Two-layer optimal scheduling of integrated energy systems for ultra-fast EV charging under multiple uncertainties

Ultra-fast charging electric vehicle charging stations (EVCSs) integrated with photovoltaic and energy storage systems play an increasingly important role in integrated energy systems (IESs), but their coordinated operation is challenged by the conflict between electric vehicle charging satisfaction, system economy, and multiple uncertainties. To address this issue, this paper proposes a two-layer optimal scheduling model for an IES–EVCS system. In the lower layer, an EVCS scheduling model is developed to maximize satisfaction-converted benefits of ultra-fast charging EVs. In the upper layer, an IES scheduling model is established to minimize operating cost while coordinating energy interaction with the EVCS. A Stackelberg game framework is adopted to link the two decision layers, and a robust optimization–distributionally robust optimization framework is introduced to handle multiple uncertainties. To solve the resulting large-scale optimization problem efficiently, a two-layer, three-stage nested column-and-constraint generation algorithm is designed. Case studies show that the proposed method increases electric vehicle satisfaction-converted benefits by about 4.8 times, reduces IES scenario deterioration compensation costs by 37.5%, and shortens solution time by 87.7%. These results demonstrate that the proposed framework can effectively improve EV charging service quality, reduce system operating risk, and provide an efficient scheduling tool for IES–EVCS coordinated operation under uncertainty.