考虑电动汽车的联合电力系统多目标调度
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摘要
为了消除电动汽车的无序充电和风力发电的随机性对电力系统带来的负面影响,利用相关数据得到了电动汽车充电概率,将风电的不确定性表示为一个具有零均值、呈正态分布的预测误差,由此建立了考虑电动汽车无序充电的风-水-火联合的多目标调度模型。采用多目标布谷鸟搜索算法对该联合电力系统调度模型进行求解,并用算例分析了三种情形下水电站的调峰能力。分析结果表明,风-水-火联合调度模型和调度策略能够提高系统接纳风电能力,满足电动汽车无序充电,降低火电机组出力的峰谷差,为解决大规模风电入网和大量电动汽车充电提供可借鉴的策略。
In order to eliminate the negative impact of the disorderly charging and randomness of wind power generation on electric power system,the charging probability of electric vehicles is obtained by using the relevant data,and the uncertainty of wind power is expressed as a prediction error with zero mean and normal distribution. A multi-objective scheduling model of wind-water-fire combination considering the disorderly charging of electric vehicles is established. The multi-objective cuckoo search algorithm is adopted to solve the dispatching model of the combined power system,and the peak-shaving capacity of hydropower station in three situations is analyzed by an example. The analysis results show that the proposed wind-water-fire joint dispatching model and dispatching strategy can improve the system's wind power capacity,meet the disorderly charging of electric vehicles,reduce the peak-valley difference of thermal power unit output,and provide a reference strategy for solving large-scale wind power grid entry and large-scale charging of electric vehicles.
In order to eliminate the negative impact of the disorderly charging and randomness of wind power generation on electric power system,the charging probability of electric vehicles is obtained by using the relevant data,and the uncertainty of wind power is expressed as a prediction error with zero mean and normal distribution. A multi-objective scheduling model of wind-water-fire combination considering the disorderly charging of electric vehicles is established. The multi-objective cuckoo search algorithm is adopted to solve the dispatching model of the combined power system,and the peak-shaving capacity of hydropower station in three situations is analyzed by an example. The analysis results show that the proposed wind-water-fire joint dispatching model and dispatching strategy can improve the system's wind power capacity,meet the disorderly charging of electric vehicles,reduce the peak-valley difference of thermal power unit output,and provide a reference strategy for solving large-scale wind power grid entry and large-scale charging of electric vehicles.
引文
[1]杨国清,付菁,王德意,等.基于一主三从博弈的风-水-气区域电力系统调度研究[J].电网技术,2018,42(2):495-503.YANG Guoqing,FU Jing,WANG Deyi,et al. Study on one leader and three followers game dispatching of regional power system with wind-water-gas power[J]. Power System Technology,2018,42(2):495-503.
[2]刘东奇,王耀南,袁小芳.电动汽车充放电与风力/火力发电系统的协同优化运行[J].电工技术学报,2017,32(3)18-26.LIU Dongqi,WANG Yaonan,YUAN Xiaofang. Cooperative dispatch of large-scale electric vehicles with wind-thermal power generating system[J]. Transactions of China Electrotechnical Society,2017,32(3):18-26.
[3]杨文荣,马晓燕,徐茂林,等.基于改进鸟群算法的微电网并网优化调度研究[J].电工电能新技术,2018,37(2):53-60.YANG Wenrong,MA Xiaoyan,XU Maolin,et al. Research on scheduling optimization of grid-connected micro-grid based on improved bird swarm algorithm[J]. Advanced Technology of Electrical Engineering and Energy,2018,37(2):53-60.
[4]张程翔,刘天琪,卜涛,等.风水火联合电力系统动态优化调度[J].现代电力,2017,34(4):8-14.ZHANG Chengxiang,LIU Tianqi,BU Tao,et al. Dynamic optimal dispatch of wind-hydro-thermal power system[J]. Modern Electric Power,2017,34(4):8-14.
