With the development of renewable energy, loads of thermal power units fluctuate, resulting in the trade-off between the frequent switching of auxiliary equipment and the economic-emission benefits in the wet flue gas desulfurization (WFGD) system. In this paper, the predictive scheduling problem is formalized, considering the power consumption, emission punishment and the switching frequency of slurry circulation pumps with finite prediction sequence of load and sulfur in coal. Model-free off-policy reinforcement learning (RL) is applied to solve the unclear and drifting system dynamic. Considering a real system, the framework setting and an emulator is introduced. Compared with traditional scheduling policies and the case without prediction, the proposed framework shows obvious advantages in terms of comprehensive performance and approximates the theoretical optimal solution at the steady-state. Moreover, the policy keeps the performance by adapting to the drifting without manual intervention, which demonstrates a broad application prospect in similar scenarios.

随着可再生能源的发展，火电机组的负荷波动，导致辅助设备的频繁切换与湿法烟气脱硫（WFGD）系统的经济排放效益之间的平衡。本文根据煤的负荷和硫含量的有限预测序列，结合能耗，排污和泥浆循环泵的切换频率，对预测调度问题进行了形式化。应用无模型的非政策强化学习（RL）解决了不确定性和漂移的系统动态问题。考虑到实际系统，介绍了框架设置和仿真器。与传统的调度策略和没有预测的情况相比，提出的框架在综合性能方面显示出明显的优势，并且近似于稳态下的理论最优解。此外，该策略通过适应漂移而无需人工干预即可保持性能，这表明了在类似情况下的广阔应用前景。