Date of Award
2026
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Industrial and Systems Engineering and Engineering Management
Committee Chair
Ana Wooley
Committee Member
Dale Thomas
Committee Member
Howard Chen
Committee Member
Dongsheng Wu
Research Advisor
Hieu Pham
Subject(s)
Electric power systems--United States--Management, Demand-side management (Electric utilities)--United States, Electric power systems--United States--Reliability
Abstract
Historical evidence indicates that the United States electric power grid continues to face persistent criticism for reliability challenges during peak demand conditions. Extreme ambient temperatures are a primary driver of such peaks, rendering the grid most vulnerable precisely when electricity demand is highest. These demand surges are largely driven by coincident operation of heating, ventilation, and air-conditioning systems across customer classes, a trend expected to intensify as sectors such as transportation increasingly rely on electrification. Efforts to mitigate demand related unreliability have traditionally focused on system-level demand forecasting and planning, typically at the scale of cities or entire utilities. However, historical system-level forecasts have frequently underestimated extreme demand conditions, and, more critically such aggregate projections cannot be hierarchically translated into actionable operational metrics for downstream grid subsystems. Knowledge of an anticipated system wide demand increase during an extreme temperature event does not readily enable reliable estimation of demand at the distribution and transmission networks, or substation levels, where operational decisions are ultimately executed. Although this limitation is well understood by practitioners, demand management analytical frameworks for distribution-level decision-making remain scarce in the existing literature. This dissertation examines the hierarchical structure of the electric power grid and systematically reviews prior analytical efforts focused on proactive demand planning under extreme temperature conditions. Addressing identified gaps in decision support systems at the distribution level, this work develops and evaluates integrated machine learning, statistical, and optimization-based frameworks for predictive and prescriptive demand planning within distribution systems during peak demand scenarios. Beyond theoretical development, the proposed frameworks are empirically validated using real-world operational data and are applied to guide the planning and operational deployment of the first utility-scale battery energy storage system in North Alabama. The primary contribution of this research lies in advancing distribution-level decision support capabilities and also in establishing a predictive-prescriptive modeling effort for spatial resource planning and allocation towards enhanced grid resilience and robustness during extreme peak scenarios.
Recommended Citation
Tettey, Anyama Hanson, "A hierarchical, data-driven analytical paradigm for strategic planning, operational decision-making, and resilience enhancement in United States electric power systems under extreme demand conditions" (2026). Dissertations. 489.
https://louis.uah.edu/uah-dissertations/489