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Scenarios of near future wind power are synthesized by considering the power spectral density (PSD), statistical characteristics, and the future capacity. The PSD of the wind power follows different power laws over different frequency ranges and is approximated by a piecewise function. A scaling exponent of the power law for a particular piece can be approximated by the slope of an affine function fitted to a logarithmic plot of the PSD. Each piece of the function has a different trend as the total capacity increases. Slope trends, the first PSD value, and the last PSD value are trained to forecast the PSD. Then, future wind power scenarios are synthesized from the forecasted PSD. In this process, phase angles are searched using a genetic algorithm while satisfying forecasted statistical characteristics for the given capacity. Our approach is simulated and validated for wind power for seven years in ERCOT and is used to synthesize a future wind power scenario at 10,000 MW capacity. Our approach could also be used to generate wind power scenarios at present capacity for many stochastic optimization problems in power systems.