Integrated Seasonal Demand Response for AC-OPF with Precise and Innovative Modeling of Thermal Energy Storage and Optimal ESS Allocation

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 improve forecasting accuracy for electrical loads and energy production from solar and wind sources over one year using long short-term memory (LSTM) neural networks. By analyzing historical load data using the K-means clustering method, we identified key scenarios for effective seasonal 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 mixed-integer nonlinear programming (MINLP) 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.

Keywords: Ice thermal energy storage (ITES), AC optimal power flow (AC-OPF), Energy storage system (ESS), Demand Response (DR), HVAC aggregator, Mmulti-objective optimization (MOO)