计及灵活性的检修—运行协同优化模型及算法
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摘要
为提升风电并网条件下电力系统的运行灵活性,提出一种结合场景分析和协同优化的检修—运行决策方法。采用K-medoids聚类方法获得风电出力典型场景集,同时根据典型性评估指标确定目标场景集规模。以经济性和灵活性为目标,建立融合多场景经济调度的"检修—运行"分层协同优化模型。为提高模型求解效率,引入对潮流安全违约"轻容忍"的改进Benders分解法,降低运行优化主问题的约束矩阵维数。通过拉格朗日松弛技术及乘子更新机制,实现检修和运行的协同寻优。算例测试验证了所述模型及算法能有效实现经济性及灵活性更优的检修—运行决策。
To improve the operation flexibility of power system with integrated wind power,a method of maintenance and operation decision based on scenario analysis and collaborative optimization is proposed.The typical scenario set of wind power output is obtained via K-medoids clustering method,and the target scenario set size is determined according to the typicality evaluation indices.With economy and flexibility as the goal,a maintenance-operation bilevel collaborative optimization model combined with multi-scenario economic dispatch is formulated.To improve the solving efficiency,an improved Benders decomposition method with light tolerance of power flow limit violation is introduced to reduce the dimension of the constraint matrix in the main problem of operation optimization.Lagrangian relaxation technique and multiplier update mechanism help to coordinate the maintenance schedule and operation optimization.The case study shows that the proposed method can effectively optimize the economy and flexibility of maintenance and operation decision.
To improve the operation flexibility of power system with integrated wind power,a method of maintenance and operation decision based on scenario analysis and collaborative optimization is proposed.The typical scenario set of wind power output is obtained via K-medoids clustering method,and the target scenario set size is determined according to the typicality evaluation indices.With economy and flexibility as the goal,a maintenance-operation bilevel collaborative optimization model combined with multi-scenario economic dispatch is formulated.To improve the solving efficiency,an improved Benders decomposition method with light tolerance of power flow limit violation is introduced to reduce the dimension of the constraint matrix in the main problem of operation optimization.Lagrangian relaxation technique and multiplier update mechanism help to coordinate the maintenance schedule and operation optimization.The case study shows that the proposed method can effectively optimize the economy and flexibility of maintenance and operation decision.
引文
[1]YU Y,PETER B L,EUGENE L,et al.Transmission contingency-constrained unit commitment with high penetration of renewables via interval optimization[J].IEEE Transactions on Power Systems,2017,32(2):1410-1421.
[2]JI Guoqiang,WU Wenchuan,ZHANG Boming.Robust generation maintenance scheduling considering wind power and forced outages[J].IET Renewable Power Generation,2016,10(5):634-641.
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[9]汪洋,钟海旺,夏清,等.机组检修与运行组合的协同优化[J].电力系统自动化,2014,38(21):26-31.DOI:10.7500/AEPS20140403002.WANG Yang,ZHONG Haiwang,XIA Qing,et al.Collaborative optimization of unit maintenance and securityconstrained unit commitment[J].Automation of Electric Power Systems,2014,38(21):26-31.DOI:10.7500/AEPS20140403002.
[10]李本新,韩学山.发输电检修与机组组合联合决策的Benders分解方法[J].电工技术学报,2015,30(3):224-231.LI Benxin,HAN Xueshan.Benders decomposition algorithm to coordination of generation and transmission maintenance scheduling with unit commitment[J].Transactions of China Electrotechnical Society,2015,30(3):224-231.
[11]WANG Yifei,LI Zhiyi,SHAHIDEHPOUR M,et al.Stochastic co-optimization of midterm and short-term maintenance outage scheduling considering covariates in power system[J].IEEE Transactions on Power Systems,2016,31(6):4795-4805.
[12]WU Lei,SHAHIDEHPOUR M,FU Yong.Securityconstrained generation and transmission outage scheduling with uncertainties[J].IEEE Transactions on Power Systems,2010,25(3):1674-1685.
