含电动汽车与可控负荷的光伏智能小区两阶段优化调度
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摘要
考虑光伏预测、气温预测偏差对智能小区内电动汽车、可控负荷协同调度的影响,提出了日前协同规划与实时动态修正相结合的两阶段优化调度策略。首先,基于住宅区面积、可控负荷使用特性以及热力学定理建立可控负荷出力模型。其次,在日前调度中以电动汽车有序充电后的运营方收益最大为目标制定实时电价。再次,通过引入实时调度来修正由于日前预测所导致的供求不平衡量。在实时调度中以日前制定的实时电价为依据,优化电动汽车日前充电方案进而实现实时调度费用最小的目标。最后,对所提模型和算法进行了仿真验证,结果表明:文中所提出的两阶段优化调度策略能够降低智能小区协同调度成本,充分发挥电动汽车与可控负荷的调度潜能,提升运营方收益,降低用户费用,改善系统负荷特性。
Considering deviation of PV forecast and temperature forecast,a two-stage dispatch optimization approach combining collaborative planning with real-time dynamic correction was proposed in this paper.Firstly,based on residential area,controllable load characteristics and thermodynamic theorems,a controllable load output model was established.Secondly,day-ahead dispatch able to get appropriate time-of-use(TOU) price was established to ensure profit maximization for operators after orderly charging of electric vehicle.Real-time dispatch was introduced to modify imbalance between power supply and demand due to recent prediction error.In real-time dispatch,electric vehicle charging scheme was optimized based on TOU price to achieve minimized real-time dispatch cost.Finally,through simulation verification on the proposed model and algorithm,results show that the proposed two-stage dispatch optimization approach can reduce intelligent community's cooperative dispatch cost.This approach can fully release potential dispatch capacity of electric vehicles and controllable load,thus enhancing operator revenue,reducing user costs and improving system load characteristics.
Considering deviation of PV forecast and temperature forecast,a two-stage dispatch optimization approach combining collaborative planning with real-time dynamic correction was proposed in this paper.Firstly,based on residential area,controllable load characteristics and thermodynamic theorems,a controllable load output model was established.Secondly,day-ahead dispatch able to get appropriate time-of-use(TOU) price was established to ensure profit maximization for operators after orderly charging of electric vehicle.Real-time dispatch was introduced to modify imbalance between power supply and demand due to recent prediction error.In real-time dispatch,electric vehicle charging scheme was optimized based on TOU price to achieve minimized real-time dispatch cost.Finally,through simulation verification on the proposed model and algorithm,results show that the proposed two-stage dispatch optimization approach can reduce intelligent community's cooperative dispatch cost.This approach can fully release potential dispatch capacity of electric vehicles and controllable load,thus enhancing operator revenue,reducing user costs and improving system load characteristics.
引文
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[13]高亚静,李瑞环,梁海峰,等.考虑间歇性电源与负荷不确定性情况下基于多场景技术的主动配电系统两步优化调度[J].中国电机工程学报,2015,35(7):1657-1665.Gao Yajing,Li Ruihuan,Liang Haifeng,et al.Two step optimal dispatch based on multiple scenarios technique considering uncertainties of intermittent distributed generations and loads in the active distribution system[J].Proceedings of the CSEE,2015,35(7):1657-1665(in Chinese).
[14]鞠立伟,于超,谭忠富.计及需求响应的风电储能两阶段调度优化模型及求解算法[J].电网技术,2015,39(5):1287-1293.Ju Liwei,Yu Chao,Tan Zhongfu.A two-stage scheduling optimization model and corresponding solving algorithm for power grid containing wind farm and energy storage system considering demand response[J].Power System Technology,2015,39(5):1287-1293(in Chinese).
[15]Federal High Way Administration.US Department of Transportation[EB/OL].2011[2011-06].http://nhts.ornl.gov/2009/pub/stt.pdf.
[16]Ziadi Z,Oshiro M,Senjyu T,et al.Optimal voltage control using inverters interfaced with PV systems considering forecast error in a distribution system[J].IEEE Transactions on Sustainable Energy,2014,5(2):682-690.
[17]黄平.粒子群算法改进及其在电力系统的应用[D].广州:华南理工大学,2012.
[18]黄宇,杨健维,何正友.基于双层离散粒子群优化的智能小区车辆与家庭互动调度策略[J].电网技术,2015,39(10):2690-2696.Huang Yu,Yang Jianwei,He Zhengyou.A dispatching strategy for V2H of intelligent community based on bilayer discrete particle swarm optimization[J].Power System Technology,2015,39(10):2690-2696(in Chinese).
[19]北京市发展和改革委员会.北京市发展和改革委员会关于本市电动汽车充电服务收费有关问题的通知[EB/OL].[2015-09-06].http://www.bjpc.gov.cn/tztg/201505/t9040414.htm.
[20]王怡,刘加平.西安住宅环境现状调查及空调设备使用情况分析[J].暖通空调,2005,35(12):44-46.Wang Yi,Liu Jiaping.Investigation on residential building indoor environment in Xi’an and usage of air conditioners[J].HV&AC,2005,35(12):44-46(in Chinese).
[2]Yilmaz M,Krein P T.Review of the impact of vehicle-to-grid technologies on distribution systems and utility interfaces[J].IEEE Transactions on Power Electronics,2013,28(12):5673-5689.
[3]林少伯,韩民晓,赵国鹏,等.基于随机预测误差的分布式光伏配网储能系统容量配置方法[J].中国电机工程学报,2013,33(4):25-33.Lin Shaobo,Han Minxiao,Zhao Guopeng,et al.Capacity allocation of energy storage in distributed photovoltaic power system based on stochastic prediction error[J].Proceedings of the CSEE,2013,33(4):25-33(in Chinese).
