A Feature-Based Cost Estimation Model for Wind Turbine Blade Spar Caps

James Clarke, AT McIlhagger, E Archer, Thomas Dooher, Tomas Flanaga, Peter Schubel

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Abstract

A problem for wind turbine operators is decreasing prices for wind-generated electricity. Many turbines are approaching their rated 20-year lives. A more economically viable and sustainable solution that reduces Levelized Cost of Energy (LCOE) and avoids expensive turbine replacement is retrofitting new spar caps blades. A new cost model assesses the feasibility of retrofitting 35 to 75
m turbines with GFRP (glass fiber reinforced polymer composite) and longer length CFRP (carbon fiber reinforced composite) spar caps. Spar cap cost scales with features such as mass, volume fraction and complexity. Organizational learning is a cost factor. Material and direct labor increase as proportions of total cost while tooling, capital, utilities, and indirect labor decrease. There is good agreement between a manufacturer and the model. Twenty-year turbines were compared with retrofitted spar caps over 25 years for LCOE. Same length GFRP and longer length CFRP spar cap retrofits decrease LCOE. Longer length CFRP spar caps decrease LCOE compared with GFRP retrofits over 25 years. CFRP material cost impacts CFRP retrofit feasibility. Retrofitted turbines must meet engineering, operational performance, and planning requirements criteria. Software algorithms may improve human learning and enable automatic updates from varying design and cost inputs, thereby increasing cost prediction accuracy.
Original languageEnglish
Article numberdoi:10.3390/asi3020017
Number of pages26
JournalApplied System Innovation
Volume3
Issue number17
DOIs
Publication statusPublished - 30 Mar 2020

Keywords

  • Spar cap
  • complexity
  • features
  • cost model
  • data constraints
  • organizational learning

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