中国电机工程学报2026，Vol.46Issue(14)：5808-5821,中插9,15.DOI:10.13334/j.0258-8013.pcsee.250630
基于纳什谈判的用户侧共享储能联盟月前负荷最大需量两阶段鲁棒优化
Two-stage Robust Optimization for Pre-monthly Maximum Demand of User-side Shared Energy Storage Alliance Based on Nash Bargaining
摘要
Abstract
For large industrial electricity consumers,electricity expenses constitute a significant portion of operational costs.Configuring energy storage systems for peak shaving and valley filling can effectively reduce both energy charges and demand charges.To determine a reasonable monthly declared maximum demand value,this study selects industrial users with different load profiles as research subjects.Considering load forecast uncertainty and scenarios where users share self-built energy storage systems,a two-stage robust optimization model based on Nash bargaining is proposed for shared energy storage.The model decomposes the Nash bargaining problem into two subproblems:a cost minimization problem and a bargaining transfer problem.The cost minimization problem employs a two-stage robust optimization method to determine the monthly declared maximum demand value,ensuring optimal electricity costs under the worst-case scenario.The bargaining transfer problem further solves the cost allocation scheme for charging and discharging operations of shared energy storage among users,building upon the cost minimization results.This model not only accounts for the impact of load forecast uncertainty on maximum demand but also provides a theoretical framework for incentivizing coordinated cooperation among shared energy storage alliance members and exploring equitable transaction mechanisms.Finally,case simulations verify the feasibility and effectiveness of the proposed monthly maximum demand optimization model.关键词
用户侧/共享储能/最大需量/负荷不确定性/纳什谈判Key words
user-side/shared energy storage/maximum demand/load uncertainty/Nash bargaining分类
信息技术与安全科学引用本文复制引用
王丹妮,江岳文,温步瀛..基于纳什谈判的用户侧共享储能联盟月前负荷最大需量两阶段鲁棒优化[J].中国电机工程学报,2026,46(14):5808-5821,中插9,15.基金项目
国家自然科学基金项目(52307087).Project Supported by National Natural Science Foundation of China(52307087). （52307087）
