基于概率潮流的电动汽车充放电负荷参与源荷互动策略
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摘要
伴随着大规模电动汽车和可再生能源发电接入主动配电网,给电网带来诸多不确定性因素,给系统稳定运行造成一定影响。为了应对风电的波动性,研究提出通过价格信号引导电动汽车有序充放电的调度策略。研究首先,建立以电网调度成本期望值最小为目标函数,以调度电动汽车充放电负荷平抑风电波动为约束条件的源荷互动模型,采用粒子群算法求解最优电价下的充放电负荷;然后,计及电动汽车充放电负荷响应风电波动的不确定性,分析电动汽车充放电负荷不确定性对配电网潮流的影响,采用半不变量法计算主动配电网概率潮流;最后用34节点算例进行仿真验证。仿真结果表明,基于源荷互动的有序充放电策略,能够平衡风电功率波动,改善配网电压的波动水平,有利于提高系统运行的稳定性。
With the large-scale electric vehicles and renewable energy power generation access to the active distribution network and the formation of the uncertainty affect the stability of the system.The resarch through the price signal dispatch electric vehicle charge and discharge load to promote active distribution network consumption of wind power and other intermittent energy,to improve the stability of the grid.The source-load interaction model is established to minimize the expected cost of power grid dispatching.The source-load interaction model is used to suppress the fluctuation of wind power.Based on the uncertainty of the injection power of the distribution network,taking into account the uncertainty of the response of charge and discharge load,the semi-invariant method is used to calculate the probability flow of the active distribution network.Through the 34-node example,it is shown that the stability of the system can be improved by dispatching the electric power consumption of the electric vehicle to supplement the wind power fluctuation.
With the large-scale electric vehicles and renewable energy power generation access to the active distribution network and the formation of the uncertainty affect the stability of the system.The resarch through the price signal dispatch electric vehicle charge and discharge load to promote active distribution network consumption of wind power and other intermittent energy,to improve the stability of the grid.The source-load interaction model is established to minimize the expected cost of power grid dispatching.The source-load interaction model is used to suppress the fluctuation of wind power.Based on the uncertainty of the injection power of the distribution network,taking into account the uncertainty of the response of charge and discharge load,the semi-invariant method is used to calculate the probability flow of the active distribution network.Through the 34-node example,it is shown that the stability of the system can be improved by dispatching the electric power consumption of the electric vehicle to supplement the wind power fluctuation.
引文
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[12] CIRIC R M,PADIHA-FELTRIN A,DENIS I F E D.Observing the Performance of Distribution Systems with Embedded Generators[J].European Transactions on Electrical Power,2004,14(06):347-359.
[2]汪德夫,张树冰.考虑时空分布的电动汽车充电负荷预测及对电网负荷影响分析[J].电力学报,2017,32(06):483-489.WANG De-fu,ZHANG Shu-bing.Prediction of Electric Vehicle Load and Its Influence on Power Load Considering Time and Apace Distribution[J].Journal of Electric Power,2017,32(06):483-489.
[3]刘伟佳,文福拴,薛禹胜,等.电动汽车参与电力系统优化调度的谈判策略[J].电力系统自动化,2015,39(17):192-200.LIU Jia-wei,WEN Fu-shuan,XUE Yu-sheng,et al.Negotiation Strategies for Participation of Electric Vehicles in Power System Optimal Dispatching[J].Automation of Electric Power Systems,2015,39(17):192-200.
[4]王文超,刘天羽,汪笃军,等.基于改进粒子群算法的电动汽车群充电优化方法[J].电力学报,2017,32(06):439-446.WANG Wen-chao,LIU Tian-yu,WANG Du-jun,et al.Optimization Method of Electric Vehicle Group Charging Based on Improved Particle Swarm Optimization Algorithm[J].Journal of Electric Power,2017,32(06):439-446.
[5]孙辉,沈钟浩,周玮,等.主动配电网源荷协调多目标阻塞调度[J].电力系统自动化,2017,41(16):88-95+170.SUN Hui,SHEN Zhong-hao,ZHOU We,et al.Multi-objective Congestion Dispatch of Active Distribution Network Based on Source-load Coordination[J].Automation of Electric Power Systems,2017,41(16):88-95+170..
[6]郭小敏,杨健维,何正友.V2G模式下城市电网电压质量概率评估方法[J].电网技术,2015,39(10):2986-2992.GUO Xiao-min,YANG Jian-wei,He Zheng-you.Probabilistic Evaluation Method of Voltage Quality for Urban Power Network Considering Vehicle to Grid[J].Power System Technology,2015,39(10):2986-2992.
[7] Kong S,Cho H C,Lee J U,et al.Probabilistic Modeling of Electric Vehicle Charging Load for Probabilistic Load Flow[C]//IEEE VPPC.2012IEEE Vehicle Power and Propulsion Conference.IEEE:Seoul,2012:1010-1013.
[8]杨家豪,欧阳森,吴裕生,等.计及风—储联合系统概率模型的配电网随机潮流[J].电网技术,2016,40(01):234-241.YANG Jia-hao,OUYANG Sen,WU Yu-sheng,et al.Stochastic Power Flow of Distribution Network Considering Probability Model of Wind-Storage Combined Systems[J].Power System Technology,2016,40(01):234-241.
[9]蔡德福,钱斌,陈金富,等.含电动汽车充电负荷和风电的电力系统动态概率特性分析[J].电网技术,2013,37(03):590-596.CAI De-fu,QIAN Bin,CHEN Jin-fu,et al.Analysis on Dynamic Probabilistic Characteristic of Power Grid Connected With Electric Vehicle Charging Load and Wind Power[J].Power System Technology,2013,37(3):590-596.
[10]葛少云,连恒辉,刘洪,等.电动汽车时序响应能力模型与控制策略[J].电力系统自动化,2016,40(03):33-38+151.GE Shao-yun,LIAN Heng-hui,LIU Hong,et al.Timing Response Capability Modeland Control Method of Electric Vehicle[J].Automation of Electric Power Systems,2016,40(3):33-38+151.
[11]曾丹,姚建国,杨胜春,等.应对风电消纳中基于安全约束的价格型需求响应优化调度建模[J].中国电机工程学报,2014,34(31):5571-5578.ZENG Dan,YAO Jian-guo,YANG Sheng-chun,et al.Optimization Dispatch Modeling for Price-based Demand Response Considering Security Constraints to Accommodate the Wind Power[J].Proceedings of the CSEE,2014,34(31):5571-5578.
[12] CIRIC R M,PADIHA-FELTRIN A,DENIS I F E D.Observing the Performance of Distribution Systems with Embedded Generators[J].European Transactions on Electrical Power,2004,14(06):347-359.