Multi-time-domain hierarchical scheduling of integrated energy systems with frequency decomposition considering large-scale grid connection of electric vehicles

Amid global decarbonization efforts, Integrated Energy Systems (IES) face critical challenges from the inherent uncertainties of high-penetration renewable energy sources and the large-scale, potentially disorderly integration of Electric Vehicles (EVs), leading to significant supply-demand imbalances and system volatility. Existing approaches struggle to effectively coordinate diverse device response times and capture complex EV user behavior. To address these, this paper proposes a multi-time domain control model-driven bi-level optimization framework for Integrated Energy Systems. The upper level employs a multi-objective intelligent algorithm to generate Pareto-optimal solutions balancing economic costs and environmental impacts. The lower level implements a singular perturbation theory-based multi-time domain control model, dynamically partitioning devices into fast/slow subsystems for differentiated scheduling, significantly enhancing resilience against uncertainties. Additionally, a prospect theory-driven EV dynamic charging response model accurately incorporates user psychology (e.g., loss aversion) to guide orderly charging via price incentives. Furthermore, a Fourier-based power decoupling strategy for hybrid storage (LIB-SC) reduces battery degradation by 28.7%. Case studies demonstrate superior performance: a 12.2% reduction in economic costs, an 11.5% reduction in emissions, and robustly maintained system volatility below 5% under extreme uncertainties, showcasing significant advancements in balancing economic-environmental objectives while accommodating high renewable and EV penetration.