基于深度子领域自适应的需求响应潜力评估方法  

Demand Response Potential Assessment Method Based on Deep Subdomain Adaptation Network

作　　者：孔祥玉[1] 刘超[1] 王成山[1] 李思维 陈宋宋 KONG Xiangyu;LIU Chao;WANG Chengshan;LI Siwei;CHEN Songsong(Key Laboratory of Smart Grid of Ministry of Education(Tianjin University),Nankai District,Tianjin 300072,China;Beijing Fibrlink Communications Co.,Ltd.,Fengtai District,Beijing 100071,China;China Electric Power Research Institute,Haidian District,Beijing 100192,China)

机构地区：[1]智能电网教育部重点实验室(天津大学),天津市南开区300072 [2]北京中电飞华通信有限公司,北京市丰台区100071 [3]中国电力科学研究院有限公司,北京市海淀区100192

出　　处：《中国电机工程学报》2022年第16期5786-5797,共12页Proceedings of the CSEE

基　　金：国家重点研发计划项目(2017YFB0902900,2017YFB0902902)。

摘　　要：随着新型电力系统发展,规模化灵活需求侧互动响应资源挖掘成为必然。针对需求响应聚合商缺少聚合用户需求响应历史数据时的潜力分析问题,提出一种基于深度子领域自适应的需求响应潜力评估方法。该方法首先分析影响电力用户参与需求响应的关键因素,建立建筑用户典型用电模式下的二次回归参数库;在此基础上,采用基于参数特征相似度的需求响应潜力评估方法,得到用户参与响应的初始估计值;将用户参与响应关键影响因素和参与响应的降负荷率数据应用于训练深度子领域自适应神经网络,并对网络全连接层提取的特征参数进行特征对齐,实现了缺少响应历史数据时潜力评估神经网络的训练。通过算例分析,验证所提方法的有效性。With the development of new power system and the participation of renewable energy,excavating the resource of large-scale demand side interactive response with high flexibility is in demand.Aiming at solving the problem of demand response(DR)potential analysis with a lack of historical data of load aggregators,a demand response potential assessment method based on deep subdomain adaptation was proposed.Firstly,key factors affecting users’participation in DR were proposed,and a quadratic regression parameter database was established on typical power consumption modes in buildings.Then,a DR potential evaluation method based on similarity evaluation was performed to obtain an estimate value of power users’participation value in DR.Key influencing factors and the load reduction rate of users when participating DR were applied to train the depth subdomain adaptive neural network.Besides,feature parameters extracted from the full connection layer of the network was aligned to attain the training of the potential evaluation neural network with a lack of historical data.The validity of the proposed method was verified by case studies.

关 键 词：特征相似度 需求响应 潜力评估 子领域自适应 

分 类 号：TM73[电气工程—电力系统及自动化]