Prediction of wind farm energy yield using NWP considering within-cell wake losses

Numerical Weather Prediction (NWP) models such as Weather Research and Forecasting (WRF) are widely used for predicting the wind resource at potential wind farm deployment sites and, increasingly, for energy yield prediction. Sub-grid models have previously been developed to represent wind farms by modification of momentum sink and turbulence kinetic energy source terms within cells containing wind turbines. In this study, a subgroup of turbines are parameterized by thrust and power curves determined using semi-empirical wake models to assess influence of within-cell wake losses on net yield. Variation of thrust and power with wind speed and direction was obtained for groups of turbines using the modified PARK and Eddy Viscosity methods in Openwind. Sensitivity to turbine number and spacing relative to the cell were determined. The influence of such wake losses on yield was evaluated by comparison of energy yield from a power curve and predicted wind speed, from use of a standard turbine representation within WRF and from a modified parameterization to represent wake losses. The case study is based on the Horns Rev farm for time intervals selected to represent the annual wind speed distribution. The parameterization developed provided an energy yield that is within 0.5% of the annual, when scaled for a year, compared to predictions within range 2-4% of measured by standard methods.