[5]潘华,梁作放,薛强中,等.虚拟电厂中的储能技术及其应用[J].山东电力技术,2018,45(7):55-61.PAN Hua,LIANG Zuofang,XUE Qiangzhong,et al. Energy storage technology in virtual power plant and its application[J]. Sandong Electeic Power,2018,45(7):55-61.
[6]贺建波,胡志坚,仉梦林,等.考虑系统实时响应风险水平约束的风–火–水电力系统协调优化调度[J].电网技术,2014,38(7):1898-1906.HE Jianbo,HU Zhijian,ZHANG Menglin,et al. Coordinated optimal dispatching of wind-thermal-hydro power system considering constraint of real-time expected demand not supplied[J]. Power System Technology,2014,38(7):1898-1906.
[7]董皎皎,高峰,管晓宏.平抑风电场随机波动的风-水-燃气系统优化设计[J].中国电机工程学报,2017,37(10):2878-2886.DONG Jiaojiao,GAO Feng,GUAN Xiaohong. Optimal design of wind-hydro-gas system for stochastic power fluctuation smoothing in wind farms[J]. Proceedings of the CSEE,2017,37(10):2878-2886.
[8]卢志刚,隋玉珊,何守龙,等.基于场景分析的含风电系统经济爬坡调度[J].电工电能新技术,2016,35(2):38-43.LU Zhigang,SUI Yushan,HE Shoulong,et al. Economic/unit ramp dispatching for power grid integrated with wind power based on scenario analysis[J]. Advanced Technology of Electrical Engineering and Energy,2016,35(2):38-43.
[9]吴红斌,侯小凡,赵波,等.计及可入网电动汽车的微网系统经济调度[J].电力系统自动化,2014,38(9):77-84.WU Hongbin,HOU Xiaofan,ZHAO Bo,et al. Economical dispatch of microgrid considering plug-in electric vehicles[J]. Automation of Electric Power Systems,2014,38(9):77-84.
[10]肖浩,裴玮,孔力.含大规模电动汽车接入的主动配电网多目标优化调度方法[J].电工技术学报,2017,32(2):179-189.XIAO Hao,PEI Wei,KONG Li. Multi-objective optimization scheduling method for active distribution network with large scale electric vehicles[J]. Transactions of China Electrotechnical Society,2017,32(2):179-189.
[11] Jian L,Xue H,Xu G,et al. Regulated Charging of Plug-in Hybrid Electric Vehicles for Minimizing Load Variance in Household Smart Microgrid. IEEE Transactions on Industrial Electronics,2013,60(8):3218-3226
[12]项顶,胡泽春,宋永华,等.通过电动汽车与电网互动减少弃风的商业模式与日前优化调度策略[J].中国电机工程学报,2015,35(24):6293-6303.XIANG Ding,HU Zechun,SONG Yonghua,et al. Business model and day-ahead dispatch strategy to reduce wind power curtailment through vehicle-to-grid[J]. Proceedings of the CSEE,2015,35(24):6293-6303.
[13]曲荣海,秦川.电动汽车及其驱动电机发展现状与展望[J].南方电网技术,2016,10(3):82-86.QU Ronghai,QIN Chuan. Development status and prospects of electric vehicles and their drive motors[J]. Southern Power System Technology,2016,10(3):82-86.
[14]曾爽,陈海洋,刘秀兰,等.考虑用户行为特性的电动乘用车充电负荷建模[J].电气应用,2016,35(1):36-41.ZENG Shuang,CHEN Haiyang,LIU Xiulan,et al. Charging load modeling of electric vehicle considering user behavior characteristics[J]. Electrical Application,2016,35(1):36-41.
[15]王开艳,罗先觉,吴玲,等.清洁能源优先的风–水–火电力系统联合优化调度[J].中国电机工程学报,2013,33(13):27-35.WANG Kaiyan,LUO Xianjue,WU Ling,et al. Optimal dispatch of wind-hydro-thermal power system with priority given to clean energy[J]. Proceedings of the CSEE,2013,33(13):27-35.
[16] Yang X S,Deb S. Cuckoo search via Lévy flights[C]//2009World Congress on Nature&Biologically Inspired Computing(Na BIC). IEEE,2009.
