  Power grid expansion planning requires making large investment decisions in
the present that will impact the future cost and reliability of a system
exposed to wide-ranging uncertainties. Extreme temperatures can pose
significant challenges to providing power by increasing demand and decreasing
supply and have contributed to recent major power outages. We propose to
address a modeling challenge of such high-impact, low-frequency events with a
bi-objective stochastic integer optimization model that finds solutions with
different trade-offs between efficiency in normal conditions and risk to
extreme events. We propose a conditional sampling approach paired with a risk
measure to address the inherent challenge in approximating the risk of
low-frequency events within a sampling based approach. We present a model for
spatially correlated, county-specific temperatures and a method to generate
both unconditional and conditionally extreme temperature samples from this
model efficiently. These models are investigated within an extensive case study
with realistic data that demonstrates the effectiveness of the bi-objective
approach and the conditional sampling technique. We find that spatial
correlations in the temperature samples are essential to finding good solutions
and that modeling generator temperature dependence is an important
consideration for finding efficient, low-risk solutions.
