WAsP model performance verification using lidar data

Int J Energy Environ Eng (2016) 7:105–113 DOI 10.1007/s40095-015-0189-6 ORIGINAL RESEARCH WAsP model performance verification using lidar data Victor S. Indasi1 • M. Lynch1 • B. McGann1 • Frank Yu1 • F. Jeanneret1 • J. Sutton1 Received: 27 March 2015 / Accepted: 21 August 2015 / Published online: 6 November 2015 Ó The Author(s) 2015. This article is published with open access at Springerlink.com Abstract This study describes the verification of Wind Atlas Analysis and Application program (WAsP) modelled average wind speeds in a complex terrain. WAsP model was run using data collected at 3 masts: Kalkumpei, Nyiru and Sirima using cup anemometers and wind vanes for the entire 2009 calendar year and verified using data collected by WindTracer LIDAR (light detection and ranging) for 2 weeks from 11th to 24th July 2009. Evaluating WAsP mean wind speed map using LIDAR data showed that Nyiru station provides the best data to model mean wind speed over the wind farm domain with a mean difference of 0.16 m/s, root mean square error of 0.85 m/s and Index of Agreement of 0.61. Construction of a 310 MW windfarm has commenced at this site. Once completed, the windfarm will be operating 365 vestas V52-850kW turbines. Keywords WAsP
LIDAR
Complex terrain
Wind resource assessment Introduction WAsP is the wind energy industry standard model used to assess the mean wind speed and energy output at a specific site or at a high resolution over a wider area. WAsP, whose sub-models were first developed by the Risø National Laboratory in 1987, is commonly used throughout the world in the wind energy industry to get an estimate of available regional wind resources, to site turbines at & Victor S. Indasi 1 Department of Physics and Astronomy, Curtin University, Bentley, WA, Australia specific locations, and to estimate wind farm production [1]. WAsP is based on physical principles of flows in the boundary layer and attempts to solve the Navier–Stokes momentum equations, estimates the regional wind climate, as well as the wind speed at any specific location and height. This is done by horizontally and vertically extrapolating a record of wind data within the region using steps that take into consideration elevation or topography changes, land use or classification/surface roughness, and local obstacles [2]. WAsP has been applied to a wide variety of situations including flat, open terrain [3]; offshore locations [4, 5]; coastal locations [6]; mountainous terrain; [7]; forested terrain [8]; extreme winds [9, 10] and short-range weather forecasting [11]. Barthelmie et al. [4] found that for offshore locations WAsP tended to over-predict the mean speed. The differences were thought to be due to the incorrect assignment of roughness lengths and stability effects. Romeo and Magri [6] found that WAsP produced good estimates of the mean speed for a coastal site in southeast Sicily. They used data from a numerical model as the starting point for the analysis. Suárez et al. [8] studied an area of mountainous, forested terrain in western Scotland. Using an anemometer on an exposed ridge as their reference site, they found that WAsP produced an accurate estimate of the mean speed at another nearby hill-top site (7.5 km to the east southeast). However, for two valley locations in the same area, the mean speed was underestimated by around 15 %, and for a site in a saddle and a site on the side of a valley the WAsP estimates were too high by 15–20 %. Landberg and Mortensen [11] compared WAsP and Measure-Correlate-Predict (MCP) using data from six complex terrain stations in northern Portugal. They 123 106 Int J Energy Environ Eng (2016) 7:105–113 demonstrated that WAsP will produce poor results if the reference and target site are in different climatic zones. Onat and Ersoz [12] used five-layer sugeno-type model scripted in MATLAB and WAsP to describe the characteristics of wind climate and energy potential for three regions in Turkey. Their analysis produced detailed wind resource maps and concluded that the regions are well located for the installation of parallel-connected wind plants to the national network in terms of the reliability of wind and capacity usage rates. Palaiologou et al. [13] used GIS and WAsP as basic calculation platforms to test and evaluate measurements from 15 wind turbine sites by creating six alternative scenarios in the island of Lesvos, Greece. They demonstrated that topography plays a critical role in the accuracy of WAsP calculations. Djamai and Merzouk [14] used WAsP to investigate the possibility of setting up a 10 MW wind farm in Adrar, a region located in the south of the country. Lima and Filho [15] conducted a wind resource evaluation and wind energy assessment for São João do Cariri in Paraiba state of northeast. They both demonstrated that WAsP program is a robust and reliable tool to make wind characterization and wind energy potential assessment. The predictions from WAsP for wind flows over simple isolated hills compare well with the measured data from the two benchmark field measurements [16]. Additional independent assessments of WAsP for more complex terrain situations, which lie largely within its operating envelope, generally confirm the reliability of the predictions under these conditions. There are several factors which influence the accuracy of a power estimates using WAsP. These factors are broadly classified into four categories: Atmospheric conditions [17, 18], Orography [19], Weibull fit error and wind data records [18]. Due to some of the simplifications made in the numerical models used within WAsP, the program can produce somewhat inaccurate results when used outside its recommended operational envelope [18]. When a site has complex, rugged terrain or very complex atmospheric conditions, the accuracy of WAsP can be unreliable [18]. This problem can be solved using several reference sites and cross-referencing sites where wind observations are available. There is also the option for some user corrections at problematic sites which can significantly improve the accuracy of the model in complex terrain [20]. The aim of this study was to evaluate the accuracy of the WAsP model using high spatial resolution LIDAR data. To achieve this, WAsP was run using wind data from 3 instrumented meteorological masts; the resulting mean wind speeds were compared to mean wind speed retrieved from LIDAR. 123 Area of study The region of study is north-western Kenya where the winds are generated by a low-level jet called the Turkana easterly low-level jet. The jet stream is created by the much bigger East African low-level jet. The Turkana Channel jet blows lasting through the year from the South East through the valley between the East African and the Ethiopian Highlands extending from the Ocean to the deserts in Sudan [21]. The wind is enhanced locally between Mt. Kulal (2300 m ASL) and the Mt Nyiru Range (2750 m ASL). Both Kinuthia and Asnani [21, (...truncated)