计及需求响应的电动汽车和可再生能源多阶段动态经济环境调度优化模型
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摘要
提出了一种计及需求响应的电动汽车和可再生能源多阶段入网调度模型,首先通过分时电价机制引导用户合理用电,得出精确的用户负荷曲线;然后在车网互动阶段和风光消纳阶段,在以平滑系统负荷波动为目的的基础上,分别以车主用电成本最低和风、光发电消纳最大为目标,采用基于求取帕累托最优前沿的NSGA-II算法和模糊隶属函数对电动汽车充放电、风力发电和光伏发电出力进行优化;最后在火电机组调度出力阶段,以火电机组经济和环境成本最低为目标,对火电机组出力进行优化。算例结果表明:合理的分时电价机制能够改变车主的充放电行为和用户的用电行为,减小负荷峰谷差,提升风、光发电消纳能力,减小火电机组的总运行成本和污染物排放量。
This paper presents an related to demand response of electric vehicles and renewable energy multi stage grid dispatchingmodel.Firstly,adopting time-of-use price mechanism to guide the user using electricity reasonably to obtain accurate user load curve.Then in the vehicles gridinteracting stage and wind and photovoltaic consumptive scenery stage,for the purpose of smoothing system load fluctuation,respectively taking the owner of vehicles' lowest cost of using electricity,wind and photovoltaic power consumption as the goal,applying the NSGA-II algorithm to obtain the pareto optimal frontier and fuzzy membership function cope with the electric vehicles charging and discharging,wind power and photovoltaic power output.Finally,in the power generation dispatching stage,taking the thermal power units' economic and environmental costs as the goaloptimize the output of thermal power units.The simulation results show that the reasonable mechanism of time-of-use price can change the owner of the vehicles charging and discharging behavior and users' using electricity behavior,reducing the peak load,improving wind and photovoltaicpower consumptive capacity,reducing the total cost of operation and the pollutant emissions of thermal power units.
This paper presents an related to demand response of electric vehicles and renewable energy multi stage grid dispatchingmodel.Firstly,adopting time-of-use price mechanism to guide the user using electricity reasonably to obtain accurate user load curve.Then in the vehicles gridinteracting stage and wind and photovoltaic consumptive scenery stage,for the purpose of smoothing system load fluctuation,respectively taking the owner of vehicles' lowest cost of using electricity,wind and photovoltaic power consumption as the goal,applying the NSGA-II algorithm to obtain the pareto optimal frontier and fuzzy membership function cope with the electric vehicles charging and discharging,wind power and photovoltaic power output.Finally,in the power generation dispatching stage,taking the thermal power units' economic and environmental costs as the goaloptimize the output of thermal power units.The simulation results show that the reasonable mechanism of time-of-use price can change the owner of the vehicles charging and discharging behavior and users' using electricity behavior,reducing the peak load,improving wind and photovoltaicpower consumptive capacity,reducing the total cost of operation and the pollutant emissions of thermal power units.
引文
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[15]刘旭,杨德友,孟涛,等.计及需求响应的含风电场日前两阶段动态环境经济调度[J].电力建设,2016,37(9):146-154.LIU Xu,YANG Deyou,MENG Tao,et al.Day-ahead two-stage dynamic economic emission dispatching in wind power integrated system incorporating demand response[J].Electric Power Construction,2016,37(9):146-154.
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[18]鞠立伟,李欢欢,陈致宏,等.基于两步制自适应求解算法的风电-电动汽车多种并网模式效益对比分析模型[J].电网技术,2014,38(6):1492-1498.JU Liwei,LI Huanhuan,CHEN Zhihong,et al.A benefit contrastive analysis model of multi grid-connected modes for wind power and plug-in hybrid electric vehicles based on two-step adaptive solving algorithm[J].Power System Technology,2014,38(6):1492-1498.
[19]刘柏良,黄学良,李军,等.含分布式电源及电动汽车充电站的配电网多目标规划研究[J].电网技术,2015,39(2):450-456.LIU Bailiang,HUANG Xueliang,LI Jun,et al.Multiobjective planning of distribution network containing distributed generation and electric vehicle charging stations[J].Power System Technology,2015,39(2):450-456.
