Tie-line Power Adjustment Method Based on Proximal Policy Optimization Algorithm

Abstract

The tie-line power adjustment is an essential part of the power system operation state calculation. Various existing algorithms to solve the tie-line power adjustment problem are mainly implemented by introducing tie-line power equation constraints into conventional power flow calculations. Such methods have low calculation efficiency, not enough automation, and are prone to non-convergence in the power flow calculation. In this paper, the tie-line power adjustment problem is formulated as a Markov decision process, and the proximal policy optimization algorithm is introduced to optimize the decision policy. In order to enhance the effectiveness of the proposed method, a new deep neural network structure suitable for the proximal policy optimization algorithm is designed. The proposed method is verified with the IEEE 39-bus system.