  With the increasing frequency of natural disasters, operators must prioritize
improvements in the existing electric power grid infrastructure to enhance the
resilience of the grid. Resilience to extreme weather events necessitates
lowering the impacts of high-impact, low-probability (HILP) events, which is
only possible when such events are considered during the planning stage. This
paper proposes a two-stage stochastic planning model where the generation
dispatch, line hardening, line capacity expansion, and distributed generation
sizing and siting decisions are proactively decided to minimize the overall
load shed and its risk for extreme weather scenarios, where the risk is modeled
using conditional value-at-risk. To alleviate computational complexity without
sacrificing solution quality, a representative scenario sampling method is
used. Finally, the overall framework is tested on a standard IEEE reliability
test system to evaluate the effectiveness of the proposed approach. Several
planning portfolios are presented that can help system planners identify
trade-offs between system resilience, planning budget, and risk aversion.
