# Impact of Economic Indicators on the Integrated Design of Wind Turbine Systems
## 4 Results and Discussion
### Effects of Blade Length  
Increasing blade length will increase energy production, but it will also increase the costs of material, labor and delivery. Generally, the initial capital cost and maintenance cost will also increase. Figure 3: Flow chart of the optimization methodology. To analyze how blade length impacts the economic effectiveness of a design, we recorded the trends in energy production, loading, mass, and costs from the properties of all the intermediate cases during the optimization. These data points are results for all metrics put together, show clearly the relationships between various turbine properties and the blade length. The annual capacity factor is commonly used as an indicator of energy performance. It is defined as the energy generated during the year divided by wind turbine rated power multiplied by the number of hours in the year. Figure 4 shows the capacity factor against blade length. Each point in the scatter plot represents an individual turbine configuration evaluated during the genetic algorithm. Although wind energy is proportional to the square of blade length, due to the limitation of generator capacity, the output electrical power is not permitted to exceed the rated power beyond the rated wind speed. The total energy production increases with the blade length while the effect is reduced as the blade length increases. The capital expenditure also increases with blade length, however in this case the effect increases as blade length increases (Figure 5). The same comparison was made for blade length against blade root loading and against blade mass, as shown in Figures 6 and 7 respectively. Power law relationships are fitted to this data to quantify the correlations. The flap-wise bending moment (Mf) is the critical load in blade structure design. This research shows that the flap-wise bending moment of blade root increases with length, L, as L\({}^{2.84}\), while the mass of the blades scales with L\({}^{2.52}\). The enveloping functions on the outer border of the scattered points are also presented to indicate the boundary of the design results. Figure 4: Equivalent full-loaded hours against blade length. Figure 5: Initial capital cost against blade length. Torsional moment (Mx) under fixed hub coordinates greatly influences the gearbox design so this is examined closely in this work. The optimization data trend predicts that Mx increases linearly with L as depicted in Figure 8. Meanwhile, Figure 9 shows the gearbox mass scales as L\({}^{1.78}\). The loading and mass of the remaining components such as the low speed shaft (Figure 10) and the bed-plate (Figure 11) each have different scale exponents as described in the figures. Figure 8: Hub Mx against blade length. Figure 6: Blade root Mf against blade length. Figure 7: Blade mass against blade length. In the scatter plots of economic indicators as a function of blade length, the boundaries of these scatter cloud appear in the form of a parabola. The data points to an optimal blade length for each of these economic metrics are shown in Figures 12-15. Figure 11: Bed-plate mass against blade length. Figure 9: Gearbox mass against blade length. Figure 14: IRR against blade length. Figure 4:0 LCoE against blade length. Figure 4:1 NPV against blade length. A large proportion of the initial capital costs are unrelated to the rotor diameter or design loads, for example the generator, converter and control system costs. This leads to a higher LCoE and lower NPV when blade length is comparatively short. For example, a 30 m-38 m blade is uneconomical for a 2 MW turbine since the energy production is inadequate compared with the costs (Figure 13). As the blade length increases beyond 38 m, the benefits outweigh the economic costs. The economic efficiency improves until it reaches a maximum at a certain blade length.