Microgrids are increasingly relied upon to maintain power supply when the main grid fails, but the transition from connected to islanded operation is more complex than theory suggests. Real-world islanding events involve sudden voltage and frequency swings, unpredictable load changes, variable renewable output, and the risk that distributed energy resources may trip offline during the disturbance. Existing resilience metrics typically assume equipment survives the transition and focus only on minimizing outage duration or magnitude—an optimistic view that can lead to inadequate planning.
Researchers have now proposed a stochastic scheduling framework that explicitly models the probability of successful islanding (PSI), treating it as a measurable risk metric. The PSI represents the likelihood that a microgrid can restore balance between supply and demand immediately after main grid disconnection, accounting for all sources of uncertainty: load volatility, renewable generation variability, and the failure rates of individual DERs. Rather than assuming survival, the model acknowledges that components may fail under extreme transients.
The nonlinear mathematical constraints defining PSI are converted into a mixed-integer linear programming (MILP) formulation using a multi-interval approximation technique, making the model computationally tractable for real power system scheduling. Case studies demonstrate that the new approach estimates PSI with less than 8% error, compared to roughly 28% error using conventional methods that ignore DER tripping.
Sensitivity analyses confirmed that accounting for equipment failure rates is essential to accurate PSI calculation. The framework allows grid operators and microgrid planners to set target PSI thresholds and adjust scheduled generation, storage, and load shedding strategies to meet them reliably. This more rigorous quantification of islanding success probability enables safer, more resilient microgrid designs and operational protocols.