[20]李军,颜辉,张仰飞,等.基于改进萤火虫算法的农村配电网分布式电源优化配置[J].电力电容器与无功补偿,2016,37(6):82-87.LI Jun,YAN Hui,ZHANG Yangfei,et al.Distribution power optimization configuration of rural distribution grid based on improved firefly algorithm[J].Power Capacitor Reactive Power Compensation,2016,37(6):82-87.
[21]TING T O,RAO M V C,LOO C K.A novel approach for unit commitment problem via an effective hybrid particle swarm optimization[J].IEEE Transactions on Power Systems,2006,21(1):411-418.
[22]王抒祥.减排约束下电力资源综合利用优化模型与方法研究[D].北京:华北电力大学,2013.
[23]王亦,吴俊明,翟云峰,等.考虑大规模风电和电动汽车入网的协同优化调度[J].电力科学与工程,2015,31(4):11-17.WANG Yi,WU Junming,ZHAI Yunfeng,et al.Synergistic optimization scheduling considering large-scale wind power and electric vehicles integration[J].Electric Power Science and Engineering,2015,31(4):11-17.
[24]谭忠富,陈广娟,赵建保,等.以节能调度为导向的发电侧与售电侧峰谷分时电价联合优化模型[J].中国电机工程学报,2009,29(1):55-62.TAN Zhongfu,CHEN Guangjuan,ZHAO Jianbao,et al.Optimization model for designing peak-valley time-of-use power price of generation side and sale side at the direction of energy conservation dispatch[J].Proceeding of the CSEE,2009,29(1):55-62.
[2]邬捷龙,杨健.能源互联网技术的现状及发展趋势研究[J].电网与清洁能源,2016,32(3):8-12.WU Jielong,YANG Jian.Research on the current situation and development trend of the technology of energy internet[J].Power System and Clean Energy,2016,32(3):8-12.
[3]郑建涛,徐越,许世森.太阳能-燃气联合循环发电技术[J].热力发电,2014,43(8):24-28.ZHENG Jiantao,XU Yue,XU Shisen.An overview of integrated solar combined cycle technology[J].Thermal Power Generation,2014,43(8):24-28.
[4]吴晨曦.风光发电及电动汽车充放电随机性对配电系统的影响研究[D].杭州:浙江大学,2015.
[5]唐晓瑭,倪桦,王永生.电动汽车供电能源优化组合及智能充电策略[J].电力建设,2013,34(12):111-115.TANG Xiaotang,NI Hua,WANG Yongsheng.Energy supply optimization and intelligent charging strategy of electric vehicles[J].Electric Power Construction,2013,34(12):111-115.
[6]张智晟,温令云,李国,等.基于改进化学反应优化算法的电动汽车与可再生能源多目标协同调度[J].电网技术,2014,38(3):633-637.ZHANG Zhisheng,WENG Lingyun,LI Guo,et al.Multiobjective coordinated scheduling of electric vehicles and renewable generation based on improved chemical reaction optimization algorithm[J].Power System Technology,2014,38(3):633-637.
[7]黄晓倩,汪沨,谭阳红,等.考虑V2G模式的电动汽车与可再生能源协同调度[J].工程设计学报,2016,23(1):67-73.HUANG Xiaoqian,WANG Feng,TAN Yanghong,et al.Coordinated scheduling of electric vehicles and renewable generation considering vehicle-to-grid mode[J].Journal of Engineering Design,2016,23(1):67-73.
[8]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.ZHU Yongsheng,WANG Jie,QU Boyang,et al.Dynamic environmental and economic dispatch of power system with EVs[J].Automation of Electric Power Systems,2016,36(10):16-23.
[9]鞠立伟,秦超,吴鸿亮,等.计及多类型需求响应的风电消纳随机优化调度模型[J].电网技术,2015,39(7):1839-1846.JU Liwei,QIN Chao,WU Hongliang,et al.Wind power accommodation stochastic optimization model with multitype demand response[J].Power System Technology,2015,39(7):1839-1846.
[10]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.HOU Jianzhao,HU Qunfeng,TAN Zhongfu.Multi objective optimization model of collaborative WP-EV dispatch consdering demand response[J].Automation of Electric Power Systems,2016,36(7):22-27.
[11]AHMED F,DEB K.Multi-objective optimal path planning using elitist non-dominated sorting genetic algorithms[J].Soft Computing,2013,17(7):1283-1299.
