Wind Speed and Energy at Different Heights on the Latvian Coast of the Baltic Sea

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1 J. Energy Power Sources Vol. 1, No. 2, 2014, pp Received: July 1, 2014, Published: August 30, 2014 Journal of Energy and Power Sources Wind Speed and Energy at Different Heights on the Latvian Coast of the Baltic Sea Valerijs Bezrukovs and Vladislavs Bezrukovs Ventspils University College, Inzenieru Str. 101a, Ventspils, LV3600, Latvia Corresponding author: Valerijs Bezrukovs Abstract: The paper is devoted to the investigation into the wind energy potential in Latvia based on long-term observations of the wind energy density fluctuations at heights from 10 to 60 m on the Baltic Sea coast in the northland south-west. The wind speed values were measured in using a LOGGER 9200 Symphonie measuring system, and since June of 2011 a lidar ZephIR for the heights up to 160 m. The results are presented in the form of tables, bar charts and graphs for three areas with different terrain types. The histograms are composed for the relative repetition frequency of wind speed. In the paper, the coefficients of approximating functions were derived. The extrapolation results are shown for the distribution curves of averaged wind speed and energy density values at heights up to 200 m. Comparative results of the wind speed distribution in height are given for two areas spaced 30 km apart. Key words: Wind energy density, wind speed approximating functions at 200 m, lidar, metrological mast. 1. Introduction The availability of large unpopulated coastal areas and the developed infrastructure of electric power networks in the Baltic countries make attractive the use of these areas for siting large WPP (Wind Power Plants). According to data from the European Bank for Reconstruction and Development Renewable Energy Programme, the technical potential for wind energy production in Latvia has been estimated to be around 1,277 GWh/year or 25% of annual production of electricity in our country. Up to this date projects on the construction of 450 MW coastal WPPs have been approved in Latvia. Besides, a project of a 900 MW offshore wind park is now under consideration. The locations of these WPPs on the Baltic offshore and Sea coast of Latvia are shown in Fig. 1. The location of planned WPPs is concentrated in the coastal region due to the dominance of wind energy streams coming from the south-west coast of the Baltic Sea. However, currently no reliable maps exist that would show the distribution of wind energy in height on the territory of Latvia, and it is sometimes problematic to plan the construction of WPPs in the inland areas. Systematic long-term measurements of wind speeds in Latvia since 2007, taking into account the wind speed distribution at several heights, have been carried out at two sites on the north-west coast of the Baltic Sea in the Ventspils region and on the north of the country Fig. 1 Map of the Latvian coast of the Baltic Sea with planned WWP locations (blue labels) and wind measurement sites 1, 2 and 3 (red stars).

2 Wind Speed and Energy at Different Heights on the Latvian Coast of the Baltic Sea 107 in the Ainaži region, 35 km from the sea shore [1]. The places where the metrological equipment is located are shown on the map of Fig. 1 by red stars 1, 2 and Wind Measurement Devices and Methodology of the Research The measurements of wind speed on sites 1 and 2 were carried out using certified sensors of wind speed and those indicating the direction of air stream. All measuring sensors are arranged on metallic NRG masts with the height of 53 and 60 m above the ground (see Table 1) [2]. For storing information from the sensors at all height levels an LOGGER 9200 Symphonie complex was used. The complex has an independent energy supply from batteries and it stores average wind speed values for every 10 min intervals from nine sensors in its memory card. Wind data retrieving and filtering from both sites were done using Symphonie Data Retriever. Further data analysis was done using Microsoft Excel 2007 with additional scripts. Installation of the 60 m long mast with the measuring complex LOGGER 9200 Symphonie are shown in Fig. 2. Because of new improved installing kit installation of mast was significantly simplified. However, all appropriate preparatory works may take several weeks. It should be taken into account that installation of the mast can be made only in windless time. On site 3 an optical remote sensing complex lidar ZephIR (Fig. 3) is used, which can measure wind speed and direction at a distance. The complex is installed on the top floor of an eight-storey apartment building and Fig. 3 Optical remote sensing complex lidar ZephIR for measuring wind speed and direction on the distance till height 160 m on five height levels located in Ventspils site 3 near Baltic Sea coast. Fig. 2 Installation of the 60 m long mast with a measuring complex LOGGER 9200 Symphonie at the Ainaži region (wind measuring site 2). Fig. 4 The user interface of Waltz software which allow configure height levels of wind speed measurement complex ZephIR.

