Power System Operation Mode Calculation Based on Improved Deep Reinforcement Learning

Ziyang Yu, Bowen Zhou (), Dongsheng Yang, Weirong Wu, Chen Lv and Yong Cui
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Ziyang Yu: College of Information Science and Engineering, Northeastern University, Shenyang 110004, China
Bowen Zhou: College of Information Science and Engineering, Northeastern University, Shenyang 110004, China
Dongsheng Yang: College of Information Science and Engineering, Northeastern University, Shenyang 110004, China
Weirong Wu: College of Information Science and Engineering, Northeastern University, Shenyang 110004, China
Chen Lv: China Electric Power Research Institute, Beijing 100192, China
Yong Cui: State Grid Shanghai Municipal Electric Power Company, Shanghai 201507, China

Mathematics, 2023, vol. 12, issue 1, 1-14

Abstract: Power system operation mode calculation (OMC) is the basis for unit commitment, scheduling arrangement, and stability analyses. In dispatch centers at all levels, OMC is usually realized by manually adjusting the parameters of power system components. In a new-type power system scenario, a large number of new energy sources lead to a significant increase in the complexity and uncertainty of a system structure, thus further increasing the workload and difficulty of manual adjustment. Therefore, improving efficiency and quality is of particular importance for power system OMC. This paper first considers generator power adjustment and line switching, and it then models the power flow adjustment process in OMC as a Markov decision process. Afterward, an improved deep Q-network (improved DQN) method is proposed for OMC. A state space, action space, and reward function that conform to the rules of the power system are designed. In addition, the action mapping strategy for generator power adjustment is improved to reduce the number of action adjustments and to speed up the network training process. Finally, 14 load levels under normal and N-1 fault conditions are designed. The experimental results on an IEEE-118 bus system show that the proposed method can effectively generate the operation mode under a given load level, and that it has good robustness.

Keywords: deep reinforcement learning; DQN; operation mode calculation; power flow convergence; power system (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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