Multi-time scale optimization study of integrated energy system considering dynamic energy hub and dual demand response

The integrated energy system (IES) is one of the current hot topics in energy research field. However, simplifying models often assumes constant energy conversion device efficiency during optimized operation, which leads to impractical operating modes for the IES. Additionally, the outcomes of device outputs vary across different time scales. The inaccuracy and temporal dispersion of energy supply are becoming more and more prominent, and the modeling and optimal scheduling of the IES are facing great challenges. To effectively address these issues, a multi-timescale optimal scheduling strategy for integrated energy systems was constructed, which considers the variable operating conditions of the equipment. First, a dynamic energy hub model with iterative correction between the equipment efficiency and load rate was established. Subsequently, on this basis, a multi-time scale optimization model of day-ahead, intraday, and real-time dispatch was constructed. Further, a day-ahead and intra-day demand response strategy was introduced to encourage customers to actively participate in demandside management. Finally, a case study based on the energy system in the Xiongan area of China was conducted. From the optimization results, it was demonstrated that the proposed model in this work can solve the problem of inaccurate equipment efficiency in the fixed condition model and significantly improve the accuracy of system operation and dispatch. The implementation of a multi-time scale optimization and regulation strategy for IES can reduce the cost by 5.82%. Combined also with the dual demand response strategy, the total costs can be reduced by up to 10.07%.