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\begin{document}
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\title{Learning-Based, Cost-Effective Distribution Grid Emergency Resource
Planning for Extreme Weather Events}



\author[1]{Shuva Paul}%
\author[1]{Fei Ding}%
\author[2]{Santiago Grijalva}%
\affil[1]{National Renewable Energy Laboratory}%
\affil[2]{Georgia Institute of Technology}%


\vspace{-1em}



  
  \date{April 25, 2026}


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\begin{abstract}
Weather events can cause power outages and damage the grid
infrastructure. Resilience is the ability of the power grid to quickly
recover from disruptions such as weather events. Enhancing the
resilience of the distribution systems is required for continued grid
operation. A solution to enhance the resilience of a distribution system
is to proactively deploy resources (e.g., backup generators, repair
crews, additional conductors) in the most vulnerable regions to provide
faster service restoration. In this paper, first, we propose a method to
model the impact of extreme weather events on power grids. Then, we use
Q-learning to identify the worst impact zones while considering possible
propagating paths of extreme weather. Finally, we develop a
game-theoretic approach to allocate resources to the worst impact zones
at the minimum cost. Simulations are conducted on a modified IEEE
123-bus test system. The results prove the effectiveness of the proposed
work on enhancing grid resilience.%
\end{abstract}%



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