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Harnessing the unknown: Quantifying uncertainty in offshore wind Levelised Cost of Energy
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DOI：10.1016/j.apor.2026.105073.png)
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Abstract
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The Levelised Cost of Energy (LCOE) is a critical metric used to determine the cost-effectiveness of offshore wind farms. However, inherent uncertainties in the processes of constructing, operating, and decommissioning offshore wind farms propagate through to the LCOE, resulting in varying ranges of potential values. To address this challenge, a stochastic model is developed to quantify uncertainties associated with the variables in each stage of an offshore wind farm. Given the limited availability of open-source data due to the competitive market, Monte Carlo simulations are employed to expand datasets by identifying the probability distribution of each variable. The resulting uncertainties are propagated through to the LCOE calculation. A total of 22 scenarios are simulated by varying the following variables: foundation type, turbine removal strategy, distance from shore, turbine rating, number of turbines, and learning rate. Our methodology is also tested on different real offshore wind farms in UK waters, uncovering interesting findings. The results reveal significant cost inefficiencies when fewer than 30 turbines are installed. For projects with similar LCOEs, this study demonstrates that variations in uncertainties above the mean could lead to substantial cost savings. This finding is particularly relevant to the UK's Contracts for Difference (CfD) scheme, where decisions often involve offshore wind farms with closely aligned LCOE values. Moreover, this paper provides valuable guidance for smaller developers and new investors by quantifying uncertainty and identifying conditions under which projects become economically viable.
Keywords:
Offshore wind
LCOE
Uncertainty
Monte Carlo simulations
Contract for Differences (CfD)
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