风电接入下基于半监督谱聚类的备用动态分区方法
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摘要
随着新能源的不断接入和电网结构的日益复杂,电力系统运行状态复杂多变,基于固定分区的旋转备用计算方法无法有效应对源-网-荷的波动性以及由此引起的线路阻塞问题。为此,首先研究了能量-备用联合优化机组组合问题;其次考虑风电出力的不确定性和线路N-1故障,建立了线路阻塞的风险评估方法;然后以阻塞线路的功率转移分布因子(power transfer distribution factor,PTDF)为相似性测度,提出了基于半监督谱聚类的分区迭代划分方法;最后基于动态分区结果确定备用容量配置。文章分别采用39节点及118节点系统进行算例仿真,结果表明本文所提出的备用动态分区方法可自动确定分区数目,合理进行备用分区,备用动态分区后可有效保证旋转备用可用性。
With the continuous access of new energy and the increasingly complex structure of the power grid,the operating state of the power system is complex and changeable. The spinning reserve calculation method based on fixed zone partitioning is unable to effectively deal with the volatility of the source-network-load and the resulting congestion transmission problem. Firstly a energy and reserve co-optimization unit commitment is studied. Secondly congestion transmissions are evaluated considering wind power uncertainty and N-1 contingency. Then,taking the power transfer distribution factor( PTDF) of congestion line as similarity measure,partition iterative method is proposed based on semisupervised spectral clustering. Finally,reserve capacity configuration is determined based on the result of dynamic zone partitioning. This paper use a 39 bus system and a 118 bus system separately for simulation. The result show s that the proposed reserve dynamic zone partitioning method can automatically determine the number of zone partitioning and is reasonable to reserve partition; the zonal reserve could guarantee the delivery of spinning reserve.
With the continuous access of new energy and the increasingly complex structure of the power grid,the operating state of the power system is complex and changeable. The spinning reserve calculation method based on fixed zone partitioning is unable to effectively deal with the volatility of the source-network-load and the resulting congestion transmission problem. Firstly a energy and reserve co-optimization unit commitment is studied. Secondly congestion transmissions are evaluated considering wind power uncertainty and N-1 contingency. Then,taking the power transfer distribution factor( PTDF) of congestion line as similarity measure,partition iterative method is proposed based on semisupervised spectral clustering. Finally,reserve capacity configuration is determined based on the result of dynamic zone partitioning. This paper use a 39 bus system and a 118 bus system separately for simulation. The result show s that the proposed reserve dynamic zone partitioning method can automatically determine the number of zone partitioning and is reasonable to reserve partition; the zonal reserve could guarantee the delivery of spinning reserve.
引文
[1]曹喜民,刘天琪,赵达维,等.含风电系统旋转备用的多目标鱼群-蜂群优化方法[J].电力自动化设备,2014,34(5):35-39.CAO Ximin,LIU Tianqi,ZHAO Dawei,et al.Multi-objective spinning reserve optimization based on fish colony-bee colony algorithm for system containing wind power[J].Electric Power Automation Equipment,2014,34(5):35-39.
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[14]颜伟,王芳,唐文左,等.基于电源分区与短路阻抗距离的电压无功分区方法[J].电力系统保护与控制,2013,41(7):109-115.YAN Wei,WANG Fang,TANG Wenzuo,et al.Network partitioning for reactive power/voltage control based on power sources clustering and short-circuit impedance distance[J].Power System Protection and Control,2013,41(7):109-115.
[15]杨洪明,刘建华.电力市场环境下电价区域划分问题的尺度空间层次聚类算法[J].电网技术,2004,28(22):53-60.YANG Hongming,LIU Jianhua.A scale space hierarchical clustering method for price zone partitioning in power market environment[J].Power System Technology,2004,28(22):53-60.
[16]梁海平,顾雪平.基于谱聚类的黑启动子系统划分[J].电网技术,2013,37(2):372-377.LIANG Haiping,GU Xueping.Black-start network partitioning based on spectral clustering[J].Power System Technology,2013,37(2):372-377.
[17]高琰,谷士文,唐琎,等.机器学习中谱聚类方法的研究[J].计算机科学,2007,34(2):201-203.GAO Yan,GU Shiwen,TANG Jin,et al.Research on spectral clustering in machine learning[J].Computer Science,2007,34(2):201-203.
[18]赵凤,焦李成,刘汉强,等.半监督谱聚类特征向量选择算法[J].模式识别与人工智能,2011,24(1):48-56.ZHAO Feng,JIAO Licheng,LIU Hanqiang,et al.Semi-supervised eigenvector selection for spectral clustering[J].Pattern Recognition and Artificial Intelligence,2011,24(1):48-56.
[19]VON LUXBURG U.A tutorial on spectral clustering[J].Statistics and Computing,2007,17(4):395-416.
[20]WAGSTAFF K,CARDIE C,ROGERS S,et al.Constrained kmeans clustering with background knowledge[C]//Proceedings of the Eighteenth International Conference on Machine Learning.Morgan Kaufmann Publishers Inc.,2001:577-584.
[2]葛炬,王飞,张粒子.含风电场电力系统旋转备用获取模型[J].电力系统自动化,2010,34(6):32-36.GE Ju,WANG Fei,ZHANG Lizi.Spinning reserve model in the wind power integrated power system[J].Automation of Electric Power Systems,2010,34(6):32-36.
