A reduced-dimensional evolutionary framework for modeling supercritical power plants

Supercritical generation technologies have emerged as a promising pathway toward clean and flexible energy conversion. Advancing these technologies demands highly accurate mathematical models capable of supporting innovative control strategies and enhancing energy efficiency. The modeling of industrial systems has evolved from simple first-principle formulations to sophisticated frameworks that preserve their physical foundations while being enhanced through optimization algorithms. As these optimization problems increase in size, sensitivity analysis becomes essential for identifying the most influential parameters, thereby reducing computational complexity without sacrificing accuracy. These critical parameters are then refined using an adaptive Non-dominated Sorting Genetic Algorithm II (NSGA-II) optimization method. The results demonstrate notable gains in both predictive accuracy and computational efficiency. The proposed model holds significant potential for power generation applications, particularly in advanced control system design and real-time performance monitoring. This study developed a supercritical power plant (SCPP) model that achieved improved accuracy while maintaining simplicity compared to existing models. A model of a 600 MW SCPP is first constructed and systematically analyzed to capture its key characteristics. Subsequently, a sensitivity analysis is conducted to identify the parameters with the greatest influence on model performance. These critical parameters are then tuned using an adaptive Non-dominated Sorting Genetic Algorithm II (NSGA-II) optimization method. The results demonstrate notable gains in accuracy. The proposed model offers promising potential for power generation applications, particularly in the areas of advanced control system design and real-time performance monitoring.