主动配电网多目标PMU最优配置
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摘要
随着大规模的分布式电源(distributed generation,DG)接入及电网与用户互动增加,主动配电网状态估计结果与量测配置需要考虑更多不确定性因素。为加强对配电网的实时监测与控制,提高配电网运行态势感知能力,需要发展同步相量测量装置(phasor measurement unit,PMU)。在计及DG与负荷不确定性的基础上,建立了考虑经济性、配电网状态估计精度以及节点电压越限概率的多目标PMU最优配置模型。以拟蒙特卡洛方法模拟DG与负荷的不确定性;基于两步式加权最小二乘方法,构建混合量测配电网状态估计模型,并利用改进的自适应多目标二进制差分进化算法进行求解,从而得到特定状态估计误差精度下的PMU最优配置Pareto非劣解集。通过IEEE 33节点配电网系统进行仿真计算分析,验证了所提模型与算法的可行性与有效性。
Due to integration of distributed generations(DGs) and interaction between grids and consumers, more uncertain factors are needed to be considered in state estimation and meter deployment of active distribution network(ADN). To improve controllability and situation awareness ability, phasor measurement units(PMUs) should be developed in ADN. In this paper, a multi-objective model of PMUs placement is constructed considering cost, accuracy of state estimation and the probability voltage magnitude exceeds limit. Quasi-Monte-Carlo is used to simulate uncertainty of DGs and loads, and two-step weighted least squared method is used to solve the state estimation. Extended adaptive multi-objective binary differential evolution is adopted to obtain Pareto set of PMUs deployment. The proposed model and method is tested on IEEE 33-bus distribution network, and the results indicate their feasibility and effectiveness.
Due to integration of distributed generations(DGs) and interaction between grids and consumers, more uncertain factors are needed to be considered in state estimation and meter deployment of active distribution network(ADN). To improve controllability and situation awareness ability, phasor measurement units(PMUs) should be developed in ADN. In this paper, a multi-objective model of PMUs placement is constructed considering cost, accuracy of state estimation and the probability voltage magnitude exceeds limit. Quasi-Monte-Carlo is used to simulate uncertainty of DGs and loads, and two-step weighted least squared method is used to solve the state estimation. Extended adaptive multi-objective binary differential evolution is adopted to obtain Pareto set of PMUs deployment. The proposed model and method is tested on IEEE 33-bus distribution network, and the results indicate their feasibility and effectiveness.
引文
[1]周孝信,鲁宗相,刘应梅,等.中国未来电网的发展模式和关键技术[J].中国电机工程学报,2014,34(29):4999-5008.Zhou Xiaoxin,Lu Zongxiang,Liu Yingmei,et al.Development models and key technologies of future grid in China[J].Proceedings of the CSEE,2014,34(29):4999-5008(in Chinese).
[2]徐航,鞠力,董树锋,等.提高配电网状态估计精度的智能配电单元优化布点方法[J].电网技术,2018,42(4):1210-1216Xu Hang,Ju Li,Dong Shufeng,et al.Optimization of intelligent distribution unit placement to improve accuracy of distribution network state estimation[J].Power System Technology,2018,42(4):1210-1216(in Chinese)
[3]王晓辉,陈乃仕,李烨,等.基于态势联动的主动配电网多源优化调度框架[J].电网技术,2017,41(2):349-354.Wang Xiaohui,Chen Naishi,Li Ye,et al.Multi-source optimal dispatch architecture for active distribution network based on situational linkage[J].Power System Technology,2017,41(2):349-354(in Chinese).
[4]王红,张文,刘玉田.考虑分布式电源出力不确定性的主动配电网量测配置[J].电力系统自动化,2016,40(12):9-14.Wang Hong,Zhang Wen,Liu Yutian.Measurement placement in active distribution networks considering output uncertainty of distributed generators[J].Automation of Electric Power Systems,2016,40(12):9-14(in Chinese).
[5]辛开远,高赐威,杨玉华.配电网状态估计中的量测变换技术[J].电网技术,2002,26(9):67-70.Xin Kaiyuan,Gao Ciwei,Yang Yuhua.A discussion on measurement transformation technology in state estimation for distribution network[J].Power System Technology,2002,26(9):67-70(in Chinese).
[6]李滨,杜孟远,祝云,等.基于准实时数据的智能配电网状态估计[J].电工技术学报,2016,31(1):34-44.Li Bin,Du Mengyuan,Zhu Yun,et al.A state estimator for smart distribution networks with quasi-real time data[J].Transactions of China Electrotechnical Society,2016,31(1):34-44(in Chinese).
