MDRPASS: A Multi-Dimensional Demand Response Potential Assessment Based Scheduling Strategy for Smart Grid

The high penetration of renewable energy presents significant challenges for the power grid in balancing supply and demand. Demand response (DR) is crucial for stable grid operation, yet existing research often overlooks actual electricity consumption patterns for the following day and the diversity among user types, compromising assessment accuracy and applicability. To address these shortcomings, this paper proposes an integrated load regulation system comprising “Identification-Prediction-Scheduling”. First, we identify specific user groups and utilize load prediction models to accurately forecast future loads. By analyzing predicted loads alongside historical data, we uncover electricity consumption patterns and DR potential for the following day, providing better insights for power grid scheduling. Finally, we implement a load control strategy aimed at optimizing grid stability. Our results show that the load forecasting model achieves average errors of 17.86% and 16.16% at intervals of 15 minutes and 1 hour, respectively. When demand is set at 1500 kW, the proposed load control strategy predicts a response load curtailment of 2216.85 kW and a DPI score of 50.75, significantly outperforming both the random selection strategy (1887.11 kW, DPI score of 18.39) and the priority to large FBC strategy (288.86 kW, DPI score of 40.78). This approach ensures better enterprise selection, contractual compliance, and improved benefits for electrical users.