Keywords

Energy Resilience, Power Outage, Renewable Energy

Abstract

Existing power outage datasets lack the spatial and temporal resolution required for neighborhood- level risk assessment, and conventional energy system design optimizes for cost without accounting for community-level demand - resulting in inefficient and inequitable asset de- ployment. This dissertation presents IONCORTEX, a framework integrating three technical contributions: a heterogeneous sensor fusion pipeline combining social media signals with municipal infrastructure records for intersection-level outage detection at six-hour tempo- ral resolution, maintaining detection capability during telecommunications disruptions; a techno-economic analysis pipeline evaluating photovoltaic, battery storage, hydrogen, and diesel portfolios using NREL’s System Advisor Model with Monte Carlo uncertainty quan- tification; and a mixed-integer linear programming optimization that allocates energy assets by weighting outage frequency against vulnerability-adjusted critical load demand. Analy- sis of financing structures demonstrates that financing terms, not technology costs, are the primary driver of levelized cost of energy disparities across communities. The framework is validated against 319 confirmed outage incidents across 194 Orlando intersections span- ning 2023-2025, including Hurricane Ian. IONCORTEX provides a replicable pipeline from high-resolution outage detection to optimized energy asset deployment

Completion Date

2026

Semester

Spring

Committee Chair

Gurupur, Varadraj

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Electrical and Computer Engineering

Format

PDF

Document Type

Dissertation

Identifier

DP0053308

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