Improved ECMWF forecasts of direct normal irradiance: A tool for better operational strategies in concentrating solar power plants

Abstract

To contribute for improved operational strategies of concentrating solar power plants with accurate forecasts of direct normal irradiance, this work describes the use of several post-processing methods on numerical weather prediction. Focus is given to a multivariate regression model that uses measured irradiance values from previous hours to improve next-hour predictions, which can be used to refine daily strategies based on day-ahead predictions. Short-term forecasts provided by the Integrated Forecasting System, the global model from the European Centre for Medium-Range Weather Forecasts (ECMWF), are used together with measurements in southern Portugal. As a nowcasting tool, the proposed regression model significantly improves hourly predictions with a skill score of z0.84 (i.e. an increase of z27.29% towards the original hourly forecasts). Using previous-day measured availability to improve next-day forecasts, the model shows a skill score of z0.78 (i.e. an increase of z6% towards the original forecasts), being further improved if larger sets of data are used. Through a power plant simulator (i.e. the System Advisor Model), a preliminary economic analysis shows that using improved hourly predictions of electrical energy allows to enhance a power plant's profit in z0.44 MV/year, as compared with the original forecasts. Operational strategies are proposed accordingly.

Development of a Simple Methodology Using Meteorological Data to Evaluate Concentrating Solar Power Production Capacity

“…For DNI measurements, pyrheliometers were used, particularly with the model CHP1, a World Meteorological Organization (WMO) first class instrument with an estimated uncertainty on a daily basis of <1% [37]. The instruments used were mounted on a SOLYS2 Sun-Tracker (Kipp&Zonen) [25].…”

“…In this context, a multivariate regression model (MRM) is used to adjust hourly modelled DNI to the observations. The MRM model [25] uses an interactive stepwise function, with an adjusted r-squared criterion, to improve DNI predictions. The method allows to perform all possible combinations between predictors, until the best fitting option is found.…”

“…Engerer2 parameters and regression coefficients are then calibrated using DNI data measurements in Évora (reference station) along with other meteorological datasets. The post-processing model is based on the Multivariate Regression Model (MRM) implemented by Lopes et al [25], which allows to adjust DNI predictions, provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), to ground observations. Additionally, calculation of typical meteorological years (TMY) is performed with the aim of providing representative data of a given region considering a longer period, which is a key step for elaboration of projects of such dimensions [26][27][28].…”

Revolutionizing Solar Power Forecasts by Correcting the Outputs of the WRF-SOLAR Model

“…Once a regression function is fitted, it can be used to predict the bias. To enhance the solar irradiance forecasts made by NWP models, various regression methods have been employed, such as multivariate linear regressions with stepwise variable selection [13][14][15], probability-based distributions [16,17], and other bias correction techniques. That is, there are numerous options for regression methods.…”

An analytical study of direct normal irradiaance for concentrated solar energy applications for the mechria region of Algeria

“…The model's effectiveness is evident in its ability to significantly enhance hourly forecasts, with a skill score of approximately 0.84. Furthermore, it is estimated that this model has the potential to augment a power plant's profit by approximately 0.44 M€/year [3].Devon Kesseli and fellow authors unveil a comprehensive guide document for precise modeling of hazards, expenses, and electrical productivity in projects involving the concentration of solar energy. The guidelines were succinctly summarized in a spreadsheet-based checklist and subsequently executed on the System Advisor Model (SAM) software of the National Renewable Energy Laboratory (NREL), culminating in valuable feedback and recommended enhancements to SAM [4]Kelvin, Tsz Hei Choi, along with their coauthors, present in this paper four distinct methods for estimating spectrally resolved direct normal irradiance (DNI) for solar energy applications.…”