配电网电动汽车接纳能力分析
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摘要
针对传统建模方法精度不高的特点,提出剩余电量安全裕度的概念,用来定量描述电动汽车充电可能性。为了定量分析电动汽车的日充电需求和配电网的常规日负荷之间的关系,提出置信准入比的概念,并从NHTS数据库直接采样模拟私家车的充电负荷。考虑到大规模电动汽车接入配电网充电的安全运行约束,构建了考虑配电网接纳能力的机会约束方程,研究了配电网电动汽车接纳能力与机会约束置信水平的关系。建立了计及配电网电动汽车接纳能力、电网节点电压越限惩罚的多目标函数,采用带精英策略的非支配排序遗传算法(NSGA–Ⅱ),对IEEE 33节点算例进行分析,研究结果表明所提出的电动汽车充电模型精度更高,在给定置信水平的前提下,为保证电网的运行可靠性需将置信准入比控制在一定范围内。
In view of the low accuracy of traditional modeling methods of Electric Vehicles charging demand,this paper proposes the concept of residual power security margin,which is used to quantitatively describe the charging possibility of electric vehicles.In order to quantitatively analyze the relationship between the daily charging demand of electric vehicles and power load,the concept of confidence admission ratio is proposed,and the charging load of private cars is sampled directly from National Household Travel Survey(NHTS) database.Considering the safe operation constraints of distribution network when large-scale electric vehicles are integrated into,this paper constructs an opportunity constrained equation considering distribution network hosting capability,and studies the relationship between distribution network electric vehicle hosting capability and the confidence level of opportunity constraints.A multi-objective function considering distribution network electric vehicle hosting capability and the penalty of bus voltage off-limit in distribution network is established.With the method of non-dominated sorting genetic algorithm with elite strategy(NSGA-Ⅱ) an IEEE 33-bus test is analyzed,the results show that the proposed charging model of EV has higher accuracy.Under the premise of given confidence level,in order to ensure the operation reliability of power grid,the confidence admission ratio should be controlled within a certain range.
In view of the low accuracy of traditional modeling methods of Electric Vehicles charging demand,this paper proposes the concept of residual power security margin,which is used to quantitatively describe the charging possibility of electric vehicles.In order to quantitatively analyze the relationship between the daily charging demand of electric vehicles and power load,the concept of confidence admission ratio is proposed,and the charging load of private cars is sampled directly from National Household Travel Survey(NHTS) database.Considering the safe operation constraints of distribution network when large-scale electric vehicles are integrated into,this paper constructs an opportunity constrained equation considering distribution network hosting capability,and studies the relationship between distribution network electric vehicle hosting capability and the confidence level of opportunity constraints.A multi-objective function considering distribution network electric vehicle hosting capability and the penalty of bus voltage off-limit in distribution network is established.With the method of non-dominated sorting genetic algorithm with elite strategy(NSGA-Ⅱ) an IEEE 33-bus test is analyzed,the results show that the proposed charging model of EV has higher accuracy.Under the premise of given confidence level,in order to ensure the operation reliability of power grid,the confidence admission ratio should be controlled within a certain range.
引文
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[16]马燕峰,陈磊,李鑫,等.基于机会约束混合整数规划的风火协调滚动调度[J].电力系统自动化,2018,42(5):127-132,175.MA Yanfeng,CHEN Lei,LI Xin,et al.Rolling dispatch of wind-coal coordinated system based on chance-constrained mixed integer programming[J].Automation of Electric Power Systems,2018,42(5):127-132,175.
[17]RAVICHANDRAN A,SIROUSPOUR S,MALYSZ P,et al.A chance-constraints-based control strategy for microgrids with energy storage and integrated electric vehicles[J].IEEE Transactions on Smart Grid,2018,9(1):346-359.
[18]WANG Z W,SHEN C,LIU F,et al.An adjustable chance-constrained approach for flexible ramping capacity allocation[J].IEEE Transactions on Sustainable Energy,2018,9(4):1798-1811.
[19]蒋永华,许妙忠,成刚.利用种群扩张与稀疏化策略改进NSGA-II-DE算法[J].控制与决策,2019,34(1):55-62.JIANG Yonghua,XU Miaozhong,CHENG Gang.Using population expansion and sparsity strategy to improve NSGA-II-DEalgorithm[J].Control and Decision,2019,34(1):55-62.
[20]WANG B H,LIANG Y T,ZHENG T C,et al.Multiobjective site selection optimization of the gas-gathering station using NSGA-II[J].Process Safety and Environmental Protection,2018,119:350-359.
[21]王聪,赵文玲.基于自适应多目标指数罚函数的NSGA-II算法[J].山东理工大学学报(自然科学版),2016,30(3):11-14.WANG Cong,ZHAO Wenling.NSGA-II based on adaptive multi-objective exponential penalty function[J].Journal of Shandong University of Technology(Natural Science Edition),2016,30(3):11-14.
[2]VELDMAN E,VERZIJLBERGH R A.Distribution grid impacts of smart electric vehicle charging from different perspectives[J].IEEE Transactions on Smart Grid,2015,6(1):333-342.
