Multi-time Scale Dispatching Strategy for EV Charging Considering Wind Power
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- 英文论文题名：Multi-time Scale Dispatching Strategy for EV Charging Considering Wind Power
- 论文作者：Yueshuang Bao ; Qilong Huang ; Zhifeng Qiu ; Chunsheng Wang ; Weihua Gui
- 英文论文作者：Yueshuang Bao ; Qilong Huang ; Zhifeng Qiu ; Chunsheng Wang ; Weihua Gui ; School of Information Science and Engineering ; Central South University ; Intelligent and Network System Research Center ; Tsinghua University
- 年：2017
- 作者机构：School of Information Science and Engineering,Central South University;Intelligent and Network System Research Center,Tsinghua University;
- 英文论文关键词：Electric vehicle ; optimal dispatching ; wind power ; randomness ; Markov decision process ; matching degree ; network losses
- 会议召开时间：2017-07-26
- 会议录名称：第36届中国控制会议论文集（B）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（B）
- 语种：英文
- 分类号：TM73
- 学会代码：KZLL
- 会议名称：第36届中国控制会议
- 会议地点：中国辽宁大连
- 主办单位：中国自动化学会控制理论专业委员会
- 学会名称：中国自动化学会控制理论专业委员会
- 页数：7
- 文件大小：512k
- 原文格式：D
- 会议级别：全国
摘要
This paper investigates the double randomness matching mechanism between electric vehicle(EV) charging and wind power in a distribution network. The Markov Decision Process is employed to model the uncertainties of both EV and wind power. The optimized dispatching models and strategies for EV charging under multi-time scale are developed to achieve the objective function.The goal of this function is to maximize matching degree between EV charging and wind power as well as minimize the power losses fully taking the physical constraints of distribution network into account. This actualizes dynamic control of EV charging. The simulation results on IEEE 5 node system and IEEE 30 node system demonstrate that the optimal dispatching method can reduce the network power losses and improve the matching degree between the EV charging and wind power.
This paper investigates the double randomness matching mechanism between electric vehicle(EV) charging and wind power in a distribution network. The Markov Decision Process is employed to model the uncertainties of both EV and wind power. The optimized dispatching models and strategies for EV charging under multi-time scale are developed to achieve the objective function.The goal of this function is to maximize matching degree between EV charging and wind power as well as minimize the power losses fully taking the physical constraints of distribution network into account. This actualizes dynamic control of EV charging. The simulation results on IEEE 5 node system and IEEE 30 node system demonstrate that the optimal dispatching method can reduce the network power losses and improve the matching degree between the EV charging and wind power.
This paper investigates the double randomness matching mechanism between electric vehicle(EV) charging and wind power in a distribution network. The Markov Decision Process is employed to model the uncertainties of both EV and wind power. The optimized dispatching models and strategies for EV charging under multi-time scale are developed to achieve the objective function.The goal of this function is to maximize matching degree between EV charging and wind power as well as minimize the power losses fully taking the physical constraints of distribution network into account. This actualizes dynamic control of EV charging. The simulation results on IEEE 5 node system and IEEE 30 node system demonstrate that the optimal dispatching method can reduce the network power losses and improve the matching degree between the EV charging and wind power.
引文
[1]D.W.Wu,A.Q.Yu,Research on nucleolus theory based profit distribution method for joint delivery of large-scale hybrid power generation,Power System Technology,2016,(10):2975-2981.
[2]D.Y.Yu,S.G.Song,B.Zhang,and X.S.Han,Synergistic dispatch of PEVs charging and wind power in Chinese regional power grids,Automation electric power system,2011,(14):24-29.
[3]J.H.Zhao,F.S.Wen,and Y.S.Xue,Power system stochastic economic dispatch considering uncertain outputs from plug-in electric vehicles and wind generators,Automation electric power system,2010,(20):22-29.
[4]X.F.Wang,C.C.Shao,X.L.Wang,and C.Du.Survey of electric vehicle charging load and dispatch control strategies,Proceedings of the CSEE,2013,(01):1-10.
[5]J.S.Chen,J.H.Zhang,and,J.Y.Lin,Strategies for electric vehicle charging with aiming at reducing network losses,Proceedings of the CSU-EPSA,2012,(03):139-144.
[6]Q.S.Jia,J.X.Shen,Z.B.Xu,and X.H.Guan,Simulation-based policy improvement for energy management in commercial office building,IEEE Transactions on Smart Grid,2012,3(4):2211-2223.
[7]D.P.Bertsekas,“Rollout algorithms for discrete optimization:A Survey”,Handbook of Combination Optimization,Springer,2013.A 19-page expository article providing a summary overview of the subject.
[8]D.P.Bertsekas.Dynamic programing and optimal control:Volume I.Massachusetts:Athena Scientific,2005.
