Date of Award:

8-2024

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Mechanical and Aerospace Engineering

Committee Chair(s)

Matthew Harris

Committee

Matthew Harris

Committee

Hailei Wang

Committee

Greg Droge

Committee

Paul Talbot

Abstract

Technoeconomic analysis is a key element in the study of integrated energy systems. The goal of this analysis is the sizing of technologies resulting in the best economic outcome for the system. The evaluation of this system involves sizing the components and simulating the resulting market to determine an outcome. This simulation incorporates multiple possible values of uncertain parameters like grid price and wind generation. This problem is currently approached with the gradient descent optimization method. An alternative approach, Bayesian optimization, sees success on simple problems of a similar nature to technoeconomic analyses. These results motivate applying Bayesian optimization as a substitute for gradient descent. For simple test problems, Bayesian optimization sees improved efficiency in comparison to gradient descent. A thorough comparison of Bayesian optimization variations identify the best optimizer configurations. Using these preferred variations, a final comparison on realistic technoeconomic analyses suggests that it is a better approach for solving this class of problems.

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