[3]GRAY M K,MORSI W G.Power quality assessment in distribution systems embedded with plug-in hybrid and battery electric vehicles[J].IEEE Transactions on Power Systems,2015,30(2):663-671.
[4]BOSOVIC A,MUSIC M,SADOVIC S.Analysis of the impacts of plug-in electric vehicle charging on the part of a real low voltage distribution network[C]//2015 IEEE Eindhoven Power Tech,29 June-2 July 2015,Eindhoven,Netherlands:1-5.
[5]王威,贺旭,王晴,等.电动汽车充电对电网负荷和电气设备的影响[J].供用电,2018,35(9):79-84.WANG Wei,HE Xu,WANG Qing,et al.Impacts of electric vehicles charging on grid load and electrical equipment[J].Distribution&Utilization,2018,35(9):79-84.
[6]RONG-CENG L O,SU C L,LU C N.Stochastic analyses of electric vehicle charging impacts on distribution network[J].IEEETransactions on Power Systems,2014,29(3):1055-1063.
[7]ZHAO J,WANG J H,XU Z,et al.Distribution network electric vehicle hosting capacity maximization:a chargeable region optimization model[J].IEEE Transactions on Power Systems,2017,32(5):4119-4130.
[8]左琦,贺海磊,梁才浩,等.计及有序充电的电动汽车接纳能力量化评估方法[J].电力系统及其自动化学报,2017,29(6):1-6.ZUO Qi,HE Hailei,LIANG Caihao,et al.Quantitative evaluation method of system acceptance capacity of electric vehicles in ordered charging mode[J].Proceedings of the CSU-EPSA,2017,29(6):1-6.
[9]张祥文,江星星,王龙,等.配电网接纳电动汽车能力评估方法研究[J].电力系统保护与控制,2015,43(12):14-20.ZHANG Xiangwen,JIANG Xingxing,WANG Long,et al.Research on assessment methods of distribution network’s ability of admitting electric vehicles[J].Power System Protection and Control,2015,43(12):14-20.
[10]ZUO Q,ZHOU M,HE H L,et al.Research on quantitatively evaluating system acceptance capacity of electric vehicles considering coordinated strategy[C]//2015 5th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies(DRPT),26-29 Nov.2015,Changsha,China:2614-2619.
[11]National Household Travel Survey.Metropolitan Travel Survey Archive[EB/OL].[2016-01-02].http.://www.surveyarchive.org/.
[12]DE HOOG J,ALPCAN T,BRAZIL M,et al.Optimal charging of electric vehicles taking distribution network constraints into account[J].IEEE Transactions on Power Systems,2015,30(1):365-375.
[13]林海英,郭正雄,宋宝松,等.电动汽车充电服务一体化平台研发与应用[J].供用电,2019,36(3):32-36.LIN Haiying,GUO Zhengxiong,SONG Baosong,et al.Development and application of integrated platform for electric vehicle charging service[J].Distribution&Utilization,2019,36(3):32-36.
[14]DE HOOG J,ALPCAN T,BRAZIL M,et al.Optimal charging of electric vehicles taking distribution network constraints into account[J].IEEE Transactions on Power Systems,2015,30(1):365-375.
[15]DE HOOG J,ALPCAN T,BRAZIL M,et al.A market mechanism for electric vehicle charging under network constraints[J].IEEE Transactions on Smart Grid,2016,7(2):827-836.
[16]马燕峰,陈磊,李鑫,等.基于机会约束混合整数规划的风火协调滚动调度[J].电力系统自动化,2018,42(5):127-132,175.MA Yanfeng,CHEN Lei,LI Xin,et al.Rolling dispatch of wind-coal coordinated system based on chance-constrained mixed integer programming[J].Automation of Electric Power Systems,2018,42(5):127-132,175.
[17]RAVICHANDRAN A,SIROUSPOUR S,MALYSZ P,et al.A chance-constraints-based control strategy for microgrids with energy storage and integrated electric vehicles[J].IEEE Transactions on Smart Grid,2018,9(1):346-359.
[18]WANG Z W,SHEN C,LIU F,et al.An adjustable chance-constrained approach for flexible ramping capacity allocation[J].IEEE Transactions on Sustainable Energy,2018,9(4):1798-1811.
[19]蒋永华,许妙忠,成刚.利用种群扩张与稀疏化策略改进NSGA-II-DE算法[J].控制与决策,2019,34(1):55-62.JIANG Yonghua,XU Miaozhong,CHENG Gang.Using population expansion and sparsity strategy to improve NSGA-II-DEalgorithm[J].Control and Decision,2019,34(1):55-62.
[20]WANG B H,LIANG Y T,ZHENG T C,et al.Multiobjective site selection optimization of the gas-gathering station using NSGA-II[J].Process Safety and Environmental Protection,2018,119:350-359.
[21]王聪,赵文玲.基于自适应多目标指数罚函数的NSGA-II算法[J].山东理工大学学报(自然科学版),2016,30(3):11-14.WANG Cong,ZHAO Wenling.NSGA-II based on adaptive multi-objective exponential penalty function[J].Journal of Shandong University of Technology(Natural Science Edition),2016,30(3):11-14.