负荷水平约束条件下新能源出力场景生成新方法  

A New Method for Generating New Energy Output Scenarios with Load Level Constraints

作　　者：许琴 刘一鸣 陈志刚 庞学跃 盛奥 林济铿[2] XU Qin;LIU Yiming;CHEN Zhigang;PANG Xueyue;SHENG Ao;LIN Jikeng(China Energy Engineering Group Guangdong Electric Power Design Institute Co.,Ltd.,Guangzhou 510663,China;School of Electronic and Information Engineering,Tongji University,Shanghai 200084,China)

机构地区：[1]中国能源建设集团广东省电力设计研究院有限公司,广州510663 [2]同济大学电子与信息工程学院,上海201804

出　　处：《南方电网技术》2025年第1期51-62,共12页Southern Power System Technology

基　　金：国家自然科学基金资助项目(51177107);中国能源建设集团广东省电力设计研究院有限公司科技项目“新能源与负荷随机性特征及时间序列生成方法研究”(EV10271W)。

摘　　要：场景序列是场景分析及优化的基础,其精度直接影响相关的分析及优化计算的有效性。基于此,提出了一种基于人工智能计及负荷水平约束的新能源出力场景生成新方法,可明显提升所生成场景的精度,并克服调度中心没有有效方法的困难。该新方法的基本过程为:首先采取基于卡尔曼增益信息融合技术进行历史数据扩充,实现了对应历史样本的有效扩充;然后,基于自组织映射网络将历史日负荷曲线进行聚类得到多个日负荷类型簇,把与每个负荷簇内的日负荷曲线在同一天的新能源出力序列划分为相应的簇,得到了负荷水平约束下的源荷信息;最后,构建基于马尔科夫链(Markov chain,MC)日类型转换关系的状态转移矩阵,滚动抽样生成未来一段时间的日类型序列,并结合利用各簇源荷数据训练好的生成对抗网络(generative adversarial network,GAN),生成对应日类型下的96点源荷序列场景。算例证实了所提方法的有效性和先进性。Scenarios sequence generation is the basis of scenario analysis and optimization problem,and its accuracy directly affects the effectiveness of related analysis and optimization calculation.Based on this,a new method of generating new energy output scenarios with load level constraints based on artificial intelligence is proposed,significantly improving the accuracy of the generated scenarios and overcoming the difficulties without effective methods faced by the dispatch center.The process of the new method is as follows:firstly,the historical data expansion strategy based on Kalman gain information fusion technology is adopted,realizing the effective data expansion of corresponding historical samples.Then,based on the self-organizing mapping network,the historical daily load curves are clustered to obtain multiple daily load type clusters,and each of the new energy output sequences on the same day as the daily load curves within each load cluster is corresponding classified into clusters,and the source load clusters with the specific load level constraint are achieved.Finally,a state transfer matrix based on Markov chain(MC)daily type transformation relationship is constructed,and then a sequence of daily types in the near future is generated by rolling sampling.And a generative adversarial network(GAN)already trained on each of source-load cluster data is applied to generate 96-point source-load scenarios sequences for the corresponding daily types.The numerical experiments verify the effectiveness and advancement of the proposed method.

关 键 词：场景系列生成 人工智能 数据扩展 负荷水平约束 马尔科夫链 新能源 

分 类 号：TM614[电气工程—电力系统及自动化] TM73