An urban boundary layer wind measurement campaign in Spain based on the Köppen-Geiger climate classification

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An urban boundary layer wind measurement campaign in Spain based on the Köppen-Geiger climate classification Aya Aihara 1, Bahri Uzunoğlu 1, Fernando Aznar 2, Javier Magdalena 2 and Anders Goude 1 1 Department of Engineering Sciences,Division of Electricity Centre for Renewable Electric Energy Conversion Uppsala University,The Ångström Laboratory Box 534, 751 21 Uppsala, Sweden 2 SOLUTE Ingenieros Av. Cerro del Aguila 3 Edificio 2, Of 3 Izd 28703 San Sebastian de los Reyes, Madrid E-mail: aya.aihara@angstrom.uu.se, bahri.uzunoglu@angstrom.uu.se, fernando.aznar@solute.es,javier.magdalena@solute.es, anders.goude@angstrom.uu.se Abstract. The small scale wind turbines mainly corresponds to turbines installed in rural and isolated areas. As 80 % of European population lives in cities and the EU Directive 2010/31/EU on Energy Performance of Buildings requires that Member States shall ensure that by 31 December 2020 all new buildings are nearly zero-energy buildings. This is a commercial opportunity that also provides a motivation to investigate technical challenges related to the peculiarities of urban wind regime. Urban wind resource assessment for small scale wind applications presents several challenges and complexities that are different from large-scale wind power generation. Urban boundary layers relevant to this kind of flows have different horizontal profiles impacted by the buildings, low speed wind regimes, separation and different turbulence characteristics. In order to have better insight into the physics of the urban wind turbines, European Framework project with acronym WINDUR has been undertaken. As part of this work, the complexity of the problem motivated us to look at the physics of urban flow problems first by measurements on several sites in Spain based on different climate classification regions defined by the Köppen-Geiger climate classification. The results of this measurement campaign will be presented. A separate work employing Computational Fluid Dynamics (CFD) has also been initiated as part of this study via EU WINDUR project [1]. However this study in this paper will solely concentrate on the measurement campaign. 1. Introduction Based on the challenge of where to place the measurement masts, Solute, a Spanish engineering company that is partner of the project has taken the initiative to survey several sites in Spain for Uppsala based on Wladimir Köppen climate classification map. The most frequently used climate classification map is the one of Wladimir Köppen [2] [3]. Subsequent publications

adopted this or a former release of the Köppen-Geiger map [2] [3]. Their processes were constrained by the requirements on the permissions to buildings and commercial constraints. Taking into consideration project financial constraints and the permission issues and commercial cities sites in the context of the Spanish market and project financial constraints, eight measurement mast locations were selected as illustrated in Figure 1 (a). Two of these masts were placed in Huesco in neighbouring buildings to be employed for further CFD validation studies. Of the six climate regimes in Spain, five were addressed while the Bsh climate regime that can be observed in limited areas in Spain was not prioritized. The measurement campaign was at 3.5m from the roof of buildings based on the design considerations of the wind turbine that is being developed. The Köppen-Geiger climate classification is calculated from observed temperature and precipitation data on a regular 0.5 degree latitude/longitude grid. The classification is based on three sub-classes namely main main climate, main precipitations and main temperatures. The temperature and precipitation ranges for these classifications are summarized below. The main climates that are of interest to this study in Spain are B which is Arid that is T min +18 C and C which is warm temperature which is 3 C < T min < +18 C [2]. The main precipitations that are of interest in Spain are s which is dry summer that is P smin < P wmin, P wmax > 3P smin, P smin < 40mm and f fully humid which is not s and also not P wmin < P smin, P smax > 10P wmin. Herein P smin, P smax, P wmin, P wmax are summer and winter minimum and maximum monthly precipitations [2]. The main temperatures that are of interest in Spain are h which is hot Arid for T ann +18 C, k which is cold Arid for T ann < +18 C where T ann defines annual mean nearsurface (2 m) temperature, a which is hot summer for T max +22 C and b which is warm summer for case a is not true and for which at least 4 T mon +10 C where T mon defines mean monthly temperature and T max defines monthly mean temperatures of the warmest months [2]. The Köppen-Geiger climate classification is depicted on the map of Spain in Figure 1 (b)-(f). These maps refer to data sets provided by the Climatic Research Unit (CRU) and the Global Precipitation Climatology Centre (GPCC) and can be accessed in the website [4]. The main results are estimated by considering different IPCC scenarios, which are A1FI, A2, B1 and B2 data set in Figure 1 (c)-(f). The classification of all test sites is summarized in Table 1. Table 1: Climate classification of the test sites

Figure 1: The legends of this map is illustrated in Table 1. Measurement mast location region climates a) Measurement mast locations b) Observed 1976-2000 data set c) Simulated 20012025 A1FI data set d) Simulated 2001-2025 A2 data set e) Simulated 2001-2025 B1 data set f) Simulated 2001-2025 B2 data set (a) Measurement mast locations (b) Observed 1976-2000 data set (c) Simulated 2001-2025 A1F1 data set (d) Simulated 2001-2025 A2 data set (e) Simulated 2001-2025 B1 data set (f) Simulated 2001-2025 B2 data set

