Journal article
DC energy yield prediction in large monocrystalline and polycrystalline PV plants: Time-domain integration of Osterwald's model
Energy (Oxford), Vol.114, pp.951-960
11/01/2016
DOI: 10.1016/j.energy.2016.07.064
Abstract
The energy produced by a large PV plant is a paramount parameter for predicting the profitability of the PV system. This prediction generally consists of first estimating the DC energy and then estimating the AC energy. At present, the well-known behavior and reliability of the inverters available on the market make the estimation of the DC energy the most important source of uncertainty in the prediction of the energy produced by a PV installation. This paper presents an experimental validation of a method based on a time-domain integration of Osterwald's model for predicting the DC energy produced by a large PV system. The statistical error indicators RMSEE and MBEE, as well as a study based on scatter plots and best-fit lines, were used to validate the method. Ten large PV systems under operation in Spain were tested. Some of the PV generators exhibited hot spots, snail tracks, blown fuses and, as a result, remarkable drops in their nominal power. Despite such remarkable power decreases, the validated method was demonstrated to perform remarkably well, particularly when the systems operate under high irradiances, displaying values of RMSEE, MBEE and R2 of up to 0.56 %, 0.30 % and 0.999974, respectively.
•The DC energy was estimated by a time-domain integration of Osterwald's model.•Ten large PV systems under operation in Spain were tested.•Some PV generators exhibited problems and remarkable drops in their nominal power.•The method demonstrated to perform remarkably well, particularly for high irradiances.
Details
- Title: Subtitle
- DC energy yield prediction in large monocrystalline and polycrystalline PV plants: Time-domain integration of Osterwald's model
- Creators
- J.V MuñozG NofuentesM FuentesJ de la CasaJ Aguilera
- Resource Type
- Journal article
- Publication Details
- Energy (Oxford), Vol.114, pp.951-960
- Publisher
- Elsevier Ltd
- DOI
- 10.1016/j.energy.2016.07.064
- ISSN
- 0360-5442
- eISSN
- 1873-6785
- Language
- English
- Date published
- 11/01/2016
- Academic Unit
- Statistics and Actuarial Science; President; Biostatistics
- Record Identifier
- 9984065888402771
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