3 108 Wind Speed and Energy at Different Heights on the Latvian Coast of the Baltic Sea Table 1 Characteristics of the meteorological equipment. Ventspils (site 1) Ainaži (site 2) Ventspils (site 3) Instrument: NRG Metrological mast, height 53 m NRG Metrological mast, height 60 m Site elevation at sea level 15 m 60 m 42 m Terrain type: Baltic Sea coast, plane wooded terrain (8-10 m high trees). Mast distance from the sea: ~ 5 km An open plain terrain remote from the sea. Mast distance from the sea: ~ 35 km. Date of installation: Sensors: Anemometer: Wind direction: Temperature: NRG #40, digital sensor, time of integration 10 s; Height (m): 20; 30; 40; 50; 53 NRG #200P Wind Vane; Height (m): 20; 53 NRG #110S Temp; Height (m): 20 NRG #40, digital sensor, time of integration 10 s; Height (m): 10; 20; 30; 40; 50; 60 NRG #200P Wind Vane; Height (m):50; 60 NRG #110S Temp; Height (m): 6 Barometer: NRG #BR20 Barometer Not installed Natural Power infrared lidar ZephIR Baltic Sea coast, urban area with highest building ~50m, Lidar distance from the sea: < 800 m ZephIR lidar equipped with 2 m mast with wind speed and wind direction sensors, temperature sensor, barometer, rain sensor, humidity sensor. Laser beam set for levels (m): 60; 80; 100; 130; 160 has a direct connection to the power grid and internet. Site 3 is located 800 m from the sea coast and at an elevation of 42 m above the sea level. In Fig. 4 the interface of ZephIR complex for configuration of the height levels is presented. To calculate the mean density of available wind power the Weibull probability density function is used [3]: p V = k c V c k-1 exp - V c k (1) where k shape factor and c scale factor. For 1 k 10 approximation the following equation is used: k = δ V (2) V avg Scale factor c can be found from the following empirical approximation: c = k V k avg (3) Mean of average wind speed V avg for the measurement interval is: V avg = n i V i (4) n where V i is the average wind speed for the 10 min measurement interval; i = 1, 2,. n is the number of the measurement intervals for whole measurement period. The standard deviation of wind speed δ V can be calculated by the equation: V max. δ V = (V i -V avg ) 2 i=0 F i (5) where V i is the wind speed in the interval 0 V i V max and its corresponding F i value on the frequency distribution curve. The total sum of all means F i for the measurement interval is: i = 0 F i (V i ) = 1 (6) The mean available wind power density, P avg W A m2 is: P avg A = 1 ρv 3 2 avg.cub (7) where ρ is the air density is 1.23 kg/m 3 for standard condition at the sea level and temperature 15 ; V avg.cub is the wind average cubic speed which can be found as: 3 V avg.cub = V max i=0 V i 3 F i (8) Here, V i and F i are average wind speed for the 10 min measurement interval and its respective frequency distribution (see Fig. 13). 3. Results In the observation period since summer 2007 a database has been built that contains the wind speed values at heights of 10, 20, 30, 40, 50 and 60 m, the

4 Wind Speed and Energy at Different Heights on the Latvian Coast of the Baltic Sea 109 wind directions and air temperatures in two regions of Latvia. Currently, the wind database stores more than 4 million measurement records. The charts of seasonal fluctuations of average wind speed V avg (m/s) for measurement period T (month-averaged) at the heights 20, 30, 50 m in the Ventspils region (site 1) and 10, 30, 50, 60 m in the Ainaži region (site 2) are shown in Figs In Fig. 7 the wind roses typical for these regions are depicted, where for site 1 and 2 the data for height above the ground 53 m and 60 m, respectively, are used. In this figure outer numbers are average TIs for the wind speed greater than 4.5 m/s. The black areas stand for the total wind energy, the checked ones for the total time of wind blowing. The results of wind speed