• Wind Turbine Site.

Based on the analysis of the resource grid obtained from the ERA 5 wind data, which is detailed in Section 3.1, the wind turbine site has been selected. The criteria used was: wind resource and the proximity to the point of consumption and connections. Taking advantage of the fact that the photovoltaic installation has a security fence and enough space to locate the wind turbine inside, it has been decided to propose to place it within that area. The proposed site for the wind turbine is depicted in Figure 13.

**Figure 13.** Resource grid at 10 m with the fence surrounding the solar PV area and the proposed location of the wind turbine.

• Influence of Obstacles.

In order to take into account the influence of the surrounding obstacles, the myWind-Turbine software has been used, identifying the main obstacles and using the software to evaluate the losses due to them. In Figure 14, the general view of the site facing East and the predominant wind direction are shown, along with the main obstacles considered for their assessment.

**Figure 14.** (**a**) General view to the predominant wind direction, East-Southeast; (**b**) Considered obstacles (in red).

The results of myWindTurbine analysis show a very small decrease in the annual energy production of 0.5% due to obstacles; the influence of obstacles will be neglected for this study.

• Wind Resource.

The wind resource assessment has been widely explained in the previous chapters. As a summary and neglecting the influence of obstacles in this case, according to the previous analysis the average speeds obtained in the site during 2019 for each month are detailed in Table 4 below and extrapolated at 24 m height using WAsP.

**Table 4.** Average monthly wind speed at the site at 24 m (extrapolated with WAsP from ERA5 10 m height wind speed data).


On the other hand, Figure 15 shows the wind rose obtained for the proposed site, showing East-Southeast as the clear predominant wind direction.

**Figure 15.** Wind rose on the proposed site showing East-Southeast as the predominant wind direction.

• Wind Turbine.

For the selection of the wind turbine, five wind turbines of different sizes have been evaluated. The sizes of the wind turbines selected correspond to 10 kW, 15 kW, 20 kW, 25 kW, and 30 kW. The established cost criteria are the following:


In Table 5 the input variables defined to carry out the simulation are shown: the selected turbines, their nominal power, and hub height (Hh) together with the costs determined for the simulation.



Considering the overall performance of the wind turbines in this application at this site, the wind turbine finally selected from this analysis corresponds to the Eoclycle EO25 Class IIA [47] rated at 25 kW, using a 23 m hub height, with a LV connection to the system: It is not the highest and it is not the cheapest, but it results in the lowest overall system LCOE.

• Results of the Simulation.

The wind speed data together with the components (and sizes) of the current system and the selected wind turbine, reference values of the components, and the current consumption load of the village were considered as inputs to establish the configuration of the system in the simulation with HOMER PRO. As a remarkable fact, diesel fuel is currently being subsidized by the state of Uruguay; therefore, the cost for the diesel fuel is low (0.58 €/L).

Table 6 shows the result of the simulation of the current system operation (first row) and the optimal result considering a possible incorporation of wind energy in the current system (second row), showing the principal parameters for both cases.

**Table 6.** Results of the simulation for the alternative, including wind generation, compared to the existing installation.


When comparing both results, the positive impact of the inclusion of a wind generator in the system can be highlighted. The cost of energy fuel consumption is reduced and the renewable fraction exceeds 50%.

• A Sensibility Analysis.

A sensitivity analysis has been carried out to evaluate the impact that changes in the three main affecting parameters may have on the optimal configuration. The following variables have been considered: A variation considering a variation range in the annual average wind speed from 4.5 to 5.5 m/s (estimated value was 5.35 m/s); an increase of 5 and 15% in the village's consumption and a range in the price of fuel from 0.58 (present value) to 0.8 €/L. Figure 16 shows the impact of the variability of the wind speed and diesel fuel cost for the existing load consumption.

Figure 16 shows the relationship between the average wind speed at the site and the cost of fuel. The area in green represents the inclusion of wind power, while the black area represents the preservation of the current system. For wind speeds lower than 4.7 m/s and fuel costs lower than 0.7 €/L, the current system is optimal. Similar graphs can be derived for higher load consumptions, with a slight increase in the dark area as the energy consumption increases.

**Figure 16.** Sensitivity analysis result for diesel fuel cost (0.58–0.8 EUR/l, x axis) and average wind speed (4.5–5.5 m/s, y axis): The black area represents the existing configuration, whereas the blue are represents the configuration including wind generation.

In order to assist in the decision making, it is helpful to perform a long term analysis in order to have an estimation of the variation of wind speed compared to the period used during the analysis. This can be achieved easily using reanalysis data. In Figure 17, a histogram is presented, comparing the annual average wind speed values to the one used for the analysis (corresponding to 2018) for the last 42 years that is downloaded from ERA5 database. From this figure, at least two conclusions can be drawn: one, that the varying range is approximately ±5% in relation to 2018 (which might be helpful to move in Figure 16); second, that the year used as the reference for the simulation is in the average low area of the distribution and, therefore, it may be considered as relatively conservative (there are 14 years with lower average wind speed and 28 years with similar or higher wind speed).

As a concluding remark, the aim of this analysis is to raise a discussion on the different scenarios and to provide information for decision making.

**Figure 17.** Long term analysis for annual average wind speed using ERA5 42 years' data.
