Dynamic wind farm power simulation

Wind energy represents one of the least cost methods of electricity generation, produces no carbon emissions, and is highly scaleable. However, the intermittency of wind and the passive reaction of wind turbines means spinning and scheduled reserves are required to ensure grid security. It is vital for transmission network planning that temporally and spatially accurate models of wind power are formulated. While there are many approaches to the simulation of wind speed time-series there is less information available to aid in characterising the dynamic transformation from wind speed time-series to wind power time-series. Wind farms comprise arrays of wind turbines, and individual wind turbines have well defined power curves, however the aggregation of the individual wind turbine power time-series is not independent; thus a temporally consistent model for the spatial correlation of the wind resource is required. Measurements made at a wind farm in New Zealand are used to construct two models and simulations compared with measured power. The first applies the Sandia method coupled with the convolution of a Gaussian function and the wind turbine power curve. The second method uses the Measure Correlate Predict (MCP) methodology and Wavelet Multi-resolution Analysis (WMA). The MCP / WMA model is then applied to generate wind farm power curves for farms of differing topographies, and a simple model comprising Gaussian smoothing and a first order low pass filter matched to the results.