考虑高比例电动汽车及风电协同参与的机组组合策略
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摘要
针对高比例电动汽车(EV)接入电网,常规量化模型难以解决由大规模决策变量导致的运算效率低下的问题,提出一种由下至上的方法来描述集群EV的灵活性。首先,计及车主行为及EV电池的物理特性,通过描述功率、电能边界实现单位EV功率可行域的量化。然后,基于其统一的表达形式,对边界进行闵氏求和建立虚拟电池模型描述集群EV的功率可行域。最后将所提模型应用于高比例电动汽车及风电协调参与的机组组合问题,并通过仿真算例验证了其可行性。
Aiming at the problem that the conventional quantitative model is difficult to solve the computational inefficiency problem caused by large-scale decision variables when the high proportion of electric vehicles(EVs) connecting to grid, a bottom-up approach is proposed to describe the flexibility of the EV cluster. Firstly, taking into account the behavior of the owner and the physical characteristics of the EV battery, the unit EV power feasible domain is described quantitatively by characterizing the power and energy boundaries. Then, a virtual battery model is established to describe the power feasible domain of the EV cluster by adopting the Minkowski sum of the boundaries based on its unified expression. Finally, the proposed model is applied to the unit combination problem considering cooperated dispatch of high proportion of EVs and wind power generation, and the feasibility and superiority are verified by simulation examples. It is also proved that the system operation cost can effectively reduce with the unit commitment strategy considering the EV scheduling flexibility and cooperated dispatch of electric vehicles and wind power generation.
Aiming at the problem that the conventional quantitative model is difficult to solve the computational inefficiency problem caused by large-scale decision variables when the high proportion of electric vehicles(EVs) connecting to grid, a bottom-up approach is proposed to describe the flexibility of the EV cluster. Firstly, taking into account the behavior of the owner and the physical characteristics of the EV battery, the unit EV power feasible domain is described quantitatively by characterizing the power and energy boundaries. Then, a virtual battery model is established to describe the power feasible domain of the EV cluster by adopting the Minkowski sum of the boundaries based on its unified expression. Finally, the proposed model is applied to the unit combination problem considering cooperated dispatch of high proportion of EVs and wind power generation, and the feasibility and superiority are verified by simulation examples. It is also proved that the system operation cost can effectively reduce with the unit commitment strategy considering the EV scheduling flexibility and cooperated dispatch of electric vehicles and wind power generation.
引文
[1]田廓,董文杰.需求侧响应及输电约束条件下大规模新能源发电并网机组组合模型[J].智慧电力,2019(1):54-58+71.TIAN Kuo, DONG Wenjie. Unit commitment model of large scale new energy generation integrated into grid with demand response resources based on price and transmission capacity constraints[J]. Smart Power, 2019(1):54-58+71.
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[3]简俊威,吴杰康,莫超,等.风电与电动汽车协同并网的电力系统动态环境经济多目标模糊优化调度模型[J].广东电力,2018,31(4):49-58.JIAN Junwei, WU Jiekang, MO Chao, et al.Scheduling for cooperative grid-connection of wind power and electric vehicle[J]. Guangdong Electric Power, 2018,31(4):49-58.
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[5]樊平,齐凌成,王庆丰,等.考虑电动汽车充放电集成的配电网短期负荷预测[J].智慧电力,2018(12):50-55+87.FAN Ping, QI Lingchen, WANG Qinfeng, et al. Shortterm load forecasting for distribution network considering electric vehicles charging and discharging integration[J].Smart Power,2018(12):50-55+87.
[6]张舒,胡泽春,宋永华,等.考虑电动汽车换电站与电网互动的机组组合问题研究[J].中国电机工程学报,2012,32(10):49-55.ZHANG Shu, HU Zechun, SONG Yonghua, et al.Research on unit commitment considering interaction between battery swapping station and power grid[J].Proceedings of the CSEE,2012,32(10):49-55.
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[9]王闪闪,赵晋斌,毛玲,等.基于电动汽车移动储能特性的直流微网控制策略[J].电力系统保护与控制,2018,46(20):37-44.WANG Shanshan, ZHAO Jinbin, MAO Ling, et al. A control strategy based on mobile energy storage characteristic of electric vehicles in DC micro-grid[J].Power System Protection and Control,2018,46(20):37-44.
[10]师景佳,袁铁江,Saeed Ahmed Khan等.计及电动汽车可调度能力的风/车协同参与机组组合策略[J].高电压技术,2018,44(10):3433-3440.SHUAI Jingjia, YUAN Tiejiang,SAEED Ahmed Khan, et al. Unit commitment strategy considering cooperateddispatch of electric vehicles based on scheduling capacity and wind power generation[J]. High Voltage Engineering,2018,44(10):3433-3440.
[11]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.
[12] BAROT S, TAYLOR J A. A concise, approximate representation of a collection of loads described by polytopes[J]. International Journal of Electrical Power&Energy Systems, 2017, 84:55-63.
[13] TRANGBAEK K, BENDTSEN J. Exact constraint aggregation with applications to smart grids and resource distribution[C]. Hawai:2012 IEEE 51st IEEE Conference on Decision and Control(CDC),2012:4181-4186.
[14] ZHAO L, HAO H, ZHANG W. Extracting flexibility of heterogeneous deferrable loads via polytopic projection approximation[C]. Lasvegas:2016 IEEE 55th Conference on Decision and Control(CDC), 2016:6651-6656.
[15] MULLER F L, SZABO J, SUNDSTROM O, et al.Aggregation and disaggregation of energetic flexibility from distributed energy resources[J]. IEEE Transactions on Smart Grid, 2017:1-1.
