Hybrid modeling-based digital twin for performance optimization with flexible operation in the direct air-cooling power unit

The thermal power sector, one of the major CO2 emissions sources, is urged quicker steps to come up with an action that enables coal savings and carbon emissions reduction in China. Flexible operation is a growing trend among thermal power plants owing to the ever-increasing installation of renewable energy power resources. However, flexible operation comes with performance degradation, energy consumption growing and CO2 emissions increment. This study proposes a digital twin system for a direct air-cooling unit to determine the optimal back-pressure and corresponding fan frequency modulation under various operating conditions to minimize the coal consumption and maximize the unit profit. Thus, a hybrid model of the digital twin system integrating an extreme gradient boosting algorithm tuned by a particle swarm optimization algorithm and a domain knowledge-based analytical formulation for the optimal back-pressure is proposed. The proposed digital twin system is applied to a 600 MW on-duty direct air-cooling power unit. In the industrial case study, the proposed system saved 535.526 tons of standard coal and decreased CO2 emissions by 1335.068 tons within two weeks, which is remarkable.