含分布式光伏与储能配电网时变最优潮流追踪的分布式算法
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摘要
在量测、通信技术具备的前提下,在常规控制设施给定条件下,针对快速电力电子化技术中,大量逆变或整流控制点设定与控制点设定的优化在速度上难以跟随的问题,提出含分布式光储配电网时变最优潮流追踪的模型和分布式在线的算法。在建立时变优化模型和分析在线优化原理的基础上,就已有算法存在信息统一获取和集中计算这一弊端,将其改进为在配电网各区域仅局部及边界信息可知条件下,实现对配电网时变最优潮流分布式的追踪求解。首先,基于配电网开环运行的辐射状结构特点将配电网划分为若干区域,并对时变最优潮流的追踪求解在区域之间进行解耦。进一步,各区域可完全基于局部及边界信息,分布式的优化计算各自内部分布式光储的控制设定点,在设定点不断下发执行的过程中,实现对时变最优潮流快速分布式的追踪求解。文中对提出算法的收敛性进行了证明,并通过IEEE 123节点配电网算例分析验证了这一算法的有效性。
With the fast development of measurement and communication technologies, this paper developed a time-varying optimal power flow(OPF) tracking model and a distributed online algorithm in distribution networks with a large number of power-electronics interfaced photovoltaics and energy storage devices, fully excavating the fast following capability of inverters to set point. On the basis of formulating the time-varying online optimization model and online optimization theory, to the issue of unified information accessing and centralized calculation of existing online optimization algorithms, a distributed method was developed.It can be realized only with local and boundary information of each area being known, solving the optimal power flow tracking problem in a distributed way. Firstly, a distribution network was divided into several areas based on the radial structure characteristic of distribution networks, and the time-varying optimal power flow tracking problem was decoupled among areas. Further, each area calculated set point for photovoltaics and energy storage devices only based on local and boundary information. With the calculated set point being executed continuously, the output of photovoltaics and energy storage devices was driven to the solution of OPF in time-varying condition, solving the OPF tracking problem in a distributed way on line. The convergence of the proposed algorithm was proved mathematically and the effectiveness of the algorithm was verified through testing on IEEE 123-node distribution system.
With the fast development of measurement and communication technologies, this paper developed a time-varying optimal power flow(OPF) tracking model and a distributed online algorithm in distribution networks with a large number of power-electronics interfaced photovoltaics and energy storage devices, fully excavating the fast following capability of inverters to set point. On the basis of formulating the time-varying online optimization model and online optimization theory, to the issue of unified information accessing and centralized calculation of existing online optimization algorithms, a distributed method was developed.It can be realized only with local and boundary information of each area being known, solving the optimal power flow tracking problem in a distributed way. Firstly, a distribution network was divided into several areas based on the radial structure characteristic of distribution networks, and the time-varying optimal power flow tracking problem was decoupled among areas. Further, each area calculated set point for photovoltaics and energy storage devices only based on local and boundary information. With the calculated set point being executed continuously, the output of photovoltaics and energy storage devices was driven to the solution of OPF in time-varying condition, solving the OPF tracking problem in a distributed way on line. The convergence of the proposed algorithm was proved mathematically and the effectiveness of the algorithm was verified through testing on IEEE 123-node distribution system.
引文
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[15]Tang Yujie,Dvijotham K,Low S.Real-time optimal power flow[J].IEEE Transactions on Smart Grid,2017,8(6):2963-2973.
[16]Dall’Anese E,Dhople S V,Giannakis G B.Photovoltaic inverter controllers seeking AC optimal power flow solutions[J].IEEE Transactions on Power Systems,2016,31(4):2809-2823.
[17]Gan Lingwen,Low S H.An online gradient algorithm for optimal power flow on radial networks[J].IEEE Journal on Selected Areas in Communications,2016,34(3):625-638.
[18]Zhang Yijian,Hong Mingyi,Dall’Anese E,et al.Distributed controllers seeking AC optimal power flow solutions using ADMM[J].IEEE Transactions on Smart Grid,2018,9(5):4525-4537.
[19]Dall’Anese E,Simonetto A.Optimal power flow pursuit[J].IEEE Transactions on Smart Grid,2018,9(2):942-952.
[20]Zhou Xinyang,Dall’Anese E,Chen Lijun,et al.An incentive-based online optimization framework for distribution grids[J].IEEE Transactions on Automatic Control,2018,63(7):2019-2031.
[21]Zhu Hao,Liu H J.Fast local voltage control under limited reactive power:optimality and stability analysis[J].IEEETransactions on Power Systems,2016,31(5):3794-3803.
[22]Bolognani S,Carli R,Cavraro G,et al.Distributed reactive power feedback control for voltage regulation and loss minimization[J].IEEE Transactions on Automatic Control,2015,60(4):966-981.
[23]Trippe A E,Arunachala R,Massier T,et al.Charging optimization of battery electric vehicles including cycle battery aging[C]//IEEE PES Innovative Smart Grid Technologies.Istanbul:IEEE,2014.
[2]梁有伟,胡志坚,陈允平.分布式发电及其在电力系统中的应用研究综述[J].电网技术,2003,27(12):71-75,88.Liang Youwei,Hu Zhijian,Chen Yunping.A survey of distributed generation and its application in power system[J].Power System Technology,2003,27(12):71-75,88(in Chinese).