[16] CARRION M, ARROYO J M.A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem[J]. IEEE Transactions on power systems,2006,21(3):1371-1378.
[17]吴界辰,艾欣,张艳,等.配售分离环境下高比例分布式能源园区电能日前优化调度[J].电网技术,2018,42(6):1709-1717.WU Jiechen, AI Xin, ZHANG Yan, et al. Day-Ahead optimal scheduling for high penetration of distributed energy resources in community under separated distribution and retail operational environment[J]. Power System Technology, 2018,42(6):1709-1717.
[18]童光毅.关于当代能源转型方向的探讨[J].智慧电力,2018,46(10):1-3.TONG Guangyi.Probe into Modern Energy Structure Transition[J].Smart Power,2018,46(10):1-3.
[2]卢鹏铭,温步瀛,江岳文.基于多时间尺度协调机组组合的含风电系统旋转备用优化研究[J].电力系统保护与控制,2015, 43(5):94-100.LU Pengming, WEN Buying, JIANG Yuewen. Study on optimization of spinning reserve in wind power integrated power system based on multiple timescale and unit commitment coordination[J]. Power System Protection&Control, 2015, 43(5):94-100.
[3]简俊威,吴杰康,莫超,等.风电与电动汽车协同并网的电力系统动态环境经济多目标模糊优化调度模型[J].广东电力,2018,31(4):49-58.JIAN Junwei, WU Jiekang, MO Chao, et al.Scheduling for cooperative grid-connection of wind power and electric vehicle[J]. Guangdong Electric Power, 2018,31(4):49-58.
[4]陈芳,王艳,尹自力,等.电动汽车多因素负荷对湖南某地区电网的影响研究[J].智慧电力,2018,46(2):40-44.CHEN Fang, WANG Yan, YIN Zili, et al. Study on influence of electric vehicle multi-factor load on regional power grid in hunan province[J].Smart Power,2018,46(2):40-44.
[5]樊平,齐凌成,王庆丰,等.考虑电动汽车充放电集成的配电网短期负荷预测[J].智慧电力,2018(12):50-55+87.FAN Ping, QI Lingchen, WANG Qinfeng, et al. Shortterm load forecasting for distribution network considering electric vehicles charging and discharging integration[J].Smart Power,2018(12):50-55+87.
[6]张舒,胡泽春,宋永华,等.考虑电动汽车换电站与电网互动的机组组合问题研究[J].中国电机工程学报,2012,32(10):49-55.ZHANG Shu, HU Zechun, SONG Yonghua, et al.Research on unit commitment considering interaction between battery swapping station and power grid[J].Proceedings of the CSEE,2012,32(10):49-55.
[7] SABER A Y, VENAYAGAMOORTHY G K.Optimization of vehicle-to-grid scheduling in constrained parking lots[C]. Calgary:IEEE Power&Energy Society General Meeting.2009.
[8]陆凌蓉,文福拴,薛禹胜,等.计及可人网电动汽车的电力系统机组最优组合[J].电力系统自动化,2011,35(21):16-20.LU Lingrong, WEN Fushuan, XUE Yusheng, et al.Unit commitment in power systems with plug-in hybrid electric vehicles[J]. Automation of Electric Power Systems, 2011,35(21):16-20.
[9]王闪闪,赵晋斌,毛玲,等.基于电动汽车移动储能特性的直流微网控制策略[J].电力系统保护与控制,2018,46(20):37-44.WANG Shanshan, ZHAO Jinbin, MAO Ling, et al. A control strategy based on mobile energy storage characteristic of electric vehicles in DC micro-grid[J].Power System Protection and Control,2018,46(20):37-44.
[10]师景佳,袁铁江,Saeed Ahmed Khan等.计及电动汽车可调度能力的风/车协同参与机组组合策略[J].高电压技术,2018,44(10):3433-3440.SHUAI Jingjia, YUAN Tiejiang,SAEED Ahmed Khan, et al. Unit commitment strategy considering cooperateddispatch of electric vehicles based on scheduling capacity and wind power generation[J]. High Voltage Engineering,2018,44(10):3433-3440.
[11]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.
[12] BAROT S, TAYLOR J A. A concise, approximate representation of a collection of loads described by polytopes[J]. International Journal of Electrical Power&Energy Systems, 2017, 84:55-63.
[13] TRANGBAEK K, BENDTSEN J. Exact constraint aggregation with applications to smart grids and resource distribution[C]. Hawai:2012 IEEE 51st IEEE Conference on Decision and Control(CDC),2012:4181-4186.
[14] ZHAO L, HAO H, ZHANG W. Extracting flexibility of heterogeneous deferrable loads via polytopic projection approximation[C]. Lasvegas:2016 IEEE 55th Conference on Decision and Control(CDC), 2016:6651-6656.
[15] MULLER F L, SZABO J, SUNDSTROM O, et al.Aggregation and disaggregation of energetic flexibility from distributed energy resources[J]. IEEE Transactions on Smart Grid, 2017:1-1.
[16] CARRION M, ARROYO J M.A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem[J]. IEEE Transactions on power systems,2006,21(3):1371-1378.
[17]吴界辰,艾欣,张艳,等.配售分离环境下高比例分布式能源园区电能日前优化调度[J].电网技术,2018,42(6):1709-1717.WU Jiechen, AI Xin, ZHANG Yan, et al. Day-Ahead optimal scheduling for high penetration of distributed energy resources in community under separated distribution and retail operational environment[J]. Power System Technology, 2018,42(6):1709-1717.
[18]童光毅.关于当代能源转型方向的探讨[J].智慧电力,2018,46(10):1-3.TONG Guangyi.Probe into Modern Energy Structure Transition[J].Smart Power,2018,46(10):1-3.