[13]QIN Zhijun,HOU Yunhe,LEI Shunbo,et al.Quantification of intra-hour security-constrained flexibility region[J].IEEETransactions on Sustainable Energy,2017,8(2):671-684.
[14]王彩霞,鲁宗相.风电功率预测信息在日前机组组合中的应用[J].电力系统自动化,2011,35(7):13-17.WANG Caixia,LU Zongxiang.Unit commitment based on wind power forecast[J].Automation of Electric Power Systems,2011,35(7):13-17.
[15]陈雁,文劲宇,程时杰.考虑输入变量相关性的概率潮流计算方法[J].中国电机工程学报,2011,31(22):80-87.CHEN Yan,WEN Jinyu,CHENG Shijie.Probabilistic load flow analysis considering dependencies among input random variables[J].Proceedings of the CSEE,2011,31(22):80-87.
[16]仉梦林,胡志坚,王小飞,等.基于动态场景集和需求响应的二阶段随机规划调度模型[J].电力系统自动化,2017,41(11):68-76.DOI:10.7500/AEPS20160908008.ZHANG Menglin,HU Zhijian,WANG Xiaofei,et al.Twostage stochastic programming scheduling model based on dynamic scenario sets and demand response[J].Automation of Electric Power Systems,2017,41(11):68-76.DOI:10.7500/AEPS20160908008.
[17]毛典辉.基于MapReduce的Canopy-Kmeans改进算法[J].计算机工程与应用,2012,48(27):22-26.MAO Dianhui.Improved Canopy-Kmeans algorithm based on MapReduce[J].Computer Engineering and Applications,2012,48(27):22-26.
[18]覃岭,林济铿,戴赛,等.基于改进轻鲁棒优化模型的风、火机组组合[J].中国电机工程学报,2016,36(15):4108-4118.QIN Ling,LIN Jikeng,DAI Sai,et al.Improved light robust optimization model based wind-thermal unit commitment[J].Proceedings of the CSEE,2016,36(15):4108-4118.
[19]ZHAO X,LUH P B,WANG J.Surrogate gradient algorithm for Lagrangian relaxation[J].Journal of Optimization Theory&Applications,1999,100(3):699-712.
[2]JI Guoqiang,WU Wenchuan,ZHANG Boming.Robust generation maintenance scheduling considering wind power and forced outages[J].IET Renewable Power Generation,2016,10(5):634-641.
[3]周明,夏澍,李琰,等.含风电的电力系统月度机组组合和检修计划联合优化调度[J].中国电机工程学报,2015,35(7):1586-1595.ZHOU Ming,XIA Shu,LI Yan,et al.A joint optimization approach on monthly unit commitment and maintenance scheduling for wind power integrated power systems[J].Proceedings of the CSEE,2015,35(7):1586-1595.
[4]MA Xiyuan,SUN Yuanzhang,FANG Hualiang,et al.Scenario-based multiobjective decision-making of optimal access point for wind power transmission corridor in the load centers[J].IEEE Transactions on Sustainable Energy,2013,4(1):229-239.
[5]WANG Jianhui,SHAHIDEPOUR M,LI Zuyi,et al.Security constrained unit commitment with volatile wind power generation[J].IEEE Transactions on Power Systems,2008,23(3):1319-1327.
[6]李海波,鲁宗相,乔颖,等.大规模风电并网的电力系统运行灵活性评估[J].电网技术,2015,39(6):1672-1678.LI Haibo,LU Zongxiang,QIAO Ying,et al.Assessment on operational flexibility of power grid with grid-connected largescale wind farms[J].Power System Technology,2015,39(6):1672-1678.
[7]MUHAMAD RAZALI N M,HASHIM A H.Backward reduction application for minimizing wind power scenarios in stochastic programming[C]//IEEE 4th International Power Engineering and Optimization Conference,August 30,2010,Shah Alam,Malaysia.
