Parametric CAPEX, OPEX, and LCOE expressions for offshore wind farms based on global deployment parameters

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Abstract

Installed wind energy capacity has been rapidly increasing over the last decade, with deployments in deeper waters and further offshore, with higher turbine ratings within new farms. Understanding the impact of different deployment factors on the overall cost of wind farms is pertinent toward benchmarking the potential of different investment decision alternatives. In this article, a set of parametric expressions for capital expenditure, operational expenditure, and levelized cost of energy are developed as a function of wind turbine capacity (P), water depth (WD), distance from port (D), and wind farm capacity (P). These expressions have been developed through a series of simulations based on a fully integrated, tested cost model which are then generalized through the application of appropriate nonlinear regression equations for a typical offshore wind farm investment and taking into account most current published cost figures. The effectiveness of the models are countersigned through a series of cases, estimating the predicted values with a maximum error of 3.3%. These expressions will be particularly useful for the preliminary assessment of available deployment sites, offering cost estimates based on global decision variables.

Original languageEnglish
Pages (from-to)281-290
Number of pages10
JournalEnergy Sources, Part B: Economics, Planning and Policy
Volume13
Issue number5
DOIs
Publication statusPublished - 4 May 2018

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Offshore wind farms
Costs
Water
Benchmarking
Wind turbines
Wind power
Turbines

Keywords

  • CAPEX
  • LCOE
  • nonlinear regression
  • offshore wind farm
  • OPEX
  • parametric expressions

Cite this

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title = "Parametric CAPEX, OPEX, and LCOE expressions for offshore wind farms based on global deployment parameters",
abstract = "Installed wind energy capacity has been rapidly increasing over the last decade, with deployments in deeper waters and further offshore, with higher turbine ratings within new farms. Understanding the impact of different deployment factors on the overall cost of wind farms is pertinent toward benchmarking the potential of different investment decision alternatives. In this article, a set of parametric expressions for capital expenditure, operational expenditure, and levelized cost of energy are developed as a function of wind turbine capacity (P), water depth (WD), distance from port (D), and wind farm capacity (P). These expressions have been developed through a series of simulations based on a fully integrated, tested cost model which are then generalized through the application of appropriate nonlinear regression equations for a typical offshore wind farm investment and taking into account most current published cost figures. The effectiveness of the models are countersigned through a series of cases, estimating the predicted values with a maximum error of 3.3{\%}. These expressions will be particularly useful for the preliminary assessment of available deployment sites, offering cost estimates based on global decision variables.",
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author = "Anastasia Ioannou and Andrew Angus and Feargal Brennan",
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AU - Ioannou, Anastasia

AU - Angus, Andrew

AU - Brennan, Feargal

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N2 - Installed wind energy capacity has been rapidly increasing over the last decade, with deployments in deeper waters and further offshore, with higher turbine ratings within new farms. Understanding the impact of different deployment factors on the overall cost of wind farms is pertinent toward benchmarking the potential of different investment decision alternatives. In this article, a set of parametric expressions for capital expenditure, operational expenditure, and levelized cost of energy are developed as a function of wind turbine capacity (P), water depth (WD), distance from port (D), and wind farm capacity (P). These expressions have been developed through a series of simulations based on a fully integrated, tested cost model which are then generalized through the application of appropriate nonlinear regression equations for a typical offshore wind farm investment and taking into account most current published cost figures. The effectiveness of the models are countersigned through a series of cases, estimating the predicted values with a maximum error of 3.3%. These expressions will be particularly useful for the preliminary assessment of available deployment sites, offering cost estimates based on global decision variables.

AB - Installed wind energy capacity has been rapidly increasing over the last decade, with deployments in deeper waters and further offshore, with higher turbine ratings within new farms. Understanding the impact of different deployment factors on the overall cost of wind farms is pertinent toward benchmarking the potential of different investment decision alternatives. In this article, a set of parametric expressions for capital expenditure, operational expenditure, and levelized cost of energy are developed as a function of wind turbine capacity (P), water depth (WD), distance from port (D), and wind farm capacity (P). These expressions have been developed through a series of simulations based on a fully integrated, tested cost model which are then generalized through the application of appropriate nonlinear regression equations for a typical offshore wind farm investment and taking into account most current published cost figures. The effectiveness of the models are countersigned through a series of cases, estimating the predicted values with a maximum error of 3.3%. These expressions will be particularly useful for the preliminary assessment of available deployment sites, offering cost estimates based on global decision variables.

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