Litcius/Paper detail

Photovoltaic modules degradation assessment using different statistical techniques

Ahmed Abdelouahed, Saad Elmamoun, Asmae Berrada, Arechkik Ameur

2022International Journal of Energy Research17 citationsDOI

Abstract

Summary Long‐term energy production of photovoltaic (PV) systems is predicted and evaluated using a degradation rate analysis. It is one of the most important factors to consider before investing in PV power plants since it indicates the system's dependability and, hence, profitability. This paper examines the degradation rates of three different PV module technologies in a mountain/cold region, over a 5‐year period; including monocrystalline silicon (mono‐Si), polycrystalline silicon (poly‐Si), and amorphous silicon (a‐Si). This study has been performed using different statistical techniques such as classical and seasonal decomposition (CSD), seasonal and trend decomposition using loess (STL), Holt winters (HW), and linear regression (LR). According to the obtained results, a‐Si modules have the highest degradation rate with values varying between 1.12 and 1.17%/year, followed by mono‐Si (0.69–0.98%/year) and poly‐Si (0.11–0.75%/year) technology respectively. This research included an economic analysis to determine the Levelized Cost of Electricity (LCOE) of the three investigated systems. The findings show that crystalline technologies (poly‐Si and mono‐Si) are more cost‐effective than a‐Si, with an LCOE of 0.099 USD/kWh, 0.108 USD/kWh, and 0.138 USD/KWh respectively.

Topics & Concepts

Cost of electricity by sourcePhotovoltaic systemMonocrystalline siliconCrystalline siliconSiliconEnvironmental scienceDegradation (telecommunications)Polycrystalline siliconMaterials scienceProcess engineeringElectricity generationEngineeringPower (physics)Electrical engineeringMetallurgyNanotechnologyPhysicsLayer (electronics)Thin-film transistorQuantum mechanicsPhotovoltaic System Optimization TechniquesSolar Radiation and PhotovoltaicsPhotovoltaic Systems and Sustainability
Photovoltaic modules degradation assessment using different statistical techniques | Litcius