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OACSTPCD
Regional Power Grid Group Collaborative Optimization Dispatching Strategy Based on Multi-agent Deep Reinforcement Learning
充分发挥可调控资源群体的调控特性可以极大提升地区电网动态调节能力.为此,文章提出一种面向可调控资源群体的协同优化调度方法,并利用多智能体深度强化学习技术求解多群体复杂协同问题.首先,对考虑多可调控资源群体的地区电网优化调度问题进行建模,设定电网优化目标及系统安全运行约束等条件;其次,阐述多智能体深度确定性策略梯度算法基本原理;然后,利用策略梯度更新算法,进行"集中训练-分散执行"寻求可调控资源群体协同最优调度策略,并定义相应评价指标分别测试智能体的离线训练效果和在线应用效果;最后,基于改进的IEEE测试系统,验证所提方法的有效性.
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.
陆亚楠;杨胜春;李亚平;姚建国;高冠中;毛文博
中国电力科学研究院有限公司(南京),江苏省 南京市 210003
动力与电气工程
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
multi-agentdata drivendeep reinforcement learningoptimize schedulingcontrollable resource groups
《电力信息与通信技术》 2024 (004)
1-10 / 10
国家自然科学基金项目(U2066212).
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