measurements taken at Ventspils site 3 using complex ZephIR are shown in Fig. 8, where the measurement results are presented for heights from 44 m up to 160 m above the ground level taking into account the site elevation of measurement complex. (a) Average wind speed V avg, m/s Ventspils, site 1 Height 50 (m) Height 30 (m) Height 20 (m) Jul.2007 Jan.2008 Jul.2008 Jan.2009 Jul.2009 Jan.2010 Jul.2010 Jan.2011 Jul.2011 Jan.2012 Time T, months Fig. 5 Average wind speed V avg for measurement period T 2007/2011 at heights 20, 30 and 50 m. Average wind speed V avg, m/s Ainaži, site 2 Height 60 (m) Height 30 (m) Height 10 (m) Time T, months Fig. 6 Average wind speed values V avg for measurement period T 2009/2011 at heights 10, 30 and 60 m. (b) Fig. 7 Wind rose: (a) for measurement period 2007/2011, Ventspils, site 1; (b) for measurement period 2009/2011, Ainaži, site 2. Height H, m Ventspils, site 3 Min Mean Max Latest Fig. 8 Distribution curves of the wind speed at heights from 44 m to 160 m, for a short-time period (24 h) obtained using complex ZephIR software.

5 110 Wind Speed and Energy at Different Heights on the Latvian Coast of the Baltic Sea Table 2 Wind data of Ventspils region site 1. Period of measurement Height above ground (m) V avg (m/s) V avg.cub. (m/s) V avg.cub. / V avg Weibull parameter k Weibull parameter c P avg. Weibull (W/m 2 ) P avg. Real (W/m 2 ) / Average / Average P avg. Real / P avg. Weibull Table 3 Wind data of Ventspils region site 3. Period of measurement Height above ground (m) V avg (m/s) V avg.cub. (m/s) V avg.cub. / V avg Weibull parameter k Weibull parameter c P avg. Weibull (W/m 2 ) P avg. Real (W/m 2 ) / Average P avg. Real / P avg. Weibull In Fig. 8 short-time scale wind speed curves obtained by software of complex ZephIR for the 24 h time interval are presented. Points on the curves are updated in real time and correspond to the minimum, maximum, mean and latest wind speed for all levels. It should be noted that, despite the nature of wind speed fluctuations, on the curve of wind speed short-interval distribution in height corresponds to a standard pattern and is close to the wind speed distribution obtained for the entire period of measurements (see Fig. 16). The results of wind speed measurements for sites 1, 2 and 3 are presented in Tables 2-3. Mean wind speed values V avg and average cubic wind speed V avg.cub. for all height levels at sites 1, 2 and 3 are calculated employing statistical analysis of the wind data from the tables. Based on the measured values, Weibull s parameters were calculated using Eqs. (1)-(3): shape factor k and scale factor c (see Tables 2-3). The tabulated values of average wind power density P avg.real for the given period were calculated using the data on the wind speed frequency distribution. The average wind power density P avg.weibull for the calculated Weibull parameters k and c was obtained employing the WAsP 10 package Weibull Distribution explorer tool [4]. From Tables 2-3 it can be seen that the magnitude of wind average cubic speed V avg.cub. (calculated taking

6 Wind Speed and Energy at Different Heights on the Latvian Coast of the Baltic Sea 111 into account the wind speed frequency distribution F(V)), is on average 25 % higher than V avg. The results of wind speed measurements at site 3 show evidence that the velocity ratio varies from 1.36 at height of 44 m to 1.21 at height of 160 m, due to strong air turbulence caused by surrounding buildings. Furthermore, the value of real wind power P avg.real is greater than P avg.weibull by average of 11%. In Figs the wind speed frequency distribution curves F(V) for sites 1 and 3 are presented, which are constructed based on actual values of wind speed for periods of 2007/2011 and / In Figs Weibull s probability density functions (1) for the corresponding values of the parameters k and c are