The challenge of integrating offshore wind power in the U.S. electric grid. Part I: Wind forecast error

Using an auto-regressive moving-average (ARMA) model, 21 equally-plausible sample paths of wind power forecast errors are generated and calibrated for each season at a control onshore wind farm, chosen because of its horizontally uniform landscape and large size. The spatial correlation between pairs of onshore wind farms is estimated with an exponential function and the matrix of error covariance is obtained. Validation at the control farm and at all other onshore farms is satisfactory. The ARMA model for the wind power forecast error is then applied to the offshore wind farms at the various build-out levels and combined with the matrix of error covariance to generate multiple samples of forecast errors at the offshore farms. The samples of forecast errors are lastly added to the WRF forecasts to generate multiple samples of synthetic, onshore-based, actual offshore wind power for use in Part II.