优选状态数的MCMC算法在风电功率序列生成中的应用
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摘要
传统马尔科夫链蒙特卡洛(MCMC)法的状态数选择常依赖于人工经验,应用于风电功率序列建模时难以较好地同时模拟原始风电功率序列的概率分布特性和自相关特性。针对该问题,提出一种优选状态数的MCMC(OSN-MC)算法。首先给出MCMC方法状态数的选取范围,其次在该范围内以生成序列与原始序列的自相关函数的误差平方和最小为原则确定优选状态数,然后利用各状态对应功率范围内的累积分布函数抽样生成随机风电功率,提高优选状态数下生成风电功率序列对于原始序列分布特性的模拟精度。应用OSN-MC法和MCMC法对中国、美国和欧洲的12个风电场生成风电功率序列,并与原始实测序列进行特性比较,结果表明:OSN-MC法生成的风电功率序列对原始序列的分布特性和自相关特性的模拟效果均优于MCMC法所生成的风电功率序列。
The state number selection of traditional MCMC(Markov Chain Monte Carlo) method often depends on personal experience,it is difficult to simulate the probability distribution and autocorrelation characteristics of original wind power series at the same time when the method is applied in wind power series modelling,for which,an OSN-MC(Optimizing State Number Markov Chain Monte Carlo) algorithm is proposed. Firstly,the state number selection range of MCMC method is given. Secondly,the optimal state number is determined based on the principle of minimizing the square sum of autocorrelation function error of generated series and original series. Then the cumulative distribution function in the corresponding power range of each state is adopted to generate random wind power,which improves the simulation accuracy of generated wind power series to the probability distribution of original series. The OSN-MC and MCMC methods are applied to generate wind power series for 12 wind farms in China,the United States and Europe,and the comparison with the original measured series shows that the simulation effect of wind power series generated by OSN-MC algorithm on the distribution and autocorrelation characteristics of original series is better than that of MCMC algorithm.
The state number selection of traditional MCMC(Markov Chain Monte Carlo) method often depends on personal experience,it is difficult to simulate the probability distribution and autocorrelation characteristics of original wind power series at the same time when the method is applied in wind power series modelling,for which,an OSN-MC(Optimizing State Number Markov Chain Monte Carlo) algorithm is proposed. Firstly,the state number selection range of MCMC method is given. Secondly,the optimal state number is determined based on the principle of minimizing the square sum of autocorrelation function error of generated series and original series. Then the cumulative distribution function in the corresponding power range of each state is adopted to generate random wind power,which improves the simulation accuracy of generated wind power series to the probability distribution of original series. The OSN-MC and MCMC methods are applied to generate wind power series for 12 wind farms in China,the United States and Europe,and the comparison with the original measured series shows that the simulation effect of wind power series generated by OSN-MC algorithm on the distribution and autocorrelation characteristics of original series is better than that of MCMC algorithm.
引文
[1] 姜欣,陈红坤,向铁元,等.考虑调峰特性的电网风电接入能力分析[J].电力自动化设备,2014,34(12):13-18.JIANG Xin,CHEN Hongkun,XIANG Tieyuan,et al.Wind power penetration capacity considering peak regulation characteristics[J].Electric Power Automation Equipment,2014,34(12):13-18.
[2] 孙辉,徐箭,孙元章,等.基于混合整数线性规划的风电场有功优化调度[J].电力系统自动化,2016,40(22):27-33.SUN Hui,XU Jian,SUN Yuanzhang,et al.Active power optimization scheduling of wind farm based on mixed-integer linear programming[J].Automation of Electric Power Systems,2016,40(22):27-33.
[3] 秦超,曾沅,苏寅生,等.基于安全域的大规模风电并网系统低频振荡稳定分析[J].电力自动化设备,2017,37(5):100-106.QIN Chao,ZENG Yuan,SU Yinsheng,et al.Low-frequency oscillatory stability analysis based on security region for power system with large-scale wind power[J].Electric Power Automation Equipment,2017,37(5):100-106.
[4] 张丽英,叶廷路,辛耀中,等.大规模风电接入电网的相关问题及措施[J].中国电机工程学报,2010,30(25):1-9.ZHANG Liying,YE Tinglu,XIN Yaozhong,et al.Problems and mea-sures of power grid accommodating large scale wind power[J].Proceedings of the CSEE,2010,30(25):1-9.
[5] 邹斌,李冬.基于有效容量分布的含风电场电力系统随机生产模拟[J].中国电机工程学报,2012,32(7):23-31.ZOU Bin,LI Dong.Power system probabilistic production simulation with wind generation based on available capacity distribution[J].Proceedings of the CSEE,2012,32(7):23-31.
[6] 丰颖,贠志皓,周琼,等.考虑风电接入的在线风险评估和预防控制[J].电力自动化设备,2017,37(2):61-68.FENG Ying,YUN Zhihao,ZHOU Qiong,et al.Online risk assess-ment and preventive control considering wind-power integration[J].Electric Power Automation Equipment,2017,37(2):61-68.
