面向热电联产经济调度优化的自适应噪声注入PPO强化学习算法OA

In order to deal with the current urgent energy crisis,improve the utilization rate of energy,this paper proposes an adaptive noise injection-based proximal policy optimization algorithm for economic optimization scheduling in cogeneration systems.By introducing adaptive noise in the clipping range,this method allowed the policy to dynamically adjust the corresponding clipping range during the policy training,enhancing the exploration capability and improving the economic efficiency of the model.The experiments show that compared with the traditional proximal policy optimization algorithm,the improved algorithm in this paper has lower energy consumption and lower operating cost in the overall energy scheduling task.