考虑风光出力波动性的配电网无功优化策略
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摘要
针对风机及光伏出力的波动性对系统网损和电压的影响,本文以全天有功网损和电压偏移最小为目标,建立兼顾电压安全稳定和配电网经济运行的多目标无功优化模型。利用三点估计法原理计算风光出力和负荷值,通过概率潮流判断状态变量是否满足概率约束,利用权重法将多目标转化为单目标,同时结合粒子群优化算法对所建模型进行求解,最后利用改进的IEEE33节点系统进行仿真计算。仿真结果表明,本文所建模型对配电网进行无功优化,有效降低了网损,改善了节点电压质量。该研究为含高比例可再生能源配电网的无功优化提供了有效方法。
The power loss and voltage quality of the system can be affected by the fluctuant output of wind turbines and photovoltaics.In this paper,a multi-objective reactive power optimization model considering both voltage stability and economic operation of distribution network is established with the objective of minimizing all-day active power loss and voltage excursion.First,the output of wind turbines,photovoltaics and load value are calculated by the three-point estimation method,and then state variables are judged whether or not to satisfy probabilistic constraints by probabilistic power flow.After that,the multi-objective is transformed into the single-objective by Weight Method,and the model is solved by combining the particle swarm optimization algorithm.In the end,the simulation of the improved IEEE 33-bus system shows that the proposed method can effectively reduce the power loss and improve the voltage quality.An effective reactive power optimization method can be provided for distribution networks with a high percentage of renewable energy by this study.
The power loss and voltage quality of the system can be affected by the fluctuant output of wind turbines and photovoltaics.In this paper,a multi-objective reactive power optimization model considering both voltage stability and economic operation of distribution network is established with the objective of minimizing all-day active power loss and voltage excursion.First,the output of wind turbines,photovoltaics and load value are calculated by the three-point estimation method,and then state variables are judged whether or not to satisfy probabilistic constraints by probabilistic power flow.After that,the multi-objective is transformed into the single-objective by Weight Method,and the model is solved by combining the particle swarm optimization algorithm.In the end,the simulation of the improved IEEE 33-bus system shows that the proposed method can effectively reduce the power loss and improve the voltage quality.An effective reactive power optimization method can be provided for distribution networks with a high percentage of renewable energy by this study.
引文
[1]国家发改委能源研究所.中国2050年高比例可再生能源发展情景暨途径研究[R].北京:国家能源局,2015.
[2]吴丽珍,蒋力波,郝晓弘.基于最优场景生成算法的主动配电网无功优化[J].电力系统保护与控制,2017,45(15):152-159.
[3]陈继明,祁丽志,孙名妤,等.多场景下含风电机组的配电网无功优化的研究[J].电力系统保护与控制,2016,44(9):129-134.
[4]尹青,杨洪耕,马晓阳.考虑多重不确定参数的配电网概率无功优化[J].电力系统保护与控制,2017,45(7):141-147.
[5]骆晨,陶顺,肖湘宁,等.主动配电网的多阶段鲁棒综合电压控制[J].现代电力,2016,33(4):44-51.
[6]黄伟,刘斯亮,王武,等.长时间尺度下计及光伏不确定性的配电网无功优化调度[J].电力系统自动化,2018,42(5):154-162.
[7]张世达,孙永辉,赵景涛,等.基于随机响应面法的主动配电网无功优化[J].电力系统自动化,2017,41(13):30-38.
[8]刘宇,高山,杨胜春,等.电力系统概率潮流算法综述[J].电力系统自动化,2014,38(23):127-135.
[9]丁明,王京景,李生虎.基于扩展拉丁超立方采样的电力系统概率潮流计算[J].中国电机工程学报,2013,33(4):163-170.
[10]Saunders C S.Point estimate method addressing correlated wind power for probabilistic optimal power flow[J].IEEE Transactions on Power Systems,2014,29(3):1045-1054.
[11]朱星阳,刘文霞,张建华.考虑大规模风电并网的电力系统随机潮流[J].中国电机工程学报,2013,33(7):77-85.
[12]张立波,程浩忠,曾平良,等.基于Nataf逆变换的概率潮流三点估计法[J].电工技术学报,2016,31(6):187-194.
[13]陈璨,吴文传,张伯明,等.考虑光伏出力相关性的配电网概率潮流[J].电力系统自动化,2015,39(9):41-47.
