Intelligent perception and regulation of fragmented flexible load resources in microgrid based on deep learning

With the rapid development of smart grid and renewable energy technology, microgrid, as a new type of distributed power system, has important potential in ensuring energy supply and improving energy efficiency. However, the optimal scheduling of microgrids still faces complex computational challenges. Based on deep learning technology, this paper discusses the optimal scheduling strategy of microgrid, especially how to solve the problem of continuous action in microgrid through deep reinforcement learning algorithm (such as DDPG). By comparing and analyzing the performance of DQN and DDPG, the advantages of DDPG in energy storage management, grid electricity trading and pollutant emissions are demonstrated. The framework of the paper includes an overview of deep learning technology, a microgrid collaborative optimization model, a flexible load control method, and an optimal dispatching strategy based on DDPG.