Zhao, ChaoyueZhao, Xinyi2026-04-202026-04-202026Zhao_washington_0250E_29246.pdfhttps://hdl.handle.net/1773/55514Thesis (Ph.D.)--University of Washington, 2026This dissertation develops decision-making frameworks for planning and operations in power systems under decision-dependent uncertainty (DDU), motivated by two emerging challenges: equity-aware electrification of public transit and wildfire-driven reliability risks. In both settings, key uncertainties are not purely exogenous; instead, they are shaped by infrastructure investments or operational controls, creating feedback loops among decisions, system states, and future risk. On the planning side, we study the deployment of on-route fast-charging infrastructure for battery electric buses (BEBs). Unlike depot charging, on-route charging introduces stringent constraints on safe operation and local power supply capacity, and can substantially change distribution-grid operating costs. We propose an integrated planning framework that couples the bus route network with the power network and formulate the problem as a mixed-integer second-order cone program to minimize total cost, including charging equipment investment, power facility upgrades, and grid operation. To explicitly address transit equity, we introduce a fairness metric, the Regional Proportion of BEB Routes, and incorporate it into the planning model either through Jain’s index constraints or a Rawlsian objective, enabling planners to balance economic efficiency with equitable regional adoption. To capture long-term grid impacts from evolving BEB charging behaviors, we further develop a two-stage distributionally robust optimization model in which the load uncertainty set depends on siting and sizing decisions, yielding grid-aware investment plans that hedge against decision-dependent demand growth. On the operations side, we address wildfire resilience in distribution systems, where utilities must dynamically trade off service continuity against ignition risk. We model proactive switching decisions using a Markov decision process whose state transitions are governed by both exogenous wildfire conditions and endogenous operational effects. In particular, we adopt a DDU framework in which line-failure probabilities explicitly depend on power flow levels. To handle the resulting large state and action spaces, we propose an approximate dynamic programming method based on post-decision states to efficiently compute risk-aware switching policies that minimize total operational cost during wildfire events. Case studies on 54-node and 138-node systems demonstrate the scalability and effectiveness of the proposed approach. Together, these contributions provide a unified view of how DDU links long-term infrastructure planning and short-term operational control in power systems, and offer practical tools for enabling equity-aware transit electrification and wildfire-resilient grid operations.application/pdfen-USnoneIndustrial engineeringElectrical engineeringIndustrial engineeringThesis