Stochastic production simulation-based capacity economic optimization study for thermal power flexibility modification

This paper employs stochastic production simulation to study the economic optimization of capacity for flexible retrofitting of non-heating thermal power units. The research addresses the issue of wind and solar energy abandonment in the “Three North” regions of China due to a lack of system flexibility. To minimize transaction costs for peak regulation, deep peak regulation costs, and system abandonment costs, a flexibility capacity optimization model is developed, considering factors such as reducing minimum stable power, increasing power regulation rates, and enhancing start-stop flexibility of thermal power units. Using data from a regional power grid in northern China, the study finds that flexible retrofitting of large-capacity units is economically optimal. Genetic algorithm is utilized to solve the objective function, demonstrating the model’s practicality. This research contributes to improving the operational efficiency and integration of renewable energy in power systems.