Solar Irradiance Nowcasting Case Studies near Sacramento

Abstract

The Sun4Cast solar power forecasting system, designed to predict solar irradiance and power generation at solar farms, is composed of several component models operating on both the nowcasting (0–6 h) and day-ahead forecast horizons. The different nowcasting models include a statistical forecasting model (StatCast), two satellite-based forecasting models [the Cooperative Institute for Research in the Atmosphere Nowcast (CIRACast) and the Multisensor Advection-Diffusion Nowcast (MADCast)], and a numerical weather prediction model (WRF-Solar). It is important to better understand and assess the strengths and weaknesses of these short-range models to facilitate further improvements. To that end, each of these models, including four WRF-Solar configurations, was evaluated for four case days in April 2014. For each model, the 15-min average predicted global horizontal irradiance (GHI) was compared with GHI observations from a network of seven pyranometers operated by the Sacramento Municipal Utility District (SMUD) in California. Each case day represents a canonical sky-cover regime for the SMUD region and thus represents different modeling challenges. The analysis found that each of the nowcasting models perform better or worse for particular lead times and weather situations. StatCast performs best in clear skies and for 0–1-h forecasts; CIRACast and MADCast perform reasonably well when cloud fields are not rapidly growing or dissipating; and WRF-Solar, when configured with a high-spatial-resolution aerosol climatology and a shallow cumulus parameterization, generally performs well in all situations. Further research is needed to develop an optimal dynamic blending technique that provides a single best forecast to energy utility operators.

Assessment of Simulated Solar Irradiance on Days of High Intermittency Using WRF-Solar

“…Similarly, WRF-Solar simulations in 2017 in Kuwait produced RMSEs in GHI and DNI of 101Wm −2 and 137 Wm −2 , respectively [44]. Despite regional weather events dominating error statistics, results from WRF-Solar simulations in other countries show the consensus on misrepresentation of clouds and aerosols has not been fully resolved, but has improved from native WRF simulations [36][37][38]41,[43][44][45].…”

“…All these changes have been incorporated in a specific augmentation of the advanced research version of the WRF Model [35] designed for solar energy predictions known as WRF-Solar [36]. WRF-Solar has been extensively tested in the USA [36][37][38][39][40][41] and other countries, such as Spain [42], Singapore [43], Kuwait [44], and Saudi Arabia [45]. Most studies have reported significant improvements in solar irradiance predictions under different sky conditions with WRF-Solar in comparison to standard WRF simulations.…”

Building the Sun4Cast System: Improvements in Solar Power Forecasting

“…To better understand the performance of the various Nowcast components in specific situations, a series of inter-comparison case studies was undertaken by Lee et al (2017). Fifteen-minute average GHI predictions were compared against observations from seven pyranometers near Sacramento, California, that are owned and operated by SMUD.…”

Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications: Second Edition

“…The Sun4Cast project has contributed also to the development of the Multisensor Advection Diffusion nowCast (MADCast) system (Descombes et al 2014), which is a particular configuration of the WRF model for fast assimilation of satellite reflectance images to obtain a proxy field to cloud fraction that can be subsequently advected in the WRF and used to compute solar radiation nowcasts. A comparative evaluation of WRF-Solar, MADCast, and satellite-based forecasts is presented in Lee et al (2017).…”

“…A nonexhaustive list includes methods based on statistical inference on ground-observed time series (Huang et al 2013;Lonij et al 2013;Voyant et al 2014;Boland and Soubdhan 2015;Graditi, Ferlito, and Adinolfi 2016), use of cloud motion vectors and other cloud advection techniques on all-sky cameras and satellite imagery (Hammer et al 1999;Perez et al 2010;Chow et al 2011;Quesada-Ruiz et al 2014;Schmidt et al 2016;Lee et al 2017;Arbizu-Barrena et al 2017), forecasts based on numerical weather prediction (NWP) models (Mathiesen and Kleissl 2011;Lara-Fanego et al 2012;Pelland, Galanis, and Kallos 2013;Ohtake et al 2013Jimenez et al 2016a;Jimenez et al 2016b) or even hybrid techniques (Marquez and Coimbra 2011;Marquez, Pedro, and Coimbra 2013;Perez et al 2014;Dambreville et al 2014;Wolff et al 2016;Mazorra Aguiar et al 2016). All these methods are explained in Section 7.2.…”