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Title: Building the Sun4Cast System: Improvements in Solar Power Forecasting

Journal Article · · Bulletin of the American Meteorological Society
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  1. National Center for Atmospheric Research, Boulder, CO (United States). Research Applications Lab.
  2. National Center for Atmospheric Research, Boulder, CO (United States). Research Applications Lab.; Ascend Analytics, Boulder, CO (United States)
  3. Colorado State Univ., Fort Collins, CO (United States). Cooperative Inst. for Research of the Atmosphere
  4. National Renewable Energy Lab. (NREL), Golden, CO (United States)
  5. Univ. of Washington, Seattle, WA (United States)
  6. Brookhaven National Lab. (BNL), Upton, NY (United States)
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The Sun4Cast System results from a research-to-operations project built on a value chain approach, and benefiting electric utilities' customers, society, and the environment by improving state-of-the-science solar power forecasting capabilities. As integration of solar power into the national electric grid rapidly increases, it becomes imperative to improve forecasting of this highly variable renewable resource. Thus, a team of researchers from public, private, and academic sectors partnered to develop and assess a new solar power forecasting system, Sun4Cast. The partnership focused on improving decision-making for utilities and independent system operators, ultimately resulting in improved grid stability and cost savings for consumers. The project followed a value chain approach to determine key research and technology needs to reach desired results. Sun4Cast integrates various forecasting technologies across a spectrum of temporal and spatial scales to predict surface solar irradiance. Anchoring the system is WRF-Solar, a version of the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model optimized for solar irradiance prediction. Forecasts from multiple NWP models are blended via the Dynamic Integrated Forecast (DICast) System, the basis of the system beyond about 6 h. For short-range (0-6 h) forecasts, Sun4Cast leverages several observation-based nowcasting technologies. These technologies are blended via the Nowcasting Expert System Integrator (NESI). The NESI and DICast systems are subsequently blended to produce short to mid-term irradiance forecasts for solar array locations. The irradiance forecasts are translated into power with uncertainties quantified using an analog ensemble approach, and are provided to the industry partners for real-time decision-making. The Sun4Cast system ran operationally throughout 2015 and results were assessed. This paper analyzes the collaborative design process, discusses the project results, and provides recommendations for best-practice solar forecasting.

Research Organization:
Brookhaven National Laboratory (BNL), Upton, NY (United States); National Renewable Energy Laboratory (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:
SC0012704; AC36-08GO28308
OSTI ID:
1362154
Alternate ID(s):
OSTI ID: 1395117
Report Number(s):
BNL-113871-2017-JA; NREL/JA-5D00-67622; R&D Project: DE-EE0006016; SL0200000
Journal Information:
Bulletin of the American Meteorological Society, Vol. 99, Issue 1; ISSN 0003-0007
Publisher:
American Meteorological SocietyCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 45 works
Citation information provided by
Web of Science

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Cited By (5)

Terawatt-scale photovoltaics: Transform global energy journal May 2019
Comparison of Implicit vs. Explicit Regime Identification in Machine Learning Methods for Solar Irradiance Prediction journal February 2020
Assessing Evidence for Weather Regimes Governing Solar Power Generation in Kuwait journal November 2019
Assessment of Simulated Solar Irradiance on Days of High Intermittency Using WRF-Solar journal January 2020
A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods journal May 2019

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