[7]Chilard O,Grenard S,Devaux O,et al.Distribution state estimation based on voltage state variables:Assessment of results and limitations[C]//International Conference and Exhibition on Electricity Distribution.IET,Prague,Czech Republic,2009:1-4.
[8]Zhou M,Centeno V A,Thorp J S,et al.An alternative for including phasor measurements in state estimators[J].IEEE Transactions on Power Systems,2006,21(4):1930-1937.
[9]Singh R,Pal B C,Vinter R B.Measurement placement in distribution system state estimation[J].IEEE Transactions on Power Systems,2009,24(2):668-675.
[10]徐俊俊,戴桂木,吴在军,等.计及电动汽车和光伏不确定性的主动配电网量测优化配置[J].电力系统自动化,2017,41(1):57-64.Xu Junjun,Dai Guimu,Wu Zaijun,et al.Optimal meter placement for active distribution network considering uncertainties of plug-in electric vehicles and photovoltaic systems[J].Automation of Electric Power Systems,2017,41(1):57-64(in Chinese)
[11]Liu J,Tang J,Ponci F,et al.Trade-offs in PMU deployment for state estimation in active distribution grids[J].IEEE Transactions on Smart Grid,2012,3(2):915-924.
[12]王克英,穆钢,陈学允,等.计及PMU的状态估计精度分析及配置研究[J].中国电机工程学报,2001,21(8):29-33.Wang Keying,Mu Gang,Chen Xueyun,et al.Precision improvement and PMU placement studies on state estimation of a hybrid measurement systems with PMUs[J].Proceedings of the CSEE,2001,21(8):29-33(in Chinese).
[13]王少芳,刘广一,黄仁乐,等.多采样周期混合量测环境下的主动配电网状态估计方法[J].电力系统自动化,2016,40(19):30-36.Wang Shaofang,Liu Guangyi,Huang Renle,et al.State estimation method for active distribution networks under environment of hybrid measurements with multiple sampling periods[J].Automation of Electric Power Systems,2016,40(19):30-36(in Chinese).
[14]Singh R,Pal B C,Jabr R A,et al.Meter placement for distribution system state estimation:an ordinal optimization approach[J].IEEETransactions on Power Systems,2012,26(4):2328-2335.
[15]Zhang C,Jia Y,Xu Z,et al.Optimal PMU placement considering state estimation uncertainty and voltage controllability[J].IETGeneration Transmission&Distribution,2018,11(18):4465-4475.
[16]丁军策,蔡泽祥,王克英.基于广域测量系统的混合量测状态估计算法[J].中国电机工程学报,2006,26(2):58-63.Ding Junce,Cai Zexiang,Wang Keying.Mixed measurements state estimation based on WAMS[J].Proceedings of the CSEE,2006,26(2):58-63(in Chinese).
[17]Karaki S H,Chedid R B,Ramadan R.Probabilistic performance assessment of autonomous solar-wind energy conversion systems[J].IEEE Transactions on Energy Conversion,1999,14(3):766-772.
[18]Abouzahr I,Ramakumar R.An approach to assess the performance of utility-interactive wind electric conversion systems[J].IEEETransactions on Energy Conversion,1991,6(4):627-638.
[19]曹佳,严正,李建华,等.含风电场交直流混联系统的概率潮流计算[J].电力自动化设备,2016,36(11):94-101.Cao Jia,Yan Zheng,Li Jianhua,et al.Probabilistic power flow calculation for AC/DC hybrid system with wind farms[J].Electric Power Automation Equipment,2016,36(11):94-101(in Chinese).
[20]赵建伟,李禹鹏,杨增辉,等.基于拟蒙特卡罗模拟和核密度估计的概率静态电压稳定计算方法[J].电网技术,2016,40(12):3833-3839.Zhao Jianwei,Li Yupeng,Yang Zenghui,et al.Probabilistic static voltage stability calculation based on Quasi-Monte Carlo and kernel density estimation[J].Power System Technology,2016,40(12):3833-3839(in Chinese).
[21]Goswami R,Bhattacharyya D K,Dutta M.Multiobjective differential evolution algorithm using binary encoded data in selecting views for materializing in data warehouse[C]//International Conference on Swarm,Evolutionary,and Memetic Computing.Springer,Cham,2013:95-106.
[22]陈亮.改进自适应差分进化算法及其应用研究[D].上海:东华大学,2012.