[4]Ma Y,Houghton T,Cruden A,et al.Modeling the benefits of vehicle-to-grid technology to a power system[J].IEEE Transactions on Power Systems,2012,27(2):1012-1020.
[5]刘伟佳,吴秋伟,文福拴,等.电动汽车和可控负荷参与配电系统阻塞管理的市场机制[J].电力系统自动化,2014,38(24):26-33.Liu Weijia,Wu Qiuwei,Wen Fushuan,et al.A market mechanism for participation of electric vehicles and dispatchable loads in distribution system congestion management[J].Automation of Electric Power Systems,2014,38(24):26-33(in Chinese).
[6]Fattori F,Anglani N,Muliere G.Combining photovoltaic energy with electric vehicles,smart charging and vehicle-to-grid[J].Solar Energy,2014(110):438-451.
[7]王贵斌,赵俊华,文福拴,等.配电系统中电动汽车与可再生能源的随机协同调度[J].电力系统自动化,2012,36(19):22-29.Wang Guibin,Zhao Junhua,Wen Fushuan,et al.Stochastic optimization dispatching of plug-in hybrid electric vehicles in coordination with renewable generation in distribution systems[J].Automation of Electric Power Systems,2012,36(19):22-29(in Chinese).
[8]于大洋,黄海丽,雷鸣,等.电动汽车充电与风电协同调度的碳减排效益分析[J].电力系统自动化,2012,36(10):14-18.Yu Dayang,Huang Haili,Lei Ming,et al.CO2 reduction benefit by coordinated dispatch of electric vehicle charging and wind power[J].Automation of Electric Power Systems,2012,36(10):14-18(in Chinese).
[9]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,Wen Lingyun,Li Guo,et al.Multi-objective coordinated scheduling of electric vehicles and renewable generation based on improved chemical reaction optimization algorithm[J].Power System Technology,2014,38(3):633-637(in Chinese).
[10]姚伟锋,赵俊华,文福拴,等.基于双层优化的电动汽车充放电调度策略[J].电力系统自动化,2012,36(11):30-37.Yao Weifeng,Zhao Junhua,Wen Fushuan,et al.A charging and discharging dispatching strategy for electric vehicles based on bi-level optimization[J].Automation of Electric Power Systems,2012,36(11):30-37(in Chinese).
[11]罗卓伟,胡泽春,宋永华,等.大规模电动汽车充放电优化控制及容量效益分析[J].电力系统自动化,2012,36(10):19-26.Luo Zhuowei,Hu Zechun,Song Yonghua,et al.Coordinated charging and discharging of large-scale plug-in electric vehicles with cost and capacity benefit analysis[J].Automation of Electric Power Systems,2012,36(10):19-26(in Chinese).
[12]张良,严正,冯冬涵,等.采用两阶段优化模型的电动汽车充电站内有序充电策略[J].电网技术,2014,38(4):967-973.Zhang Liang,Yan Zheng,Feng Donghan,et al.Two-stage optimization model based coordinated charging for EV charging station[J].Power System Technology,2014,38(4):967-973(in Chinese).
[13]高亚静,李瑞环,梁海峰,等.考虑间歇性电源与负荷不确定性情况下基于多场景技术的主动配电系统两步优化调度[J].中国电机工程学报,2015,35(7):1657-1665.Gao Yajing,Li Ruihuan,Liang Haifeng,et al.Two step optimal dispatch based on multiple scenarios technique considering uncertainties of intermittent distributed generations and loads in the active distribution system[J].Proceedings of the CSEE,2015,35(7):1657-1665(in Chinese).
[14]鞠立伟,于超,谭忠富.计及需求响应的风电储能两阶段调度优化模型及求解算法[J].电网技术,2015,39(5):1287-1293.Ju Liwei,Yu Chao,Tan Zhongfu.A two-stage scheduling optimization model and corresponding solving algorithm for power grid containing wind farm and energy storage system considering demand response[J].Power System Technology,2015,39(5):1287-1293(in Chinese).
[15]Federal High Way Administration.US Department of Transportation[EB/OL].2011[2011-06].http://nhts.ornl.gov/2009/pub/stt.pdf.
[16]Ziadi Z,Oshiro M,Senjyu T,et al.Optimal voltage control using inverters interfaced with PV systems considering forecast error in a distribution system[J].IEEE Transactions on Sustainable Energy,2014,5(2):682-690.
[17]黄平.粒子群算法改进及其在电力系统的应用[D].广州:华南理工大学,2012.
[18]黄宇,杨健维,何正友.基于双层离散粒子群优化的智能小区车辆与家庭互动调度策略[J].电网技术,2015,39(10):2690-2696.Huang Yu,Yang Jianwei,He Zhengyou.A dispatching strategy for V2H of intelligent community based on bilayer discrete particle swarm optimization[J].Power System Technology,2015,39(10):2690-2696(in Chinese).
[19]北京市发展和改革委员会.北京市发展和改革委员会关于本市电动汽车充电服务收费有关问题的通知[EB/OL].[2015-09-06].http://www.bjpc.gov.cn/tztg/201505/t9040414.htm.
[20]王怡,刘加平.西安住宅环境现状调查及空调设备使用情况分析[J].暖通空调,2005,35(12):44-46.Wang Yi,Liu Jiaping.Investigation on residential building indoor environment in Xi’an and usage of air conditioners[J].HV&AC,2005,35(12):44-46(in Chinese).