Unit commitment is a key issue in the economic dispatch of power system.For solving this kind of complex mixed-integer nonlinear optimization problem

most optimization algorithms have shortcomings in solving speed

solving efficiency and computational complexity.With the continuous development of artificial intelligence technology

intelligent algorithms have gradually been applied in decision-making optimization problems in power system and achieved better results.This paper proposes a unit commitment algorithm based on deep reinforcement learning.Firstly

a unit commitment mathematical model is constructed with the objective function of minimizing generation cost and the constraints of power balance

generation limit and minimum uptime and downtime.Secondly under the framework of the reinforcement learning

a mathematical description of the unit commitment problem is established.And the corresponding state space

action space and reward function are defined.Finally

the DQN algorithms is applied to train the model to solve the unit commitment problem.The well-trained model verified the effectiveness and superiority of the method proposed in solving the unit commitment problem by analyzing the test results.