电力系统自动化2026，Vol.50Issue(7)：180-192,13.DOI:10.7500/AEPS20250423010
计及电动汽车的多能微网电-碳协同经济调度策略
Electricity-Carbon Coordinated Economic Dispatch Strategy for Multi-energy Microgrid Considering Electric Vehicles
摘要
Abstract
Aiming at the challenge of how to fairly allocate the costs of electricity and carbon markets to electric vehicle(EV)users through pricing strategies while incentivizing their collaboration in promoting low-carbon operation of the system under the environment of dual electricity and carbon markets,this paper proposes a low-carbon economic dispatch strategy for the multi-energy microgrid with EVs based on the electricity-carbon pricing mechanism using Stackelberg game.First,based on an extended carbon emission flow model for the multi-energy microgrid with EVs,a carbon emission accounting model for charging and discharging of EV clusters is constructed.Next,a Stackelberg game market framework is designed for the multi-energy microgrid operator and EV users,and a dynamic electricity-carbon pricing model for EVs is developed based on the carbon emission accounting model.Finally,a particle swarm optimization(PSO)algorithm is employed in conjunction with the CPLEX solver to solve this bi-level Stackelberg game model,yielding a low-carbon economic dispatch strategy for the multi-energy microgrid with EVs.Simulation results validate that the proposed strategy ensures economic benefits for both parties while effectively utilizing the nodal carbon emission factors combined with the carbon price.Employing the dynamic electricity-carbon price as an economic incentive,it guides EV users toward low-carbon energy consumption behaviors and enhances the renewable energy accommodation and carbon emission reduction of the multi-energy microgrid.关键词
微网/低碳经济调度/电动汽车/车碳因子/主从博弈/电-碳价格/扩展碳排放流Key words
microgrid/low-carbon economic dispatch/electric vehicle(EV)/electric vehicle carbon storage factor/Stackelberg game/electricity-carbon price/extended carbon emission flow引用本文复制引用
张谦,何展浩,黄升炜,秦天喜,谭兴辰,毕克凡..计及电动汽车的多能微网电-碳协同经济调度策略[J].电力系统自动化,2026,50(7):180-192,13.基金项目
国家自然科学基金资助项目(52277081). This work is supported by National Natural Science Foundation of China(No.52277081). （52277081）
