基于多任务灰狼算法的大型充放电场站有源装置联合优化配置方法

Joint Optimal Allocation of Active Devices in Large-Scale Charging/Discharging Stations Based on Multi-Task Grey Wolf Optimizer

中文关键词:交直流混合微电网  多任务灰狼优化算法  电能质量抑制能力指数  协同优化

keywords:AC/DC hybrid microgrid  multi-task grey wolf optimization algorithm  power quality suppression capability ratio  collaborative optimization

中文摘要:针对电动汽车自由充放电场景下大型充电场站交直流混合微电网优化运行与电能质量管理问题，以微电网中光伏、储能、车网交互充电桩及有源滤波器等的优化配置为核心，提出一种基于多任务灰狼优化算法的有源装置联合优化配置方法。所提方法以日生命周期经济成本最小与电能质量抑制能力指数（power quality suppression capability index，PQSCI）最大为优化任务目标，针对多类型有源装置间的深度耦合与多约束运行状态问题，通过引入任务划分、因素等级与技能因子机制，实现了多设备优化任务间的信息共享与知识迁移，显著提升了全局搜索能力与计算效率。算例结果表明，所提方法可在保证系统稳定性的前提下，有效降低了运行与交互成本约19.9%，并使PQSCI指数提升约6.4%，实现了经济性与电能质量的协同最优。

Abstract:To address the optimal operation and power quality management of AC/DC hybrid microgrids in large-scale electric vehicle （EV） charging stations under the free charging and discharging scenario， this paper proposes a joint optimal allocation method for active devices based on multi-task grey wolf optimizer （MTGWO）. The method focuses on the optimal configuration of photovoltaic systems， energy storage systems， V2G chargers， active power filters and other equipment in the microgrid. The optimization objectives are set to minimize the daily lifecycle economic cost and maximize the power quality suppression capability index （PQSCI）. To solve the problems of deep coupling and multi-constraint operation states among various types of active devices， the mechanisms of task division， factor grading and skill factor are introduced to realize information sharing and knowledge transfer among multi-equipment optimization tasks， which significantly improves the global search ability and computational efficiency. The case results show that the proposed method can effectively reduce the operation and interaction costs by about 19.9% on the premise of ensuring system stability， and increase the PQSCI by about 6.4%， realizing the collaborative optimization of economy and power quality.