Dynamic scheduling for integrated energy systems: A variable timescale approach with process parameter deviation-driven feedback correction

The integration of renewable energy into integrated energy systems presents significant challenges due to stochastic generation, system complexity, and model uncertainties. Optimal scheduling of integrated energy systems is crucial to ensuring the system operates economically and safely. Conventional fixed-timescale scheduling methods fail to balance responsiveness to real-time fluctuations and computational efficiency, while open-loop optimization overlooks prediction errors and dynamic discrepancies. To address these limitations, this study proposes a variable timescale approach with process parameter deviation-driven feedback correction. The proposed framework combines a dynamic scheduling layer that adaptively adjusts optimization intervals based on net load volatility to balance computational load and responsiveness, and a feedback correction layer that employs a multivariate predictive controller to dynamically rectify scheduling commands using real-time process parameter deviations. Simulation results demonstrate that the proposed approach reduces total energy supply deviations by 60.76 % (from 66.54 kWh to 26.11 kWh) and improves computational efficiency by 19.47 % compared to conventional methods. These results highlight the framework's ability to manage uncertainties and optimize integrated energy systems operations under renewable dominance, offering a scalable solution for sustainable energy system management.