Sub-Hour Solar Data for Power System Modeling From Static Spatial Variability Analysis: Preprint

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High penetration renewable integration studies need high quality solar power data with spatial-temporal correlations that are representative of a real system. This paper will summarize the research relating sequential point-source sub-hour global horizontal irradiance (GHI) values to static, spatially distributed GHI values. This research led to the development of an algorithm for generating coherent sub-hour datasets that span distances ranging from 10 km to 4,000 km. The algorithm, in brief, generates synthetic GHI values at an interval of one-minute, for a specific location, using SUNY/Clean Power Research, satellite-derived, hourly irradiance values for the nearest grid cell to that location and … continued below

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Hummon, M.; Ibanez, E.; Brinkman, G. & Lew, D. December 1, 2012.

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This article is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided by the UNT Libraries Government Documents Department to the UNT Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 117 times. More information about this article can be viewed below.

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High penetration renewable integration studies need high quality solar power data with spatial-temporal correlations that are representative of a real system. This paper will summarize the research relating sequential point-source sub-hour global horizontal irradiance (GHI) values to static, spatially distributed GHI values. This research led to the development of an algorithm for generating coherent sub-hour datasets that span distances ranging from 10 km to 4,000 km. The algorithm, in brief, generates synthetic GHI values at an interval of one-minute, for a specific location, using SUNY/Clean Power Research, satellite-derived, hourly irradiance values for the nearest grid cell to that location and grid cells within 40 km.

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  • Presented at the 2nd International Workshop on Integration of Solar Power in Power Systems, 12-13 November 2012, Lisbon, Portugal

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  • Report No.: NREL/CP-6A20-56204
  • Grant Number: AC36-08GO28308
  • Office of Scientific & Technical Information Report Number: 1059579
  • Archival Resource Key: ark:/67531/metadc842802

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  • December 1, 2012

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  • May 19, 2016, 9:45 a.m.

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  • April 6, 2017, 3:28 p.m.

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Hummon, M.; Ibanez, E.; Brinkman, G. & Lew, D. Sub-Hour Solar Data for Power System Modeling From Static Spatial Variability Analysis: Preprint, article, December 1, 2012; Golden, Colorado. (https://digital.library.unt.edu/ark:/67531/metadc842802/: accessed June 12, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; crediting UNT Libraries Government Documents Department.

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