Method for Evaluating and Predicting the Reliable Regulation Capacity of Industrial Loads

This paper proposes a method for evaluating and predicting the reliable regulation capacity of industrial electricity loads. First, by performing clustering analysis on historical load data, an initial baseline load curve is generated, and then dynamically adjusted using temperature and date correction factors to obtain a more accurate baseline load curve. Next, combined with load forecasting, the theoretical regulation capacity is calculated, and the uncertainty of regulation capacity is assessed by analyzing the response probabilities under different regulation periods and demands. Subsequently, the distribution of reliable regulation capacity is analyzed based on the theoretical regulation capacity and uncertainty. Finally, the effectiveness of the method is verified through simulation modeling. The results indicate that different regulation periods and reduction levels significantly impact load regulation capacity, and the development of regulation strategies should be based on actual demand and reliable regulation capacity. This study provides theoretical support for the assessment of industrial load regulation and the optimization of regulation strategies.