Power system intelligent operation knowledge learning model based on reinforcement learning and data-driven

With the expansion of power grid scale and deepening component coupling, operation behavior system becomes more complex, traditional function decoupling dispatching architecture is not available anymore. Firstly, this paper studies corresponding relationship between reinforcement learning method decision problem, constructs artificial intelligent knowledge model based on (AIDLM). Then, a data-driven proposed, interpretable obtained. Finally, efficiency evaluation indexes proposed used to guide extraction original acquired knowledge. The economic problem regional analyzed. results show that AIDLM can intelligently give strategy generation according time series changing load, which effectively reduces cost in grid. make up for shortcomings methods provide strong support modern dispatching.