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Abstract:
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Traditionally the power grid has been a one-way street with power flowing from the distribution network to the load consumer. 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 temporally detailed datasets available to help grid researchers understand how expansion of DERs could affect future operations. Realistic simulations of by-the-second solar irradiances are needed to study how variability affects DERs. Irradiance data are highly nonstationary 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 observed irradiances. We illustrate our approach on a fine resolution dataset from Hawaii, where our approach outperforms standard nonlinear time series models.
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