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Title: Understanding Inter-Annual Variability of PV Energy Production in the Contiguous United States

Abstract

Year-to-year variability of photovoltaic (PV) generation is an important factor for project financing as well as for modeling the reliability and resource adequacy of power systems. In this work, we analyze inter-annual variability of PV generation across the contiguous United States using the National Solar Radiation Database (NSRDB) from 1998 to 2014. We compare the estimated PV generation from the typical meteorological year (TMY) against the long-term mean, and find that on average the TMY tends to overpredict estimated PV production. However, we also found significant regional bias and spatial clustering in that comparison, such that there are many regional pockets of underprediction as well. Finally, we show that there is not a single year of data that most closely approximates a representative 'resource year' for all regions of the United States. These results point to the need to understand inter-annual variability at individual sites and to be aware of the shortcomings of using TMY or a single year of data for project or grid modeling.

Authors:
 [1];  [1];  [1];  [1];  [1];  [1]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Solar Energy Technologies Office (EE-4S)
OSTI Identifier:
1476245
Report Number(s):
NREL/CP-6A20-70726
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), 24-28 June 2018, Boise, Idaho
Country of Publication:
United States
Language:
English
Subject:
14 SOLAR ENERGY; 29 ENERGY PLANNING, POLICY, AND ECONOMY; probability exceedance; national solar radiation database; capacity value; PV; long-term variability

Citation Formats

Maclaurin, Galen J, Cole, Wesley J, Lopez, Anthony J, Reimers, Andrew, Rosenlieb, Evan, and Roberts, Billy J. Understanding Inter-Annual Variability of PV Energy Production in the Contiguous United States. United States: N. p., 2018. Web. doi:10.1109/PMAPS.2018.8440289.
Maclaurin, Galen J, Cole, Wesley J, Lopez, Anthony J, Reimers, Andrew, Rosenlieb, Evan, & Roberts, Billy J. Understanding Inter-Annual Variability of PV Energy Production in the Contiguous United States. United States. doi:10.1109/PMAPS.2018.8440289.
Maclaurin, Galen J, Cole, Wesley J, Lopez, Anthony J, Reimers, Andrew, Rosenlieb, Evan, and Roberts, Billy J. Mon . "Understanding Inter-Annual Variability of PV Energy Production in the Contiguous United States". United States. doi:10.1109/PMAPS.2018.8440289.
@article{osti_1476245,
title = {Understanding Inter-Annual Variability of PV Energy Production in the Contiguous United States},
author = {Maclaurin, Galen J and Cole, Wesley J and Lopez, Anthony J and Reimers, Andrew and Rosenlieb, Evan and Roberts, Billy J},
abstractNote = {Year-to-year variability of photovoltaic (PV) generation is an important factor for project financing as well as for modeling the reliability and resource adequacy of power systems. In this work, we analyze inter-annual variability of PV generation across the contiguous United States using the National Solar Radiation Database (NSRDB) from 1998 to 2014. We compare the estimated PV generation from the typical meteorological year (TMY) against the long-term mean, and find that on average the TMY tends to overpredict estimated PV production. However, we also found significant regional bias and spatial clustering in that comparison, such that there are many regional pockets of underprediction as well. Finally, we show that there is not a single year of data that most closely approximates a representative 'resource year' for all regions of the United States. These results point to the need to understand inter-annual variability at individual sites and to be aware of the shortcomings of using TMY or a single year of data for project or grid modeling.},
doi = {10.1109/PMAPS.2018.8440289},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {8}
}

Conference:
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