基于强化学习与多目标模型预测控制的热电联产机组灵活经济运行双层优化策略

Abstract Enhancing the comprehensive performance of the combined heat and power (CHP) units is crucial for accommodating renewable energy and achieving energy conservation. To this end, a bi-level optimization strategy based on reinforcement learning (RL) and multi-objective model predictive control (MOMPC) is proposed to enhance the CHP units flexibility and economic performance. Firstly, a CHP unit model is constructed, and its various parameters are incorporated into the rolling optimization of the MOMPC, serving as the lower-level follower to solve the fundamental control. Secondly, a bi-level optimization strategy integrating the twin delayed deep deterministic policy gradient (TD3) algorithm with MOMPC (TD3-MOMPC) is proposed. The TD3 agent is designated as the upper-level leader. By decomposing the complex flexibility requirements and the optimization control sequence of the CHP unit, tasks are assigned to both the upper-level leader and the lower-level follower for bi-level interactive optimization. Thirdly, with power flexibility, heating quality, and operational economy serving as leader guidance, a multi-criterion optimization reward function is designed for the upper-level. Then, the actions of the upper-level TD3 agent are designed as dynamic weights and time-varying prediction horizons for the rolling optimization of MOMPC, serving as a bridge to connect and guide the bi-level optimization. Finally, to verify the effectiveness of the bi-level optimization strategy, extensive tests on load variation and disturbance rejection were conducted on a 300 MW CHP unit. The results show that the proposed strategy enhances the unit's load flexibility, heating quality, and operational economy.