文章摘要
基于Copula函数的风电时序联合出力典型场景生成
Generation of typical sequential joint output scenarios of wind power based on Copula function
投稿时间:2019-06-12  修订日期:2019-10-09
DOI:
中文关键词: 场景生成  场景缩减  Copula函数  风电联合出力  拟合优度
英文关键词: scenario generation  scenario reduction  Copula function  joint output of wind power  goodness of fit
基金项目:基金项目:国家电网江苏省公司科技项目(521080170006);国家重点研发计划(2016YFB0900100);国家自然科学基金(51777185)
作者单位E-mail
汤向华 国网江苏省电力公司海门市供电公司 tangxh88@126.com 
李秋实 国网江苏省电力公司海门市供电公司  
侯丽钢 国网江苏省电力公司海门市供电公司  
陈昌铭 浙江大学工程师学院  
刘晟源 浙江大学电气工程学院  
林振智 浙江大学电气工程学院 linzhenzhi@zju.edu.cn 
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中文摘要:
      针对风、光出力进行典型场景生成是电力系统规划和运行中应对风、光出力不确定性的常用方法,然而现有的典型场景生成方法未考虑不同时刻出力分布函数的差异性。在此背景下,针对分布式可再生能源发电的不确定性和相关性,创新性地考虑了不同时刻出力分布函数的差异性,运用Copula函数建立了多风电场时序联合出力模型;对模型进行概率抽样、拼接生成大量初始场景集,采用K-means聚类算法进行场景缩减生成风电时序联合出力典型场景。算例分析表明,所得的风电时序联合出力典型场景符合出力的相关性,并可以体现出力分布函数在不同时刻的差异性,在反映同一地区多风电场实际出力方面具有更高的准确性,可以更加有效地指导电力系统的优化运行。
英文摘要:
      Typical scenarios generating is one of methods for dealing with uncertainty of wind and PV outputs in power system planning and operation. However, the differences among output distribution functions at different times are not considered by the existing generation methods for typical scenarios. Given this background, in view of the uncertainty and correlation of outputs for dis-tributed renewable energy generation, the differences among outputs’ distribution functions at different times are considered innovatively, the Copula function is used to establish the joint output model for multi-wind farm time-series. A large number of initial scenario sets are generated by probability sampling and splicing from the Copula model, and K-means clustering algorithm is used for reducing scenarios and generating typical joint output scenarios. Case studies show that the typical joint output scenarios of wind power are consistent with the correlation among wind power output and the differences among outputs’ distribution functions at different times. The output scenarios of multiple wind farms in the same area can be generated by the proposed algorithm with higher accuracy, which can guide the optimal operation of power systems more effectively.
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