shown for sites 1 and site 3 during the same time period and same height levels. As can be seen, the real wind speed frequency distribution curves in Figs noticeably differ from Fig. 9 Wind speed frequency distribution curves F(V), 2007/ % 7% 5% 3% 1% Ventspils, site 1 Height 50 (m) Height 40 (m) Height 30 (m) Height 20 (m) Ventspils, site 3 Height 160 (m) Height 100 (m) Height 80 (m) Height 60 (m) Height 44 (m) Fig. 10 Wind speed frequency distribution curves F(V), / the Weibull probability density functions shown in Figs At the same time, for the wind speed frequency distribution curves obtained in a short-time period (6 months) as shown in Figs the differences are more pronounced. For longer measurement periods (5 years) the measured curves tend to be close to the established laws (Weibull s distribution) with some deviations. For comparison, Figs show a combined frequency distribution diagram of the wind speed bins and the equivalent Weibull probability density function curve for sites 1 and 3. In both cases, the ideal curve of Weibull s probability density function is close to the actual distribution of wind speed bins. The wind energy flow fluctuations during a year have a periodic character, with deviations ± from the average long-term values [1]. At the same time, the mean monthly values of wind power may differ many times as can be seen in Fig. 15, which shows the wind Ventspils, site 1 Height 50 (m) Height 40 (m) Height 30 (m) Height 20 (m) Fig. 11 Weibull probability density function, 2007/ % 7% 5% 3% 1% Ventspils, site 3 Height 160 (m) Height 100 (m) Height 80 (m) Height 60 (m) Height 44 (m) Fig. 12 Weibull probability density function, /

7 112 Wind Speed and Energy at Different Heights on the Latvian Coast of the Baltic Sea Ventspils, site 1, height 50 m Frequency distribution Weibull distribution Height H, m Ventspils site 1, 5 years Ventspils site 1, 6 months Ventspils site 3, 6 months Ventspils site 3, 5 years (model) H 1 (v)= (v+1.4) H 2 (v)= (v+1.4) H 3 (v)= v H 4 (v)=0.189 v Fig. 13 Frequency and Weibull distributions curves, 2007/ % 7% 5% 3% 1% Ventspils, site 3, height 44 m Frequency distribution Weibull distribution Fig. 14 Frequency and Weibull distributions curves, / Wind power density P avg, W/m Ventspils, site 3 Height 160 (m) Height 130 (m) Height 100 (m) Height 80 (m) Height 60 (m) Height 44 (m) 0 Jun.2011 Jul.2011 Aug.2011 Sep.2011 Oct.2011 Nov.2011 Dec.2011 Jan.2012 Time T, months Fig. 15 Wind power density curves for heights up to 160 m above ground level, / , Ventspils, site 3. power curves calculated according to Eq. (7) based on the measured wind parameters. Measurements were carried out using complex ZephIR for the time period / , in Ventspils, site 3 at heights 44, 60, 80, 100, 130 and 160 m above ground level sequentially Wind speed v, m/s Fig. 16 The approximating functions of the height vs. average wind speed H(v) for the Ventspils regions sites 1 and 3 extrapolated up to the height of 200 m. for each level with measurement time 3 s at each point. The nature of wind energy flow distribution high above the ground in real conditions is significantly affected by the type of surrounding landscape. Site 1 is located in a small woody area, while site 3 is located on outskirts of the town and is surrounded by apartment houses. Under these conditions, turbulence of the air flow decreases wind speed near the ground and has a significant impact on the slope pattern of wind speed distribution in height. General rules that govern this process were defined comparing the wind speed distribution curves from both sites. Fig. 16 presents approximation curves of the wind speed distribution at a height of 200 m based on the data obtained from site 1 for 6 months and 5 years periods and from site 3 for the 6 months period. Assuming the wind speed distribution processes to be similar at both sites, it was possible to create the following models of exponential approximating functions H(v) connecting the wind speed and the height (up to 200 m above the ground level): H 1 (v) = v (9) H 2(v) = v (10) H 3 (v) = v (11) H 4 (v)=0.189 v (12) where v wind speed (m/s);

