Reinforcement Learning with Model Predictive Control for Coordinated Operation of Power Plant

To address the challenges of rapid and accurate regulation of power generation load due to significant model nonlinearity, strong inertia, and substantial variable coupling in the load control process of boiler and turbine units in coal-fired power plants, this paper proposes a control policy that combines model predictive control with reinforcement learning based on a multi-operating condition model. By making real-time adjustments according to the operating state, the policy corrects model predictive control towards optimal control, thereby achieving optimal variable load control of a coal-fired power unit. A case study of a typical 300MW power plant in China demonstrates the superior control performance of the proposed method compared to traditional predictive control models.