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Title: Characterizing local high-frequency solar variability and its impact to distribution studies

Abstract

Accurately representing the local solar variability at timescales relevant to distribution grid operations (30-seconds and shorter) is essential to modeling the impact of solar photovoltaics (PV) on distribution feeders. Due to a lack of available high-frequency solar data, some distribution grid studies have used synthetically-created PV variability or measured PV variability from a different location than their study location. In this work, we show the importance of using accurate solar PV variability inputs in distribution studies. Using high-frequency solar irradiance data from 10 locations in the United States, we compare the ramp rate distributions at the different locations, use a quantitative metric to describe the solar variability, and run distribution simulations using representative 1-week samples from each location to determine the impact of solar variability on the number of tap change operations. Results indicate up to a 300% difference in the number of tap change operations when using PV variability profiles from the different locations.

Authors:
 [1];  [2];  [2]
  1. Sandia National Lab. (SNL-CA), Livermore, CA (United States)
  2. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-CA), Livermore, CA (United States); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
OSTI Identifier:
1497655
Report Number(s):
SAND-2014-16368J
Journal ID: ISSN 0038-092X; 672037
Grant/Contract Number:  
AC04-94AL85000
Resource Type:
Accepted Manuscript
Journal Name:
Solar Energy
Additional Journal Information:
Journal Volume: 118; Journal Issue: C; Journal ID: ISSN 0038-092X
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
14 SOLAR ENERGY; Solar variability; Distribution grid integration; Voltage regulator; Tap changes

Citation Formats

Lave, Matthew Samuel, Reno, Matthew J., and Broderick, Robert Joseph. Characterizing local high-frequency solar variability and its impact to distribution studies. United States: N. p., 2015. Web. doi:10.1016/j.solener.2015.05.028.
Lave, Matthew Samuel, Reno, Matthew J., & Broderick, Robert Joseph. Characterizing local high-frequency solar variability and its impact to distribution studies. United States. doi:https://doi.org/10.1016/j.solener.2015.05.028
Lave, Matthew Samuel, Reno, Matthew J., and Broderick, Robert Joseph. Thu . "Characterizing local high-frequency solar variability and its impact to distribution studies". United States. doi:https://doi.org/10.1016/j.solener.2015.05.028. https://www.osti.gov/servlets/purl/1497655.
@article{osti_1497655,
title = {Characterizing local high-frequency solar variability and its impact to distribution studies},
author = {Lave, Matthew Samuel and Reno, Matthew J. and Broderick, Robert Joseph},
abstractNote = {Accurately representing the local solar variability at timescales relevant to distribution grid operations (30-seconds and shorter) is essential to modeling the impact of solar photovoltaics (PV) on distribution feeders. Due to a lack of available high-frequency solar data, some distribution grid studies have used synthetically-created PV variability or measured PV variability from a different location than their study location. In this work, we show the importance of using accurate solar PV variability inputs in distribution studies. Using high-frequency solar irradiance data from 10 locations in the United States, we compare the ramp rate distributions at the different locations, use a quantitative metric to describe the solar variability, and run distribution simulations using representative 1-week samples from each location to determine the impact of solar variability on the number of tap change operations. Results indicate up to a 300% difference in the number of tap change operations when using PV variability profiles from the different locations.},
doi = {10.1016/j.solener.2015.05.028},
journal = {Solar Energy},
number = C,
volume = 118,
place = {United States},
year = {2015},
month = {6}
}

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Figure 1 Figure 1: Map of high-frequency data.

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