# A Feature-Based Cost Estimation Model for Wind Turbine Blade Spar Caps
## 1 Introduction  
A major problem for wind turbine operators is that electricity prices are predicted to fall from \(\copyright\)0.078/kWh in 2014 to \(\copyright\)0.045/kWh by 2020, thereby impacting the economic feasibility of wind turbine projects [1]. A system-level design involving rotor, blades and tower is very expensive, so a possible solution is retrofitting existing turbine designs with longer length carbon fiber reinforced composite (CFRP) spar cap blades [2] for increased electricity generation [3] and lower Levelized Cost of Energy (LCOE) [4].  
Another issue is resource sustainability, as wind turbines produce low carbon energy but contain large amounts of materials. Turbine life extension by blade replacement/retrofit is a potential strategy for maximizing resource effectiveness [5]. Turbines below 1 MW are now either approaching or exceeding their rated design lives of 20 years, while many energy generating classes of turbine from 1 to 6 MW are over 10 years old [6]. Blades are considered to wear out the fastest compared with other turbine components, therefore life extension by retrofitting new blades is a route to maximize resource effectiveness [7]. However, if blade retrofitting is to become widely adopted as a strategy for reducing LCOE and maximizing resource effectiveness, retrofitted blades must perform within the same designand operational constraints as before in terms of wind loading regimes, allowable fatigue, permitted blade deflection, and required electricity generation.  
The research objective of this study was to develop a cost model for assessing the economic feasibility, in terms of LCOE, of replacing spar cap blades in the 35 to 75 m range with new spar cap blades of the same or longer lengths. The model will focus on how part features and production rate affect resource costs such as tooling, capital, labor, and electricity, and is intended to be used concurrently with blade design so that retrofitted blades meet existing engineering and operational power generation performance criteria. A key issue was unavailability of certain cost data so a resource-based technical cost model (TCM) approach was employed. The model is capable of improvement in predictive power as more data becomes available. A novel expression is introduced relating manufacturing complexity and organizational learning as cost factors.  
The model will compare three scenarios for LCOE:  
* LCOE for turbines with glass fiber reinforced polymer composite (GFRP) blades from 35 to 75 m for 20 years, with no blade retrofits.
* LCOE for 25 years: retrofitted GFRP blades installed at 20 years with a 5-year extension.
* LCOE for 25 years: retrofitted longer length CFRP blades installed at 20 years with a 5-year extension.  
The feature-based model was derived from resource cost equations derived by [PERSON] [8]. Available cost data was kindly provided by a spar cap manufacturing company [9]. The co-authors of this study provided valuable insights and ideas while one co-author ([PERSON]) suggested an algorithm that automatically calculates various constants from cost data in order to enhance the methodologies underlying the model, thereby improving model effectiveness.