2. Measurement result The following is the measurement result obtained in the eight mast locations. For validation purposes there were two measurement stations on the same site, Huesca in Spain. One site is located in Portugal and the rest of the sites are located in Spain. They were measured every 10 minutes continuously over approximately one year. 2.1. Huesca technopark site The data measured in the Huesca site is presented as follows. In this site two masts were placed in the building, namely EDIF 3 and EDIF 4. Table 2 lists the measurement location, the coordinate system expressed by longitude and latitude and the measurement period of the Huesca site. Table 2:, coordinates and measurement period EDIF 3 Huesca, Spain N42 60 29.3200 W0 270 33.2200 14/08/2014-29/09/2015 EDIF 4 N42 60 29.2500 W0 270 24.5400 (a) Picture of the building EDIF 3 (b) Picture of the 3.5m mast in EDIF 3 (c) Picture of the building EDIF 4 (d) Picture of the 3.5m mast in EDIF 4 Figure 2: a) Picture of the building, Huesca (EDIF 3) b) Picture of the mast 3.5m measurement, Huesca (EDIF 3) c) Picture of the building, Huesca (EDIF 4) d) Picture of the mast 3.5m measurement, Huesca (EDIF 4) Figure 2 displays the characteristics of the orography and buildings involved in the Huesca site. As shown in the figure, the building EDIF 3 is located to the East of EDIF 4. Figure 3 and 4 show the monthly and diurnal wind profile (a) and the wind rose (b) of EDIF 3 and EDIF 4, respectively. For the wind rose, the mean, maximum and minimum wind speed and the standard deviation are calculated at every 7.5 degree based on the annual wind data. They are represented by red, green, blue and black lines, but the minimum values are almost close to zero and not visible in the graph. Figure 5 and 6 are the wind distribution plotted in each direction of EDIF 3 and EDIF 4. This shows the wind speed varies considerably with wind direction. The wind speed is generally

(a) (b) Figure 3: Case of EDIF 3 (a) Monthly (left) and diurnal (right) profile (b) Mean, maximum, minimum wind speed and standard deviation of each sector (a) (b) Figure 4: Case of EDIF 4 (a) Monthly (left) and diurnal (right) profile (b) Mean, maximum, minimum wind speed and standard deviation of each sector high in areas with a strongly prevailing wind direction, which is around the East and the West. This tendency can also be observed in the mean wind speed.

Figure 5: Wind rose (Case of EDIF 3) Figure 6: Wind rose (Case of EDIF 4)

2.2. Rivas-Vaciamadrid The data measured in the Rivas-Vaciamadrid site is presented as follows. Table 3 lists the measurement location, the coordinate system expressed by longitude and latitude and the measurement period of the Rivas-Vaciamadrid site. Figure 7 displays the characteristics of the orography and buildings involved in the Rivas-Vaciamadrid site. Table 3:, coordinates and measurement period Calle Picos de Urbion, Rivas-Vaciamadrid, Madrid, Spain N40 220 25.9200 W3 310 41.6100 08/08/2014-14/10/2015 Figure 7: Site map (top-left), the mast (top-right) and picture of the building (bottom) Figure 8: Wind distribution (left), Mean, maximum, minimum wind speed and standard deviation of each sector (right) Figure 8 shows the wind distribution (left) and the mean, maximum, minimum wind speed and standard deviation of each sector (right). On the right graph, the mean, maximum, minimum wind speed and the standard deviation are represented by red, green, blue and black lines at every 7.5 degree.

2.3. A Corun a The data measured in the A Corun a site is presented as follows. Table 4 lists the measurement location, the coordinate system and the measurement period of A Corun a site. Figure 9 displays the characteristics of the orography and buildings involved in the A Corun a site. Table 4:, coordinates and measurement period Praza do Galatea, A Corun a, Spain N43 230 3.0000 W8 230 58.9600 01/08/2014-29/09/2015 Figure 9: Site map (left), picture of the building (right-top) and the mast (right-bottom) Figure 10: Wind distribution (left), Mean, maximum, minimum wind speed and standard deviation of each sector (right) Figure 10 shows the wind distribution (left) and the mean, maximum, minimum wind speed and standard deviation of each sector (right). On the right graph, the mean, maximum, minimum wind speed and the standard deviation are represented by red, green, blue and black lines at every 7.5 degree.

2.4. Guadalajara The data measured in the Guadalajara site is presented as follows. Table 5 lists the measurement location, the coordinate system and the measurement period of the Guadalajara site. Figure 11 displays the characteristics of the orography and buildings involved in the Guadalajara site. Table 5:, coordinates and measurement period Guadalajara, Spain N40 380 40.0400 W3 80 40.6500 04/08/2014-29/09/2015 Figure 11: Site map (left), picture of the building (right-top) and the mast (right-bottom) Figure 12: Wind distribution (left), Mean, maximum, minimum wind speed and standard deviation of each sector (right) Figure 12 shows the wind distribution (left) and the mean, maximum, minimum wind speed and standard deviation of each sector (right). On the right graph, the mean, maximum, minimum wind speed and the standard deviation are represented by red, green, blue and black lines at every 7.5 degree.

