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Ever increasing penetration of wind power generation along with the integration of energy storage systems (ESSs) makes the successive states of the power system interdependent and more stochastic. Appropriate stochastic modeling of wind power is required to deal with the existence of uncertainty either in observations of the data (spatial) or in the characteristics that drive the evolution of the data (temporal). Particularly, for capturing spatiotemporal interdependencies and determining energy storage requirements, this paper proposes a versatile model using advanced statistical modeling based on the vine-copula theory. To tackle the complexity and computational burden of modeling highdimensional wind data, a systematic truncation method is utilized that significantly reduces computational burden of the method while preserving the required accuracy. By constructing a graphical dependency model, unlike existing autoregressive and Markov chain models, the proposed method can replicate the exact autocorrelation function (ACF) and cross-correlation function (CCF), while retaining the correct distribution of the original data as well as the effective dependence between different sites under study. The practical importance of the proposed model is demonstrated through an example of ESS sizing for wind power.

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