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Title: Data Challenges in Estimating the Capacity Value of Solar Photovoltaics

Abstract

We examine the robustness of solar capacity-value estimates to three important data issues. The first is the sensitivity to using hourly averaged as opposed to subhourly solar-insolation data. The second is the sensitivity to errors in recording and interpreting load data. The third is the sensitivity to using modeled as opposed to measured solar-insolation data. We demonstrate that capacity-value estimates of solar are sensitive to all three of these factors, with potentially large errors in the capacity-value estimate in a particular year. If multiple years of data are available, the biases introduced by using hourly averaged solar-insolation can be smoothed out. Multiple years of data will not necessarily address the other data-related issues that we examine. Our analysis calls into question the accuracy of a number of solar capacity-value estimates relying exclusively on modeled solar-insolation data that are reported in the literature (including our own previous works). Our analysis also suggests that multiple years' historical data should be used for remunerating solar generators for their capacity value in organized wholesale electricity markets.

Authors:
; ;
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:
1365700
Report Number(s):
NREL/JA-6A20-66888
Journal ID: ISSN 2156-3381
DOE Contract Number:
AC36-08GO28308
Resource Type:
Journal Article
Resource Relation:
Journal Name: IEEE Journal of Photovoltaics; Journal Volume: 7; Journal Issue: 4
Country of Publication:
United States
Language:
English
Subject:
14 SOLAR ENERGY; 29 ENERGY PLANNING, POLICY, AND ECONOMY; capacity value; power system reliability; photovoltaic solar power generation

Citation Formats

Gami, Dhruv, Sioshansi, Ramteen, and Denholm, Paul. Data Challenges in Estimating the Capacity Value of Solar Photovoltaics. United States: N. p., 2017. Web. doi:10.1109/JPHOTOV.2017.2695328.
Gami, Dhruv, Sioshansi, Ramteen, & Denholm, Paul. Data Challenges in Estimating the Capacity Value of Solar Photovoltaics. United States. doi:10.1109/JPHOTOV.2017.2695328.
Gami, Dhruv, Sioshansi, Ramteen, and Denholm, Paul. 2017. "Data Challenges in Estimating the Capacity Value of Solar Photovoltaics". United States. doi:10.1109/JPHOTOV.2017.2695328.
@article{osti_1365700,
title = {Data Challenges in Estimating the Capacity Value of Solar Photovoltaics},
author = {Gami, Dhruv and Sioshansi, Ramteen and Denholm, Paul},
abstractNote = {We examine the robustness of solar capacity-value estimates to three important data issues. The first is the sensitivity to using hourly averaged as opposed to subhourly solar-insolation data. The second is the sensitivity to errors in recording and interpreting load data. The third is the sensitivity to using modeled as opposed to measured solar-insolation data. We demonstrate that capacity-value estimates of solar are sensitive to all three of these factors, with potentially large errors in the capacity-value estimate in a particular year. If multiple years of data are available, the biases introduced by using hourly averaged solar-insolation can be smoothed out. Multiple years of data will not necessarily address the other data-related issues that we examine. Our analysis calls into question the accuracy of a number of solar capacity-value estimates relying exclusively on modeled solar-insolation data that are reported in the literature (including our own previous works). Our analysis also suggests that multiple years' historical data should be used for remunerating solar generators for their capacity value in organized wholesale electricity markets.},
doi = {10.1109/JPHOTOV.2017.2695328},
journal = {IEEE Journal of Photovoltaics},
number = 4,
volume = 7,
place = {United States},
year = 2017,
month = 7
}
  • We examine the robustness of solar capacity-value estimates to three important data issues. The first is the sensitivity to using hourly averaged as opposed to subhourly solar-insolation data. The second is the sensitivity to errors in recording and interpreting load data. The third is the sensitivity to using modeled as opposed to measured solar-insolation data. We demonstrate that capacity-value estimates of solar are sensitive to all three of these factors, with potentially large errors in the capacity-value estimate in a particular year. If multiple years of data are available, the biases introduced by using hourly averaged solar-insolation can be smoothedmore » out. Multiple years of data will not necessarily address the other data-related issues that we examine. Our analysis calls into question the accuracy of a number of solar capacity-value estimates relying exclusively on modeled solar-insolation data that are reported in the literature (including our own previous works). Lastly, our analysis also suggests that multiple years’ historical data should be used for remunerating solar generators for their capacity value in organized wholesale electricity markets.« less
  • We estimate the capacity value of concentrating solar power (CSP) plants without thermal energy storage in the southwestern U.S. Our results show that CSP plants have capacity values that are between 45% and 95% of maximum capacity, depending on their location and configuration. We also examine the sensitivity of the capacity value of CSP to a number of factors and show that capacity factor-based methods can provide reasonable approximations of reliability-based estimates.
  • Here, we estimate the capacity value of concentrating solar power (CSP) plants without thermal energy storage in the southwestern U.S. Our results show that CSP plants have capacity values that are between 45% and 95% of maximum capacity, depending on their location and configuration. We also examine the sensitivity of the capacity value of CSP to a number of factors and show that capacity factor-based methods can provide reasonable approximations of reliability-based estimates.
  • We estimate the capacity value of concentrating solar power (CSP) plants with thermal energy storage (TES) in the southwestern U.S. Our results show that incorporating TES in CSP plants significantly increases their capacity value. While CSP plants without TES have capacity values ranging between 60% and 86% of maximum capacity, plants with TES can have capacity values between 79% and 92%. Here, we demonstrate the effect of location and configuration on the operation and capacity value of CSP plants. Finally, we also show that using a capacity payment mechanism can increase the capacity value of CSP, since the capacity valuemore » of CSP is highly sensitive to operational decisions and energy prices are not a perfect indicator of scarcity of supply.« less