Wildfire Risk-Informed Preventive-Corrective Decision Making under Renewable Uncertainty

TL;DR

This paper presents a novel decision-making framework that integrates wildfire risk assessment with power system operation to enhance resilience in renewable-rich grids, using stochastic optimization and stability constraints.

Contribution

It introduces a new stochastic preventive-corrective decision-making scheme that accounts for wildfire risks and renewable variability in power system operations.

Findings

The approach improves system resilience against wildfire risks.

It maintains economic viability under various wildfire scenarios.

Demonstrated on a 240-bus US Western Interconnection system.

Abstract

The increasing frequency and intensity of wildfires poses severe threats to the secure and stable operation of power grids, particularly one that is interspersed with renewable generation. Unlike conventional contingencies, wildfires affect multiple assets, leading to cascading outages and rapid degradation of system operability and stability. At the same time, the usual precursors of large wildfires, namely dry and windy conditions, are known with high confidence at least a day in advance. Thus, a coordinated decision-making scheme employing both day-ahead and real-time information has a significant potential to mitigate dynamic wildfire risks in renewable-rich power systems. Such a scheme is developed in this paper through a novel stochastic preventive-corrective cut-set and stability-constrained unit commitment and optimal power flow formulation that also accounts for the variability of renewable generation. The results obtained using a reduced 240-bus system of the US Western Interconnection demonstrate that the proposed approach increases the resilience of power systems across multiple levels of wildfire risks while maintaining economic viability.