含不同集群电动汽车的微电网优化调度
|
摘要
文章考虑微电网中的可再生能源出力,针对不接受微电网直接调度的电动汽车集群,提出一种基于等效负荷的实时电价策略进行电动汽车有序充、放电引导。该策略与微电网直接调度策略相结合,对不同集群电动汽车进行充、放电管理,并综合考虑环境效益、车主收益与微网运行状态,建立了以微电网综合运行成本最低、负荷波动最小为目标的优化模型,并用灰狼优化算法求解。优化结果表明,所提出的策略能够合理调度各可控发电单元,引导电动汽车有序充、放电,减少微电网运行成本,降低等效负荷的波动。
The interaction between the electric vehicle and the micro-grid can effectively solve many negative problems caused by the disorderly charging of a large number of electric vehicles. For electric vehicle clusters that do not receive direct dispatch from microgrids, a real-time pricing strategy based on equivalence load is proposed to guide the charging and discharging of electric vehicles. This strategy is combined with the micro-grid direct dispatch strategy to perform charge and discharge management of different clusters of electric vehicles. Based on this consideration, considering the environmental benefits, the owner's revenue, and the microgrid operating status, an optimization model was established with the goal of minimizing the overall operating costs and minimizing the load fluctuation of the microgrid, and was solved using the gray wolf optimization algorithm. The optimization results show that by reasonably scheduling the controllable power generation units and the charging and discharging of the electric vehicles, the operating costs of the microgrid can be effectively reduced and fluctuations in equivalent loads can be reduced. Finally, by comparing with the basic particle swarm algorithm and genetic algorithm, the advantage of the gray wolf optimization algorithm for solving the microgrid optimization scheduling problem is verified.
The interaction between the electric vehicle and the micro-grid can effectively solve many negative problems caused by the disorderly charging of a large number of electric vehicles. For electric vehicle clusters that do not receive direct dispatch from microgrids, a real-time pricing strategy based on equivalence load is proposed to guide the charging and discharging of electric vehicles. This strategy is combined with the micro-grid direct dispatch strategy to perform charge and discharge management of different clusters of electric vehicles. Based on this consideration, considering the environmental benefits, the owner's revenue, and the microgrid operating status, an optimization model was established with the goal of minimizing the overall operating costs and minimizing the load fluctuation of the microgrid, and was solved using the gray wolf optimization algorithm. The optimization results show that by reasonably scheduling the controllable power generation units and the charging and discharging of the electric vehicles, the operating costs of the microgrid can be effectively reduced and fluctuations in equivalent loads can be reduced. Finally, by comparing with the basic particle swarm algorithm and genetic algorithm, the advantage of the gray wolf optimization algorithm for solving the microgrid optimization scheduling problem is verified.
引文
[1]胡洪权,朱自伟,王佗,等.分布式电源电动汽车充电站储能系统电价分析[J].可再生能源,2018,36(2):283-289.
[2]朱永胜,王杰,瞿博阳,等.含电动汽车的电力系统动态环境经济调度[J].电力自动化设备,2016,36(10):16-23.
[3]梁子鹏,陈皓勇,王勇超,等.含电动汽车的微电网鲁棒经济调度[J].电网技术,2017,41(8):2647-2658.
[4]Yang H,Pan H,Luo F,et al.Operational planning of electric vehicles for balancing wind power and load fluctuations in a microgrid[J].IEEE Transactions on Sustainable Energy,2017,8(2):592-604.
[5]Xiaogang Guo,ZhejingBaoZ,Hongji Lai,et al.Model predictive control considering scenario optimisation for microgrid dispatching with wind power and electric vehicle[J].Journal of Engineering,2017,13:2539-2543.
[6]苏粟,蒋小超,王玮,等.计及电动汽车和光伏-储能的微网能量优化管理[J].电力系统自动化,2015,39(9):164-171.
[7]周天沛,孙伟.基于微电网的电动汽车与电网互动技术[J].电力系统自动化,2018,42(3):98-104,117.
[8]赵兴勇,王帅,吴新华,等.含分布式电源和电动汽车的微电网协调控制策略[J].电网技术,2016,40(12):3732-3740.
