Load aggregation management strategies for demand response: a dual forecasting approach for cost minimization

The rising demand for electricity presents significant challenges to grid stability. Demand response programs address this issue by incentivizing consumers to adjust consumption during peak periods. Load aggregators facilitate these programs by coordinating load reductions across participants; however, they face challenges in maintaining profitability and minimizing operational costs, particularly in nascent demand response markets. In this study, we evaluate three participant selection strategies: duration based, price based, and forecast based, within the context of Thailand’s pilot demand response programs. We propose a dual forecasting methodology that combines short-term load profile forecasting using XGBoost and long-term load duration curve predictions using SARIMAX. This integrated approach improves forecast accuracy and enables more strategic participant selection. Simulation results demonstrate that the dual forecasting strategy consistently minimizes operational costs and reduces the number of participant calls, outperforming conventional strategies even under resource-constrained, high-risk scenarios. These findings suggest that the dual forecasting strategy offers a cost-effective and reliable solution for demand response management, particularly in environments with limited participant availability, making it well-suited for deployment in emerging markets.