[23]王砚帛,谭永东,文菓,等.基于Fisher判别和元胞自动机模型的微电网格局计算方法[J].电网技术,2012,36(11):12-17.Wang Yanbo,Tan Yongdong,Wen Guo,et al.An approach to calculate micro-grid’s configuration based on fisher discriminant and cellular automata model[J].Power System Technology,2012,36(11):12-17(in Chinese).
[24]Beumea N,Emmerich M.SMS-EMOA:multiobjective selection based on dominated hypervolume[J].European Journal of Operational Research,2007,181(3):1653-1669.
[25]Awad N H,Ali M Z,Duwairi R M.Multi-objective differential evolution based on normalization and improved mutation strategy[J].Natural Computing,2016,16(4):1-15.
[26]蔡伟,杨洪,熊飞,等.考虑电力通信网可靠性的业务路由优化分配方法[J].电网技术,2013,37(12):3541-3545.Cai Wei,Yang Hong,Xiong Fei,et al.An optimized service routing allocation method for electric power communication network considering reliability[J].Power System Technology,2013,37(12):3541-3545(in Chinese).
[27]李枝勇,马良,张惠珍.蝙蝠算法在多目标多选择背包问题中的应用[J].计算机仿真,2013,30(10):350-353.Li Zhiyong,Ma Liang,Zhang Huizhen.Application of bat algorithm in multi-objective and multi-choice knapsack problem[J].Computer Simulation,2013,30(10):350-353(in Chinese).
[2]徐航,鞠力,董树锋,等.提高配电网状态估计精度的智能配电单元优化布点方法[J].电网技术,2018,42(4):1210-1216Xu Hang,Ju Li,Dong Shufeng,et al.Optimization of intelligent distribution unit placement to improve accuracy of distribution network state estimation[J].Power System Technology,2018,42(4):1210-1216(in Chinese)
[3]王晓辉,陈乃仕,李烨,等.基于态势联动的主动配电网多源优化调度框架[J].电网技术,2017,41(2):349-354.Wang Xiaohui,Chen Naishi,Li Ye,et al.Multi-source optimal dispatch architecture for active distribution network based on situational linkage[J].Power System Technology,2017,41(2):349-354(in Chinese).
[4]王红,张文,刘玉田.考虑分布式电源出力不确定性的主动配电网量测配置[J].电力系统自动化,2016,40(12):9-14.Wang Hong,Zhang Wen,Liu Yutian.Measurement placement in active distribution networks considering output uncertainty of distributed generators[J].Automation of Electric Power Systems,2016,40(12):9-14(in Chinese).
[5]辛开远,高赐威,杨玉华.配电网状态估计中的量测变换技术[J].电网技术,2002,26(9):67-70.Xin Kaiyuan,Gao Ciwei,Yang Yuhua.A discussion on measurement transformation technology in state estimation for distribution network[J].Power System Technology,2002,26(9):67-70(in Chinese).
[6]李滨,杜孟远,祝云,等.基于准实时数据的智能配电网状态估计[J].电工技术学报,2016,31(1):34-44.Li Bin,Du Mengyuan,Zhu Yun,et al.A state estimator for smart distribution networks with quasi-real time data[J].Transactions of China Electrotechnical Society,2016,31(1):34-44(in Chinese).
[7]Chilard O,Grenard S,Devaux O,et al.Distribution state estimation based on voltage state variables:Assessment of results and limitations[C]//International Conference and Exhibition on Electricity Distribution.IET,Prague,Czech Republic,2009:1-4.
[8]Zhou M,Centeno V A,Thorp J S,et al.An alternative for including phasor measurements in state estimators[J].IEEE Transactions on Power Systems,2006,21(4):1930-1937.
[9]Singh R,Pal B C,Vinter R B.Measurement placement in distribution system state estimation[J].IEEE Transactions on Power Systems,2009,24(2):668-675.
[10]徐俊俊,戴桂木,吴在军,等.计及电动汽车和光伏不确定性的主动配电网量测优化配置[J].电力系统自动化,2017,41(1):57-64.Xu Junjun,Dai Guimu,Wu Zaijun,et al.Optimal meter placement for active distribution network considering uncertainties of plug-in electric vehicles and photovoltaic systems[J].Automation of Electric Power Systems,2017,41(1):57-64(in Chinese)
[11]Liu J,Tang J,Ponci F,et al.Trade-offs in PMU deployment for state estimation in active distribution grids[J].IEEE Transactions on Smart Grid,2012,3(2):915-924.
