电力系统自动化2024，Vol.48Issue(1)：41-49,9.DOI:10.7500/AEPS20230528001
基于画像的工业园区需求响应潜力评估
Portrait-based Assessment on Demand Response Potential of Industrial Parks
摘要
Abstract
The accurate assessment of demand response potential prorides data support for the power grid dispatch departments to formulate scientific and reasonable dispatch plans,which is of great significance to various stakeholders involved in the implementation process of demand response.Due to the high and regular electricity consumption of industrial loads,the regulatory potential that can be tapped is enormous,making it one of the most ideal demand response resources.First,for typical adjustable users,cluster analysis is conducted on their main types of electricity consumption plans,and each electricity consumption plan is depicted based on the maximal relevance and minimal redundancy criterion.Then,a dataset of typical adjustable users with different electricity consumption plans is used as the training set for the ultra-short-term load forecasting to obtain the adjustable capacity of typical users during the dispatch period.Finally,based on the proposed framework for assessing the response potential of the park,the typical adjustable load categories of each factory are determined based on the electricity consumption behavior clustering,and the adjustable capacity of the corresponding type of loads in the park is obtained based on the proportion of each type of adjustable load,thereby obtaining the total regulatory potential of the park.Simulation results show that the proposed assessment method can analyze the regulatory potential of various adjustable loads in the park,and the effectiveness of the assessment method is verified by combining actual data on the total regulatory potential of the park.关键词
需求响应/可调节容量/工业园区/工业负荷/聚类分析/用电行为/画像Key words
demand response/adjustable capacity/industrial park/industrial load/clustering analysis/electricity consumption behavior/portrait引用本文复制引用
范宇辉,姜婷玉,黄奇峰,鞠平..基于画像的工业园区需求响应潜力评估[J].电力系统自动化,2024,48(1):41-49,9.基金项目
国家自然科学基金资助项目(51837004) （51837004）
江苏省自然科学基金青年项目(SBK2023043599). This work is supported by National Natural Science Foundation of China(No.51837004)and Jiangsu Provincial Natural Science Foundation of China(No.SBK2023043599). （SBK2023043599）
