基于强化学习算法的微电网优化策略OA

Distributed energy has the characteristics of small-scale fluctuations and intermittency,making it difficult to formulate operational strategies for microgrids.As an effective way to integrate multiple distributed energy sources and external grids,multi-energy microgrid management is becoming a very complex task.A microgrid real-time optimal operation strategy was proposed under the influence of comprehensive factors such as load demand,renewable energy sources and energy storage devices.Firstly,based on the reinforcement learning framework,the microgrid operation problem was modeled as a Markov decision process,and then a microgrid strategy optimization model was constructed with the aim of minimizing voltage fluctuations and operational losses in the microgrid.In order to effectively utilize the interconnection structure of the distribution network,a graph attention proximal policy optimization(GT-PPO)algorithm was designed on the basis of the proximal policy optimization algorithm.This algorithm uses an attention mechanism and a graph neural network to learn the correlation of distribution network nodes to formulate the optimal scheduling strategy for multi-energy distribution networks at different times under various environments.Simulation experiments were conducted using two specifications of the improved IEEE 33 node and IEEE 118 node distribution networks.The experimental results show that the optimization strategy can achieve real-time optimization of microgrids,and the results are better than the traditional proximal policy optimization(PPO)algorithm and double deep Q network(DDQN)algorithm.