[7] WOODS M J,RUSSELL C J,DAVY R J,et al.Simulation of wind power at several locations using a measured time-series of wind speed[J].IEEE Transactions on Power Systems,2013,28(1):219-226.
[8] 张宏宇,印永华,申洪,等.基于概率测度变换的风速时间序列建模方法[J].电力系统自动化,2013,37(2):7-10.ZHANG Hongyu,YIN Yonghua,SHEN Hong,et al.A wind speed time series modeling method based on probability measure transformation[J].Automation of Electric Power Systems,2013,37(2):7-10.
[9] 吴峰,孔卫亚,周宇,等.考虑多风电场风速变化规律的模拟数据生成方法[J].电网技术,2016,40(7):2038-2044.WU Feng,KONG Weiya,ZHOU Yu,et al.A wind speed modeling method for multiple wind farms considering variation regularities[J].Power System Technology,2016,40(7):2038-2044.
[10] LASLETT D,CREAGH C,JENNINGS P.A simple hourly wind power simulation for the south-west region of western Australia using MERRA data[J].Renewable Energy,2016,96:1003-1014.
[11] 邹金,赖旭,汪宁渤.风电随机出力的时间序列模型[J].电网技术,2014,38(9):2416-2421.ZOU Jin,LAI Xu,WANG Ningbo.Time series model of stochastic wind power generation[J].Power System Technology,2014,38(9):2416-2421.
[12] PAPAEFTHYMIOU G,KL?CKL B.MCMC for wind power simulation[J].IEEE Transactions on Energy Conversion,2008,23(1):234-240.
[13] 刘纯,吕振华,黄越辉,等.长时间尺度风电出力时间序列建模新方法研究[J].电力系统保护与控制,2013,41(1):7-13.LIU Chun,Lü Zhenhua,HUANG Yuehui,et al.A new method to simulate wind power time series of large time scale[J].Power System Protection and Control,2013,41(1):7-13.
[14] 李驰,刘纯,黄越辉,等.基于波动特性的风电出力时间序列建模方法研究[J].电网技术,2015,39(1):208-214.LI Chi,LIU Chun,HUANG Yuehui,et al.Study on the modeling me-thod of wind power time series based on fluctuation characteristics[J].Power System Technology,2015,39(1):208-214.
[15] DENAXAS E A,BANDYOPADHYAY R,PATINO-ECHEVERRI D,et al.SynTiSe:a modified multi-regime MCMC approach for generation of wind power synthetic time series[C]//IEEE International Conference on Systems,Man,and Cybernetics.Hong Kong,China:[s.n.],2015:668-674.
[16] 于鹏,黎静华,文劲宇,等.含风电功率时域特性的风电功率序列建模方法[J].中国电机工程学报,2014,34(22):3715-3723.YU Peng,LI Jinghua,WEN Jinyu,et al.A wind power time series modeling method based on its time domain characteristics[J].Proceedings of the CSEE,2014,34(22):3715-3723.
[17] 刘次华.随机过程[M].武汉:华中科技大学出版社,2014:56-200.
[18] 吴桐.风电功率的特性分析及其时间序列生成方法研究[D].武汉:华中科技大学,2013.WU Tong.Analysis of wind power characteristics and study of its time series generation[D].Wuhan:Huazhong University of Science and Technology,2013.
[19] 魏绍凯,谢明,郑叔芳.叶型曲线的自适应分段回归[J].中国电机工程学报,1993,13(4):54-58.WEI Shaokai,XIE Ming,ZHENG Shufang.Adaptive segmented regression for blade profile[J].Proceedings of the CSEE,1993,13(4):54-58.
[20] 林卫星,文劲宇,艾小猛,等.风电功率波动特性的概率分布研究[J].中国电机工程学报,2012,32(1):38-46.LIN Weixing,WEN Jinyu,AI Xiaomeng,et al.Probability density function of wind power variations[J].Proceedings of the CSEE,2012,32(1):38-46.
[21] 杨明,刘先忠.矩阵论[M].武汉:华中科技大学出版社,2005.
[2] 孙辉,徐箭,孙元章,等.基于混合整数线性规划的风电场有功优化调度[J].电力系统自动化,2016,40(22):27-33.SUN Hui,XU Jian,SUN Yuanzhang,et al.Active power optimization scheduling of wind farm based on mixed-integer linear programming[J].Automation of Electric Power Systems,2016,40(22):27-33.
[3] 秦超,曾沅,苏寅生,等.基于安全域的大规模风电并网系统低频振荡稳定分析[J].电力自动化设备,2017,37(5):100-106.QIN Chao,ZENG Yuan,SU Yinsheng,et al.Low-frequency oscillatory stability analysis based on security region for power system with large-scale wind power[J].Electric Power Automation Equipment,2017,37(5):100-106.
