  Investigation of path dependence in commercial lithium-ion cells for pure electric bus applications: Aging mechanism identification , 2015 .

[10]  Aoife Foley,et al.  Impacts of Electric Vehicle charging under electricity market operations , 2013 .

[11]  Hongwen He,et al.  Simulation Research on an Electric Vehicle Chassis System Based on a Collaborative Control System , 2013 .

[12]  Zita Vale,et al.  Enhanced Multi-Objective Energy Optimization by a Signaling Method , 2016 .

[13]  Christoph M. Flath,et al.  Impact of electric vehicles on distribution substations: A Swiss case study , 2015 .

[14]  Tom Molinski,et al.  PEV Charging Profile Prediction and Analysis Based on Vehicle Usage Data , 2012, IEEE Transactions on Smart Grid.

[15]  Michael R. Frey,et al.  An Introduction to Stochastic Modeling (2nd Ed.) , 1994 .

[16]  Damian Flynn,et al.  Local Versus Centralized Charging Strategies for Electric Vehicles in Low Voltage Distribution Systems , 2012, IEEE Transactions on Smart Grid.

[17]  Morten Lind,et al.  Electric vehicle fleet management in smart grids: A review of services, optimization and control aspects , 2016 .

[18]  Yue Yuan,et al.  Modeling of Load Demand Due to EV Battery Charging in Distribution Systems , 2011, IEEE Transactions on Power Systems.

[19]  Yaonan Wang,et al.  Electric Vehicle Charging and Discharging Coordination on Distribution Network Using Multi-Objective Particle Swarm Optimization and Fuzzy Decision Making , 2016 .

[20]  Johan Driesen,et al.  Multiobjective Battery Storage to Improve PV Integration in Residential Distribution Grids , 2013, PES 2013.

[21]  Maria Huhtala,et al.  Random Variables and Stochastic Processes , 2021, Matrix and Tensor Decompositions in Signal Processing.

[22]  Jian Liu,et al.  Electric vehicle charging infrastructure assignment and power grid impacts assessment in Beijing , 2012 .

[23]  R. Ash,et al.  Real analysis and probability , 1975 .

[24]  Qiuwei Wu,et al.  Day-Ahead Energy Planning with 100% Electric Vehicle Penetration in the Nordic Region by 2050 , 2014 .

[25]  Huang Yu,et al.  Economic Operation of Electric Vehicle Battery Swapping Station Based on Genetic Algorithms , 2013 .

[26]  Kenneth Dixon,et al.  Introduction to Stochastic Modeling , 2011 .

[27]  Ehab F. El-Saadany,et al.  A Multistage Centralized Control Scheme for Islanded Microgrids With PEVs , 2014, IEEE Transactions on Sustainable Energy.

[28]  Juuso Lindgren,et al.  Effectiveness of smart charging of electric vehicles under power limitations , 2014 .

[29]  D. Rajan Probability, Random Variables, and Stochastic Processes , 2017 .

[30]  Mattia Marinelli,et al.  Phase-wise enhanced voltage support from electric vehicles in a Danish low-voltage distribution grid , 2016 .

[31]  Hewu Wang,et al.  Optimal decentralized valley-filling charging strategy for electric vehicles , 2014 .

[32]  Le Yi Wang,et al.  Robust and Scalable Management of Power Networks in Dual-Source Trolleybus Systems: A Consensus Control Framework , 2016, IEEE Transactions on Intelligent Transportation Systems.

[33]  Johan Driesen,et al.  Electric Vehicle Charging in an Office Building Microgrid With Distributed Energy Resources , 2014, IEEE Transactions on Sustainable Energy.

[34]  Antonio Colmenar-Santos,et al.  Planning Minimum Interurban Fast Charging Infrastructure for Electric Vehicles: Methodology and Application to Spain , 2014 .

[35]  Zita Vale,et al.  A stochastic model for energy resources management considering demand response in smart grids , 2017 .

[36]  Hortensia Amaris,et al.  Optimal Charging Scheduling of Electric Vehicles in Smart Grids by Heuristic Algorithms , 2014 .

[37]  Robert C. Green,et al.  The impact of plug-in hybrid electric vehicles on distribution networks: a review and outlook , 2010, PES 2010.

[38]  Le Yi Wang,et al.  Topology of a Bidirectional Converter for Energy Interaction between Electric Vehicles and the Grid , 2014 .

[39]  Li Zhang,et al.  Fuel reduction and electricity consumption impact of different charging scenarios for plug-in hybrid , 2011 .