Abstract
Uncertainty in renewable energy resources and variations in the demand response (DR) of participation pose significant challenges for accurately predicting participation levels, particularly across seasons. This study offers a holistic approach to seasonal DR scheduling within the AC optimal power flow (AC-OPF) framework, focusing on advanced thermal energy storage (TES) and optimal energy storage system (ESS) allocation. In this study, we categorized the electrical loads and energy generation from solar and wind sources on a seasonal basis. By analyzing historical load data using the K-means clustering method, we identified key scenarios for implementing effective seasonal demand response (DR) strategies. Additionally, we forecast the optimal participation rates for subscribers, enabling the design of targeted DR incentives that meet the seasonal system needs. Our research specifically targets the optimization of heating and cooling demands within HVAC systems, emphasizing the impact of ambient temperature on the efficiency of TES. This relaxed mixed-integer nonlinear programming (RMINLP) problem was solved using GAMS software with the CONOPT3 solver, specifically applied to the IEEE 24 and 118 bus networks. This multi-objective optimization framework enhances energy management and facilitates the integration of renewable resources, thereby contributing to grid stability and sustainability. Our findings highlight the crucial role of seasonal DR strategies for enhancing the overall efficiency of energy systems. For each season, an optimal range and point for DR performance was obtained to be offered to subscribers by the system operator.