[8]王群,董文略,杨莉.基于Wasserstein距离和改进K-mediods聚类的风电/光伏经典场景集生成算法[J].中国电机工程学报,2015,35(11):2654-2661.WANG Qun,DONG Wenlüe,YANG Li.A wind power/photovoltaic typical scenario set generation algorithm based on Wasserstein distance metric and revised K-mediods cluster[J].Proceedings of the CSEE,2015,35(11):2654-2661.
[9]汪洋,钟海旺,夏清,等.机组检修与运行组合的协同优化[J].电力系统自动化,2014,38(21):26-31.DOI:10.7500/AEPS20140403002.WANG Yang,ZHONG Haiwang,XIA Qing,et al.Collaborative optimization of unit maintenance and securityconstrained unit commitment[J].Automation of Electric Power Systems,2014,38(21):26-31.DOI:10.7500/AEPS20140403002.
[10]李本新,韩学山.发输电检修与机组组合联合决策的Benders分解方法[J].电工技术学报,2015,30(3):224-231.LI Benxin,HAN Xueshan.Benders decomposition algorithm to coordination of generation and transmission maintenance scheduling with unit commitment[J].Transactions of China Electrotechnical Society,2015,30(3):224-231.
[11]WANG Yifei,LI Zhiyi,SHAHIDEHPOUR M,et al.Stochastic co-optimization of midterm and short-term maintenance outage scheduling considering covariates in power system[J].IEEE Transactions on Power Systems,2016,31(6):4795-4805.
[12]WU Lei,SHAHIDEHPOUR M,FU Yong.Securityconstrained generation and transmission outage scheduling with uncertainties[J].IEEE Transactions on Power Systems,2010,25(3):1674-1685.
[13]QIN Zhijun,HOU Yunhe,LEI Shunbo,et al.Quantification of intra-hour security-constrained flexibility region[J].IEEETransactions on Sustainable Energy,2017,8(2):671-684.
[14]王彩霞,鲁宗相.风电功率预测信息在日前机组组合中的应用[J].电力系统自动化,2011,35(7):13-17.WANG Caixia,LU Zongxiang.Unit commitment based on wind power forecast[J].Automation of Electric Power Systems,2011,35(7):13-17.
[15]陈雁,文劲宇,程时杰.考虑输入变量相关性的概率潮流计算方法[J].中国电机工程学报,2011,31(22):80-87.CHEN Yan,WEN Jinyu,CHENG Shijie.Probabilistic load flow analysis considering dependencies among input random variables[J].Proceedings of the CSEE,2011,31(22):80-87.
[16]仉梦林,胡志坚,王小飞,等.基于动态场景集和需求响应的二阶段随机规划调度模型[J].电力系统自动化,2017,41(11):68-76.DOI:10.7500/AEPS20160908008.ZHANG Menglin,HU Zhijian,WANG Xiaofei,et al.Twostage stochastic programming scheduling model based on dynamic scenario sets and demand response[J].Automation of Electric Power Systems,2017,41(11):68-76.DOI:10.7500/AEPS20160908008.
[17]毛典辉.基于MapReduce的Canopy-Kmeans改进算法[J].计算机工程与应用,2012,48(27):22-26.MAO Dianhui.Improved Canopy-Kmeans algorithm based on MapReduce[J].Computer Engineering and Applications,2012,48(27):22-26.
[18]覃岭,林济铿,戴赛,等.基于改进轻鲁棒优化模型的风、火机组组合[J].中国电机工程学报,2016,36(15):4108-4118.QIN Ling,LIN Jikeng,DAI Sai,et al.Improved light robust optimization model based wind-thermal unit commitment[J].Proceedings of the CSEE,2016,36(15):4108-4118.
[19]ZHAO X,LUH P B,WANG J.Surrogate gradient algorithm for Lagrangian relaxation[J].Journal of Optimization Theory&Applications,1999,100(3):699-712.