电力信息与通信技术2024，Vol.22Issue(4)：1-10,10.DOI:10.16543/j.2095-641x.electric.power.ict.2024.04.01
基于多智能体深度强化学习的地区电网群体协同优化调度策略
Regional Power Grid Group Collaborative Optimization Dispatching Strategy Based on Multi-agent Deep Reinforcement Learning
摘要
Abstract
Giving full play to the regulatory characteristics of the controllable resource group can greatly improve the dynamic regulation capacity of the regional power grid.Therefore,a collaborative optimal scheduling method for controllable resource groups is proposed,and multi-agent deep reinforcement learning technology is used to solve multi-group complex collaboration problems.Firstly,the regional power grid optimization and dispatching problem considering multiple controllable resource groups is modeled,and the power grid optimization goals and system safety operation constraints are set.Secondly,the basic principle of multi-agent deep deterministic strategy gradient algorithm is expounded.Then,the policy gradient update algorithm is used to seek the optimal scheduling strategy of controllable resource group collaboration,and the corresponding evaluation indicators are defined to test the offline training effect and online application effect of the agent respectively.Finally,based on the improved IEEE test system,the effectiveness of the proposed method is verified.关键词
多智能体/数据驱动/深度强化学习/优化调度/可调控资源群体Key words
multi-agent/data driven/deep reinforcement learning/optimize scheduling/controllable resource groups分类
信息技术与安全科学引用本文复制引用
陆亚楠,杨胜春,李亚平,姚建国,高冠中,毛文博..基于多智能体深度强化学习的地区电网群体协同优化调度策略[J].电力信息与通信技术,2024,22(4):1-10,10.基金项目
国家自然科学基金项目(U2066212). （U2066212）
