摘要
针对电动汽车大规模接入配电系统导致的负荷波动增大、功率损耗上升及供电质量下降等问题,提出一种基于优先级的车辆到电网(vehicle-to-grid,V2G)协调调度方法。该方法构建了以“负荷方差”“有功功率损耗降低指标(power loss reduction,PLR)”和“无功功率损耗降低指标(power loss reduction,QLR)”为核心的多目标优化模型,并引入最有价值球员(most valuable player,MVP)算法求解,以同时实现负荷曲线平滑化与配电系统供电质量提升。设计基于SOC的优先级充放电策略,通过MVP算法搜索最优调度方案,并与GA、ABC、PSO、CSO、OCSO等多种元启发式算法进行对比。结果表明:所提出方法能够显著降低负荷波动,PLR和QLR的最小化效果均优于对比算法,功率损耗降低幅度最高可达29.20%,计算效率亦具有明显优势。基于IEEE 69节点径向配电系统的仿真验证了该方法的有效性和鲁棒性,证明该方法能够为电动汽车有序用电规划提供更合理的非支配解集,为后续实时调度及可再生能源接入提供较为重要参考。
To address the issues of increased load fluctuations,rising power losses,and degraded power supply quality caused by large-scale integration of electric vehicles into distribution systems,a priority-based vehicle-to-grid(V2G)coordinated scheduling method is proposed.A multi-objective optimization model is established using load variance,the power loss reduction(PLR)index,and the power loss reduction(QLR)index as key metrics.The most valuable player(MVP)algorithm is employed to obtain optimal charge-discharge schedules that simultaneously smooth the load curve and enhance distribution power quality.A state-of-charge(SOC)-based priority charging and discharging strategy is designed,and the MVP algorithm is used to explore optimal solutions.Comparative analyses with GA,ABC,PSO,CSO,and OCSO algorithms are conducted.The results demonstrate that the proposed method significantly reduces load variations and achieves superior minimization of PLR and QLR,with the maximum reduction in power loss reaching up to 29.20%.It also exhibits higher computational efficiency.Simulations on the IEEE 69-bus radial distribution system verify the effectiveness and robustness of the proposed approach.It is concluded that this method provides a more reasonable set of non-dominated solutions for orderly EV power consumption planning,offering valuable insights for real-time scheduling and the integration of renewable energy.
作者
华陈君
陆志刚
王敏霞
徐媛
Hua Chenjun;Lu Zhigang;Wang Minxia;Xu Yuan(State Grid Wuxi Power Supply Company,Wuxi 214000,China)
出处
《兵工自动化》
北大核心
2026年第1期73-78,共6页
Ordnance Industry Automation
基金
国网江苏省电力有限公司科技项目资助(J2024197)。
关键词
电动汽车
优先充电和放电
负荷差异
多目标
有序充电
electric vehicles
priority-based charging and discharging
load variance
multi-objective
orderly charging