[12]钱国超,马御棠,刘渝根,等.遗传算法在接地网腐蚀故障诊断中的应用研究[J].电瓷避雷器,2014(2):52-59,65.QIAN Guochao,MA Yutang,LIU Yugen,et al.Research on the application of genetic algorithm in fault diagnosis of grounding grid corrosion[J].Insulators and Surge Arresters,2014(2):52-59,65.
[13]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].Proceeding of the CSEE,2013,33(13):27-3.
[14]李浩闪,李燕青.分时电价下考虑用户侧响应的日前调度计划建模与分析[J].华东电力,2014,42(2):314-318.LI Haoshan,LI Yanqing.Day-ahead generation dispacth modeling and analysis considering demand response under time-of-use price[J].East China Electric Power,2014,42(2):314-318.
[15]刘旭,杨德友,孟涛,等.计及需求响应的含风电场日前两阶段动态环境经济调度[J].电力建设,2016,37(9):146-154.LIU Xu,YANG Deyou,MENG Tao,et al.Day-ahead two-stage dynamic economic emission dispatching in wind power integrated system incorporating demand response[J].Electric Power Construction,2016,37(9):146-154.
[16]WHITE C D,ZHANG K M.Using vehicle-to-grid technology for frequency regulation and peak-load reduction[J].Journal of Power Sources,2011,196(8):3972-3980.
[17]杨甲甲,赵俊华,文福拴,等.含电动汽车和风电机组的虚拟发电厂竞价策略[J].电力系统自动化,2014,38(13):92-102.YANG Jiajia,ZHAO Junhua,WEN Fushuan,et al.Development of bidding strategies for virtual power plants considering uncertain outputs from plug-in electric vehicles and wind generators[J].Automation of Electric Power Systems,2014,38(13):92-102.
[18]鞠立伟,李欢欢,陈致宏,等.基于两步制自适应求解算法的风电-电动汽车多种并网模式效益对比分析模型[J].电网技术,2014,38(6):1492-1498.JU Liwei,LI Huanhuan,CHEN Zhihong,et al.A benefit contrastive analysis model of multi grid-connected modes for wind power and plug-in hybrid electric vehicles based on two-step adaptive solving algorithm[J].Power System Technology,2014,38(6):1492-1498.
[19]刘柏良,黄学良,李军,等.含分布式电源及电动汽车充电站的配电网多目标规划研究[J].电网技术,2015,39(2):450-456.LIU Bailiang,HUANG Xueliang,LI Jun,et al.Multiobjective planning of distribution network containing distributed generation and electric vehicle charging stations[J].Power System Technology,2015,39(2):450-456.
[20]李军,颜辉,张仰飞,等.基于改进萤火虫算法的农村配电网分布式电源优化配置[J].电力电容器与无功补偿,2016,37(6):82-87.LI Jun,YAN Hui,ZHANG Yangfei,et al.Distribution power optimization configuration of rural distribution grid based on improved firefly algorithm[J].Power Capacitor Reactive Power Compensation,2016,37(6):82-87.
[21]TING T O,RAO M V C,LOO C K.A novel approach for unit commitment problem via an effective hybrid particle swarm optimization[J].IEEE Transactions on Power Systems,2006,21(1):411-418.
[22]王抒祥.减排约束下电力资源综合利用优化模型与方法研究[D].北京:华北电力大学,2013.
[23]王亦,吴俊明,翟云峰,等.考虑大规模风电和电动汽车入网的协同优化调度[J].电力科学与工程,2015,31(4):11-17.WANG Yi,WU Junming,ZHAI Yunfeng,et al.Synergistic optimization scheduling considering large-scale wind power and electric vehicles integration[J].Electric Power Science and Engineering,2015,31(4):11-17.
[24]谭忠富,陈广娟,赵建保,等.以节能调度为导向的发电侧与售电侧峰谷分时电价联合优化模型[J].中国电机工程学报,2009,29(1):55-62.TAN Zhongfu,CHEN Guangjuan,ZHAO Jianbao,et al.Optimization model for designing peak-valley time-of-use power price of generation side and sale side at the direction of energy conservation dispatch[J].Proceeding of the CSEE,2009,29(1):55-62.