Monitoring versus prediction of the power of three different PV technologies in the coast of Lima-Peru

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This article presents the benefits of two simple analytical models for estimating the outdoor performance of three different photovoltaic technologies in Lima, Peru. The Osterwald and the constant fill factor models are implemented to estimate the maximum power delivered by three photovoltaic module...

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Detalles Bibliográficos
Autores: Calsi B.X., Conde L.A., Angulo J.R., Montes-Romero J., Guerra Torres, Jorge Andrés, De La Casa J., Tofflinger J.A.
Formato: artículo
Fecha de Publicación:2021
Institución:Consejo Nacional de Ciencia Tecnología e Innovación
Repositorio:CONCYTEC-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.concytec.gob.pe:20.500.12390/2367
Enlace del recurso:https://hdl.handle.net/20.500.12390/2367
https://doi.org/10.1088/1742-6596/1841/1/012001
Nivel de acceso:acceso abierto
Materia:Thin films
Forecasting
Heterojunctions
Photovoltaic cells
Solar energy
Thin film solar cells
http://purl.org/pe-repo/ocde/ford#2.02.03
Descripción
Sumario:This article presents the benefits of two simple analytical models for estimating the outdoor performance of three different photovoltaic technologies in Lima, Peru. The Osterwald and the constant fill factor models are implemented to estimate the maximum power delivered by three photovoltaic module technologies: aluminum back surface field, heterojunction with intrinsic thin-layer and amorphous/microcrystalline thin-film tandem. A 12-months experimental campaign is carried out through measurements of current-voltage curves, irradiance and module temperature. The results show that both models overestimate the modelled power when compared to the measured one. In order to correct the maximum power predicted by both models, a correction factor is introduced. This correction factor allows us to estimate losses and a respective effective nominal power to minimize the prediction error on a monthly and yearly basis. These parameters demonstrate a unique behavior for each technology during different months implying different seasonal impacts of the ambient variables on the module performance. The effectiveness of this correction factor is demonstrated through accuracy measures. It enables the photovoltaic power prediction with an error < 1% for the particular climate in Lima, Peru. © 2021 Published under licence by IOP Publishing Ltd.
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