[14]胡源,别朝红,宁光涛,等.计及风电不确定性的多目标电网规划期望值模型与算法[J].电工技术学报,2016,31(10):168-175.
[15]艾小猛,文劲宇,吴桐,等.基于点估计和Gram-Charlier展开的含风电电力系统概率潮流实用算法[J].中国电机工程学报,2013,33(16):16-22.
[16]吴栋梁,王扬,郭创新,等.电力市场环境下考虑风电预测误差的经济调度模型[J].电力系统自动化,2012,36(6):23-28.
[17]廖秋萍,吕林,刘友波,等.考虑重构的含可再生能源配电网电压控制模型与算法[J].电力系统自动化,2017,41(18):32-39.
[18]Hong H P.An efficient point estimate method for probabilistic analysis[J].Reliability Engineering&System Safety,1998,59(3):261-267.
[19]Morales J M,Perez-Ruiz J.Point estimate schemes to solve the probabilistic power flow[J].IEEE Transactions on Power Systems,2007,22(4):1594-1601.
[20]潘炜,刘文颖,杨以涵.概率最优潮流的点估计算法[J].中国电机工程学报,2008,28(16):28-33.
[2]吴丽珍,蒋力波,郝晓弘.基于最优场景生成算法的主动配电网无功优化[J].电力系统保护与控制,2017,45(15):152-159.
[3]陈继明,祁丽志,孙名妤,等.多场景下含风电机组的配电网无功优化的研究[J].电力系统保护与控制,2016,44(9):129-134.
[4]尹青,杨洪耕,马晓阳.考虑多重不确定参数的配电网概率无功优化[J].电力系统保护与控制,2017,45(7):141-147.
[5]骆晨,陶顺,肖湘宁,等.主动配电网的多阶段鲁棒综合电压控制[J].现代电力,2016,33(4):44-51.
[6]黄伟,刘斯亮,王武,等.长时间尺度下计及光伏不确定性的配电网无功优化调度[J].电力系统自动化,2018,42(5):154-162.
[7]张世达,孙永辉,赵景涛,等.基于随机响应面法的主动配电网无功优化[J].电力系统自动化,2017,41(13):30-38.
[8]刘宇,高山,杨胜春,等.电力系统概率潮流算法综述[J].电力系统自动化,2014,38(23):127-135.
[9]丁明,王京景,李生虎.基于扩展拉丁超立方采样的电力系统概率潮流计算[J].中国电机工程学报,2013,33(4):163-170.
[10]Saunders C S.Point estimate method addressing correlated wind power for probabilistic optimal power flow[J].IEEE Transactions on Power Systems,2014,29(3):1045-1054.
[11]朱星阳,刘文霞,张建华.考虑大规模风电并网的电力系统随机潮流[J].中国电机工程学报,2013,33(7):77-85.
[12]张立波,程浩忠,曾平良,等.基于Nataf逆变换的概率潮流三点估计法[J].电工技术学报,2016,31(6):187-194.
[13]陈璨,吴文传,张伯明,等.考虑光伏出力相关性的配电网概率潮流[J].电力系统自动化,2015,39(9):41-47.
[14]胡源,别朝红,宁光涛,等.计及风电不确定性的多目标电网规划期望值模型与算法[J].电工技术学报,2016,31(10):168-175.
[15]艾小猛,文劲宇,吴桐,等.基于点估计和Gram-Charlier展开的含风电电力系统概率潮流实用算法[J].中国电机工程学报,2013,33(16):16-22.
[16]吴栋梁,王扬,郭创新,等.电力市场环境下考虑风电预测误差的经济调度模型[J].电力系统自动化,2012,36(6):23-28.
[17]廖秋萍,吕林,刘友波,等.考虑重构的含可再生能源配电网电压控制模型与算法[J].电力系统自动化,2017,41(18):32-39.
[18]Hong H P.An efficient point estimate method for probabilistic analysis[J].Reliability Engineering&System Safety,1998,59(3):261-267.
[19]Morales J M,Perez-Ruiz J.Point estimate schemes to solve the probabilistic power flow[J].IEEE Transactions on Power Systems,2007,22(4):1594-1601.
[20]潘炜,刘文颖,杨以涵.概率最优潮流的点估计算法[J].中国电机工程学报,2008,28(16):28-33.