MAPEAMENTO DO POTENCIAL EÓLICO DE MICRO E MINIGERAÇÃO COMO ALTERNATIVA SUSTENTÁVEL PARA UM PEQUENO MUNICÍPIO DO SUL DO BRASIL

This study aimed to map the wind potential for micro and mini power generation for Campo Mourão, State of Paraná, as an alternative power generation in southern Brazil. Monthly data from a historical series of 36 years (1980 2015) of INMET were analyzed and linked to land use. For this purpose, the SASPlanet application, with Bing image, was used to collect the urban area. The software QGis 2.14 was used for photointerpretation, which defined the height of the obstacles and their respective class of roughness factor. These data were linked in the formula in a Spring 5.4.2 algorithm, in order to estimate the speeds in 10, 20, 30 and 40 meters in height besides their respective generation potentials. The climatological normal had a mean total speed of 2.49 m.s, a mean minimum speed of 2.05 m.s, a mean maximum speed of 2.92 m.s 1 and a predominant direction from the East. The photointerpretation identified 37.61% area with obstacles up to 2 meters high, 59.77% between 2 and 10 meters and 2.63% with more than 10 meters. The resulting speeds of the algorithm identified the largest standard deviations at the highest speeds. The maps pointed out that regardless of the average, the highest potential for power generation occurs at 40 meters.


INTRODUCTION
Over the years, the intensive use of fossil fuels, together with high crude oil prices and environmental problems, has led to increased attention worldwide to obtain new sources of energy resources (Hosseinalizadeh et al. 2017).
In this scenario, to reduce dependence on fossil fuels, it became necessary to diversify the portfolio of energy supply to cleaner and more sustainable sources of energy, especially renewable energies .
As a response to the oil crisis, climate change and environmental issues, in the 1970s, there was an incentive for technologies to implement wind energy . According to , among all renewable energy sources, wind energy is the only one with the most developed technologies of use. Currently, the world is in a moment of transition between the current economic model and a sustainable model, based on renewable energies . In this scenario, the Brazilian electricity matrix is based mainly on hydroelectric generation and, according to the National Energy Balance (BEN, 2017), it accounted for 68.1% of all generation in 2016. However,  warn that water shortage that regularly affects the Northeast region of Brazil, has also affected the Southeast region in recent years (2014)(2015), because since 2013 this region has reduced levels of its reservoirs.
The water crisis interferes not only at the regional level but also at the national level, but the micro and mini power generation distributed serves as an alternative and Among the renewable sources mentioned by the normative, one of the highlights that has been highlighted as an alternative source of energy for the production of electricity is the use of technologies for wind energy. In the State of Paraná, in southern Brazil, the study of wind power began in 1994 with the initiative of Companhia Paranaense de Energia (COPEL), in order to identify areas with better conditions for the use of wind at great heights (50, 75 and 100 meters). Different from expectations, the maps generated indicate that the most favorable areas are not the coastal areas, but those with higher altitude such as the municipality of Palmas, located in South-Central region of the state, which contains the first wind farm in the south of the country .
The studies presented in the Atlas of Brazilian Wind Potential were important for the knowledge in the implementation of large wind farms. However,  explains that the heights of interest for micro and mini power generation are between 10 and 40 meters, and the Atlas cannot be applied to this class of electricity generation.
In the work of , the authors emphasize that the Brazilian potential for wind power in small wind turbines is not yet fully known, a factor that is due to the absence of a specific Aeolian Atlas.
Thus, this study aimed at of mapping the wind potential for micro and mini power generation in the urban area of a small Brazilian municipality. It is expected that the methodology and the results obtained in the analyses contribute to the dissemination of the advantages and help in the implementation of small wind farms.

Study area
This study was developed in the municipality of Campo Mourão, State of Paraná  Campo Mourão is inserted in a climatic type defined as Cfa -mesothermal subtropical climate , characterized by hot summers, in which the warmest quarter (December, January and February) has an average of 26-28ºC, with concentration of rainfall in the summer and drought in the winter period and infrequent frosts, and the coldest quarter (June, July and August) presents average temperatures of 15-17ºC. The annual mean temperature for the municipality is defined as 20-21ºC .
The municipality is mainly agricultural, being great producer of grains. According to the Paraná Institute of Economic and Social Development (IPARDES, 2016), the main cultivated products are soybeans with 53,500 ha, followed by wheat with 14,000 ha and finally corn with 11,600 ha of mechanized planting in predominantly soft/wavy relief .

