[EN]In this paper, an adaptive solar radiation numerical model for complex terrains is described.Secretaría de Estado de Investigación, Desarrollo e Innovación del Ministerio de Economía y Competitividad del Gobierno de España; Fondos FEDER

Díaz Reyes, Felipe

Escobar, José María

Mazorra Aguiar, Luis

Montenegro Armas, Rafael

Montero, Gustavo

Rodríguez, Eduardo

English

Gestion del Repositorio Documental de la Universidad de Salamanca

CONFERENCE OF THE XVIII SPANISH-FRENCH SCHOOL Jacques-Louis LionsABOUT NUMERICAL SIMULATION IN PHYSICS AND ENGINEERINGLas Palmas de Gran Canaria, June 25-29, 2018Solar Radiation Modelling and ForecastingF. Díaz∗, G. Montero∗, R. Montenegro∗, L. Mazorra Aguiar∗, E. Rodríguez∗, J.M. Escobar∗Keywords: Solar radiation, Modelling, Forecasting, Artificial Neural NetworksMathematics Subject Classification (2010): 62H30, 62H35, 65F10, 65N50, 65Z05, 68T05, 92B20Solar energy is one of the most interesting renewable ener-gies on earth. To become an alternative, solar power needs,in addition to good storage systems, tools that allow its easymanagement, as well as its efficient prediction. Among thesetools, solar radiation prediction and characterisation models arefundamental. In this paper, an adaptive solar radiation numeri-cal model for complex terrains is described [1]. This model isan improvement of that presented in [2].Real sky radiation values are obtained for beam, diffuse andreflected components, using clear sky computed irradiance a-ffected by the clear sky index for the analysed mesh triangleand time step. Terrain discretisation is adapted to orographyand albedo to improve reflected irradiance computations. Like-wise, the use of surfaces provided by the adaptive discreti-sation, allows us the option to improve the determination ofshadows on the domain [3]. On the other hand, a good solarradiation forecasting system is needed for an optimal manage-ment of power systems with a high solar energy penetration.This is the reason to include different prediction options inthe model, either using Numerical Weather Prediction (NWP)models [4], or applying forecasting techniques like BayesianArtificial Neural Networks [5]. The use of satellite imageshas been introduced to improve the results of those mathemat-ical methods [5]. In addition to these images, measurementsand NWP models have been combined [6]. With the com-puted irradiance values, electrical power generation can be as-sessed. Photovoltaic and Solar Thermal collectors are easily in-troduced in our discretised domain, taking into account its po-sition, orientation and inclination and, moreover, consideringits capacity for acting like a Sun tracker. Both, photovoltaicand solar thermal models are incorporated to compute the elec-trical power generation. Forecasted radiation values providea predicted power generation profile for a period of time inthe whole domain. This model is a convenient tool as it com-putes reliable characteristic or predicted radiation values overa complex terrain and likewise, electrical generation by meansof photovoltaic or solar thermal facilities. Examples have beendone for Canary Islands.AcknowledgementsThis research was supported by the Spanish Government, "Se-cretaría de Estado de Investigación, Desarrollo e Innovación","Ministerio de Economía y Competitividad", and FEDER,Grant Contract: CTM2014-55014-C3-1-R.References[1] F. Díaz, G. Montero, J.M. Escobar, E. Rodríguez, R. Montenegro, AnAdaptive Solar Radiation Numerical Model . Journal of Computationaland Applied Mathematics. 236 (2012), 4611–4622.[2] G. Montero, J.M. Escobar, E. Rodríguez, R. Montenegro, Solar radia-tion and shadow modelling with adaptive triangular meshes . Solar En-ergy. 83(7) (2009), 998–1012.[3] F. Díaz, H. Montero, D. Santana, G. Montero, E. Rodríguez, L. MazorraAguiar, A. Oliver, Improving shadows detection for solar radiation nu-merical models . Applied Mathematics and Computation. 319 (2018),71–85.[4] F. Díaz, G. Montero, J.M. Escobar, E. Rodríguez, R. Montenegro, Anew predictive solar radiation numerical model . Applied Mathematicsand Computation. 267 (2015), 596–603.[5] L. Mazorra Aguiar, B. Pereira, M. David, F. Díaz, P. Lauret, Use ofsatellite data to improve solar radiation forecasting with Bayesian Arti-ficial Neural Networks . Solar Energy. 122 (2015), 1309–1324.[6] L. Mazorra Aguiar, B. Pereira, P. Lauret, F. Díaz, M. David, Combiningsolar irradiance measurements, satellite-derived data and a numericalweather prediction model to improve intra-day solar forecasting . Re-newable Energy. 97 (2016), 599–610.∗University Institute for Intelligent Systems and Numerical Applications, University of Las Palmas de Gran Canaria, Campus Universitario de Tafira, 35017Las Palmas de Gran Canaria (SPAIN). Email: felipe.diaz@ulpgc.es1

Solar Radiation Modelling and Forecasting

https://gredos.usal.es/bitstream/10366/138213/1/SINUMCC_ULPGC_FDiaz_inMem_EHF2018_Abstract.pdf

Solar Radiation Modelling and Forecasting

Abstract

Similar works

Full text

Available Versions

Gestion del Repositorio Documental de la Universidad de Salamanca