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A chance-constrained Bi-level scheduling framework for EV-integrated microgrids considering travel demand and
1College of Rail Transit and Transportation, Hunan Railway Professional Technology College, Hunan, 412001, China.
Scientific Reports
|July 1, 2026
Summary
This study introduces a chance-constrained optimization framework for electric vehicle (EV) charging in microgrids. The method effectively manages uncertainties from EV usage and renewable energy, reducing load fluctuations and costs.
Area of Science:
- * Electrical Engineering
- * Power Systems Engineering
- * Optimization Theory
Background:
- * Growing electric vehicle (EV) integration in microgrids introduces operational challenges due to stochastic charging patterns and variable renewable energy generation.
- * Existing scheduling frameworks often struggle to adequately address the multi-source uncertainties inherent in these complex systems.
- * The need for robust operational strategies is paramount to ensure stability and efficiency in EV-integrated microgrids.
Purpose of the Study:
- * To develop a chance-constrained bi-level optimization framework for scheduling EV charging and microgrid operations.
- * To effectively manage uncertainties associated with EV user behavior, renewable generation, and load demand.
- * To minimize net load fluctuation, EV charging costs, and microgrid operational expenses while ensuring system reliability.
Main Methods:
- * A bi-level optimization model was formulated, with the upper level optimizing EV orderly charging and the lower level optimizing microgrid energy storage dispatch.
- * Probabilistic models were developed to represent uncertainties in EV travel, photovoltaic generation, and load demand.
- * Chance constraints were reformulated into tractable forms using the sample average approximation method.
Main Results:
- * The proposed framework effectively smoothed the net load curve and reduced peak demand in a residential microgrid case study.
- * Economic performance of the microgrid was improved under various uncertainty levels and EV penetration scenarios.
- * The method demonstrated practical applicability for managing high EV integration in microgrids.
Conclusions:
- * The chance-constrained bi-level optimization framework provides an effective solution for scheduling operations in EV-integrated microgrids facing multi-source uncertainties.
- * The approach supports sustainable energy utilization and low-carbon community development by enhancing system efficiency and economic performance.
- * The findings highlight the framework's potential for real-world implementation in modern power systems with increasing electrification.
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