[12]王克英,穆钢,陈学允,等.计及PMU的状态估计精度分析及配置研究[J].中国电机工程学报,2001,21(8):29-33.Wang Keying,Mu Gang,Chen Xueyun,et al.Precision improvement and PMU placement studies on state estimation of a hybrid measurement systems with PMUs[J].Proceedings of the CSEE,2001,21(8):29-33(in Chinese).
[13]王少芳,刘广一,黄仁乐,等.多采样周期混合量测环境下的主动配电网状态估计方法[J].电力系统自动化,2016,40(19):30-36.Wang Shaofang,Liu Guangyi,Huang Renle,et al.State estimation method for active distribution networks under environment of hybrid measurements with multiple sampling periods[J].Automation of Electric Power Systems,2016,40(19):30-36(in Chinese).
[14]Singh R,Pal B C,Jabr R A,et al.Meter placement for distribution system state estimation:an ordinal optimization approach[J].IEEETransactions on Power Systems,2012,26(4):2328-2335.
[15]Zhang C,Jia Y,Xu Z,et al.Optimal PMU placement considering state estimation uncertainty and voltage controllability[J].IETGeneration Transmission&Distribution,2018,11(18):4465-4475.
[16]丁军策,蔡泽祥,王克英.基于广域测量系统的混合量测状态估计算法[J].中国电机工程学报,2006,26(2):58-63.Ding Junce,Cai Zexiang,Wang Keying.Mixed measurements state estimation based on WAMS[J].Proceedings of the CSEE,2006,26(2):58-63(in Chinese).
[17]Karaki S H,Chedid R B,Ramadan R.Probabilistic performance assessment of autonomous solar-wind energy conversion systems[J].IEEE Transactions on Energy Conversion,1999,14(3):766-772.
[18]Abouzahr I,Ramakumar R.An approach to assess the performance of utility-interactive wind electric conversion systems[J].IEEETransactions on Energy Conversion,1991,6(4):627-638.
[19]曹佳,严正,李建华,等.含风电场交直流混联系统的概率潮流计算[J].电力自动化设备,2016,36(11):94-101.Cao Jia,Yan Zheng,Li Jianhua,et al.Probabilistic power flow calculation for AC/DC hybrid system with wind farms[J].Electric Power Automation Equipment,2016,36(11):94-101(in Chinese).
[20]赵建伟,李禹鹏,杨增辉,等.基于拟蒙特卡罗模拟和核密度估计的概率静态电压稳定计算方法[J].电网技术,2016,40(12):3833-3839.Zhao Jianwei,Li Yupeng,Yang Zenghui,et al.Probabilistic static voltage stability calculation based on Quasi-Monte Carlo and kernel density estimation[J].Power System Technology,2016,40(12):3833-3839(in Chinese).
[21]Goswami R,Bhattacharyya D K,Dutta M.Multiobjective differential evolution algorithm using binary encoded data in selecting views for materializing in data warehouse[C]//International Conference on Swarm,Evolutionary,and Memetic Computing.Springer,Cham,2013:95-106.
[22]陈亮.改进自适应差分进化算法及其应用研究[D].上海:东华大学,2012.
[23]王砚帛,谭永东,文菓,等.基于Fisher判别和元胞自动机模型的微电网格局计算方法[J].电网技术,2012,36(11):12-17.Wang Yanbo,Tan Yongdong,Wen Guo,et al.An approach to calculate micro-grid’s configuration based on fisher discriminant and cellular automata model[J].Power System Technology,2012,36(11):12-17(in Chinese).
[24]Beumea N,Emmerich M.SMS-EMOA:multiobjective selection based on dominated hypervolume[J].European Journal of Operational Research,2007,181(3):1653-1669.
[25]Awad N H,Ali M Z,Duwairi R M.Multi-objective differential evolution based on normalization and improved mutation strategy[J].Natural Computing,2016,16(4):1-15.
[26]蔡伟,杨洪,熊飞,等.考虑电力通信网可靠性的业务路由优化分配方法[J].电网技术,2013,37(12):3541-3545.Cai Wei,Yang Hong,Xiong Fei,et al.An optimized service routing allocation method for electric power communication network considering reliability[J].Power System Technology,2013,37(12):3541-3545(in Chinese).
[27]李枝勇,马良,张惠珍.蝙蝠算法在多目标多选择背包问题中的应用[J].计算机仿真,2013,30(10):350-353.Li Zhiyong,Ma Liang,Zhang Huizhen.Application of bat algorithm in multi-objective and multi-choice knapsack problem[J].Computer Simulation,2013,30(10):350-353(in Chinese).