Typical Power Grid Operation Mode Generation Based on Reinforcement Learning and Deep Belief Network

Abstract

With the continuous expansion of power grids and the gradual increase in operational uncertainty, it is progressively challenging to meet the capacity requirements for power grid development based on manual experience. In order to further improve the efficiency of the operation mode calculation, reduce the consumption of manpower and material resources, and consider the sustainability of energy development, this paper proposes a typical power grid operation mode generation method based on Q-learning and the deep belief network (DBN) for the first time. Firstly, the operation modes of different generator combinations located in different regions are obtained through Q-learning intelligent generation. Subsequently, the generated operation modes are clustered as different operation mode sets according to the data characteristics. Furthermore, comprehensive evaluation indexes are proposed from the perspectives of the steady state, transient state, and the economy. These multi-dimensional indexes are integrated via the analytical hierarchy process–entropy weight method (AHP-EWM) to enhance the comprehensibility of the evaluation system. Finally, DBN is introduced to construct a rapid operation mode evaluation model to realize the evaluation of operation mode sets, and typical operation mode sets are obtained accordingly. In this way, the system calculator only needs to compare the composite values to obtain the typical operation modes. The proposed method is validated by the Northeast Power Grid in China. The experimental results show that the proposed method can quickly generate typical power grid operation modes according to actual demand and greatly improve the efficiency of operation mode calculation.