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Day-ahead resource forecasting for concentrated solar power integration

Lukas Nonnenmacher, Amanpreet Kaur and Carlos F.M. Coimbra

Renewable Energy, 2016, vol. 86, issue C, 866-876

Abstract: In this work, we validate and enhance previously proposed singe-input direct normal irradiance (DNI) models based on numerical weather prediction (NWP) for intra-week forecasts with over 200,000 hours of ground measurements for 8 locations. Short latency re-forecasting methods to enhance the deterministic forecast accuracies are presented and discussed. The basic forecast is applied to 15 additional locations in North America with satellite-derived DNI data. The basic model outperforms the persistence model at all 23 locations with a skill between 12.4% and 38.2%. The RMSE of the basic forecast is in the range of 204.9 W m−2 to 309.9 W m−2. The implementation of stochastic learning re-forecasting methods yields further reduction in error from 204.9 W m−2 to 176.5 W m−2. To a great extent, the errors are caused by inaccuracies in the NWP cloud prediction. Improved assessment of atmospheric turbidity has limited impact on reducing forecast errors. Our results suggest that NWP-based DNI forecasts are very capable of reducing power and net-load uncertainty introduced by concentrated solar power plants at all locations in North America. Operating reserves to balance uncertainty in day-ahead schedules can be reduced on average by an estimated 28.6% through the application of the basic forecast.

Keywords: CSP Integration; Day-ahead forecasting; NWP based DNI forecasting; Solar variability; Solar uncertainty (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:86:y:2016:i:c:p:866-876

DOI: 10.1016/j.renene.2015.08.068

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