电网技术2025，Vol.49Issue(7)：2779-2788,中插31-中插34,14.DOI:10.13335/j.1000-3673.pst.2024.0710
基于主从博弈的电动汽车参与城市电网分区精细化削峰策略
Refined Peak Shaving Strategy for EV Participation in Urban Grid Partitioning Based on Stackelberg Game
摘要
Abstract
With the rapid development of electric vehicles(EVs),more and more EVs are accessing and interacting with the distribution grid.Because of the line overload phenomenon caused by large-scale EVs access to the distribution network,and considering the communication pressure generated by scheduling large-scale EVs on the distribution network control center.This paper proposes a Stackelberg game-based electric vehicle participation in the urban power grid zoning and regulation strategy.Firstly,we propose an urban distribution network zoning method based on zonal composability,and the load similarity coefficient and the source-load matching coefficient are introduced into the zoning parameters for the first time.We propose a two-layer game scheduling model for EVs under zonal control based on the zoning results.The upper model is designed to maximize the revenue of the distribution grid control center.The lower model is designed to minimize the cost of electricity consumption for EV users in aggregators.This interaction between the two players is intended to reach an equilibrium solution.Finally,we use the synchronous alternating direction method of multipliers to realize the distributed parallel solution in multiple regions,and we perform simulation calculations based on a 266-node distribution network in a southern city to verify the validity of the proposed model and methodology.关键词
电动汽车/配电网分区/主从博弈/分布式求解/同步型交替方向乘子算法Key words
electric vehicle/distribution network partitioning/Stackelberg game/distributed computation/synchronous alternating direction method of multipliers分类
信息技术与安全科学引用本文复制引用
马铭宇,董萍,董家读,刘明波,可思为,王春玲..基于主从博弈的电动汽车参与城市电网分区精细化削峰策略[J].电网技术,2025,49(7):2779-2788,中插31-中插34,14.基金项目
国家自然科学基金项目(52077083) （52077083）
广东省自然科学基金海上风电联合基金项目(2022A1515240076).Project Supported by National Natural Science Foundation of China(52077083) （2022A1515240076）
Guangdong Natural Science Foundation Offshore Wind Power Joint Fund(2022A1515240076). （2022A1515240076）
