Abstract

To address the transient frequency limit violations triggered by the low-inertia characteristics of temporary islanded microgrids formed under extreme disasters, this paper proposes a multi-source collaborative two-stage robust optimization day-ahead scheduling model considering dynamic frequency constraints. Firstly, a collaborative architecture encompassing emergency power vehicles, grid-forming energy storage systems, and flexible loads is constructed. Through collaborative scheduling in the day-ahead pre-scheduling and real-time re-scheduling stages, this architecture effectively avoids the exorbitant costs of physical load shedding under extreme conditions. Secondly, to overcome the limitations of traditional robust box uncertainty sets—which ignore temporal correlations, tend to cause non-physical high-frequency oscillations, and hinder algorithm convergence—a time-correlated uncertainty set based on state-transition auxiliary variables is designed to accurately capture the continuous evolution characteristics of meteorological disturbances. The column-and-constraint generation algorithm is utilized for the solution methodology, combined with the big-M method to transform the subproblem containing bilinear terms into a mixed-integer linear programming model for efficient solving. Simulation results on a modified 33-node test system demonstrate that the proposed model effectively filters out high-frequency oscillation trajectories and significantly improves computational efficiency. Under the worst-case temporal disturbances, the transient frequency drop and the rate of change in frequency are strictly controlled within safe thresholds. Compared to deterministic scheduling and traditional box-based robust models, the proposed scheme effectively balances system security and economic efficiency, demonstrating exceptional system resilience and defense capabilities against varying prediction errors.

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