Spatiotemporal Modeling of Wind Generation for Optimal Energy Storage Sizing


Ever increasing penetration of wind power generation along with the mixing of energy storage systems (ESSs) makes the successive states of the ability system interdependent and more stochastic. Appropriate stochastic modeling of wind power is required to house the existence of uncertainty either in observations of the data (spatial) or in the characteristics that drive the evolution of the info (temporal). Particularly, for capturing spatiotemporal interdependencies and determining energy storage requirements, this paper proposes a flexible model using advanced statistical modeling based mostly on the vine-copula theory. To tackle the complexity and computational burden of modeling high-dimensional wind knowledge, a systematic truncation method is utilized that considerably reduces computational burden of the method while preserving the specified accuracy. By constructing a graphical dependency model, in contrast to existing autoregressive and Markov chain models, the proposed method will replicate the exact autocorrelation perform (ACF) and cross-correlation function (CCF), whereas retaining the right distribution of the first data in addition because the effective dependence between totally different sites beneath study. The sensible importance of the proposed model is demonstrated through an example of ESS sizing for wind power.

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