A Framework for Balancing Power Grid Efficiency and Risk with Bi-objective Stochastic Integer Optimization

TL;DR

This paper introduces a bi-objective stochastic integer optimization framework for power grid expansion planning that balances efficiency and risk from extreme temperature events, using a novel conditional sampling approach.

Contribution

It presents a new modeling approach combining bi-objective optimization with conditional sampling to better account for low-frequency, high-impact temperature events in power grid planning.

Findings

Spatial temperature correlations are crucial for optimal solutions.

Modeling generator temperature dependence improves risk management.

The approach effectively balances efficiency and risk in case studies.

Abstract

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.