Suzhou Electric Appliance Research Institute
期刊号: CN32-1800/TM| ISSN1007-3175

Article retrieval

文章检索

首页 >> 文章检索 >> 最新索引

新能源不确定功率预测方法综述

来源:电工电气发布时间:2018-09-14 09:14 浏览次数:632
新能源不确定功率预测方法综述
 
吴晨媛,吕干云,吴启宇,蒋小伟
(南京工程学院 电力工程学院,江苏 南京 211167)
 
    摘 要:新能源的不确定性功率预测研究能在传统预测模型基础上提高其预测精度并提供一定的概率信息和预测区间。从误差概率密度预测、区间预测两个方面对新能源功率预测的不确定性进行分析,总结归纳了各种不同的模型及其优缺点和评价指标,并探讨了新能源不确定功率预测存在的问题及今后需要深入研究的方向。
    关键词:新能源功率预测;不确定性;误差概率密度预测;区间预测
    中图分类号:TM715     文献标识码:A     文章编号:1007-3175(2018)09-0001-06
 
Survey of Uncertainty Power Prediction Technique in New Energy
 
WU Chen-yuan, LV Gan-yun, WU Qi-yu, JIANG Xiao-wei
(School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 2111 67, China)
 
    Abstract: Uncertainty power prediction for the new energy prediction study, based on the traditional prediction model, could improve its prediction accuracy and provide a certain probability information and prediction interval. This paper analyzed the uncertainty of new energy power prediction, from the aspects of error probability density prediction and interval prediction, and summarized various models and their advantages, disadvantages and evaluation indexes. Finally, this paper discussed the problem of uncertainty prediction for the new energy power and directions for further research in the future.
    Key words: new energy power prediction; uncertainty; error probability density prediction; interval prediction
 
