Power system intelligent operation knowledge learning model based on reinforcement learning and data-driven - Zhou, Yibo; Mu, Gang; An, Jun; Zhang, Liang (Frontiers in Energy Research, 2023)
With the expansion of power grid scale and the deepening of component coupling, the operation behavior of power system becomes more and more complex, and the traditional function decoupling dispatching architecture is not available anymore. Firstly, this paper studies the corresponding relationship between reinforcement learning method and power system dispatching decision problem, and constructs the artificial intelligent dispatching knowledge learning model of power system based on reinforcement learning (AIDLM). Then, a data-driven intelligent dispatching knowledge learning method is proposed, and interpretable dispatching decision knowledge is obtained. Finally, a knowledge efficiency evaluation indexes is proposed and used to guide the extraction of original acquired knowledge. The intelligent economic dispatching problem of a regional power grid is analyzed. The results show that the AIDLM method can intelligently give the dispatching strategy of power generation according to the time series changing load, which effectively reduces the cost of power generation in the grid. The method proposed in this paper can make up for the shortcomings of traditional dispatching methods and provide strong support for modern power system dispatching.