Theses and Dissertations

Advisor

Fu, Yong

Committee Member

Karimi, Masoud

Committee Member

Choi, Seungdeog

Committee Member

Saadatizadeh, Zahra

Date of Degree

5-15-2026

Original embargo terms

Immediate Worldwide Access

Document Type

Dissertation - Open Access

Major

Electrical & Computer Engineering

Degree Name

Doctor of Philosophy (Ph.D.)

College

James Worth Bagley College of Engineering

Department

Department of Electrical and Computer Engineering

Abstract

The resilience of the power system has become increasingly important in recent years and needs to be further enhanced in the future under wildfire conditions. 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. The chance-constrained optimization is employed as a foundation for managing uncertainty. 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 stochastic robust optimization (SRO) model within a tri-level Defender–Attacker–Defender (DAD) framework. This model supports expansion planning decisions such as hardening of existing transmission lines or installation of new lines to reduce power system-induced wildfire. The SRO approach 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.

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