基于新型场景划分与考虑时序相关性的光伏出力时间序列模拟方法
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摘要
针对现有光伏出力的马尔科夫链模型在原始数据分段和随机抽样方面的不足,文章提出一种基于新型场景划分与考虑时序相关性的光伏出力时间序列模拟方法。首先引入Davies-Bouldin有效性指标优化模糊C均值聚类(fuzzy Cmean clustering,FCM)法,进行场景划分,形成数据特征更清晰的原始光伏出力序列集合。然后建立不同场景的光伏出力状态转移矩阵,通过马尔科夫链蒙特卡洛法生成光伏出力时间序列,在此过程中,利用Copula理论进行条件概率抽样生成下一时刻光伏出力状态值,以降低传统蒙特卡洛抽样的随机性。实际算例表明,文章所提方法生成的光伏出力时间序列不仅在数据的概率统计特性方面比现有的模型结果更精确,而且更好地保留了原始序列的自相关性。
Focusing on the defect of raw data segmentation and random sampling for the existing Markov chains model of PV output,a simulation method of PV output time series which is based on a new type of scenario division and considering temporal correlation is proposed. Firstly,the DBI clustering effectiveness index is introduced to optimize fuzzy C-mean clustering method,and the scenes are divided into different situations and the data sets of historical PV output series with more obvious data characteristics are established. Then,a number of state transition matrixes in different scenes are generated and PV output series are simulated through Markov chains Monte Carlo method. During this process,the statevalue for the next moment can be got using the Copula theory to conduct the conditional probability sampling,so as to reduce the randomness of the traditional Monte Carlo sampling. According to actual case calculation,in the new method in this article,the PV output series are not only more accurate than the existing model in probabilistic statistical features of the data,but also preserve better autocorrelation of the original sequence.
Focusing on the defect of raw data segmentation and random sampling for the existing Markov chains model of PV output,a simulation method of PV output time series which is based on a new type of scenario division and considering temporal correlation is proposed. Firstly,the DBI clustering effectiveness index is introduced to optimize fuzzy C-mean clustering method,and the scenes are divided into different situations and the data sets of historical PV output series with more obvious data characteristics are established. Then,a number of state transition matrixes in different scenes are generated and PV output series are simulated through Markov chains Monte Carlo method. During this process,the statevalue for the next moment can be got using the Copula theory to conduct the conditional probability sampling,so as to reduce the randomness of the traditional Monte Carlo sampling. According to actual case calculation,in the new method in this article,the PV output series are not only more accurate than the existing model in probabilistic statistical features of the data,but also preserve better autocorrelation of the original sequence.
引文
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[17]赵继超,袁越,傅质馨,等.基于Copula理论的风光互补发电系统可靠性评估[J].电力自动化设备,2013,33(1):124-129.ZHAO Jichao,YUAN Yue,FU Zhixin,et al. Reliability assessment of w ind-PV hybrid generation system based on Copula theory[J].Electric Pow er Automation Equipment,2013,33(1):124-129.
[18]黎静华,文劲宇,程时杰,等.考虑多风电场出力Copula相关关系的场景生产方法[J].中国电机工程学报,2013,33(16):30-36.LI Jinghua,WEN Jinyu,CHENG Shijie,et al. A scene generation method considering Copula correlation relationship of multi-w ind farms pow er[J]. Proceedings of the CSEE,2013,33(16):30-36.#
[2]黄碧斌,李琼慧,高菲.计及电网改造的高渗透率分布式光伏优化规划[J].电力建设,2015,36(10):82-87.HUANG Bibin,LI Qionghui,GAO Fei. Distributed photovoltaic optimal planning w ith high permeability considering grid reinforcement[J]. Electric Pow er Construction,2015,36(10):82-87.
[3]娄素华,胡斌,吴耀武,等.碳交易环境下含大规模光伏电源的电力系统优化调度[J].电力系统自动化,2014,38(17):91-97.LOU Suhua,HU Bin,WU Yaow u,et al. Optimal dispatch of pow er system integrated w ith large scale photovoltaic generation under carbon trading environment[J]. Automation of Electric Pow er Systems,2014,38(17):91-97.
