Wind integration studies often focus on the capacity value of wind power without considering Unit Commitment and Economic Dispatch or resolving requirements for ancillary services. Here, a novel method for simulating wind power time series with sufficient temporal span to support capacity studies and temporal resolution to support UCED studies is developed. Wind speed time series (WSTS), with 6 h temporal and 0.7 × 0.7 degree spatial resolutions, are extracted from the ECMWF-interim reanalysis, interpolated, scaled, and imputed so that they are representative of a point wind speed measurement with a 5 min temporal resolution. Imputation is made using a wavelet multi-resolution analysis approach that ensures temporally consistent correlations while accounting for heteroskedasticity. WSTS are transformed to power using wind power plant power curves, lowpass filters, and a Markov Chain model of operational efficiencies. The wind power model is validated using a set of measurements made at wind power plants (WPPs) in New Zealand and used to simulate power time series for 2 GW portfolios of WPPs representing compact, disperse, diverse, and business-as-usual portfolios. Metrics for dependability, variability, and predictability are applied to quantify the benefits of spatial diversification.