Decarbonization, Irrigation, and Energy System Planning: Analyses in New York State and Ethiopia

Abstract

This dissertation contains two collections of analyses, both broadly focused on energy system planning, but motivated by different research objectives in distinct geographic settings. Part I – Chapters I-III – evaluates decarbonization strategies in New York. These studies are characteristic of the primary energy-related challenge faced by the Global North: How can states cost-effectively meet time-bound emissions reduction targets? A series of linear programs are developed to answer this question, culminating in the System Electrification and Capacity TRansition (SECTR) model, a high-fidelity representation of the New York State energy system that characterizes statewide emissions and allows for comparative study of various decarbonization pathways. SECTR simulations indicate that prioritizing heating and vehicle electrification alongside an expansion of instate wind and solar generation capacity allows New York to meet recently legislated climate goals more affordably than through approaches that mandate substantial low-carbon electricity targets. Additional work also explores the optimal distribution of energy infrastructure within New York to meet specified decarbonization targets, along with the value of supply-side, demand-side, and bidirectional methods of system flexibility. Part II of this dissertation – Chapters IV-VII – is concerned with the energy system challenges faced by the lowest income countries. Set in the Ethiopian Highlands, this work first aims to locate smallholder irrigated areas, as irrigation has attendant energy requirements that are larger and more likely to generate supplementary sources of revenue compared to residential demands. Here, a novel classification methodology is developed to collect labeled data, train a machine learning-based irrigation detection model, and understand the spatial extent of model applicability. Across isolated plots of land as small as 30m by 30m, the resulting model achieves >95% prediction accuracy. Further studies then explore the system planning implications of simulated electricity demands associated with these irrigated areas