[17] MISHRA C,SINGH P,ROKADIA J. Optimal power flow in the presence of wind power using modified cuckoo search[J]. IET Generation,Transmission&Distribution, 2015, 9(7):615-626.
[18]赵玉新,杨新社,刘利强.新兴元启发式优化算法[M].北京:科学出版社,2013:173-199.ZHAO Yuxin,YANG Xinshe,LIU Liqiang. New meta-heuristic optimization algorithm[M]. Beijing:Science Press,2013:173-199.
[19] YANG X S,Deb S. Multi-objective cuckoo search for design optimization[J]. Computers&Operations Research,2011,40(6):161.
[20]王嘉庚,王磊,丛龙祥,等.智能电网中分布式发电系统的多目标优化运行[J].智能电网,2017,5(5):448-452.WANG Jiageng,WANG Lei,CONG Longxiang,et al. Multi-objective optimal operation of distributed generation system in smart grid[J]. Smart Grid,2017,5(5):448-452.
[21]贺兴时,李娜,杨新社,等.多目标布谷鸟搜索算法[J].系统仿真学报,2015,27(4):731-737.HE Xingshi,LI Na,YANG Xinshe,et al. Multi-objective cuckoo search algorithm[J]. Journal of System Simulation,2015,27(4):731-737.
[2]刘东奇,王耀南,袁小芳.电动汽车充放电与风力/火力发电系统的协同优化运行[J].电工技术学报,2017,32(3)18-26.LIU Dongqi,WANG Yaonan,YUAN Xiaofang. Cooperative dispatch of large-scale electric vehicles with wind-thermal power generating system[J]. Transactions of China Electrotechnical Society,2017,32(3):18-26.
[3]杨文荣,马晓燕,徐茂林,等.基于改进鸟群算法的微电网并网优化调度研究[J].电工电能新技术,2018,37(2):53-60.YANG Wenrong,MA Xiaoyan,XU Maolin,et al. Research on scheduling optimization of grid-connected micro-grid based on improved bird swarm algorithm[J]. Advanced Technology of Electrical Engineering and Energy,2018,37(2):53-60.
[4]张程翔,刘天琪,卜涛,等.风水火联合电力系统动态优化调度[J].现代电力,2017,34(4):8-14.ZHANG Chengxiang,LIU Tianqi,BU Tao,et al. Dynamic optimal dispatch of wind-hydro-thermal power system[J]. Modern Electric Power,2017,34(4):8-14.
[5]潘华,梁作放,薛强中,等.虚拟电厂中的储能技术及其应用[J].山东电力技术,2018,45(7):55-61.PAN Hua,LIANG Zuofang,XUE Qiangzhong,et al. Energy storage technology in virtual power plant and its application[J]. Sandong Electeic Power,2018,45(7):55-61.
[6]贺建波,胡志坚,仉梦林,等.考虑系统实时响应风险水平约束的风–火–水电力系统协调优化调度[J].电网技术,2014,38(7):1898-1906.HE Jianbo,HU Zhijian,ZHANG Menglin,et al. Coordinated optimal dispatching of wind-thermal-hydro power system considering constraint of real-time expected demand not supplied[J]. Power System Technology,2014,38(7):1898-1906.
[7]董皎皎,高峰,管晓宏.平抑风电场随机波动的风-水-燃气系统优化设计[J].中国电机工程学报,2017,37(10):2878-2886.DONG Jiaojiao,GAO Feng,GUAN Xiaohong. Optimal design of wind-hydro-gas system for stochastic power fluctuation smoothing in wind farms[J]. Proceedings of the CSEE,2017,37(10):2878-2886.
[8]卢志刚,隋玉珊,何守龙,等.基于场景分析的含风电系统经济爬坡调度[J].电工电能新技术,2016,35(2):38-43.LU Zhigang,SUI Yushan,HE Shoulong,et al. Economic/unit ramp dispatching for power grid integrated with wind power based on scenario analysis[J]. Advanced Technology of Electrical Engineering and Energy,2016,35(2):38-43.
