An Intelligent Algorithm for Solving Unit Commitments Based on Deep Reinforcement Learning

With the reform of energy structures, high proportion volatile new access makes existing unit commitment (UC) theory unable to satisfy development demands day-ahead market decision-making in power system. Therefore, this paper proposes an intelligent algorithm for solving UC, based on deep reinforcement learning (DRL) technology. Firstly, DRL is used model Markov decision process UC problem, and corresponding state space, transfer function, action space reward function are proposed. Then, policy gradient (PG) solve problem. On basis, Lambda iteration output scheme start–stop state, finally a DRL-based solution The applicability effectiveness method verified simulation examples.