Wind Potential Mapping
Knowledge of wind behavior is essential for wind power projects, and many studies use observations from the historical wind speed series acquired through meteorological stations. These studies use spatial and temporal analyses for wind speed prediction modeling  or for budgetary organization .
In this study, we performed a data analysis of the historical series of 36 years  using the Meteorological Database for Teaching and Research (BDMEP), managed by the National Institute of Meteorology (INMET). Descriptive statistics were performed as monthly and annual mean, in addition to their respective standard deviations, in order to characterize wind behavior. These data served as a basis for the subsequent phase, which defined the average speed at different heights, pertinent to micro and mini power generation, from the known height.
In order to determine the best height for the wind turbines, it was necessary to know the average wind speeds. As the micro and mini power generation falls within a range between 10 and 40 meters in height, speeds were analyzed in four different heights to define the best choice, being 10, 20, 30 and 40 meters.
It is important to emphasize that the available wind data are usually collected in For this, the methodology defended by  was used, which applies the formula to estimate the speed of the wind at the height of the openings of buildings. This formula is used in architectural projects with the purpose of estimating the use of natural ventilation in buildings. This methodology was chosen due to the expected behavior of the results, since it takes into account the height and the interference caused by the contact of the wind with the obstacles.
According to , the equation that corrects the wind speed in the openings is as follows: where: V: average wind speed (m/s) at desired height; Vm: average wind speed (m/s) at the meteorological station at 10 meters high; k, a: coefficients according to terrain roughness (Table 1); z: desired height   To know the average speed of the winds in a new height, it is necessary to know the height of the buildings inserted in the urban area. For this, we used Bing Maps images from the urban area of the city of Campo Mourão, State of Paraná; the images were obtained using the SAS PLANET software and inserted in a database in Qgis and by means of photointerpretation, we identified the higher buildings according to the shadow projected by the incidence of the Sun at the moment of image capture.
For the identification of the buildings, the Google Earth software was used in Street View mode, where it was possible to identify the building and estimate the number of floors of each building, later all the buildings were visited in loco to confirm the number of floors.
According to the speeds of the winds estimated by the formula and the roughness factor of the defined terrain, the choice of the wind turbines that best fit the meteorological and urbanistic situations characterized in the area could be made. The choice of wind turbine should take into account the data analyzed so far, i.e. by the overall configuration of the wind system, allied with the type of application and expected power.
To determine the average local wind power in Watts (W),

Climatological Normal
Statistical calculations for the identification of the climatological normal, according to the historical series of 36 years defined between 1980 and 2015, resulted in the average total speed of the winds, as well as their standard deviation, upper limits (mean maximum velocity) and lower limits (mean minimum speed) and the predominance of the direction of the winds (Table 2) in the city. According to the Climatological Normals of Brazil (1961Brazil ( -1990

Wind Speed
Levando em consideração as análises estatísticas realizadas para identificar a normal climatológica da cidade de Campo Mourão -PR, obtiveram-se dados mensais de velocidades médias, os valores de desvio padrão e os valores de máximos e mínimos conforme mostra a Table 3. In a study of evaluation of the wind potential in a region of southern Brazil,   With the data of maximum, mean and minimum velocities obtained in the climatological normal, along with the formula of correction of speed of the winds, it was possible to estimate the speeds in the heights determined by this study, as listed in Table 4. For the speed (maximum, mean and minimum), it was found that the best performance in relation to height is achieved in the range of 40 meters, this can be explained, according to Sato (2015), due to the lower interference of the terrain roughness in the wind shear with the plane under which it travels.

Height of obstacles
The mapping of obstacles encountered (any wind block that may interfere with wind speed and/or wind direction, such as buildings, forests and crops), classified by building heights can be seen in Figure 2.
The urban area was classified into three major classes, on the map the yellow color represents obstacles up to two (2) meters high. Therefore, lawn areas, without vegetation, cultivated land, water, streets and mining are included in this class, totaling 37.61% of the total area covered.
In orange is the class for obstacles greater than 2 (two) and less than 10 (ten) meters, including therefore urban areas without tall buildings, forests, terrain with many trees and few structures and areas with few trees. This class represents 59.77% of the urban area of the municipality, identified as the largest portion of the area coverage.
The area identified by the red color belongs to obstacles greater than 10 (ten) meters in height. This class includes only the urban area with tall buildings. Its percentage corresponds to only 2.63%, considered as the smallest portion of coverage in the area. The major obstacles are located in the center of the city represented by the buildings (Figure 2), as Morigi and Bovo (2016) emphasized when explaining in their study that the verticalization in the city of Campo Mourão is concentrated in the central region, which justified this condensation due to the infrastructure, commerce and services installed there.
The city of Campo Mourão has been increasing since the construction in 1970 of the first high building containing 8 floors, thus achieving a considerable economic growth, which resulted in a significant increase in the number of buildings, especially from the 2000s, that is, the verticalization of the municipality is very recent. This factor explains its small portion of obstacles above 10 meters, represented by the high buildings (Morigi, 2015).