[4] 张丽英,叶廷路,辛耀中,等.大规模风电接入电网的相关问题及措施[J].中国电机工程学报,2010,30(25):1-9.ZHANG Liying,YE Tinglu,XIN Yaozhong,et al.Problems and mea-sures of power grid accommodating large scale wind power[J].Proceedings of the CSEE,2010,30(25):1-9.
[5] 邹斌,李冬.基于有效容量分布的含风电场电力系统随机生产模拟[J].中国电机工程学报,2012,32(7):23-31.ZOU Bin,LI Dong.Power system probabilistic production simulation with wind generation based on available capacity distribution[J].Proceedings of the CSEE,2012,32(7):23-31.
[6] 丰颖,贠志皓,周琼,等.考虑风电接入的在线风险评估和预防控制[J].电力自动化设备,2017,37(2):61-68.FENG Ying,YUN Zhihao,ZHOU Qiong,et al.Online risk assess-ment and preventive control considering wind-power integration[J].Electric Power Automation Equipment,2017,37(2):61-68.
[7] WOODS M J,RUSSELL C J,DAVY R J,et al.Simulation of wind power at several locations using a measured time-series of wind speed[J].IEEE Transactions on Power Systems,2013,28(1):219-226.
[8] 张宏宇,印永华,申洪,等.基于概率测度变换的风速时间序列建模方法[J].电力系统自动化,2013,37(2):7-10.ZHANG Hongyu,YIN Yonghua,SHEN Hong,et al.A wind speed time series modeling method based on probability measure transformation[J].Automation of Electric Power Systems,2013,37(2):7-10.
[9] 吴峰,孔卫亚,周宇,等.考虑多风电场风速变化规律的模拟数据生成方法[J].电网技术,2016,40(7):2038-2044.WU Feng,KONG Weiya,ZHOU Yu,et al.A wind speed modeling method for multiple wind farms considering variation regularities[J].Power System Technology,2016,40(7):2038-2044.
[10] LASLETT D,CREAGH C,JENNINGS P.A simple hourly wind power simulation for the south-west region of western Australia using MERRA data[J].Renewable Energy,2016,96:1003-1014.
[11] 邹金,赖旭,汪宁渤.风电随机出力的时间序列模型[J].电网技术,2014,38(9):2416-2421.ZOU Jin,LAI Xu,WANG Ningbo.Time series model of stochastic wind power generation[J].Power System Technology,2014,38(9):2416-2421.
[12] PAPAEFTHYMIOU G,KL?CKL B.MCMC for wind power simulation[J].IEEE Transactions on Energy Conversion,2008,23(1):234-240.
[13] 刘纯,吕振华,黄越辉,等.长时间尺度风电出力时间序列建模新方法研究[J].电力系统保护与控制,2013,41(1):7-13.LIU Chun,Lü Zhenhua,HUANG Yuehui,et al.A new method to simulate wind power time series of large time scale[J].Power System Protection and Control,2013,41(1):7-13.
[14] 李驰,刘纯,黄越辉,等.基于波动特性的风电出力时间序列建模方法研究[J].电网技术,2015,39(1):208-214.LI Chi,LIU Chun,HUANG Yuehui,et al.Study on the modeling me-thod of wind power time series based on fluctuation characteristics[J].Power System Technology,2015,39(1):208-214.
[15] DENAXAS E A,BANDYOPADHYAY R,PATINO-ECHEVERRI D,et al.SynTiSe:a modified multi-regime MCMC approach for generation of wind power synthetic time series[C]//IEEE International Conference on Systems,Man,and Cybernetics.Hong Kong,China:[s.n.],2015:668-674.
[16] 于鹏,黎静华,文劲宇,等.含风电功率时域特性的风电功率序列建模方法[J].中国电机工程学报,2014,34(22):3715-3723.YU Peng,LI Jinghua,WEN Jinyu,et al.A wind power time series modeling method based on its time domain characteristics[J].Proceedings of the CSEE,2014,34(22):3715-3723.
[17] 刘次华.随机过程[M].武汉:华中科技大学出版社,2014:56-200.
[18] 吴桐.风电功率的特性分析及其时间序列生成方法研究[D].武汉:华中科技大学,2013.WU Tong.Analysis of wind power characteristics and study of its time series generation[D].Wuhan:Huazhong University of Science and Technology,2013.
[19] 魏绍凯,谢明,郑叔芳.叶型曲线的自适应分段回归[J].中国电机工程学报,1993,13(4):54-58.WEI Shaokai,XIE Ming,ZHENG Shufang.Adaptive segmented regression for blade profile[J].Proceedings of the CSEE,1993,13(4):54-58.
[20] 林卫星,文劲宇,艾小猛,等.风电功率波动特性的概率分布研究[J].中国电机工程学报,2012,32(1):38-46.LIN Weixing,WEN Jinyu,AI Xiaomeng,et al.Probability density function of wind power variations[J].Proceedings of the CSEE,2012,32(1):38-46.
[21] 杨明,刘先忠.矩阵论[M].武汉:华中科技大学出版社,2005.