基于改进Shapley值的风电汇聚趋势性分状态量化方法
|
摘要
风电输出功率具有波动性,由于各机组出力之间的平抑效果,随着风电集群规模的增大,风电输出功率波动逐渐变缓,风电输出功率表现出"汇聚效应"。把握汇聚效应的趋势性对于规划送出线路及网架结构具有重要的意义。基于改进Shapley值法对风电汇聚效应的趋势性进行量化分析。在对不同风电输出状态量化分析的基础上,得到各状态下的持续出力曲线,进而构建基于汇聚特性分析的风电持续出力曲线分状态组合预测模型,并建立预测精度评价体系。采用改进的Shapley值法确定预测模型中的权重系数,避免了传统Shapley值法在单一模型预测结果偏差过大时仍参与组合的现象。基于实测数据对模型有效性进行检验,算例分析表明:相对于单一的预测模型,风电持续出力曲线的分状态组合预测方法能较更准确地描述风电汇聚的趋势。
Wind power output is fluctuant. Due to moderating effect of the output of each unit, the output fluctuation of wind power gradually slows down with wind power scale increase, and the wind power output shows a "convergence effect". It is of great guiding significance to grasp the trend of convergence effect for planning of outgoing transmission lines and capacity configuration. In this paper, the wind power output states are defined and the convergence trend of wind power in each output state is combined. The weight coefficients in the combination forecasting model are determined with the improved Shapley value method, avoiding the phenomenon that the traditional Shapley value method still participates in the combination when the deviation of a single model is too large. Based on quantitative calculation of continuous output curve of each state, a combined forecasting method of wind power continuous output curve is put forward based on analysis of convergence characteristics, and a prediction accuracy evaluation system is established. Validity of the method is verified with measured data. Case study shows that compared with the single prediction model, the combined forecasting method of wind power continuous output curve can accurately describe the trend of wind power convergence.
Wind power output is fluctuant. Due to moderating effect of the output of each unit, the output fluctuation of wind power gradually slows down with wind power scale increase, and the wind power output shows a "convergence effect". It is of great guiding significance to grasp the trend of convergence effect for planning of outgoing transmission lines and capacity configuration. In this paper, the wind power output states are defined and the convergence trend of wind power in each output state is combined. The weight coefficients in the combination forecasting model are determined with the improved Shapley value method, avoiding the phenomenon that the traditional Shapley value method still participates in the combination when the deviation of a single model is too large. Based on quantitative calculation of continuous output curve of each state, a combined forecasting method of wind power continuous output curve is put forward based on analysis of convergence characteristics, and a prediction accuracy evaluation system is established. Validity of the method is verified with measured data. Case study shows that compared with the single prediction model, the combined forecasting method of wind power continuous output curve can accurately describe the trend of wind power convergence.
引文
[1]国家能源局.2017年风电并网运行情况[EB/OL].[2018-07-01].http://www.nea.gov.cn/2018-02/01/c_136942234.htm.
[2]姜文玲,王勃,汪宁渤,等.多时空尺度下大型风电基地出力特性研究[J].电网技术,2017,41(2):493-499.Jiang Wenling,Wang Bo,Wang Ningbo,et al.Research on power output characteristics of large-scale wind power base in multiple temporal and spatial scales[J].Power System Technology,2017,41(2):493-499(in Chinese).
[3]黄林宏,宋丽莉,周荣卫,等.大型风电基地风电波动特征分析[J].中国电机工程学报,2017,37(6):1599-1610.Huang Linhong,Song Lili,Zhou Rongwei,et al.Characteristics analysis of wind power fluctuations for large-scale wind farms[J].Proceedings of the CSEE,2017,37(6):1599-1610(in Chinese).
[4]李剑楠,乔颖,鲁宗相,等.多时空尺度风电统计特性评价指标体系及其应用[J].中国电机工程学报,2013,33(13):53-61.Li Jiannan,Qiao Ying,Lu Zongxiang,et al.An evaluation index system for wind power statistical characteristics in multiple spatial and temporal scales and its application[J].Proceedings of the CSEE,2013,33(13):53-61(in Chinese).
[5]李剑楠,乔颖,鲁宗相,等.大规模风电多尺度出力波动性的统计建模研究[J].电力系统保护与控制,2012,40(19):7-13.Li Jiannan,Qiao Ying,Lu Zongxiang,et al.Research on statistical modeling of large-scale wind farms output fluctuations in different spatial and temporal scales[J].Power System Protection and Control,2012,40(19):7-13(in Chinese).
[6]韩杏宁,黎嘉明,文劲宇,等.风电功率状态的时域概率特性研究[J].电力系统保护与控制,2016,44(14):31-39.Han Xingning,Li Jiaming,Wen Jinyu,et al.Research on the time domain probabilistic characteristics of wind power state[J].Power System Protection and Control,2016,44(14):31-39(in Chinese).
[7]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+20.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+20(in Chinese).
[8]杨茂,杨春霖.基于模糊粒计算的风电功率实时预测研究[J].东北电力大学学报,2017,37(5):1-7.Yang Mao,Yang Chunlin.Research on wind power real-time forecasting based on fuzzy granular computing[J].Journal of Northeast Electric Power University,2017,37(5):1-7(in Chinese).
[9]杨茂,张强.风电功率超短期预测误差的非参数估计分布研究[J].东北电力大学学报,2018,38(1):15-20.Yang Mao,Zhang Qiang.The research of ultra short-term wind power prediction error distribution based on nonparametric estimation[J].Journal of Northeast Electric Power University,2018,38(1):15-20(in Chinese).