2.5. Ca diz The data measured in the Ca diz site is presented as follows. Table 6 lists the measurement location, the coordinate system and the measurement period of the Ca diz site. Figure 13 displays the characteristics of the orography and buildings involved in the Ca diz site. Table 6:, coordinates and measurement period Chipiona, Ca diz, Spain N36 430 48.8200 W6 250 56.3700 01/08/2014-29/09/2015 Figure 13: Site map (top), picture of the building (right-top) and the mast (right-bottom) Figure 14: Wind distribution (left), Mean, maximum, minimum wind speed and standard deviation of each sector (right) Figure 14 shows the wind distribution (left) and the mean, maximum, minimum wind speed and standard deviation of each sector (right). On the right graph, the mean, maximum, minimum wind speed and the standard deviation are represented by red, green, blue and black lines at every 7.5 degree.

2.6. Valladolid The data measured in the Valladolid site is presented as follows. Table 7 lists the measurement location, the coordinate system and the measurement period of Valladolid site. Figure 15 displays the characteristics of the orography and buildings involved in the Valladolid site. Table 7:, coordinates and measurement period Valladolid, Spain N41 390 50.5700 W4 420 14.8300 28/07/2014-14/10/2015 Figure 15: Site map (left) and picture of the mast (right) Figure 16: Wind distribution (left), Mean, maximum, minimum wind speed and standard deviation of each sector (right) Figure 16 shows the wind distribution (left) and the mean, maximum, minimum wind speed and standard deviation of each sector (right). On the right graph, the mean, maximum, minimum wind speed and the standard deviation are represented by red, green, blue and black lines at every 7.5 degree.

2.7. Oporto The data measured in the Oporto site is presented as follows. Table 8 lists the measurement location, the coordinate system and the measurement period of the Oporto site. Figure 17 displays the characteristics of the orography and buildings involved in the Oporto site. Table 8:, coordinates and measurement period Valongo, Portugal N41 110 42.5900 W8 310 4.1500 12/08/2014-29/09/2015 Figure 17: Site map (top), picture of the building (right-top) and the mast (right-bottom) Figure 18: Wind distribution (left), Mean, maximum, minimum wind speed and standard deviation of each sector (right) Figure 18 shows the wind distribution (left) and the mean, maximum, minimum wind speed and standard deviation of each sector (right). On the right graph, the mean, maximum, minimum wind speed and the standard deviation are represented by red, green, blue and black lines at every 7.5 degree.

2.8. Brief summary The wind roses of all test sites show that the wind speed is highly dependent on the wind direction. The urban boundary layers have large impact on the wind profile when it comes to small wind turbine installations at the roof of buildings. Figure 19: Monthly profile (left) and diurnal profile (right) of all test sites Figure 19 shows the monthly wind profile (left) and the diurnal wind profile (right) of all test sites. The markers stand for the climate classification, that is, is Cfb, BSk, + Csa and Csb. The maximum wind speed occurs at between 14 and 16 for all cases [5]. The hour which has the minimum wind speed value varies among the eight cases, but it is either in the midnight or in the early morning. Strong correlated similarities are not found in the monthly wind profile compared to the diurnal data. However, six test sites have the maximum wind speed in February, while Cádiz and Oporto sites have it in April and October. The case of Rivas-Vaciamadrid, of which the classification is BSk, has the largest difference between maximum and minimum monthly wind speeds, 3.3 m/s, while Csa has the smallest difference 1.1 m/s. However, the opposite happens in the diurnal profile. In other words, the hourly averaged wind speed changes most greatly during a day in the Csa area, but it has the least change in the BSk area. 3. Conclusions A measurement campaign has been undertaken for small wind turbine installations in Spain as part of an EU project WINDUR, and the preliminary findings are shared. An urban boundary layer wind measurement campaign in Spain based on the Köppen-Geiger climate classification is presented. The measurement result shows high dependency of the wind speed on the wind direction. References [1] A. Goude, B. Uzunoglu, G. Giovannini, J. Magdalena, and A. Fernandez. A gui for urban wind flow cfd analysis of small scale wind applications. In 2015 International Conference on Cyberworlds (CW), 7-9 Oct. 2015. [2] Markus Kottek, Jürgen Grieser, Christoph Beck, Bruno Rudolf, and Franz Rubel. World map of the köppengeiger climate classification updated. Meteorologische Zeitschrift, 15(3):259 263, 2006. [3] Franz Rubel and Markus Kottek. Observed and projected climate shifts 1901 2100 depicted by world maps of the köppen-geiger climate classification. Meteorologische Zeitschrift, 19(2):135 141, 2010. [4] World maps of köppen-geiger climate classification. http://koeppen-geiger.vu-wien.ac.at/. Accessed: 2016-05-10. [5] Stefan Emeis. Wind Energy Meteorology -Atmospheric Physics for Wind Power Generation-. Springer, 2013.