[9]庄怀东,吴红斌,刘海涛,等.含电动汽车的微电网系统多目标经济调度[J].电工技术学报,2014,29(S1):365-373.
[10]王凌云,汪德夫,马奇伟,等.源、网、荷三方利益最优的电动汽车充、放电双层优化调度[J].可再生能源,2018,36(5):713-718.
[11]杨晓东,张有兵,赵波,等.供需两侧协同优化的电动汽车充、放电自动需求响应方法[J].中国电机工程学报,2017,37(1):120-130.
[12]孙丛丛,王致杰,江秀臣,等.分时电价环境下计及电动汽车充、放电影响的微电网优化控制策略[J].可再生能源,2018,36(1):64-71.
[13]苏海锋,梁志瑞.基于峰谷电价的家用电动汽车居民小区有序充电控制方法[J].电力自动化设备,2015,35(6):17-22.
[14]侯建朝,胡群丰,谭忠富.计及需求响应的风电-电动汽车协同调度多目标优化模型[J].电力自动化设备,2016,36(7):22-27.
[15]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):77-84,99.
[16]张晓波,张保会,董梁,等.微电网中电动汽车充电模式与换电模式的运行优化[J].电力系统自动化,2016,40(9):56-63.
[17]Mohanty S,Subudhi B,Ray P K.A new mppt design using grey wolf optimization technique for photovoltaic system under partial shading conditions[J].IEEETransactions on Sustainable Energy,2016,7(1):181-188.
[2]朱永胜,王杰,瞿博阳,等.含电动汽车的电力系统动态环境经济调度[J].电力自动化设备,2016,36(10):16-23.
[3]梁子鹏,陈皓勇,王勇超,等.含电动汽车的微电网鲁棒经济调度[J].电网技术,2017,41(8):2647-2658.
[4]Yang H,Pan H,Luo F,et al.Operational planning of electric vehicles for balancing wind power and load fluctuations in a microgrid[J].IEEE Transactions on Sustainable Energy,2017,8(2):592-604.
[5]Xiaogang Guo,ZhejingBaoZ,Hongji Lai,et al.Model predictive control considering scenario optimisation for microgrid dispatching with wind power and electric vehicle[J].Journal of Engineering,2017,13:2539-2543.
[6]苏粟,蒋小超,王玮,等.计及电动汽车和光伏-储能的微网能量优化管理[J].电力系统自动化,2015,39(9):164-171.
[7]周天沛,孙伟.基于微电网的电动汽车与电网互动技术[J].电力系统自动化,2018,42(3):98-104,117.
[8]赵兴勇,王帅,吴新华,等.含分布式电源和电动汽车的微电网协调控制策略[J].电网技术,2016,40(12):3732-3740.
[9]庄怀东,吴红斌,刘海涛,等.含电动汽车的微电网系统多目标经济调度[J].电工技术学报,2014,29(S1):365-373.
[10]王凌云,汪德夫,马奇伟,等.源、网、荷三方利益最优的电动汽车充、放电双层优化调度[J].可再生能源,2018,36(5):713-718.
[11]杨晓东,张有兵,赵波,等.供需两侧协同优化的电动汽车充、放电自动需求响应方法[J].中国电机工程学报,2017,37(1):120-130.
[12]孙丛丛,王致杰,江秀臣,等.分时电价环境下计及电动汽车充、放电影响的微电网优化控制策略[J].可再生能源,2018,36(1):64-71.
[13]苏海锋,梁志瑞.基于峰谷电价的家用电动汽车居民小区有序充电控制方法[J].电力自动化设备,2015,35(6):17-22.
[14]侯建朝,胡群丰,谭忠富.计及需求响应的风电-电动汽车协同调度多目标优化模型[J].电力自动化设备,2016,36(7):22-27.
[15]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):77-84,99.
[16]张晓波,张保会,董梁,等.微电网中电动汽车充电模式与换电模式的运行优化[J].电力系统自动化,2016,40(9):56-63.
[17]Mohanty S,Subudhi B,Ray P K.A new mppt design using grey wolf optimization technique for photovoltaic system under partial shading conditions[J].IEEETransactions on Sustainable Energy,2016,7(1):181-188.