[3]李茜,刘天琪,何川,等.含风电系统的有功和备用协调优化方法[J].电力自动化设备,2016,36(7):7-14.LI Qian,LIU Tianqi,HE Chuan,et al.Coordinated optimization of active power and reserve capacity for power grid with wind farm[J].Electric Power Automation Equipment,2016,36(7):7-14.
[4]张国强,吴文传,张伯明.考虑风电接入的有功运行备用协调优化[J].电力系统自动化,2011,35(12):15-19,46.ZHANG Guoqiang,WU Wenchuan,ZHANG Boming.Optimization of operation reserve coordination considering wind power integration[J].Automation of Electric Power Systems,2011,35(12):15-19,46.
[5]元博,周明,李庚银,等.基于可靠性指标的含风电电力系统的发电和运行备用的协调调度模型[J].电网技术,2013,37(3):800-807.YUAN Bo,ZHOU Ming,LI Gengyin,et al.A coordinated dispatching model considering generation and operating reserve for wind power integrated power system based on ELNSR[J].Power System Technology,2013,37(3):800-807.
[6]雷雪姣,潘士娟,管晓宏,等.考虑传输安全裕度的电力系统发电经济调度[J].中国电机工程学报,2014,34(31):5651-5658.LEI Xuejiao,PAN Shijuan,GUAN Xiaohong,et al.Transmission safety margin constrained unit commitment in power systems[J].Proceedings of the CSEE,2014,34(31):5651-5658.
[7]王建学,王锡凡,丁晓莺.区域电力市场中的分区备用模型[J].中国电机工程学报,2006,26(18):28-33.WANG Jianxue,WANG Xifan,DING Xiaoying.Study on zonal reserve model in power market[J].Proceedings of the CSEE,2006,26(18):28-33.
[8]杨建林,严正,冯冬涵.考虑分区备用的主能量—备用联合市场均衡模型[J].电力系统自动化,2009,33(11):13-17.YANG Jianlin,YAN Zheng,FENG Donghan.Equilibrium model for integrated energy-reserve electricity market with regional reserve constraints[J].Automation of Electric Power Systems,2009,33(11):13-17.
[9]AHMADI-KHATIR A,BOZORG M,CHERKAOUI R.Probabilistic spinning reserve provision model in multi-control zone power system[J].IEEE Transactions on Power Systems,2013,28(3):2819-2829.
[10]WANG F,HEDMAN K W.Dynamic reserve zones for day-ahead unit commitment with renewable resources[J].IEEE Transactions on Power Systems,2015,30(2):612-620.
[11]ABEDI S,HE M,GIESSELMANN M.Graph partitioning-based zonal reserve allocation for congestion management in power systems with wind resources[C]//2016 North American Power Symposium(NAPS).IEEE,2016:1-6.
[12]李禹鹏,冯楠,崔勇,等.考虑风电不确定性及柔性负荷的安全约束机组组合问题研究[J].电力建设,2017,38(2):129-137.LI Yupeng,FENG Nan,CUI Yong,et al.Security constrained unit commitment problem considering wind power uncertainty and flexible load[J].Electric Power Construction,2017,38(2):129-137.
[13]邵雅宁,唐飞,王波,等.具有多目标量化评估特性的无功电压双阶段分区方法[J].中国电机工程学报,2014,34(22):3768-3776.SHAO Yaning,TANG Fei,WANG Bo,et al.A dual-stage partitioning method for reactive power and voltage with multi-objective quantitative assessment[J].Proceedings of the CSEE,2014,34(22):3768-3776.
[14]颜伟,王芳,唐文左,等.基于电源分区与短路阻抗距离的电压无功分区方法[J].电力系统保护与控制,2013,41(7):109-115.YAN Wei,WANG Fang,TANG Wenzuo,et al.Network partitioning for reactive power/voltage control based on power sources clustering and short-circuit impedance distance[J].Power System Protection and Control,2013,41(7):109-115.
[15]杨洪明,刘建华.电力市场环境下电价区域划分问题的尺度空间层次聚类算法[J].电网技术,2004,28(22):53-60.YANG Hongming,LIU Jianhua.A scale space hierarchical clustering method for price zone partitioning in power market environment[J].Power System Technology,2004,28(22):53-60.
[16]梁海平,顾雪平.基于谱聚类的黑启动子系统划分[J].电网技术,2013,37(2):372-377.LIANG Haiping,GU Xueping.Black-start network partitioning based on spectral clustering[J].Power System Technology,2013,37(2):372-377.
[17]高琰,谷士文,唐琎,等.机器学习中谱聚类方法的研究[J].计算机科学,2007,34(2):201-203.GAO Yan,GU Shiwen,TANG Jin,et al.Research on spectral clustering in machine learning[J].Computer Science,2007,34(2):201-203.
[18]赵凤,焦李成,刘汉强,等.半监督谱聚类特征向量选择算法[J].模式识别与人工智能,2011,24(1):48-56.ZHAO Feng,JIAO Licheng,LIU Hanqiang,et al.Semi-supervised eigenvector selection for spectral clustering[J].Pattern Recognition and Artificial Intelligence,2011,24(1):48-56.
[19]VON LUXBURG U.A tutorial on spectral clustering[J].Statistics and Computing,2007,17(4):395-416.
[20]WAGSTAFF K,CARDIE C,ROGERS S,et al.Constrained kmeans clustering with background knowledge[C]//Proceedings of the Eighteenth International Conference on Machine Learning.Morgan Kaufmann Publishers Inc.,2001:577-584.