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Applications of optimization and statistical models to the energy sector
Hirwa, Jusse Aline
Hirwa, Jusse Aline
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2022
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Abstract
As the world transitions towards a low-carbon future, decarbonization of the energy sector will become increasingly important. Deployment of electric vehicles, as well as investment in distributed generation systems that incorporate renewable and fuel-efficient technologies such as the combined heat and power, can reduce global pollution to meet climate-change goals. We apply optimization modeling tools to design and dispatch a system consisting of renewable technologies, combined heat and power, conventional generators, and storage to reduce energy costs for representative commercial buildings. We first highlight how to efficiently formulate an optimization model to achieve high-quality solutions within a 10-minute time limit, enabling the implementation of the model in a web-based tool. We then conduct a case study of South Africa to provide insights regarding different design and dispatch decisions and highlight the benefits of the system to address unreliability of power supply. Another aspect of our research employs statistical models to examine determinants of substitution in batteries that power electric vehicles. There are challenges in cost and performance of these batteries, as well as in supply chains of battery materials that may hinder the deployment of electric vehicles. Hence, electric-vehicle manufacturers and battery producers are investigating substitution in batteries to help mitigate these challenges. We identify two types of substitution such as nickel-for-cobalt and lithium-ion battery types for nickel-cobalt manganese, and find that the cost of materials will play an important role in driving these substitutions.
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