Biggins, Flora Anne Vindolanda (2022) Optimising investments in battery storage and green hydrogen production. PhD thesis, University of Sheffield.
Abstract
Energy systems are undergoing a transition towards low-carbon alternatives, but intermittent renewable sources like wind and solar pose challenges. Battery storage and hydrogen technologies, offer potential solutions with numerous benefits. They can enhance grid stability, improve power quality, and decarbonise industries like heavy manufacturing, heating, and shipping. Both batteries and hydrogen complement renewables by storing excess power and using curtailed energy.
To drive the widespread adoption of low-carbon energy technologies, it is crucial to establish its economic viability. This research focuses on optimising the revenues of low-carbon energy investments, specifically battery storage and green hydrogen production. It explores three key areas: determining the optimal usage of these technologies, identifying the best deployment locations, and addressing uncertainties.
In terms of usage, the research analyses various case studies and modelling techniques. It applies optimisation models to energy markets, examines community-owned battery projects, and combines machine learning with optimisation models to maximise battery revenues across different market segments. Additionally, the research explores the optimal investment and usage of PEM electrolysers within wind farms to produce green hydrogen, using optimisation models and real options analysis.
Metadata
Supervisors: | Solomon, Brown |
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Keywords: | Battery energy storage; green hydrogen production; economics; mixed integer linear programming; optimisation; real options analysis |
Awarding institution: | University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Chemical and Biological Engineering (Sheffield) |
Depositing User: | Miss Flora Anne Vindolanda Biggins |
Date Deposited: | 09 Jul 2024 09:51 |
Last Modified: | 09 Jul 2024 09:51 |
Open Archives Initiative ID (OAI ID): | oai:etheses.whiterose.ac.uk:35056 |
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