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Barbara Brown, Tara Jensen, John Halley Gotway, Randy Bullock, Eric Gilleland, Tressa Fowler, Kathryn Newman, Dan Adriaansen, Lindsay Blank, Tatiana Burek, Michelle Harrold, Tracy Hertneky, Christina Kalb, Paul Kucera, Louisa Nance, John Opatz, Jonathan Vigh, and Jamie Wolff

Capsule summary

MET is a community-based package of state-of-the-art tools to evaluate predictions of weather, climate, and other phenomena, with capabilities to display and analyze verification results via the METplus system.

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Sue Ellen Haupt, Branko Kosović, Tara Jensen, Jeffrey K. Lazo, Jared A. Lee, Pedro A. Jiménez, James Cowie, Gerry Wiener, Tyler C. McCandless, Matthew Rogers, Steven Miller, Manajit Sengupta, Yu Xie, Laura Hinkelman, Paul Kalb, and John Heiser

Abstract

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 the 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, which forms 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 midterm 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.

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