Wind power
In order to calculate wind power, it was necessary to use wind turbines as parameters, since the rotor area is one of the variables of the formula that expresses the value in Watts (W). In order to do so, we chose to analyze two wind turbine models, with Air 40 representing microgeneration and Skystream Land for mini generation (Table 5). The wind speed to start power generation of the Skystream Land wind turbine is 3.5 (m/s), i.e. according to Table 4, this wind turbine would only produce energy at least 30 meters height and using as base the mean maximum speed, where the mean at that height is recorded at 3.68 (m/s). This limitation can be justified, according to Sousa, Junior and Sousa (2018) due to the variation in the mean speed for each period and that the wind speed is directly proportional to the power generated by the turbine.
As shown above, the generation range of the mean maximum velocity is the one with the highest standard deviation, which means that there is greater variation in wind speeds, sometimes being larger and sometimes smaller, and it can therefore be impossible to generate with any inconsistency of the wind. Similar results were observed by Pearre and Swan (2018), who emphasized that wind speed events may change as they encounter different types of terrain and travel different distances.
The rotor area also defines the speed required to start the generation, the larger the area, the greater the speed required. For this reason, the study of the wind potential of Campo Mourão was carried out based on only one (1) Air 40 wind turbine (microgeneration). Like wind speed, the higher the height, the greater the wind power.
Therefore, the highest power was identified at 40 meters in height (Table 6) for all speeds (maximum, mean and minimum). With the different speed mean values (maximum, mean and minimum), together with the roughness factor of the blocks and the type of wind turbine selected, it was possible to construct the wind potential maps, containing the power estimation considering factors such as wind speeds and the height of the wind turbine.
A total of 12 maps were generated, distributed in 3 (three) groups of maps, differentiated by wind speed (minimum, mean and maximum), each group with predefined heights (10, 20, 30 and 40 meters). The first group refers to the mean minimum speed, according to Figure 3.   For both minimum ( Figure 3) and mean (Figure 4), there is a low wind potential for the urban area of the municipality in question. However, for the maximum for the heights of 30 and 40 meters, it can be observed a greater potential, however the maximums are less recurrent.
According to Sato (2015) in a study conducted in Cascavel (medium city of approximately 270 thousand inhabitants to the west of Campo Mourão), the mean speed for height of 10 meters was 4.03 m.s -1 , from 4.84 m.s -1 at 25 meters and 5.32 m.s -1 at 40 meters, but the study disregarded the roughness factor of the urban area.
In the work of Santos et al. (2006) also in the city of Cascavel and considering the roughness factor, the values did not exceed 3 m.s -1 , therefore, there is a greater similarity with the results for Campo Mourão presented in the present study.
Our results presented for Campo Mourão are also similar to the results of Ramos and Seidler (2011), who identified a mean wind speed at the Meteorological Station of the State of Rio Grande do Sul, located at the Universidade Regional Integrada do Alto Uruguai e das Missões URI Campus de Santo Ângelo, with approximately 2.5 m.s -1 , based on a historical series of 6 (six) years from 1989 to 1994, concluding that it is possible to generate energy with small wind turbines at this speed, assisting small enterprises with the costs of energy consumption.
According to Teixeira (2013) who developed a study on the wind potential in Lisbon, Portugal, it was possible to observe that the speed of the winds was registered in a range between 11.26 and 13.45 m.s -1 (2009)(2010)(2011)(2012)(2013), and concluded that for an Omniflow OM 3.8 turbine at an average speed of 12 m.s -1 , the turbine power would be 1.08 kW. In

CONCLUSION
Based on the data analyzed in the climatological normal, it was possible to identify a mean total speed of 2.49 m.s -1 with the predominant direction coming from the East (90º). The values analyzed for the means, the means of the maximum and the minimum, indicate that for the three groups of data the highest standard deviations are found at the highest speeds. In other words, the higher speeds have an inconsistency in the wind continuity, obtaining in this way greater variation during a period, interfering negatively with the generation and the life time of the wind turbine.
According to the photointerpretation, the study area presented 37.61% obstacles with up to 2 meters in height, 59.77% obstacles greater than 2 and less than 10 meters and 2.63% with obstacles greater than 10 meters in height interfering with wind speed.
The generated maps allowed to identify that regardless of maximum, mean or minimum values, the height with the highest generation potential is 40 meters.
It is concluded that urban wind power in the municipality of Campo Mourão does not present economic attractiveness, this information must be proven with future punctual studies in the urban area, because the analyses were based on the mean values found referring to the meteorological station of the municipality that is located between valleys, where the orographic factor influences the actual speed of the winds.
When there are obstacles, the wind speed suffers from influences that interfere with the data and as the calibration occurs in the Meteorological Station, it is possible that the urban wind potential is greater than the analyzed herein.