This paper analyzes the viability of an offshore energy hub consisting of wind turbines, batteries, fuel cells and electrolyzers connected to, and powering, an oil producing floating production storage and offloading unit. We assess these components considering an oil production setup that strives for reduced CO2 emissions. The problem is addressed from a probabilistic perspective. First, we utilize a quasirandom Monte Carlo approach to generate multiple scenarios regarding the uncertainties of the problem. Then, we evaluate the estimated net present value and total CO2 emissions of the system. As a highlight, our method is capable of exploiting a larger variety of data and capturing more sources of uncertainties compared to the literature. Open-source wind data is used to simulate wind power generation. Wind speed is modeled via a kernel density estimator to benefit the most from the data. The obtained results indicate that the renewable energy technologies enable outcomes with significant reduction to CO2 emissions. However, at the current prices of these technologies, operating a low emitting field links to the loss of a significant share of the expected profits.

Probabilistic Economic Assessment of an Offshore Energy Hub Supplying Electrical Power to a Floating Production Storage and Offloading Unit

Varagnolo, Damiano;
2023

Abstract

This paper analyzes the viability of an offshore energy hub consisting of wind turbines, batteries, fuel cells and electrolyzers connected to, and powering, an oil producing floating production storage and offloading unit. We assess these components considering an oil production setup that strives for reduced CO2 emissions. The problem is addressed from a probabilistic perspective. First, we utilize a quasirandom Monte Carlo approach to generate multiple scenarios regarding the uncertainties of the problem. Then, we evaluate the estimated net present value and total CO2 emissions of the system. As a highlight, our method is capable of exploiting a larger variety of data and capturing more sources of uncertainties compared to the literature. Open-source wind data is used to simulate wind power generation. Wind speed is modeled via a kernel density estimator to benefit the most from the data. The obtained results indicate that the renewable energy technologies enable outcomes with significant reduction to CO2 emissions. However, at the current prices of these technologies, operating a low emitting field links to the loss of a significant share of the expected profits.
2023
Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE
ASME 2023 42nd International Conference on Ocean, Offshore and Arctic Engineering, OMAE 2023
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3542101
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