Spatiotemporal Modeling of Wind Generation for Optimal Energy Storage Sizing

    Authors

    H. V. Haghi;S. Lotfifard

    Comments

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    Abbreviated Journal Title

    IEEE Trans. Sustain. Energy

    Keywords

    Autocorrelation; data models; distributed power generation; energy; storage; higher order statistics; renewable energy; time series; analysis; wind power generation; TIME-SERIES MODELS; DEPENDENT RANDOM-VARIABLES; POWER-SYSTEMS; VINES; INTEGRATION; SCENARIOS; SELECTION; Energy & Fuels; Engineering, Electrical & Electronic

    Abstract

    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 high-dimensional 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 auto-correlation 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.

    Journal Title

    Ieee Transactions on Sustainable Energy

    Volume

    6

    Issue/Number

    1

    Publication Date

    1-1-2015

    Document Type

    Article

    Language

    English

    First Page

    113

    Last Page

    121

    WOS Identifier

    WOS:000346733200012

    ISSN

    1949-3029

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