# A Feature-Based Cost Estimation Model for Wind Turbine Blade Spar Caps
## 2 Literature Review
### Manufacturing Cost Models
\begin{table}
\begin{tabular}{c c} \hline \hline
**Existing Cost Models** & **Key Attributes of existing Cost Model** \\ \hline [PERSON] [14] & Technical cost modelling for a generic 45 m turbine blade manufactured using vacuum infusion. \\ \hline [PERSON] et al. [15] & Technical cost model compares liquid resin transfer molding with vacuum infusion for a generic 40 m blade. \\ \hline [PERSON] et al. [16] & A techno-economic model for estimating the cost of a thermoplastic wind turbine blade relative to a baseline thermoset epoxy blade \\ \hline
**Proposed Cost Model** & **Key Attributes of proposed Cost Model** \\ \hline Technical cost model to calculate cost of wind turbine spar caps from 35 to 75 m in length. & A relationship that scales the cost of spar cap blade lengths from 35 to 75 m with Spar Cap features: mass, complexity, and volume fraction \\ - comparing LCOE for 20-year turbine life with LCOE for extended turbine life & Two cost curves, one for CFRP and one for GFRP, in spar caps from 35 to 75 m in length \\ - comparing LCOE for same length CFRP retrofits with LCOE for longer length CFRP retrofits & Capability to model scenarios for LCOE with varying inputs, e.g., material cost, capacity factor. \\ Cost data for a 35 m spar cap: tooling, capital, labor, and utilities. & A novel relationship between manufacturing complexity and organizational learning \\ Mass data for spar cap lengths 35, 40, 45, 50, 55, 60, 65, 70, & and organizational learning \\ 75 m. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cont. a baseline Calibration Standard; production rates for the 10 m analogue and 35 m spar cap; and glass and carbon composite masses for spar caps with a box spar design in the 35 to 75 m blade range [31], in which the mass of unidirectional GFRP and CFRP composite required for each spar cap length is specified for allowable tip deflections in a given wind loading regime. [PERSON] et al. [32] emphasise that engineering studies should be carried out concurrently with financial modelling for optimised cost-effective design. If retrofitted blades can meet given wind loading, tip deflection, and power generation performance criteria, while comparing extra power generation with longer blades against power reduction with increased turbulence, blade retrofitting scenarios can be modelled that extend the useful life of a wind turbine by reducing LCOE compared to LCOE for a 20-year life baseline and complete turbine replacement, thereby improving material efficiency and sustainability  
[PERSON] [33] describes a typical wind speed distribution plot. Turbine capital cost and operational and maintenance (O and M) data were taken from studies by the International Renewable Energy Agency [1] and the US National Renewable Energy Laboratory [34]. [PERSON] [35] provides a succinct description of the discount rate or cost of capital, termed the weighted average cost of capital (WACC), which is the cost of debt, the equity premium of the investors, and the share of debt and equity in the project. The discount rate is a measure of risk that will be charged by investors based on whether wind generation is offshore (higher rate) or onshore (lower rate) and location by country. Based on survey results from worldwide onshore and offshore locations, a rate of 7% was chosen for this study, which is the average rate for Europe and North America between onshore and offshore wind generation. [PERSON] [36] describes how in any iteration methods, a reasonable \"guess\" can be assumed for one or more unknown variables. Iterative methods are often the only choice for non-linear equations where powers \(x\), \(y\) and \(z\) are unknown. [PERSON] [37] describes similar methods for use in engineering when extensive data is unavailable, for example, estimating the number of stages required for a complete solvent extraction process in chemical engineering.