## 5 Optimization for PCSH Wind Turbine Tower
### Optimization Results for the PCSH Wind Turbine Tower  
#### 5.2.1 LCOE Optimization  
The relationship between the LCOE under the category IV wind energy resource area and the number of iterations is illustrated in Figure 6. According to Figure 6, by the use of the proposed PUPSO optimization approach, the LCOE of the PCSH tower defined above decreases clearly with the iteration of the approach and the minimization of the objective function is realized when the number of the iteration reaches 31. The LCOE decreases sharply in the first iterations because the algorithm in this paper strengthens the searching space diffusion and heightens the weight of particle optimization in the early stage and the weight of global optimum in the later iterations. The LCOE also drops fast in the early stage of the PSO optimization. However, the optimal result of PSO is inferior to that of the PUPSO algorithm proposed in this paper after the process is iterated four times.  
The LCOE of four types of wind energy resource areas are presented in Table 6. Compared with the LCOE of the original wind turbine tower, the LCOE of the optimized wind turbine tower reduced by about 4% due to the reduction in construction costs. Theoretically, if the LCOE is higher than the electricity price, the project is not economically feasible. Therefore, the optimized PCSH wind turbine tower can increase profits and make it economically possible to build wind farms in areas with lower electricity prices.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Wind Turbine Parameters** & **Value** \\ \hline Generator model & XE93-2000 \\ Rated power & 2 MW \\ Rotor diameter & 93.4 m \\ Nacelle and hub weight & 80 t \\ Distance from gravitational center of the nacelle and hub to the center of tower & 3000 mm \\ Weight of blades & 48.5 t \\ Distance from gravitational center of the blades to the center of tower & 4864 mm \\ IEC wind zone & IECIIIA \\ Annual average wind speed & 7.5 m/s \\ Cut-in wind speed & 3 m/s \\ Nominal wind speed & 11 m/s \\ Cut-out wind speed & 25 m/s \\ Extreme wind speed & 52.5 m/s \\ Rotational speed & 23 rpm \\ Maximum turbulence intensity & 0.18 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters of the wind turbine.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(w_{max}\) & 0.9 \\ \(w_{min}\) & 0.4 \\ \(M\) & 50 \\ \(N\) & 30 \\ Penalty term & 0.5 \\ \(\zeta\) & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameters of the PUPSO approach.  
The optimization rates, that is, the ratio of the difference of variables before and after optimization to the value before optimization, using the PUPSO are shown in Figure 7. It can be seen that the variables illustrated in the figure are less than zero, which means the variables are smaller than they were before the optimization. As the number of iterations increases, the variable tends to decrease.  
The comparison of corresponding dimensions for the PCSH wind turbine tower is listed in Table 7. Compared with the original design, the height of the upper steel segment is greatly reduced to 22 m and approximately 30% of the total height of the PCSH tower while the steel segment number is 1. The thickness of the tower, including the steel segment and concrete segment, is also reduced, which decreases the material consumption of the PCSH tower.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Category**} & **Equivalent Available** & **Electricity Price in** & **LCOE for the Benchmark** & **LCOE for the Optimized** \\  & **Duration (h)** & **2019 (Yuan/kWh)** & **PCSH Tower (Yuan/kWh)** & **PCSH Tower (Yuan/kWh)** \\ \hline I & 2850 & 0.34 & 0.3613 & 0.3474 \\ II & 2600 & 0.39 & 0.3874 & 0.3722 \\ III & 2500 & 0.43 & 0.3993 & 0.3835 \\ IV & 2000 & 0.52 & 0.4769 & 0.4571 \\ \hline \hline \end{tabular}
\end{table}
Table 6: LCOE measurement.  
Figure 6: Optimization of costs of PCSH wind tower as a function of iteration number.  
Figure 7: The optimization ratio of variables.