[9]吴红斌,侯小凡,赵波,等.计及可入网电动汽车的微网系统经济调度[J].电力系统自动化,2014,38(9):77-84.WU Hongbin,HOU Xiaofan,ZHAO Bo,et al. Economical dispatch of microgrid considering plug-in electric vehicles[J]. Automation of Electric Power Systems,2014,38(9):77-84.
[10]肖浩,裴玮,孔力.含大规模电动汽车接入的主动配电网多目标优化调度方法[J].电工技术学报,2017,32(2):179-189.XIAO Hao,PEI Wei,KONG Li. Multi-objective optimization scheduling method for active distribution network with large scale electric vehicles[J]. Transactions of China Electrotechnical Society,2017,32(2):179-189.
[11] Jian L,Xue H,Xu G,et al. Regulated Charging of Plug-in Hybrid Electric Vehicles for Minimizing Load Variance in Household Smart Microgrid. IEEE Transactions on Industrial Electronics,2013,60(8):3218-3226
[12]项顶,胡泽春,宋永华,等.通过电动汽车与电网互动减少弃风的商业模式与日前优化调度策略[J].中国电机工程学报,2015,35(24):6293-6303.XIANG Ding,HU Zechun,SONG Yonghua,et al. Business model and day-ahead dispatch strategy to reduce wind power curtailment through vehicle-to-grid[J]. Proceedings of the CSEE,2015,35(24):6293-6303.
[13]曲荣海,秦川.电动汽车及其驱动电机发展现状与展望[J].南方电网技术,2016,10(3):82-86.QU Ronghai,QIN Chuan. Development status and prospects of electric vehicles and their drive motors[J]. Southern Power System Technology,2016,10(3):82-86.
[14]曾爽,陈海洋,刘秀兰,等.考虑用户行为特性的电动乘用车充电负荷建模[J].电气应用,2016,35(1):36-41.ZENG Shuang,CHEN Haiyang,LIU Xiulan,et al. Charging load modeling of electric vehicle considering user behavior characteristics[J]. Electrical Application,2016,35(1):36-41.
[15]王开艳,罗先觉,吴玲,等.清洁能源优先的风–水–火电力系统联合优化调度[J].中国电机工程学报,2013,33(13):27-35.WANG Kaiyan,LUO Xianjue,WU Ling,et al. Optimal dispatch of wind-hydro-thermal power system with priority given to clean energy[J]. Proceedings of the CSEE,2013,33(13):27-35.
[16] Yang X S,Deb S. Cuckoo search via Lévy flights[C]//2009World Congress on Nature&Biologically Inspired Computing(Na BIC). IEEE,2009.
[17] MISHRA C,SINGH P,ROKADIA J. Optimal power flow in the presence of wind power using modified cuckoo search[J]. IET Generation,Transmission&Distribution, 2015, 9(7):615-626.
[18]赵玉新,杨新社,刘利强.新兴元启发式优化算法[M].北京:科学出版社,2013:173-199.ZHAO Yuxin,YANG Xinshe,LIU Liqiang. New meta-heuristic optimization algorithm[M]. Beijing:Science Press,2013:173-199.
[19] YANG X S,Deb S. Multi-objective cuckoo search for design optimization[J]. Computers&Operations Research,2011,40(6):161.
[20]王嘉庚,王磊,丛龙祥,等.智能电网中分布式发电系统的多目标优化运行[J].智能电网,2017,5(5):448-452.WANG Jiageng,WANG Lei,CONG Longxiang,et al. Multi-objective optimal operation of distributed generation system in smart grid[J]. Smart Grid,2017,5(5):448-452.
[21]贺兴时,李娜,杨新社,等.多目标布谷鸟搜索算法[J].系统仿真学报,2015,27(4):731-737.HE Xingshi,LI Na,YANG Xinshe,et al. Multi-objective cuckoo search algorithm[J]. Journal of System Simulation,2015,27(4):731-737.