Abstract
Deep Reinforcement Learning has shown significant progress in complex sequential decision-making tasks. Due to the uncertainty of the power system and the volatility of new energy sources, conventional scheduling methods may lose their effectiveness. This paper proposes an approach for solving the high-renewable penetrated power system scheduling problem by Proximal Policy Optimization (PPO) algorithm. Since the action in the method is constrained within a certain range, the convergence of the algorithm is better. The IEEE 30-bus system is used to verify the method and compared with the Deep Q-learning (DQN) , Policy Gradient (PG) algorithms, the results show that the method in this paper has better results in dealing with continuous action space problems.
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