南方电网技术2023，Vol.17Issue(11)：51-60,10.DOI:10.13648/j.cnki.issn1674-0629.2023.11.006
日前邀约模式下电力需求响应的聚合优化模型与方法
Aggregation Optimization Model and Method for Power Demand Response Based on the Day-Ahead Invitation Mode
摘要
Abstract
A demand response resource aggregation optimization model and method under invitation mode is proposed from the perspective of load integrators.Firstly,based on the actual situation of demand response transactions in some provinces and regions in China,the mode of day-ahead invitation response and the aggregation agent mechanism of load integrators for users are analyzed.Then,based on the mechanism conditions for organizing,assessing,and compensating for the demand response of the day-ahead invitation,and taking into account the characteristics of user resource response,effective response capacity authentication,and the impact of the revenue and assessment cost allocation ratio between load integrators and users,an aggregation optimization model is constructed to better adapt to the needs of load integrators for making day-ahead invitation response decisions.Aiming at the nonlinear coupling relationship of decision variables in the model,intermediate variables and logical constraints are introduced for linearization processing,which is then transformed into a mixed integer programming problem to reduce the difficulty of solving.The analysis of numerical examples shows that the aggregation optimization of different types of response resources can help to improve the expected returns of load integrators in the day-ahead invitation response,and can provide more detailed information for the analysis of the limit capacity of load integrators to compatible with different quality resources,which verifies the effectiveness of the model and method.关键词
需求响应/聚合优化/日前邀约/混合整数规划Key words
demand response/aggregation optimization/day-ahead invitation/mixed-integer programming分类
信息技术与安全科学引用本文复制引用
王巍,袁泉,刘春晓,苏寅生,马骞,孙宇军,陈潇婷..日前邀约模式下电力需求响应的聚合优化模型与方法[J].南方电网技术,2023,17(11):51-60,10.基金项目
中国南方电网有限责任公司科技项目(000000KK52210078). Supported by the Science and Technology Project of China Southern Power Grid Co.,Ltd.(000000KK52210078). （000000KK52210078）
