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• W4366818820 abstract "The capital expenditure (CAPEX) for the fixed-bottom offshore wind farm is assessed using a probabilistic engineering cost model and the cost reduction scenarios in Japan are analyzed. Firstly, the engineering cost model is described to assess the capital expenditure. A new export cable length model is also proposed considering the landing point distance and the vessel size model is proposed as the function of turbine rated power. The proposed engineering cost model succeeds in explaining the mechanism of the increase and decrease of CAPEX experienced in the UK. The uncertainties of model parameters are identified from the reported data and modeled by the normal distribution function. The workability is predicted using the discrete event simulation. The predicted CAPEX is then compared with the existing 30 fixed-bottom offshore wind farms in the United Kingdom. The predicted mean and standard deviation values of CAPEX show good agreement with the reported ones, while the conventional parametric model underestimates the mean value and cannot predict the standard deviation. Finally, the cost reduction scenarios and their uncertainties of offshore wind farms in Japan are analyzed using the proposed probabilistic engineering cost model. The levelized cost of wind energy reduced from 20.0 JPY/kWh to 17.0 JPY/kWh, 13.6 JPY/kWh and 10.1 JPY/kWh by the reduction of installation days using the specific installation vessel, the turbine enlargement and the improvement of operation and maintenance efficiency. The predicted supply prices for each cost reduction scenario agree well with those reported at the first auction conducted in 2021 in Japan." @default.
• W4366818820 created "2023-04-25" @default.
• W4366818820 creator A5011075756 @default.
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• W4366818820 date "2023-07-01" @default.
• W4366818820 modified "2023-09-30" @default.
• W4366818820 title "Assessment of capital expenditure for fixed-bottom offshore wind farms using probabilistic engineering cost model" @default.
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• W4366818820 doi "https://doi.org/10.1016/j.apenergy.2023.120912" @default.
• W4366818820 hasPublicationYear "2023" @default.
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