Typical models of the distribution system restoration process
This provides a data-driven stochastic model for resilience planning in power distribution systems, though it appears incremental as it builds on existing probabilistic modeling approaches with utility-specific data.
The authors developed probabilistic models for power system restoration using outage data from four utilities, decomposing restoration into three components with specific distributions (Beta for time progression, Uniform as approximation, Lognormal for total duration, Gamma for first restore time), enabling Monte Carlo simulation and uncertainty-aware decision support.
Accurate probabilistic modeling of the power system restoration process is essential for resilience planning, operational decision-making, and realistic simulation of resilience events. In this work, we develop data-driven probabilistic models of the restoration process using outage data from four distribution utilities. We decompose restoration into three components: normalized restore time progression, total restoration duration, and the time to first restore. The Beta distribution provides the best-pooled fit for restore time progression, and the Uniform distribution is a defensible, parsimonious approximation for many events. Total duration is modeled as a heteroskedastic Lognormal process that scales superlinearly with event size. The time to first restore is well described by a Gamma model for moderate and large events. Together, these models provide an end-to-end stochastic model for Monte Carlo simulation, probabilistic duration forecasting, and resilience planning that moves beyond summary statistics, enabling uncertainty-aware decision support grounded in utility data.