含虚拟电厂的风电并网系统分布式优化调度建模
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摘要
针对大规模风电并网消纳的难题,该文提出一种计及需求响应虚拟电厂的风电并网系统分布式日前经济调度优化模型。首先,将需求侧负荷以需求响应虚拟电厂形式参与系统优化调度,通过负荷转移以实现削峰填谷,促进风电消纳;结合电网中不同电力公司所辖电网具有相互独立且互联的特点,基于电网分区,采用交替方向乘子法(alternating direction method of multipliers,ADMM)构建分布式优化调度模型并提出多区域协同优化的迭代计算框架;然后,在求解该分布式优化调度模型过程中,每次优化迭代时相邻区域之间仅交换边界节点相角信息即可实现各分区系统以运行成本最低为优化目标的分布式求解,在降低区域间协调运行的信息量,保证各区域独立运行的灵活性和信息私密性的基础上,最终实现电网经济调度和风电消纳的全局最优。最后,通过对6节点测试系统和IEEE-118节点测试系统的算例分析,验证了所提模型的可行性和算法的有效性。
To solve the problem of large-scale wind power integration, this paper proposed a decentralized day-ahead optimal dispatching model for wind power integrated power system with virtual power plants(VPP) based on demand response(DR). Firstly, the demand-side load was considered to participate in optimal dispatching of power system in the form of VPP based on DR, and the load shifting was implemented to achieve peak-valley regulation to promote wind power integration. Utilized to partition the power system, the alternating direction method of multipliers(ADMM) method was adopted to construct a decentralized optimal dispatching model and iterative computing framework of regional collaborative optimization, which is suitable for independent and interconnected multi-region power grid. Furthermore, in the process of solving the decentralized optimal dispatching model, only the phase angle information of the boundary buses was exchanged between neighboring regions in each iteration,and the decentralized solving of the model with the objective function to minimize the operation cost in each partition system can be realized. The overall optimization of operation economy and wind power integration is finally achieved, and the amount of exchanged information is reduced on the basis of ensuring the flexibility and privacy of information in each region.Finally, numerical results of 6-bus and IEEE-118 bus test systems verify the rationality of the proposed model and the availability of the algorithm.
To solve the problem of large-scale wind power integration, this paper proposed a decentralized day-ahead optimal dispatching model for wind power integrated power system with virtual power plants(VPP) based on demand response(DR). Firstly, the demand-side load was considered to participate in optimal dispatching of power system in the form of VPP based on DR, and the load shifting was implemented to achieve peak-valley regulation to promote wind power integration. Utilized to partition the power system, the alternating direction method of multipliers(ADMM) method was adopted to construct a decentralized optimal dispatching model and iterative computing framework of regional collaborative optimization, which is suitable for independent and interconnected multi-region power grid. Furthermore, in the process of solving the decentralized optimal dispatching model, only the phase angle information of the boundary buses was exchanged between neighboring regions in each iteration,and the decentralized solving of the model with the objective function to minimize the operation cost in each partition system can be realized. The overall optimization of operation economy and wind power integration is finally achieved, and the amount of exchanged information is reduced on the basis of ensuring the flexibility and privacy of information in each region.Finally, numerical results of 6-bus and IEEE-118 bus test systems verify the rationality of the proposed model and the availability of the algorithm.
引文
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[12]冯汉中,刘明波,赵文猛,等.ADMM应用于求解多区域互联电网分布式无功优化问题[J].电力系统及其自动化学报,2017,29(7):20-26.Feng Hanzhong,Liu Mingbo,Zhao Wenmeng,et al.Application of ADMM to distributed reactive-power optimization of multi-area interconnected power grids[J].Proceeding of the CSU-EPSA,2017,29(7):20-26(in Chinese).
[13]赵晶晶,魏炜,王成山.基于电网分区的分布式输电能力计算[J].中国电机工程学报,2008,28(7):1-6.Zhao Jingjing,Wei Wei,Wang Chengshan.Distributed transfer capability computation of electric power system based on decomposed system network[J].Proceedings of the CSEE,2008,28(7):1-6(in Chinese).
[14]Lai Xiaowen,Xie Le,Xia Qing,et al.Decentralized multi-area economic dispatch via dynamic multiplierbased lagrangian relaxation[J].IEEE Transactions on Power Systems,2015,30(6):3225-3233.
[15]齐琛,汪可友,李国杰,等.交直流混合主动配电网的分层分布式优化调度[J].中国电机工程学报,2017,37(7):1909-1917.Qi Chen,Wang Keyou,Li Guojie,et al.Hierarchical and distributed optimal scheduling of AC/DC hybrid active distribution network[J].Proceedings of the CSEE,2017,37(7):1909-1917(in Chinese).
