Abstract:

We discuss several challenges in predicting extreme events in weather forecasting and some paths forward
in addressing them. In particular, extreme temperatures can pose significant challenges to power provision
by increasing demand and decreasing supply, contributing 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 of 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 efficiently
generate unconditional and conditionally extreme temperature samples from this model. These
models are investigated within an extensive case study with realistic data demonstrating 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 reasonable solutions and that modeling generator temperature
dependence is an important consideration for finding efficient, low-risk solutions.
Reference: https://arxiv.org/abs/2405.18538

Mihai Anitescu (present), Ramsey Rossman, Mitchell Krock, Phil Dinennis, Line Roald, James Luedtke, Julie Bessac
Argonne National Laboratory, University of Wisconsin, University of Missouri, National Renewable Energy
Laboratory

Biosketch:

Mihai Anitescu has been a computational mathematician in the Mathematics and Computer
Science Division at Argonne National Laboratory since 2002 and a part-time professor in the Department of
Statistics at the University of Chicago since 2009, with tenure since 2012. His research focuses on numerical
optimization, uncertainty quantification, and numerical analysis, as well as computational mathematics and
its applications in electricity grids and other energy-centric scientific disciplines. He is the author of more
than 150 papers in scholarly journals and conference proceedings, a senior editor of Optimization Methods
and Software, and a member of the SIAM Journal on Optimization editorial board. In the past, he was a
member of the editorial boards of the SIAM Journal on Computational Science, the SIAM/ASA Journal
on Uncertainty Quantification, and Mathematical Programming series A and B. In 2019, he was elected a
Fellow of the Society of Industrial and Applied Mathematics (SIAM). He led or is leading projects whose
total budget exceeds $40 million.