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

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 themore » 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. As a result, this paper analyzes the collaborative design process, discusses the project results, and provides recommendations for best-practice solar forecasting.« less
Authors:
 [1] ;  [1] ;  [1] ;  [1] ;  [1] ;  [1] ;  [1] ;  [1] ;  [2] ;  [3] ;  [3] ;  [4] ;  [4] ;  [5] ;  [6] ;  [6]
  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)
Publication Date:
Report Number(s):
BNL-113871-2017-JA; NREL/JA-5D00-67622
Journal ID: ISSN 0003-0007; R&D Project: DE-EE0006016; SL0200000
Grant/Contract Number:
SC0012704; AC36-08GO28308
Type:
Accepted Manuscript
Journal Name:
Bulletin of the American Meteorological Society
Additional Journal Information:
Journal Volume: 99; Journal Issue: 1; Journal ID: ISSN 0003-0007
Publisher:
American Meteorological Society
Research Org:
Brookhaven National Laboratory (BNL), Upton, NY (United States); 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)
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES
OSTI Identifier:
1362154
Alternate Identifier(s):
OSTI ID: 1395117

Haupt, Sue Ellen, Kosovic, Branko, Jensen, Tara, Lazo, Jeffrey K., Lee, Jared A., Jimenez, Pedro A., Cowie, James, Wiener, Gerry, McCandless, Tyler C., Rogers, Matthew, Miller, Steven, Sangupta, Manajit, Xie, Yu, Hinkelman, Laura, Kalb, Paul, and Heiser, John. Building the Sun4Cast System: Improvements in Solar Power Forecasting. United States: N. p., Web. doi:10.1175/BAMS-D-16-0221.1.
Haupt, Sue Ellen, Kosovic, Branko, Jensen, Tara, Lazo, Jeffrey K., Lee, Jared A., Jimenez, Pedro A., Cowie, James, Wiener, Gerry, McCandless, Tyler C., Rogers, Matthew, Miller, Steven, Sangupta, Manajit, Xie, Yu, Hinkelman, Laura, Kalb, Paul, & Heiser, John. Building the Sun4Cast System: Improvements in Solar Power Forecasting. United States. doi:10.1175/BAMS-D-16-0221.1.
Haupt, Sue Ellen, Kosovic, Branko, Jensen, Tara, Lazo, Jeffrey K., Lee, Jared A., Jimenez, Pedro A., Cowie, James, Wiener, Gerry, McCandless, Tyler C., Rogers, Matthew, Miller, Steven, Sangupta, Manajit, Xie, Yu, Hinkelman, Laura, Kalb, Paul, and Heiser, John. 2017. "Building the Sun4Cast System: Improvements in Solar Power Forecasting". United States. doi:10.1175/BAMS-D-16-0221.1. https://www.osti.gov/servlets/purl/1362154.
@article{osti_1362154,
title = {Building the Sun4Cast System: Improvements in Solar Power Forecasting},
author = {Haupt, Sue Ellen and Kosovic, Branko and Jensen, Tara and Lazo, Jeffrey K. and Lee, Jared A. and Jimenez, Pedro A. and Cowie, James and Wiener, Gerry and McCandless, Tyler C. and Rogers, Matthew and Miller, Steven and Sangupta, Manajit and Xie, Yu and Hinkelman, Laura and Kalb, Paul and Heiser, John},
abstractNote = {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. As a result, this paper analyzes the collaborative design process, discusses the project results, and provides recommendations for best-practice solar forecasting.},
doi = {10.1175/BAMS-D-16-0221.1},
journal = {Bulletin of the American Meteorological Society},
number = 1,
volume = 99,
place = {United States},
year = {2017},
month = {6}
}