[16]赵晋全,吉跃瑾,戴则梅.一种多分区互联电网分布式无功优化算法[J].电力系统保护与控制,2014,41(23):9-15.Zhao Jinquan,Ji Yuejin,Dai Zemei.A distributed reactive power optimization method for multi-area interconnected power grid[J].Power System Protection and Control,2014,41(23):9-15(in Chinese).
[17]喻洁,李扬,夏安邦.兼顾环境保护与经济效益的发电调度分布式优化策略[J].中国电机工程学报,2009,29(16):63-68.Yu Jie,Li Yang,Xia Anbang.Distributed optimization of generation dispatch schedule considering environmental protection and economic profits[J].Proceedings of the CSEE,2009,29(16):63-68(in Chinese).
[18]蒲天骄,刘威,陈乃仕,等.基于一致性算法的主动配电网分布式优化调度[J].中国电机工程学报,2017,37(6):1579-1589.Pu Tianjiao,Liu Wei,Chen Naishi,et al.Distributed optimal dispatching of active distribution network based on consensus algorithm[J].Proceedings of the CSEE,2017,37(6):1579-1589(in Chinese).
[19]陈刚,杨毅,杨晓梅,等.基于分布式牛顿法的微电网群分布式优化调度方法[J].电力系统自动化,2017,41(21):156-162.Chen Gang,Yang Yi,Yang Xiaomei,et al.Distributed optimization scheduling method for microgrid cluster based on distributed newton method[J].Automation of Electric Power Systems,2017,41(21):156-162(in Chinese).
[20]Boyd S,Parikh N,Chu E,et al.Distributed optimization and statistical learning via the alternating direction method of multipliers[J].Foundations and Trends?in Machine Learning,2010,3(1):1-122.
[21]Erseghe T,Zennaro D,Dall’Anese E,et al.Fast consensus by the alternating direction multipliers method[J].IEEETransactions on Signal Processing,2011,59(11):5523-5537.
[22]Erseghe T.Distributed optimal power flow using ADMM[J].IEEE Transactions on Power Systems,2014,29(5):2370-2380.
[23]Wang Yamin,Wu Lei,Wang Shouxiang.A fullydecentralized consensus-based ADMM approach for DC-OPF with demand response[J].IEEE Transactions on Smart Grid,2017,8(6):2637-2647.
[24]US Department of Energy.Benefits of demand response in electricity markets and recommendations for achieving them[R].Washington DC,USA:US Department of Energy,2006.
[2]李国庆,刘玢,陈厚合.大规模风电并网系统容量效益裕度计算模型研究[J].电力自动化设备,2016,36(7):1-6,14.Li Guoqing,Liu Bin,Chen Houhe.CBM calculation model for power system with large-scale wind power[J].Electric Power Automation Equipment,2016,36(7):1-6,14(in Chinese).
[3]张钦,王锡凡,王建学,等.电力市场下需求响应研究综述[J].电力系统自动化,2008,32(3):97-106.Zhang Qin,Wang Xifan,Wang Jianxue,et al.Survey of demand response research in deregulated electricity markets[J].Automation of Electric Power Systems,2008,32(3):97-106(in Chinese).
[4]宋巍,王佳伟,赵海波,等.考虑需求响应交易市场的虚拟电厂多阶段竞价策略研究[J].电力系统保护与控制,2017,45(19):35-45.Song Wei,Wang Jiawei,Zhao Haibo,et al.Research on multi-stage bidding strategy of virtual power plant considering demand response market[J].Power System Protection and Control,2017,45(19):35-45(in Chinese).
[5]牛文娟,李扬,王蓓蓓.考虑不确定性的需求响应虚拟电厂建模[J].中国电机工程学报,2014,34(22):3630-3637.Niu Wenjuan,Li Yang,Wang Beibei.Demand response based virtual power plant modeling considering uncertainty[J].Proceedings of the CSEE,2014,34(22):3630-3637(in Chinese).
[6]Bai Linquan,Li Fangxing,Jiang Tao,et al.Robust scheduling for wind integrated energy systems considering gas pipeline and power transmission N-1 contingencies[J].IEEE Transactions on Power Systems,2017,32(2):1582-1584.
[7]别朝红,胡国伟,谢海鹏,等.考虑需求响应的含风电电力系统的优化调度[J].电力系统自动化,2014,38(13):115-120.Bie Zhaohong,Hu Guowei,Xie Haipeng,et al.Optimal dispatch for wind power integrated systems considering demand response[J].Automation of Electric Power Systems,2014,38(13):115-120(in Chinese).
[8]Zhang Rufeng,Li Guoqing,Chen Houhe,et al.Atwo-stage MILP formulation for source-load coordinated dispatch with wind power considering peak-valley regulation and ramping requirements[C]//Proceedings of the 2016 IEEE Power and Energy Society General Meeting.Boston,MA,USA:IEEE,2016:1-5.