[3]Yan Ruifeng,Roediger S,Saha T K.Impact of photovoltaic power fluctuations by moving clouds on network voltage:A case study of an urban network[C]//Universities Power Engineering Conference.Brisbane:IEEE,2011.
[4]曾正,赵荣祥,汤胜清,等.可再生能源分散接入用先进并网逆变器研究综述[J].中国电机工程学报,2013,33(24):1-12.Zeng Zheng,Zhao Rongxiang,Tang Shengqing,et al.An overview on advanced grid-connected inverters used for decentralized renewable energy resources[J].Proceedings of the CSEE,2013,33(24):1-12(in Chinese).
[5]汪海宁,苏建徽,丁明,等.光伏并网功率调节系统[J].中国电机工程学报,2007,27(2):76-79.Wang Haining,Su Jianhui,Ding Ming,et al.Photovoltaic grid connected power conditioner system[J].Proceedings of the CSEE,2007,27(2):76-79(in Chinese).
[6]王成山,李霞林,郭力.基于功率平衡及时滞补偿相结合的双级式变流器协调控制[J].中国电机工程学报,2012,32(25):109-117.Wang Chengshan,Li Xialin,Guo Li.Coordinated control of two-stage power converters based on power balancing and time-delay compensation[J].Proceedings of the CSEE,2012,32(25):109-117(in Chinese).
[7]Wood A J,Wollenberg B F,ShebléG B.Power generation,operation and control[M].3rd ed.Hoboken:John Wiley&Sons,2013:491-495.
[8]陈建华,吴文传,张伯明,等.安全性与经济性协调的鲁棒区间风电调度方法[J].中国电机工程学报,2014,34(7):1033-1040.Chen Jianhua,Wu Wenchuan,Zhang Boming,et al.Arobust interval wind power dispatch method considering the tradeoff between security and economy[J].Proceedings of the CSEE,2014,34(7):1033-1040(in Chinese).
[9]王士柏,韩学山,杨明.计及机组备用响应能力的电力系统区间经济调度[J].中国电机工程学报,2013,33(7):99-108.Wang Shibo,Han Xueshan,Yang Ming.Interval economic dispatch of power system considering unit reserve responsiveness[J].Proceedings of the CSEE,2013,33(7):99-108(in Chinese).
[10]鲍海波,韦化,郭小璇.考虑风速相关性和可调度负荷不确定性的区间最优潮流[J].中国电机工程学报,2016,36(10):2628-2637.Bao Haibo,Wei Hua,Guo Xiaoxuan.Interval optimal power flow calculation considering interval correlated wind power and uncertain dispatchable load[J].Proceedings of the CSEE,2016,36(10):2628-2637(in Chinese).
[11]Joki?A,Lazar M,van den Bosch P P J.Price-based optimal control of electrical power systems[D].Eindhoven:Eindhoven University of Technology,2007.
[12]Al-Awami A T,El-Sharkawi M A.Feedback-control-based optimal power flow for real-time operation[C]//Proceedings of IEEE/PES Power Systems Conference&Exposition.Seattle,USA:IEEE,2009.
[13]Miao Zhixin,Fan Lingling.Achieving economic operation and secondary frequency regulation simultaneously through feedback control[J].IEEE Transactions on Power Systems,2016,31(4):3324-3325.
[14]Li Na,Zhao Changhong,Chen Lijun.Connecting automatic generation control and economic dispatch from an optimization view[J].IEEE Transactions on Control of Network Systems,2016,3(3):254-264.
[15]Tang Yujie,Dvijotham K,Low S.Real-time optimal power flow[J].IEEE Transactions on Smart Grid,2017,8(6):2963-2973.
[16]Dall’Anese E,Dhople S V,Giannakis G B.Photovoltaic inverter controllers seeking AC optimal power flow solutions[J].IEEE Transactions on Power Systems,2016,31(4):2809-2823.
[17]Gan Lingwen,Low S H.An online gradient algorithm for optimal power flow on radial networks[J].IEEE Journal on Selected Areas in Communications,2016,34(3):625-638.
[18]Zhang Yijian,Hong Mingyi,Dall’Anese E,et al.Distributed controllers seeking AC optimal power flow solutions using ADMM[J].IEEE Transactions on Smart Grid,2018,9(5):4525-4537.
[19]Dall’Anese E,Simonetto A.Optimal power flow pursuit[J].IEEE Transactions on Smart Grid,2018,9(2):942-952.
[20]Zhou Xinyang,Dall’Anese E,Chen Lijun,et al.An incentive-based online optimization framework for distribution grids[J].IEEE Transactions on Automatic Control,2018,63(7):2019-2031.
[21]Zhu Hao,Liu H J.Fast local voltage control under limited reactive power:optimality and stability analysis[J].IEEETransactions on Power Systems,2016,31(5):3794-3803.
[22]Bolognani S,Carli R,Cavraro G,et al.Distributed reactive power feedback control for voltage regulation and loss minimization[J].IEEE Transactions on Automatic Control,2015,60(4):966-981.
[23]Trippe A E,Arunachala R,Massier T,et al.Charging optimization of battery electric vehicles including cycle battery aging[C]//IEEE PES Innovative Smart Grid Technologies.Istanbul:IEEE,2014.