Energetica India Magazine December 2020

WIND POWER cross-prediction errors to other project and model attributes. The dataset consisted of about 105 project runs with two to ten met towers per project. Table 1 depicts the relationship between mesoscale model grid spacing and cross-prediction error (uncertainty). Not sur- prisingly, the error decreases with decreasing grid spacing. Table 1. Mesoscale Model Wind-Speed Accuracy (1) Mean absolute deviation (2) Standard deviation (3) Number of samples The relationship between cross prediction error of the meso- scale model and the degree of site complexity is explored in Table 2. For a given wind farm project area, we define terrain complexity as follows: 1. Simple: Topographic elevation variation (TEV) <25 meters 2. Moderate: TEV 25 to 75 meters 3. Complex: TEV >75 meters Table 2. Terrain Complexity and Wind-Speed Accuracy, All Resolutions. Abbreviations as in Table 1. The average cross-prediction errors increase steadily with in- creasing terrain complexity. For comparison, cross prediction errors for linear and CFD models reported in the literature show standard deviations of 4% in simple terrain and 10% in com- plex terrain. Finally, we looked at two very different countries for which Arc- Vera had large and roughly equally sized samples, USA and Brazil. USA is midlatitude, with winds driven by a variety of weather regimes; whereas Brazil is a tropical nation with winds driven primarily by the Atlantic easterly trades impinging upon terrain features in the northeastern region. Only complex-ter- rain sites (TEV > 75 m) were included here for both countries. The cross prediction error is very similar (Table 4), indicating that mesoscale modeling, when applied consistently and driv- en by a global reanalysis data set, provides consistent accura- cy across very different wind resource environments. Table 3. Model Wind-Speed Accuracy in the USA vs. Brazil, Complex Terrain. Abbreviations as in Table 1. Conclusion Mesoscale models are eminently capable of producing accu- rate, detailed wind-resource maps of project areas using mi- croscale (100-300 m) grid spacing. There are compelling rea- sons why this high-resolution mesoscale modeling approach to wind flow mapping should yield more accurate spatial wind maps than linear flow or computational fluid dynamics meth - ods. Those approaches do not account for the broad range of spatial scales and rich spectrum of meteorological phenom- ena and thermodynamics that greatly influence a site’s wind climate. Other studies confirm this improved performance to be the case. The uncertainty of mesoscale modeling for wind resource as- sessment was examined via cross prediction error statistics for a large global dataset of wind projects and meteorological towers. Uncertainty was found to vary directly with mesoscale model grid spacing and terrain complexity. However, uncer- tainty was very nearly the same for two geographically different countries (USA and Brazil), indicating that mesoscale model- ing provides consistent performance for a wide variety of wind resource types. References Orlanski, I., 1975: A rational subdivision of scales for atmo- spheric processes. Bull. Amer. Meteor. Soc., 56, 527–530. Model Grid Spacing (m) M A D ( 1 ) (m s-1) MAD (%) STDEV(2) (%) N(3) 200 0.146 1.89 2.73 353 300 0.195 2.40 3.42 102 600 0.279 3.60 4.85 39 T e r r a i n Complexity MAD (m s-1) MAD (%) S T D E V (%) N Simple 0.116 1.44 2.07 182 Moderate 0.143 1.79 2.46 140 Complex 0.236 3.08 4.17 183 Country MAD (m s-1) MAD (%) STDEV (%) N USA 0.216 3.01 3.87 82 Brazil 0.239 2.92 4.09 95 40 energetica INDIA- December_2020