Subordinated Gaussian Processes for Solar Irradiance

Caitlin Berry, William Kleiber, Bri-Mathias Hodge

Research output: Contribution to journalArticlepeer-review

Abstract

Traditionally the power grid has been a one-way street with power flowing from large transmission-connected generators through the distribution network to consumers. This paradigm is changing with the introduction of distributed renewable energy resources (DERs), and with it, the way the grid is managed. There is currently a dearth of high fidelity solar irradiance datasets available to help grid researchers understand how expansion of DERs could affect future power system operations. Realistic simulations of by-the-second solar irradiances are needed to study how DER variability affects the grid. Irradiance data are highly non-stationary and non-Gaussian, and even modern time series models are challenged by their distributional properties. We develop a subordinated non-Gaussian stochastic model whose simulations realistically capture the distribution and dependence structure in measured irradiance. We illustrate our approach on a fine resolution dataset from Hawaii, where our approach outperforms standard nonlinear time series models.

Original languageAmerican English
Article numbere2800
Number of pages22
JournalEnvironmetrics
Volume34
Issue number6
DOIs
StatePublished - 2023

Bibliographical note

Publisher Copyright:
© 2023 John Wiley & Sons Ltd.

NREL Publication Number

  • NREL/JA-6A40-85903

Keywords

  • non-Gaussian
  • nonstationary
  • renewable energy
  • simulation
  • spline

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