[9]T.Wu,Q.Yang,Z.Bao,and W.J.Yan,Coordinated energy dispatching in micro grid with wind power generation and plug-in electric vehicles,IEEE Transactions on Smart Grid,vol.4,no.3,pp.1453-1463,Sep.2013.
[10]Z.H.Pan,C.W.Gao,and S.G.Liu,Research on charging and discharging dispatch of electric vehicles based on demand side discharge bidding,Power System Technology,2016,(04):1140-1146.
[11]K.Nyns Clement,E.Haesen,and J.Driesen,The impact of charging plug-in hybrid electric vehicles on a residential distribution grid.IEEE Transactions on Power Systems,2010,25(1):371-380.
[12](Mar.28.2014).National Wind Technology[Online].Available:http//www.nrel.gov/midc/nwtc_m2/.
[13]S.Shahidinejad,E.Bibeau,and S.Filizadeh,Statistical development of a duty cycle for plug-in vehicles in a North American urban setting using fleet information.IEEE Transactions on Vehicle Technology,2010,59(8):3710-3719.
[14]T.K.Lee,Z.Bareket,T.Gordon,and Z.S.Filipi,Stochastic modeling for studies of real-world PHEV usage:Driving schedule and daily temporal distributions,IEEE Transactions on Vehicle Technology,2012,61(4):1493–1502.
[15]M.H.K.Tushar,C.Assi,M.Maier,and M.F.Uddin,"Smart micro grids:Optimal joint scheduling for electric vehicles and home appliances,"IEEE Transactions on Smart Grid,vol.5,no.1,pp.239-250,Jan.2014.
[16](Mar.21,2014).BYDe6[Online].Available:http://www.byd.com/na/uto/e6.html.
[17](Nov.13,2016).Vertas.V90-3.0MW[Online].Available:http://vestas.com/.
[2]D.Y.Yu,S.G.Song,B.Zhang,and X.S.Han,Synergistic dispatch of PEVs charging and wind power in Chinese regional power grids,Automation electric power system,2011,(14):24-29.
[3]J.H.Zhao,F.S.Wen,and Y.S.Xue,Power system stochastic economic dispatch considering uncertain outputs from plug-in electric vehicles and wind generators,Automation electric power system,2010,(20):22-29.
[4]X.F.Wang,C.C.Shao,X.L.Wang,and C.Du.Survey of electric vehicle charging load and dispatch control strategies,Proceedings of the CSEE,2013,(01):1-10.
[5]J.S.Chen,J.H.Zhang,and,J.Y.Lin,Strategies for electric vehicle charging with aiming at reducing network losses,Proceedings of the CSU-EPSA,2012,(03):139-144.
[6]Q.S.Jia,J.X.Shen,Z.B.Xu,and X.H.Guan,Simulation-based policy improvement for energy management in commercial office building,IEEE Transactions on Smart Grid,2012,3(4):2211-2223.
[7]D.P.Bertsekas,“Rollout algorithms for discrete optimization:A Survey”,Handbook of Combination Optimization,Springer,2013.A 19-page expository article providing a summary overview of the subject.
[8]D.P.Bertsekas.Dynamic programing and optimal control:Volume I.Massachusetts:Athena Scientific,2005.
[9]T.Wu,Q.Yang,Z.Bao,and W.J.Yan,Coordinated energy dispatching in micro grid with wind power generation and plug-in electric vehicles,IEEE Transactions on Smart Grid,vol.4,no.3,pp.1453-1463,Sep.2013.
[10]Z.H.Pan,C.W.Gao,and S.G.Liu,Research on charging and discharging dispatch of electric vehicles based on demand side discharge bidding,Power System Technology,2016,(04):1140-1146.
[11]K.Nyns Clement,E.Haesen,and J.Driesen,The impact of charging plug-in hybrid electric vehicles on a residential distribution grid.IEEE Transactions on Power Systems,2010,25(1):371-380.
[12](Mar.28.2014).National Wind Technology[Online].Available:http//www.nrel.gov/midc/nwtc_m2/.
[13]S.Shahidinejad,E.Bibeau,and S.Filizadeh,Statistical development of a duty cycle for plug-in vehicles in a North American urban setting using fleet information.IEEE Transactions on Vehicle Technology,2010,59(8):3710-3719.
[14]T.K.Lee,Z.Bareket,T.Gordon,and Z.S.Filipi,Stochastic modeling for studies of real-world PHEV usage:Driving schedule and daily temporal distributions,IEEE Transactions on Vehicle Technology,2012,61(4):1493–1502.
[15]M.H.K.Tushar,C.Assi,M.Maier,and M.F.Uddin,"Smart micro grids:Optimal joint scheduling for electric vehicles and home appliances,"IEEE Transactions on Smart Grid,vol.5,no.1,pp.239-250,Jan.2014.
[16](Mar.21,2014).BYDe6[Online].Available:http://www.byd.com/na/uto/e6.html.
[17](Nov.13,2016).Vertas.V90-3.0MW[Online].Available:http://vestas.com/.