Optimization Methods for Wildfire-Resilient Transmission Grid Operations and Infrastructure Planning
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Piansky, Ryan
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Abstract
Climate change-driven natural disasters pose an increasing threat to the power grid. Wildfires pose a unique challenge as power systems can ignite these destructive events, exposing utilities to liability. To mitigate the risk of ignitions, system operators proactively de-energize high-risk transmission lines in Public Safety Power Shutoff (PSPS) events. While effective for ignition risk mitigation, PSPS events can cause significant load shed. Utilities can also pursue infrastructure investments, such as line hardening or batteries, to mitigate the risk of ignition or maintain service but these decisions add complexity to already challenging optimization problems.
The contributions of this thesis include advances in modeling for optimally mitigating wildfire ignition risks, improved computational methods that leverage decomposition and machine-learning techniques for scalable algorithms on large and realistic test networks, and applications in infrastructure investments, climate resilience, and equity relevant to policymakers and utilities. This thesis provides detailed optimal power shutoff formulations in work evaluating sensitivity of decisions to ignition risk aggregation metrics and power flow formulations. Optimal power shutoff results achieve an order of magnitude reduction in load shed relative to methods comparable to industry standards. Extensions to infrastructure investment planning to support PSPS events are presented through tractable optimization algorithms, including flexibility to consider policy, equity, and alternative extreme weather events. Computational improvements are introduced through machine learning techniques and a novel temporal decomposition method that enables long time horizons to be modeled, resulting in fast, high-quality solutions that outperforms what was previously possible. These methods provide results on the order of an hour of computing time compared to days required under previous methods. In summary, this dissertation presents a detailed understanding of optimal transmission switching for PSPS events, offering further insights for engineers, utilities, and policymakers through flexible tools with realistic simulations.
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2025-12
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Dissertation (PhD)