计及负荷分类及其需求响应的配电网设备利用率提高方法  

An Optimization Method of Distribution Network Equipment Utilization Considering Load Classification and Demand Response

作　　者：张静 尚光伟[1] 龚正国 李婷婷[1] 惠峥 崔文婷 ZHANG Jing;SHANG Guangwei;GONG Zhengguo;LI Tingting;HUI Zheng;CUI Wenting(State Grid Nanyang Power Supply Company,Nanyang 473000,Henan,China;Tianjin Chuneng Power Technology Co.,Ltd.,Tianjin 300384,China)

机构地区：[1]国网南阳供电公司,河南南阳473000 [2]天津楚能电力技术有限公司,天津300384

出　　处：《电网与清洁能源》2021年第2期50-56,63,共8页Power System and Clean Energy

基　　金：国家自然科学基金项目(51877152)。

摘　　要：为提高配电网设备能源利用率,提出一种计及负荷分类及其需求响应的配电网设备能源利用率提高方法。采用LSTM自编码器对典型日负荷进行特征提取,再用改进Kmeans负荷聚类算法对特征序列进行聚类分析。基于负荷分类结果建立计及分类负荷分时需求响应的配电网设备能源利用率优化模型,该模型以设备能源利用率和用户满意度最大为目标。算例分析结果表明,所提方法能更好地激发各类负荷需求响应潜力,通过分类负荷分时需求响应引导用户削峰填谷,有效提高配电网能源利用率;通过合理设置能源利用率和用户满意度目标权重,实现系统能源利用率和用户满意度的平衡。In order to improve the distribution network equipment utilization,a method for optimizing the distribution network equipment utilization,taking into account the deep learning load classification and its demand response,is proposed in this paper.The LSTM self-encoder in deep learning is used to perform feature layer extraction and dimensionality reduction on the typical daily load curve set through training.The improved K-means load clustering algorithm is used to perform cluster analysis on feature sequences.Based on the results of load classification,an optimization model of distribution network equipment utilization that takes into account the UOT demand response of classified loads is established.The goal of the model is maximizing equipment utilization and user satisfaction.The analysis results of examples show that the proposed method can better stimulate the potential of various load demand responses,guide users to cut peaks and fill valleys through classified load demand responses;and effectively improve the distribution network equipment utilization,and the balance between the system equipment utilization and user satisfaction can be realized through rationally setting the equipment utilization and user satisfaction goal weights.

关 键 词：设备能源利用率 深度学习 分类负荷需求响应 用户满意度 负荷聚类 

分 类 号：TM73[电气工程—电力系统及自动化]