Decision Making on Transportable Resilience Delivery for Short-Term Disaster Management in Power Distribution Systems
Open Access DepositedHigh-Impact Low-Probability (HILP) incidents have increasingly threatened the operational reliability of power distribution systems (DS), leading to prolonged power outages and critical service disruptions. This dissertation develops a suite of decision-support models for short-term disaster management through the transportable delivery of resilience, focusing on the effective deployment of mobile mower sources (MPSs). The research addresses resilience planning across four key phases
post-disaster restoration, pre-disaster preparedness, disaster prevention, and renewable-integrated recovery. In the post-disaster context, a mixed-integer nonlinear programming model is proposed to optimize the routing and scheduling of MPSs while accounting for decision-dependent uncertainties (DDUs) such as waiting time for MPSs. For pre-disaster preparedness, a two-stage stochastic optimization model is introduced to determine the optimal prepositioning of MPSs and repair crews by considering infrastructure interdependence and disaster-induced uncertainties. To support disaster prevention, particularly in wildfire-prone regions, a risk-averse optimization framework is developed to balance electrically-induced wildfire ignition risk and power outage cost caused by public safety power shutoff (PSPS) actions. Lastly, the dissertation explores the deployment of mobile wind turbines (MWTs) in hydrogen-accommodated microgrids, integrating renewable energy sources into post-disaster recovery strategies under wind forecast uncertainty. The proposed models are validated using synthetic test systems, demonstrating that proactive and coordinated deployment of mobile energy assets can significantly reduce outage costs, improve adaptability under uncertainty, and enhance overall system resilience. The findings underscore the critical importance of incorporating both exogenous and decision-dependent uncertainties in infrastructure restoration and resilience planning.
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