[4]管霖,陈旭,吕耀棠,等.适用于电网规划的光伏发电概率模型及其应用[J].电力自动化设备,2017,37(11):1-7.GUAN Lin,CHEN Xu,LYaotang,et al. Probability model of PV generation for pow er system planning and its application[J]. Electric Pow er Automation Equipment,2017,37(11):1-7.
[5] KAPLANI E,KAPLANIS S. A stochastic simulation model for reliable PV system sizing providing for solar radiation fluctuation[J]. Applied Energy,2012,97(1):970-981.
[6]陆丹,袁越,杨苏.基于马尔可夫链蒙特卡洛法的独立风光柴储微网运行风险评估[J].电网技术,2017,41(3):823-830.LU Dan,YUAN Yue,YANG Su. Operation risk assessment of islanded w ind-PV-diesel-storage microgrid based on M arkov chain M onte Carlo method[J]. Pow er System Technology,2017,41(3):823-830.
[7]陈昌松,段善旭,殷进军.基于神经网络的光伏阵列发电预测模型的设计[J].电工技术学报,2009,24(9):153-158.CHEN Changsong, DUAN Shanxu, YIN Jinjun. Design of photovoltaic array pow er forecasting model based on neutral netw ork[J]. Transactions of China Electrotechnical Society,2009,24(9):153-158.
[8] SHI J,LEE W J,LIU Y,et al. Forecasting power output of photovoltaic systems based on w eather classification and support vector machines[J]. IEEE Transactions on Industry Applications,2015,48(3):1064-1069.
[9]丁明,徐宁舟.基于马尔可夫链的光伏发电系统输出功率短期预测方法[J].电网技术,2011,35(1):152-157.DING M ing,XU Ningzhou. A method to forecast short-term output pow er of photovoltaic generation system based on M arkov chain[J].Pow er System Technology,2011,35(1):152-157.
[10]罗钢,石东源,陈金富,等.风光发电功率时间序列模拟的MCMC方法[J].电网技术,2014,38(2):321-327.LUO Gang,SHI Dongyuan,CHEN Jinfu,et al. A M arkov chain M onte Carlo method for simulation of w ind and solar pow er time series[J]. Pow er System Technology,2014,38(2):321-327.
[11]丁明,鲍玉莹,毕锐.应用改进马尔科夫链的光伏出力时间序列模拟[J].电网技术,2016,40(2):459-464.DING M ing,BAO Yuying,BI Rui. Simulation of PV output time series used improved M arkov chain[J]. Pow er System Technology,2016,40(2):459-464.
[12] PAPAEFTHYMIOU G,KLOCKL B. MCMC for wind power simulation[J]. IEEE Transactions on Energy Conversion,2008,23(1):234-240.
[13] DONG R,HUANG M X. An improved FCM algorithm based on subtractive clustering for pow er load classification[J]. Advanced M aterials Research,2014(986/987):206-210.
[14] DAVIES D L,BOULDIN D W. A cluster separation measure[J].IEEE Transactions on Pattern Analysis and M achine Intelligence,1979,PAM I-1(2):224-227.
[15]韦艳华,张世英. Copula理论及其在金融分析上的应用[M].北京:清华大学出版社,2008:10-23.
[16] HAGHI H V,BINA M T,GOLKAR M A,et al. Using Copulas for analysis of large datasets in renew able distributed generation:PV and w ind pow er integration in Iran[J]. Renew able Energy,2010,35(9):1991-2000.
[17]赵继超,袁越,傅质馨,等.基于Copula理论的风光互补发电系统可靠性评估[J].电力自动化设备,2013,33(1):124-129.ZHAO Jichao,YUAN Yue,FU Zhixin,et al. Reliability assessment of w ind-PV hybrid generation system based on Copula theory[J].Electric Pow er Automation Equipment,2013,33(1):124-129.
[18]黎静华,文劲宇,程时杰,等.考虑多风电场出力Copula相关关系的场景生产方法[J].中国电机工程学报,2013,33(16):30-36.LI Jinghua,WEN Jinyu,CHENG Shijie,et al. A scene generation method considering Copula correlation relationship of multi-w ind farms pow er[J]. Proceedings of the CSEE,2013,33(16):30-36.#