418370 The Solar Integration National Dataset (SIND) Toolkit

Monday, November 9, 2015: 3:15 PM
Salon D (Salt Lake Marriott Downtown at City Creek)
Andrew Weekley, Anthony Lopez, Marissa Hummon and Bri-Mathias S. Hodge, National Renewable Energy Laboratory, Golden, CO

Solar integration studies require detailed solar power output data at a large number of locations in order to perform simulations of how the power system will operate under high solar power penetration scenarios. The solar datasets that serve as inputs to these studies must realistically reflect a number of different solar power characteristics, including: ramp rates, spatial and temporal correlations, and the capacity factors of the simulated plants. The datasets must also be time synchronized with available load profiles since the same meteorological conditions that drive load patterns also drive solar power production. The SIND Toolkit presented here fulfills these requirements as a state-of-the-art national solar resource and power dataset and is made publically available to be utilized in the next generation of solar power integration studies.

The dataset consists of one minute solar irradiance and power time series at over 120,000 locations throughout the continental United States with roughly 2 km by 2km spatial resolution. The dataset covers eight years (2005 – 2012) to allow for the examination of the impacts on inter-annual variability. The one minute data is created through a statistical upsampling of 30 minute satellite-derived solar irradiation data taken from the NREL’s National Solar Radiation Database (NSRDB). The upsampling utilizes the satellite data from neighboring regions to classify the timeperiod into one of six solar variability regimes, and the variability added to the time period is based on a statistical characterization of measured solar data from an extensive archive of ground truth data at locations throughout the continental United States. Finally, irradiance is converted to power through five different solar power configurations.

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See more of this Session: Advances in Smart Grid
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