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A simple simulator capable of generating synthetic hourly values of wind power was developed for the South West region of Western Australia. The global Modern Era Retrospective Analysis for Research and Applications (MERRA) atmospheric database was used to calibrate the simulation with wind speeds 50 m above ground level. Analysis of the MERRA data indicated that the normalised residual of hourly wind speed had a double exponential distribution. A translated square-root transformation function y n = ( ( 1 . 96 + y e ) − 1 . 4 ) / 0 . 302 was used to convert this to a normal-like distribution so that autoregressive (AR) time series analysis could be used. There was a significant dependency in this time series on the last 3 h m so a third order AR model was used to generate hourly 50 m wind speed residuals. The MERRA daily average 50 m wind speed was found to have a Weibull-like distribution, so a square root conversion was used on the data to obtain a normal distribution. The time series for this distribution was found to have a significant dependency on the values for the last two days, so a second order AR model was also used in the simulation to generate synthetic time series values for the square root of the daily average wind speed. Seasonal, daily, diurnal, and hourly components were added to generate synthetic time series values of total 50 m wind speed. To scale this wind speed to turbine hub height, a time varying wind shear factor model was created and calibrated using measured data at a coastal and an inland site. Standard wind turbine power curves were modified to produce an estimate of wind farm power output from the hub-height wind speed. Comparison with measured grid supervisory control and data acquisition (SCADA) data indicated that the simulation generated conservative power output values. The simulation was compared to two other models: a Weibull distribution model, and an AR model with normally distributed residuals. The statistical fit with the SCADA data was found to be closer than these two models. Spatial correlation using only the MERRA data was found to be higher than the SCADA data, indicating that there is still a further source of variability to be accounted for. Hence the simulation spatial correlation was calibrated to previously reported findings, which were similar to the SCADA data. [ABSTRACT FROM AUTHOR]

Abstract: An algorithm was developed to generate synthetic hourly cloudiness data for any time of the year at any location in the south west region of Western Australia (WA). To enable the algorithm to be used for simulation of the power output of both tilted photovoltaic and concentrating solar power systems, a metric of cloudiness was defined which modifies the clear sky beam, diffuse and reflected solar transmittance. Seasonally and positionally adjusted values of daily cloudiness were generated by roughly mimicking the geographic pattern of annual rainfall in WA. Rather than longitude and latitude, distance along the coastline and distance inland from the coast were used as the positional coordinates. Hourly cloudiness data was generated from the daily values using a first order autoregression algorithm with time varying mean and standard deviation. Two years of measured hourly horizontal solar irradiance data from a network of 31 weather stations was used to calibrate the algorithm. The algorithm was simple enough to run inside a world wide web page and has the potential to be adapted to other regions with a similar pattern of declining inland rainfall. [Copyright &y& Elsevier]