弹性配电系统动态负荷恢复的深度强化学习方法

Deep Reinforcement Learning Method for Dynamic Load Restoration of Resilient Distribution Systems

极端事件下,合理利用微网盈余电力供应配电系统中的关键负荷,可有效提升电网弹性。基于深度强化学习(DRL)技术,提出一种考虑微网参与的配电系统动态关键负荷恢复(DCLR)方法,支持以无模型的方式求解复杂问题,以提升在线计算效率。首先,分析含微网的配电系统DCLR问题,并在此基础上构建其马尔可夫决策过程,其中考虑了配电系统运行、微网运行和用户满意度等约束条件。其次,基于OpenDSS构建DCLR模拟环境,形成DRL应用所需的智能体-环境交互接口,进一步采用深度Q网络算法搜寻关键负荷恢复的最优控制策略,并定义收敛性、决策能力指标分别用于评价智能体的训练和应用表现。最后,基于改进的IEEE测试系统验证了所提方法的有效性。

In extreme events,it can effectively enhance the grid resilience by using surplus power of microgrids to serve the critical loads in distribution systems.Based on the deep reinforcement learning(DRL)technique,considering the participation of microgrids,a dynamic critical load restoration(DCLR)method of distribution systems is proposed to support the model-free manner to solve the complex problems,significantly improving the online computational efficiency.Firstly,the DCLR problem of distribution systems with microgrids is analyzed.On this basis,its Markov decision process(MDP)is formulated considering the complex operational constraints,including the distribution operation constraints,microgrid operation constraints,customer satisfaction degree,etc.Secondly,a DCLR simulation environment is built based on Open DSS as the agent-environment interface for applying DRL algorithms.Furthermore,a deep Q-network algorithm is adopted to search for the optimal policies of the critical load restoration.The convergence and decision-making ability indices are defined to measure the performance of the agents in training and application processes,respectively.Based on the two modified IEEE test systems,the effectiveness of the proposed method is verified.