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Title: Baseline and target values for regional and point PV power forecasts: Toward improved solar forecasting

Journal Article · · Solar Energy
 [1];  [1];  [2];  [2];  [3];  [4];  [5];  [6];  [7];  [8]
  1. National Renewable Energy Lab. (NREL), Golden, CO (United States)
  2. IBM TJ Watson Research Center, Yorktown Heights, NY (United States)
  3. Northeastern Univ., Boston, MA (United States)
  4. Univ. of Arizona, Tucson, AZ (United States)
  5. Argonne National Lab. (ANL), Lemont, IL (United States)
  6. U.S. Dept. of Energy, Washington, D.C. (United States)
  7. ISO New England, Holyoke, MA (United States)
  8. Green Mountain Power, Colchester, VT (United States)
+ Show Author Affiliations

Accurate solar photovoltaic (PV) power forecasting allows utilities to reliably utilize solar resources on their systems. However, to truly measure the improvements that any new solar forecasting methods provide, it is important to develop a methodology for determining baseline and target values for the accuracy of solar forecasting at different spatial and temporal scales. This paper aims at developing a framework to derive baseline and target values for a suite of generally applicable, value-based, and custom-designed solar forecasting metrics. The work was informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models in combination with a radiative transfer model. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of PV power output. The proposed reserve-based methodology is a reasonable and practical approach that can be used to assess the economic benefits gained from improvements in accuracy of solar forecasting. Lastly, the financial baseline and targets can be translated back to forecasting accuracy metrics and requirements, which will guide research on solar forecasting improvements toward the areas that are most beneficial to power systems operations.

Research Organization:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
Grant/Contract Number:
AC36-08GO28308; AC36-08-GO28308
OSTI ID:
1238036
Alternate ID(s):
OSTI ID: 1245234
Report Number(s):
NREL/JA-5D00-65285
Journal Information:
Solar Energy, Vol. 122; Related Information: Solar Energy; ISSN 0038-092X
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 53 works
Citation information provided by
Web of Science

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Cited By (2)

Spatial autocorrelation and entropy for renewable energy forecasting journal January 2019
Predictive analysis of photovoltaic plants specific yield with the implementation of multiple linear regression tool journal November 2018

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Related Subjects

14 SOLAR ENERGY
29 ENERGY PLANNING, POLICY, AND ECONOMY
numerical weather prediction
operating reserve
ramp forecasting
PV power forecasting
National Renewable Energy Laboratory
NREL