Direct spectral distribution characterisation using the Average Photon Energy for improved photovoltaic performance modelling. (December 2022)

• Record Type: Journal Article
• Authors: Daxini, Rajiv; Sun, Yanyi; Wilson, Robin; Wu, Yupeng
• Is Part Of: Renewable energy. Volume 201(2022)Part 1
• Page Start: 1176
• Page End: 1188
• Publication Date: 2022-12
• Subjects: Photovoltaic performance -- Average photon energy -- Spectrum -- Short-circuit current -- Solar -- Spectral correction; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2022.11.001
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: Accurate photovoltaic (PV) performance modelling is crucial for increasing the penetration of PV energy into the grid, analysing returns on investment, and optimising system design prior to investment and construction. Performance models usually correct an output value known at reference conditions for the effects of environmental and system variables at arbitrary conditions. Traditional approaches to correct for the effect of the solar spectrum on performance are based on proxy variables that represent spectral influences, such as absolute air mass (AM a ) and clearness index (K t ). A new methodology to account for the spectral influence on PV performance is proposed in this study. The proposed methodology is used to derive a novel spectral correction function based on the average energy of photons contained within the measured solar spectral distribution. The Average Photon Energy (APE) parameter contains information on the combined effects of multiple proxy variables and is not limited by climatic conditions such as cloud cover, as is the case with most traditional models. The APE parameter is shown to be capable of explaining almost 90% of the variability in PV spectral efficiency, compared to around 65% for AM a . The derived APE function is validated and shown to offer an increase of 30% in predictive accuracy for the spectral efficiency compared with the traditional AM a function, and a 17% improvement relative to the AM a -K t function. Graphical abstract: Highlights: A new methodology to analyse the spectral impact on photovoltaic performance is proposed. The proposed methodology is validated for amorphous silicon devices. A spectral correction function for photovoltaic performance forecasting is derived. The accuracy of the derived model is compared with existing models.