  Combinatorial distribution system optimization problems, such as scheduling
electric vehicle (EV) charging during evacuations, present significant
computational challenges. These challenges stem from the large numbers of
constraints, continuous variables, and discrete variables, coupled with the
unbalanced nature of distribution systems. In response to the escalating
frequency of extreme events impacting electric power systems, this paper
introduces a method that integrates sample-based conservative linear power flow
approximations (CLAs) into an optimization framework. In particular, this
integration aims to ameliorate the aforementioned challenges of distribution
system optimization in the context of efficiently minimizing the charging time
required for EVs in urban evacuation scenarios.