参考文献
[1] 杨茂,刘红柳,季本明. 基于混沌理论的风电功率超短期多步预测的误差分析[J]. 电力系统保护与控制,2017,45(4):50-55.
[2] CARRASCO J M,FRANQUELO L G, BIALASIEWICZ J T, et al.Power-electronic systems for the grid integration of renewable energy sources:a survey[J]. IEEE Transactions on Industrial Electronics,2006,53(4):1002-1016.
[3] 丁明,王伟胜,王秀丽,等. 大规模光伏发电对电力系统影响综述[J]. 中国电机工程学报,2014,34(1):2-14.
[4] SIDERATOS G, HATZIARGYRIOU N D. An Advanced Statistical Method for Wind Power Forecasting[J]. IEEE Transactions on Power System,2007,22(1):258-265.
[5] 张晓丹. 风电功率预测误差不确定性建模研究[D]. 北京:北京交通大学,2016.
[6] ZIADI Zakaria, OSHIRO Masato, SENJYU Tomonobu, et al. Optimal Voltage Control Using Inverters Interfaced with PV Systems Considering Forecast Error in a Distribution System[J]. IEEE Transactions on Sustainable Energy,2014,5(2):682-690.
[7] TEWARI S, GEYER C J, MOHAN N. A statistical model for wind power forecast error and its application to the estimation of penalties in liberalized markets[J]. IEEE Transactions on Power System,2011,26(4):2031-2039.
[8] 刘立阳, 吴军基, 孟绍良. 短期风电功率预测误差分布研究[J]. 电力系统保护与控制,2013,41(12):65-70.
[9] LI Y Q, HE W, YAN X B. Default probability of listed companies based on the generalized error distribution[C]//Proceedings of the 2010 International Conference on Multimedia Technology,2010:1-4.
[10] 杨宏,苑津莎,张铁峰. 一种基于Beta分布的风电功率预测误差最小概率区间的模型和算法[J]. 中国电机工程学报,2015,35(9):2135-2142.
[11] 刘芳,潘毅,刘辉,等. 风电功率预测误差分段指数分布模型[J]. 电力系统自动化,2013,37(18):14-19.
[12] 刘燕华,李伟花,刘冲,等. 短期风电功率预测误差的混合偏态分布模型[J]. 中国电机工程学报,2015,35(10):2375-2382.
[13] 叶林,任成,赵永宁,饶日晟,滕景竹. 超短期风电功率预测误差数值特性分层分析方法[J]. 中国电机工程学报,2016,36(3):692-700.
[14] 杨茂,董骏城. 基于混合高斯分布的风电功率实时预测误差分析[J]. 太阳能学报,2016,37(6):1594-1602.
[15] 王成福,王昭卿,孙宏斌,等. 考虑预测误差时序分布特性的含风电机组组合模型[J]. 中国电机工程学报,2016,36(15):4081-4090.
[16] EPSNECNIKOV V A. Nonparametric estimation of a multidimensional probability density[J]. Theory of Probability & Its Applications,1969,14(1):156-161.
[17] JEON J, TAYLOR J W. Using conditional kernel density estimation for wind power density forecasting[J]. Journal of the American Statistical Association,2012,107(497):66-79.
[18] 姜晓亮,李巍,吕项羽,等. 基于非参数核密度估计法的光储系统容量优化配置[J]. 高电压技术,2015,41(7):2225-2230.
[19] MATTHIAS Lange, DETLEV Heinemann. Relating the uncertainty of short-term wind speed predictions to meteorological situations with methods from synoptic climatology[C]//European Wind Energy Conference & Exhibition EWEC,2003.
[20] HAGAN K E, OYEBANJO O O, MASAUD T M, et al. A probabilistic forecasting model for accurate estimation of PV solar and windpower generation[C]//IEEE Power and Energy Conference at Illinois,2016.
[21] 王铮,王伟胜,刘纯,等. 基于风过程方法的风电功率预测结果不确定性估计[J]. 电网技术,2013,37(1):242-247.
[22] 赵唯嘉,张宁,康重庆,等. 光伏发电出力的条件预测误差概率分布估计方法[J]. 电力系统自动化,2015,39(16):8-15.
[23] 周松林,茆美琴,苏建徽. 风电功率短期预测及非参数区间估计[J]. 中国电机工程学报,2011,31(25):10-16.
[24] 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.
[25] 盛骤,谢式千,潘承毅. 概率论与数理统计[M]. 北京:高等教育出版社,2008:270-278.
[26] 王勃,刘纯,张俊,等. 基于Monte-Carlo方法的风电功率预测不确定性估计[J]. 高电压技术,2015,41(10):3385-3391.
[27] 陈建宝,丁军军. 分位数回归技术综述[J]. 统计与信息论坛,2008(3):89-96.
[28] 李智,韩学山,杨明,等. 基于分位点回归的风电功率波动区间分析[J]. 电力系统自动化,2011,35(3):83-87.
[29] WAN Can, LIN Jin, SONG Yonghua, XU Zhao, YANG Guangya. Probabilistic Forecasting of Photovoltaic Generation:An Efficient Statistical Approach[J]. IEEE Transactions on Power System,2017,32(3):2471-2472.
[30] ANTONIO Bracale,GUIDO Carpinelli,PASQUALE De Falco. A Probabilistic Competitive Ensemble Method for Short-Term Photovoltaic Power Forecasting[J]. IEEE Transactions on Sustainable Energy,2017,8(2):551-560.
[31] 杨锡运,关文渊,刘玉奇,肖运启. 基于粒子群优化的核极限学习机模型的风电功率区间预测方法[J]. 中国电机工程学报,2015,35(S1):146-153.
[32] 阎洁,刘永前,张浩,等. 基于风场景识别的动态风电功率概率预测方法[J]. 现代电力,2016,33(2):51-58.
[33] 韩爽, 刘永前, 杨勇平, 等. 风电场超短期功率预测及不确定性分析[J]. 太阳能学报,2011,32(8):1251-1256.
[34] 周同旭,周松林. 光伏发电功率区间概率预测[J]. 铜陵学院学报,2017,16(2):108-110.
[35] 董雷,周文萍,张沛,等. 基于动态贝叶斯网络的光伏发电短期概率预测[J]. 中国电机工程学报,2013,33(S1):38-45.
[36] 徐曼,乔颖,鲁宗相. 短期风电功率预测误差综合评价方法[J]. 电力系统自动化,2011,35(12):20-26.
[37] 孟岩峰,胡书举,邓雅,等. 风电功率预测误差分析及预测误差评价方法[J]. 电力建设,2013,34(7):6-9.
[38] 吴问足,乔颖,鲁宗相,等. 风电功率概率预测方法及展望[J]. 电力系统自动化,2017,41(18):167-175.
[39] 叶瑞丽,刘建楠,苗峰显,等. 风电场风电功率预测误差分析及置信区间估计研究[J]. 陕西电力,2017,45(2):21-25.
[40] 林优,杨明,韩学山,等. 基于条件分类与证据理论的短期风电功率非参数概率预测方法[J]. 电网技术,2016,40(4):1113-1119.