[9]周玮,彭昱,孙辉,等.含风电场的电力系统动态经济调度[J].中国电机工程学报,2009,29(25):13-18.Zhou Wei,Peng Yu,Sun Hui,et al.Dynamic economic dispatch in wind power integrated system[J].Proceedings of the CSEE,2009,29(25):13-18(in Chinese).
[10]Kraning M,Chu E,Lavaei J,et al.Dynamic network energy management via proximal message passing[J].Foundations and Trends?in Optimization,2014,1(2):73-126.
[11]李佩杰,陆镛,白晓清,等.基于交替方向乘子法的动态经济调度分散式优化[J].中国电机工程学报,2015,35(10):2428-2435.Li Peijie,Lu Yong,Bai Xiaoqing,et al.Decentralized optimization for dynamic economic dispatch based on alternating direction method of multipliers[J].Proceedings of the CSEE,2015,35(10):2428-2435(in Chinese).
[12]冯汉中,刘明波,赵文猛,等.ADMM应用于求解多区域互联电网分布式无功优化问题[J].电力系统及其自动化学报,2017,29(7):20-26.Feng Hanzhong,Liu Mingbo,Zhao Wenmeng,et al.Application of ADMM to distributed reactive-power optimization of multi-area interconnected power grids[J].Proceeding of the CSU-EPSA,2017,29(7):20-26(in Chinese).
[13]赵晶晶,魏炜,王成山.基于电网分区的分布式输电能力计算[J].中国电机工程学报,2008,28(7):1-6.Zhao Jingjing,Wei Wei,Wang Chengshan.Distributed transfer capability computation of electric power system based on decomposed system network[J].Proceedings of the CSEE,2008,28(7):1-6(in Chinese).
[14]Lai Xiaowen,Xie Le,Xia Qing,et al.Decentralized multi-area economic dispatch via dynamic multiplierbased lagrangian relaxation[J].IEEE Transactions on Power Systems,2015,30(6):3225-3233.
[15]齐琛,汪可友,李国杰,等.交直流混合主动配电网的分层分布式优化调度[J].中国电机工程学报,2017,37(7):1909-1917.Qi Chen,Wang Keyou,Li Guojie,et al.Hierarchical and distributed optimal scheduling of AC/DC hybrid active distribution network[J].Proceedings of the CSEE,2017,37(7):1909-1917(in Chinese).
[16]赵晋全,吉跃瑾,戴则梅.一种多分区互联电网分布式无功优化算法[J].电力系统保护与控制,2014,41(23):9-15.Zhao Jinquan,Ji Yuejin,Dai Zemei.A distributed reactive power optimization method for multi-area interconnected power grid[J].Power System Protection and Control,2014,41(23):9-15(in Chinese).
[17]喻洁,李扬,夏安邦.兼顾环境保护与经济效益的发电调度分布式优化策略[J].中国电机工程学报,2009,29(16):63-68.Yu Jie,Li Yang,Xia Anbang.Distributed optimization of generation dispatch schedule considering environmental protection and economic profits[J].Proceedings of the CSEE,2009,29(16):63-68(in Chinese).
[18]蒲天骄,刘威,陈乃仕,等.基于一致性算法的主动配电网分布式优化调度[J].中国电机工程学报,2017,37(6):1579-1589.Pu Tianjiao,Liu Wei,Chen Naishi,et al.Distributed optimal dispatching of active distribution network based on consensus algorithm[J].Proceedings of the CSEE,2017,37(6):1579-1589(in Chinese).
[19]陈刚,杨毅,杨晓梅,等.基于分布式牛顿法的微电网群分布式优化调度方法[J].电力系统自动化,2017,41(21):156-162.Chen Gang,Yang Yi,Yang Xiaomei,et al.Distributed optimization scheduling method for microgrid cluster based on distributed newton method[J].Automation of Electric Power Systems,2017,41(21):156-162(in Chinese).
[20]Boyd S,Parikh N,Chu E,et al.Distributed optimization and statistical learning via the alternating direction method of multipliers[J].Foundations and Trends?in Machine Learning,2010,3(1):1-122.
[21]Erseghe T,Zennaro D,Dall’Anese E,et al.Fast consensus by the alternating direction multipliers method[J].IEEETransactions on Signal Processing,2011,59(11):5523-5537.
[22]Erseghe T.Distributed optimal power flow using ADMM[J].IEEE Transactions on Power Systems,2014,29(5):2370-2380.
[23]Wang Yamin,Wu Lei,Wang Shouxiang.A fullydecentralized consensus-based ADMM approach for DC-OPF with demand response[J].IEEE Transactions on Smart Grid,2017,8(6):2637-2647.
[24]US Department of Energy.Benefits of demand response in electricity markets and recommendations for achieving them[R].Washington DC,USA:US Department of Energy,2006.