Real-time nested scheduling model with embedded adaptive charging–discharging strategy considering EV uncertainty

The widespread adoption of electric vehicles (EVs) offers substantial environmental benefits but presents significant challenges for power system management due to the inherent uncertainties in EV users’ behavior and charging demands. To address these issues, this study proposes a real-time nested scheduling model with an adaptive charging–discharging strategy for community, supermarket, and street parking facilities. The model employs an adaptive approach where EV users provide essential information upon arrival at the microgrid (MG), choosing between unordered or ordered participation based on their preferences. A priority-based two-stage schedule planning strategy is implemented, first planning the overall charging–discharging power for the EV cluster, then refining individual EV power based on priorities. For EVs requiring early departure, priorities are adjusted to ensure the expected State of Charge is reached before leaving. The model features a real-time nested framework with a bi-level optimization outer layer to coordinate conflicting interests among entities and an inner layer for EV power allocation. To address high uncertainties, a Model Predictive Control algorithm is employed, incorporating Karush–Kuhn–Tucker conditions, strong duality theorem, and linearization techniques. Simulation results demonstrate the model’s superior operational efficiency, reliability, and robustness while achieving a win–win outcome for both the MG operator and EV cluster.