Dissertation Title: Wildfire Risk Mitigation Methods to Enhance the Resilience of Power Systems
When: Wednesday, Oct 15, 2025, at 10:00 AM (CDT)
Where: Simrall 228
Candidate: Fasiha Zainab
Degree: Doctor of Philosophy in Electrical and Computer Engineering
Committee Members: Dr. Yong Fu, Dr. Masoud Karimi-Ghartemani, Dr. Seungdeog Choi, Dr. Zahra Saadatizadeh
Abstract:
The resilience of the power system has become increasingly important in recent years and needs to be further enhanced in the future, as potential threats from severe natural disaster events, specifically wildfires. A major concern is the two-way interaction between power systems and wildfires: power systems can ignite wildfires and be disrupted by them. Power system-induced wildfires occur when electrical components, particularly transmission lines, ignite fires due to faults exacerbated by extreme weather conditions. The presence of uncertainties, especially those related to unpredictable weather conditions, makes it difficult to handle and can significantly increase the risk of wildfire. Furthermore, these ignitions not only threaten public safety and infrastructure but also disrupt grid operations and lead to substantial economic losses. Therefore, strengthening the resilience of the power system against wildfire risk has become a pressing need.
To address these challenges, the proposed study introduces an uncertainty-aware resilience-enhancement framework that mitigates the risk of power system-induced wildfire through short-term operational strategies and long-term transmission expansion planning as a preventive measure. Firstly, the short-term operational strategy develops a wildfire risk mitigation approach using an adjustable distributionally robust chance-constrained (ADRCC) model, which ensures flexible decision-making even when the probability distributions of wildfire conditions are ambiguous or unknown. The wildfire risk mitigation (WRM) is modeled as a chance constraint,
and the risk tolerance associated with the WRM constraint is treated as a variable. This model enables proactive de-energization decisions to reduce the likelihood of wildfire ignitions, finding a balance between wildfire risk mitigation and load shedding. Secondly, the long-term transmission expansion planning strategy formulates a tri-level Defender–Attacker–Defender (DAD) model. This model supports expansion planning decisions such as hardening of existing transmission lines or installation of new lines to reduce power system-induced wildfire, and is solved by using a stochastic robust optimization (SRO) method. The SRO method combines stochastic optimization and robust optimization techniques to account for uncertainties stemming from extreme weather conditions, enabling the model to evaluate worst-case scenarios and ensure model efficiency even under the most adverse weather conditions.
Therefore, this research introduces robust approaches to proactively mitigate power system–induced wildfire risks under uncertain weather conditions, thereby enhancing the overall resilience of the power system.
