A chance-constrained Bi-level scheduling framework for EV-integrated microgrids considering travel demand and uncertainty.

With the increasing penetration of electric vehicles (EVs) in microgrids, the stochastic charging behavior of EV users and the variability of renewable generation pose significant challenges to system operation. To address these issues, this paper proposes a chance-constrained bi-level optimization scheduling framework for EV-integrated microgrids considering multi-source uncertainties. In the proposed model, the upper level focuses on EV orderly charging, aiming to minimize net load fluctuation and EV charging cost under probabilistic user satisfaction constraints. The lower level represents the microgrid operation layer, which optimizes the dispatch of energy storage systems to minimize operational costs while ensuring system reliability through chance constraints. To handle uncertainties in EV travel behavior, photovoltaic generation, and load demand, probabilistic models are constructed, and the chance constraints are reformulated into tractable forms using the sample average approximation method. Case studies on a residential microgrid demonstrate that the proposed method effectively smooths the net load curve, reduces peak demand, and improves system economic performance under different uncertainty levels and EV penetration scenarios. The results verify the effectiveness and practical applicability of the proposed framework for microgrids with high EV integration, while supporting sustainable energy utilization and low-carbon community development.