[10]申颖,赵千川,李明扬.多时空尺度下风电平滑效应的分析[J].电网技术,2015,39(2):400-405.Shen Ying,Zhao Qianchuan,Li Mingyang.Analysis on wind power smoothing effect in multiple temporal and spatial scales[J].Power System Technology,2015,39(2):400-405(in Chinese).
[11]刘燕华,田茹,张东英,等.风电出力平滑效应的分析与应用[J].电网技术,2013,37(4):987-991.Liu Yanhua,Tian Ru,Zhang Dongying,et al.Analysis and application of wind farm output smoothing effect[J].Power System Technology,2013,37(4):987-991(in Chinese).
[12]祝牧,刘吉臻,林忠伟.单一风电场平滑效应研究[J].华北电力大学学报,2016,43(3):51-55.Zhu Mu,Liu Jizhen,Lin Zongwei.Study on smoothing effect in single wind farm[J].Journal of North China Electric Power University,2016,43(3):51-55(in Chinese).
[13]穆钢,崔杨,严干贵,等.确定风电场群功率汇聚外送输电容量的静态综合优化方法[J].中国电机工程学报,2011,31(1):15-19.Mu Gang,Cui Yang,Yan Gangui,et al.A static optimization method to determine integrated power transmission capacity of clustering wind farms[J].Proceedings of the CSEE,2011,31(1):15-19(in Chinese).
[2]姜文玲,王勃,汪宁渤,等.多时空尺度下大型风电基地出力特性研究[J].电网技术,2017,41(2):493-499.Jiang Wenling,Wang Bo,Wang Ningbo,et al.Research on power output characteristics of large-scale wind power base in multiple temporal and spatial scales[J].Power System Technology,2017,41(2):493-499(in Chinese).
[3]黄林宏,宋丽莉,周荣卫,等.大型风电基地风电波动特征分析[J].中国电机工程学报,2017,37(6):1599-1610.Huang Linhong,Song Lili,Zhou Rongwei,et al.Characteristics analysis of wind power fluctuations for large-scale wind farms[J].Proceedings of the CSEE,2017,37(6):1599-1610(in Chinese).
[4]李剑楠,乔颖,鲁宗相,等.多时空尺度风电统计特性评价指标体系及其应用[J].中国电机工程学报,2013,33(13):53-61.Li Jiannan,Qiao Ying,Lu Zongxiang,et al.An evaluation index system for wind power statistical characteristics in multiple spatial and temporal scales and its application[J].Proceedings of the CSEE,2013,33(13):53-61(in Chinese).
[5]李剑楠,乔颖,鲁宗相,等.大规模风电多尺度出力波动性的统计建模研究[J].电力系统保护与控制,2012,40(19):7-13.Li Jiannan,Qiao Ying,Lu Zongxiang,et al.Research on statistical modeling of large-scale wind farms output fluctuations in different spatial and temporal scales[J].Power System Protection and Control,2012,40(19):7-13(in Chinese).
[6]韩杏宁,黎嘉明,文劲宇,等.风电功率状态的时域概率特性研究[J].电力系统保护与控制,2016,44(14):31-39.Han Xingning,Li Jiaming,Wen Jinyu,et al.Research on the time domain probabilistic characteristics of wind power state[J].Power System Protection and Control,2016,44(14):31-39(in Chinese).
[7]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+20.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+20(in Chinese).
[8]杨茂,杨春霖.基于模糊粒计算的风电功率实时预测研究[J].东北电力大学学报,2017,37(5):1-7.Yang Mao,Yang Chunlin.Research on wind power real-time forecasting based on fuzzy granular computing[J].Journal of Northeast Electric Power University,2017,37(5):1-7(in Chinese).
[9]杨茂,张强.风电功率超短期预测误差的非参数估计分布研究[J].东北电力大学学报,2018,38(1):15-20.Yang Mao,Zhang Qiang.The research of ultra short-term wind power prediction error distribution based on nonparametric estimation[J].Journal of Northeast Electric Power University,2018,38(1):15-20(in Chinese).
[10]申颖,赵千川,李明扬.多时空尺度下风电平滑效应的分析[J].电网技术,2015,39(2):400-405.Shen Ying,Zhao Qianchuan,Li Mingyang.Analysis on wind power smoothing effect in multiple temporal and spatial scales[J].Power System Technology,2015,39(2):400-405(in Chinese).
[11]刘燕华,田茹,张东英,等.风电出力平滑效应的分析与应用[J].电网技术,2013,37(4):987-991.Liu Yanhua,Tian Ru,Zhang Dongying,et al.Analysis and application of wind farm output smoothing effect[J].Power System Technology,2013,37(4):987-991(in Chinese).
[12]祝牧,刘吉臻,林忠伟.单一风电场平滑效应研究[J].华北电力大学学报,2016,43(3):51-55.Zhu Mu,Liu Jizhen,Lin Zongwei.Study on smoothing effect in single wind farm[J].Journal of North China Electric Power University,2016,43(3):51-55(in Chinese).
[13]穆钢,崔杨,严干贵,等.确定风电场群功率汇聚外送输电容量的静态综合优化方法[J].中国电机工程学报,2011,31(1):15-19.Mu Gang,Cui Yang,Yan Gangui,et al.A static optimization method to determine integrated power transmission capacity of clustering wind farms[J].Proceedings of the CSEE,2011,31(1):15-19(in Chinese).