8 Wind Speed and Energy at Different Heights on the Latvian Coast of the Baltic Sea 113 The H 1 v approximating function coefficients were obtained based on the wind speed measurements averaged over 5 years span for site 1; The H 2 v approximating function coefficients were obtained based on the wind speed measurements averaged over 6 months span for site 3; The H 3 (v) approximating function coefficients were obtained based on the wind speed measurements averaged over 6 months span for site 3; The H 4 (v) approximating function coefficients were calculated based on long-term scale averaged wind speed measurements from site 1 and 6 months averaged wind speed measurements from site 3. Curves 1 and 2 constructed based on the mean wind speed values from site 1 are well approximated by exponential Eqs. (9)-(10) for heights above 30 m, because at this site the wind flows at corresponding heights become laminar. At site 3 the wind speed distribution in height is significantly affected by the turbulent nature of wind flows at low heights. Therefore, approximation for the curve showing distribution of the mean wind speed values using exponential relationship (11) is appropriate only for the points above 100 m. The model of approximating function (12) helps more precisely estimate the long-term forecasts of generated electricity power and, respectively, the payback time for WPPs. It should be noted that the estimation of wind energy at heights up to m above the ground obtained by extrapolation of the wind speed may contain significant uncertainty, since measurements at these heights might be influenced by turbulent flows. 4. Conclusions 1. Availability of large unpopulated areas on the coasts of the Baltic countries, along with the developed infrastructure of electric power networks, makes attractive the use of these lands for siting large WPPs. 2. During long-term observations a statistical database has been accumulated on the distribution of speeds and directions of winds at different heights: 10, 20, 30, 40, 50 and 60 min the Ventspils and Ainaži regions on the Baltic Sea shore. 3. On the Baltic Sea shores near the Ventspils region calculated annual mean wind power density at height of 100 m is more than600 W/m Possibilities of correcting short-time measurements by using long-term database from adjacent areas are discussed. 5. The results of research of wind speed distribution up to 200 m are promising for evaluation of wind energy potential in Latvia and should help in assessment of prospective sites for construction of WPPs. Acknowledgment Research carried out in collaboration with Institute of Physical Energetics. Authors acknowledge support from ENCOM Ltd and express their gratitude for financing to ERDF s project SATTEH, No. 2010/0189/2DP/ /10/APIA/VIAA/019, being implemented in Engineering Research Institute <Ventspils International Radio Astronomy Centre> of Ventspils University College (VIRAC). References [1] P. Shipkovs, V. Bezrukov, V. Pugachev, Vl. Bezrukovs, V. Silutins, Research of the wind energy resource distribution in the Baltic region, in: Proceedings of Conference World Renewable Energy Congress XI, Abu Dhabi, UEA, 2010, pp [2] V. Bezrukovs, Vl. Bezrukovs, N. Levins, Problems in assessment of wind energy potential and acoustic noise distribution when designing wind power plants, Journal of Riga Technical University 6 (2011) [3] J.F. Manwell, J.G. McGowan, A.L. Rogers, Wind Energy Explained: Theory, Design and Application, John Wiley & Sons, Inc., [4] WAsP-the Wind Atlas Analysis and Application Program, Copyright Risø DTU.