基于证据理论的风速不确定性建模
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摘要
风速决定了风电场的输出功率,风速模型是研究含风电系统运行与规划的重要基础。提出一种基于证据理论的风速不确定建模方法。采用证据理论中基本可信度分配的概念描述风速;提出依据实测历史数据确定基本可信度分配的焦元和信任函数的实现方法,并设计等概率区间和等取值区间2种建模策略。以某风电场实测风速为例对基于所建模型和基于概率分布及区间分布的模型进行仿真比较,结果表明所提模型能确定风速的似然累积概率分布和信任累积概率分布,能更有效地描述和处理风速不确定性信息。
Wind speed determines the output power of a wind farm,and its model is an important basis for researching the operation and planning of power system with wind power. A modeling method of wind speed uncertainty based on the evidence theory is proposed. The concept of BPA( Basic Probability Assignment) in the evidence theory is used to describe wind speed. The implementation method of determining the focal points and trust functions of BPA based on the measured historical data is proposed,and two modeling methods of equal probability interval and equal value interval are designed. Taking the measured wind speed of a wind farm as an example,the simulative results of the proposed model are compared with those of the probability distribution and interval distribution model,and the results show that the proposed model can determine the cumulative plausibility probability distribution and the cumulative belief probability distribution of wind speed,and can describe and deal with the uncertainty information of wind speed more effectively.
Wind speed determines the output power of a wind farm,and its model is an important basis for researching the operation and planning of power system with wind power. A modeling method of wind speed uncertainty based on the evidence theory is proposed. The concept of BPA( Basic Probability Assignment) in the evidence theory is used to describe wind speed. The implementation method of determining the focal points and trust functions of BPA based on the measured historical data is proposed,and two modeling methods of equal probability interval and equal value interval are designed. Taking the measured wind speed of a wind farm as an example,the simulative results of the proposed model are compared with those of the probability distribution and interval distribution model,and the results show that the proposed model can determine the cumulative plausibility probability distribution and the cumulative belief probability distribution of wind speed,and can describe and deal with the uncertainty information of wind speed more effectively.
引文
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[14]吴京秋,孙奇,杨伟,等.基于D-S证据理论的短期负荷预测模型融合[J].电力自动化设备,2009,29(4):66-70.WU Jingqiu,SUN Qi,YANG Wei,et al. Short-term load forecast model fusion based on D-S evidence theory[J]. Electric Power Automation Equipment,2009,29(4):66-70.
[15] SHAFER G A. Mathematical theory of evidence[M]. Princeton,USA:Princeton University Press,1976.
[16]YAGER R R,KACPRZYK J,FEDRIZZI M. Advances in the Dempster-Shafer theory of evidence[M]. New York,USA:John Wiley&Sons,1994.
[2]CARTA J A,RAMIREZ P,VELZQUEZ S. A review of wind speed probability distributions used in wind energy analysis:case studies in the Canary Islands[J]. Renewable and Sustainable Energy Reviews,2009,13(5):933-955.
[3]JANGAMSHETTI S H,RAU V G. Siting of wind turbine generators[J]. IEEE Transactions on Energy Conversion,2001,16(1):8-13.
[4]JANGAMSHETTI S H,RAU V G. Site matching of wind turbine generators:a case study[J]. IEEE Transactions on Energy Conversion,1999,14(4):1537-1543.
[5]凌丹,黄洪钟,张小玲,等.混合威布尔分布参数估计的L-M算法[J].电子科技大学学报,2008,37(4):634-636.LING Dan,HUANG Hongzhong,ZHANG Xiaoling,et al. Parameter estimation for mixed Weibull distribution using L-M algorithm[J].Journal of University of Electronic Science and Technology of China,2008,37(4):634-636.
[6]洪芦诚,石立宝,姚良忠,等.计及风电场发电功率不确定性的电力系统模糊潮流[J].电工技术学报,2010,25(8):116-123.HONG Lucheng,SHI Libao,YAO Liangzhong,et al. Fuzzy modelling and solution of load flow incorporating uncertainties of wind farm generation[J]. Transactions of China Electrotechnical Society,2010,25(8):116-123.
[7] VENKATESH B,YU PENG,GOOI H B,et al. Fuzzy MILP unit commitment incorporating wind generators[J]. IEEE Transactions on Power Systems,2008,23(4):1738-1746.
[8]刘兴杰,谢春雨.基于贝塔分布的风电功率波动区间估计[J].电力自动化设备,2014,34(12):26-30.LIU Xingjie,XIE Chunyu. Wind power fluctuation interval estimation based on beta distribution[J]. Electric Power Automation Equipment,2014,34(12):26-30.
[9] PINSON P,KARINIOTAKIS G. Conditional prediction intervals of wind power generation[J]. IEEE Transactions on Power Systems,2010,25(4):1845-1856.
[10]范磊,卫志农,李慧杰,等.基于变分模态分解和蝙蝠算法-相关向量机的短期风速区间预测[J].电力自动化设备,2017,37(1):93-100.FAN Lei,WEI Zhinong,LI Huijie,et al. Short-term wind speed interval prediction based on VMD and BA-RVM algorithm[J].Electric Power Automation Equipment,2017,37(1):93-100.
[11]马瑞,张强,吴瑕,等.日风速随机模糊不确定模型[J].中国电机工程学报,2015,35(24):6351-6358.MA Rui,ZHANG Qiang,WU Xia,et al. Random fuzzy uncertain model for daily wind speed[J]. Proceedings of the CSEE,2015,35(24):6351-6358.
[12]TIAN Pauchang. Estimation of wind energy potential using different probability density functions[J]. Applied Energy,2011,88(5):1848-1856.
[13]鲍海波,韦化,郭小璇,等.考虑风电不确定性的概率区间潮流模型与算法[J].中国电机工程学报,2017,37(19):5633-5642.BAO Haibo,WEI Hua,GUO Xiaoxuan,et al. The model and algorithm of probabilistic interval power flow considering wind power uncertainty[J]. Proceedings of the CSEE,2017,37(19):5633-5642.
[14]吴京秋,孙奇,杨伟,等.基于D-S证据理论的短期负荷预测模型融合[J].电力自动化设备,2009,29(4):66-70.WU Jingqiu,SUN Qi,YANG Wei,et al. Short-term load forecast model fusion based on D-S evidence theory[J]. Electric Power Automation Equipment,2009,29(4):66-70.
[15] SHAFER G A. Mathematical theory of evidence[M]. Princeton,USA:Princeton University Press,1976.
[16]YAGER R R,KACPRZYK J,FEDRIZZI M. Advances in the Dempster-Shafer theory of evidence[M]. New York,USA:John Wiley&Sons,1994.