集群电动汽车平抑光伏波动实时调度策略
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摘要
随着光伏(photovoltaic, PV)发电渗透率的升高,光伏功率的随机波动将对电能质量与供电可靠性产生不利影响,而利用电动汽车入网(Vehicle to grid,V2G)技术下电动汽车(electric vehicles,EVs)充放电的灵活性来平抑光伏波动是一种经济、高效的解决方式。为平抑短时剧烈的光伏功率波动,首先提出了微网场景下集群电动汽车参与平抑光伏波动的控制框架,然后建立了利用EV功率跟跟踪PV出力的凸优化模型,并从数学上不失一般性地证明了凸化的有效性。所提凸化方法对系统参数没有任何要求,在优化求解前无需做任何检验。所建立的凸优化模型在求解上更加高效,且在调度容量充足时跟踪误差可控,并能在一定程度上抑制EV的过充电与过放电。最后,通过算例验证了凸优化模型的准确性与高效性及所提调度策略的优势。
With increasing penetration of photovoltaic(PV) generation, fluctuation of PV power has negative impact on power quality and power supply reliability. Based on vehicle-to-grid(V2 G) technology, the electrical vehicles(EVs) with charge flexibility are regarded as an economical and efficient solution of the problem. Firstly, to smooth the fluctuation of PV power, a control framework of aggregated EVs participating in smoothing PV power fluctuation in microgrid is proposed. Then, a convex optimization model of tracking PV output with EV power is established, and validity of the convex optimization model is proved mathematically in general. The convexification method in this paper has no requirement for system parameters and needs no inspection before solving. The convex optimization model is more efficient in solving, and the tracking error is controllable when the dispatching capacity is sufficient. Over-charge and over-discharge of EVs can be restrained to a certain extent. Finally, case studies are performed to verify accuracy and efficiency of the convex optimization model and advantages of the proposed strategy.
With increasing penetration of photovoltaic(PV) generation, fluctuation of PV power has negative impact on power quality and power supply reliability. Based on vehicle-to-grid(V2 G) technology, the electrical vehicles(EVs) with charge flexibility are regarded as an economical and efficient solution of the problem. Firstly, to smooth the fluctuation of PV power, a control framework of aggregated EVs participating in smoothing PV power fluctuation in microgrid is proposed. Then, a convex optimization model of tracking PV output with EV power is established, and validity of the convex optimization model is proved mathematically in general. The convexification method in this paper has no requirement for system parameters and needs no inspection before solving. The convex optimization model is more efficient in solving, and the tracking error is controllable when the dispatching capacity is sufficient. Over-charge and over-discharge of EVs can be restrained to a certain extent. Finally, case studies are performed to verify accuracy and efficiency of the convex optimization model and advantages of the proposed strategy.
引文
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[2]Durrani S P,Balluff S,Wurzer L,et al.Photovoltaic yield prediction using an irradiance forecast model based on multiple neural networks[J].Journal of Modern Power Systems and Clean Energy,2018,6(2):255-267.
[3]薛金花,叶季蕾,陶琼,等.采用全寿命周期成本模型的用户侧电池储能经济可行性研究[J].电网技术,2016,40(8):2471-2476.Xue Jinhua,Ye Jilei,Tao Qiong,et al.Economic feasibility of userside battery energy storage based on whole-life-cycle cost model[J].Power System Technology,2016,40(8):2471-2476(in Chinese).
[4]赵俊华,文福拴,杨爱民,等.电动汽车对电力系统的影响及其调度与控制问题[J].电力系统自动化,2011,35(14):2-10.Zhao Junhua,Wen Fushuan,Yang Aimin,et al.Impacts of electric vehicles on power systems as well as the associated dispatching and control problem[J].Automation of Electric Power Systems,2011,35(14):2-10(in Chinese).
[5]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,2011,36(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,2011,36(19):22-29(in Chinese).
[6]Liu N,Chen Q,Liu J,et al.A heuristic operation strategy for commercial building microgrids containing EVs and PV system[J].IEEE Transactions on Industrial Electronics,2015,62(4):2560-2570.
[7]Chen Q F,Wang F,Hodge B M,et al.Dynamic price vector formation model based automatic demand response strategy for PV-assisted EVcharging station[J].IEEE Transactions on Smart Grid,2017,8(6):2903-2915.
[8]Zhao T,Peng Y,Nehorai A.An optimal and distributed demand response strategy with electric vehicles in the smart grid[J].IEEETransactions on Smart Grid,2014,5(2):861-869.
[9]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):19-25.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):19-25(in Chinese).
[10]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).
[11]Fukushima M,Lin G H.Smoothing methods for mathematical programs with equilibrium constraints[C]//International Conference on Informatics Research for Development of Knowledge Society Infrastructure.Kyoto,Japan:IEEE Computer Society,2004:206-213.
[12]Chang-Ying H.Exactness of penalty functions for solving MPECmodel of the transportation network optimization problems with user equilibrium constraints[C]//International Conference on Management Science&Engineering.Lille,France:IEEE,2006:2045-2049.
[13]魏大钧,张承慧,孙波,等.基于分时电价的电动汽车充放电多目标优化调度[J].电网技术,2014,38(11):2972-2977.Wei Dajun,Zhang Chenghui,Sun Bo,et al.A time-of-use price based multi-objective optimal dispatching for charging and discharging of electric vehicles[J].Power System Technology,2014,38(11):2972-2977(in Chinese).
[14]Sousa T,Morais H,Vale Z,et al.Intelligent energy resource management considering vehicle-to-grid:a simulated annealing approach[J].IEEE Transactions on Smart Grid,2012,3(1):535-542.
[15]Omran W,Kazerani M,Salama M.Investigation of methods for reduction of power fluctuations generated from large grid-connected photovoltaic systems[J].IEEE Transactions on Energy Conversion,2011,26(1):318-327.
[16]Gill S,Kockar I,Ault G W.Dynamic optimal power flow for active distribution networks[J].IEEE Transactions on Power Systems,2014,29(1):121-131.
[17]Hao H,Wu D,Lian J,et al.Optimal coordination of building loads and energy storage for power grid and end user services[J].IEEETransactions on Smart Grid,2018,9(5):4335-4345.
[18]Li Z S,Guo Q L,Sun H B,et al.Sufficient conditions for exact relaxation of complementarity constraints for storage-concerned economic dispatch[J].IEEE Transactions on Power Systems,2016,31(2):1653-1654.
[19]Li Z S,Guo Q L,Sun H B,et al.Storage-like devices in load leveling:complementarity constraints and a new and exact relaxation method[J].Applied Energy,2015(151):13-22.
[20]Wenzel G,Negrete-Pincetic M,Olivares D E,et al.Real-time charging strategies for an electric vehicle aggregator to provide ancillary services[J].IEEE Transactions on Smart Grid,2018,9(5):5141-5151.
[21]Kouvaritakis B,Cannon M.Robust MPC for multiplicative and mixed uncertainty[M].Switzerland:Springer International Publishing,2016:175-240.
[22]Vijayakumar G,Kummert M,Klein S A,et al.Analysis of short-term solar radiation data[J].Solar Energy,2005,79(5):495-504.
[23]徐智威,胡泽春,宋永华,等.基于动态分时电价的电动汽车充电站有序充电策略[J].中国电机工程学报,2014,34(22):3638-3646.Xu Zhiwei,Hu Zechun,Song Yonghua,et al.Coordinated charging strategy for PEV charging stations based on dynamic time-of-use tariffs[J].Proceedings of the CSEE,2014,34(22):3638-3646(in Chinese).