An updated review on factors and their inter-linked influences on photovoltaic system performance

Abstract

Globally installed solar photovoltaics (PV) capacity has crossed three hundred gigawatts and is increasing each year. As the share of solar PV in the energy mix of a country increases, forecasting PV power available will be crucial. To forecast the instantaneous and long-term PV power output, understanding the factors influencing them is necessary. In this view, this work elaborates on the factors that impact the PV system through tabulation and graphical explanation. Further, a discussion of the articles related to the dust-induced change in performance is made. To understand the impact of dust on solar PV systems in depth, advanced instrumentation and methodologies have been used in the past few years. One of the methods is the measurement of spectral transmittance/reflectance/absorptance of the dust layer on the PV panel. This has led to the question whether a thin layer of some specific dust can be beneficial by absorbing infrared (IR) heat and hence allowing the PV cells to operate at a lower temperature. Many controlled experiments in the laboratory have been made using the artificial dust and sun simulators; and such studies aid in the development of numerical models. Research in modeling, mathematical analysis (from first principles) of dust deposition, and calculation of its impact on panels have been given importance in recent years. Outdoor experiments are relatively more common than other modes of research in this field. Studies involving the interaction of deposited dust with spectral radiation, improving the correlation between artificial and natural dust deposition, the interplay between dust and atmospheric parameters are to be encouraged.

Keywords: Energy.

Fig. 1

Factors effecting PV system yield. The factors discussed in are shown in bold/italicized.

Fig. 2

Factors influencing annual AC energy and range of their potential impact calculated using SANDIA model by .

Fig. 3

Factors influencing monthly AC energy and range of their potential impact estimated by .

Fig. 4

Complex interaction between the different factors.

Fig. 5

Simplified diagram showing the significance of the impact of different factors. Red and blue arrows indicate severe and mild impact respectively.

Fig. 6

Block diagram indicating the factors influencing the temperature of the panel and its influence on the reliability of the PV module.

Fig. 7

Yearly advancement of the instruments or methodology used in experiments on dust impact on PV performance.

Fig. 8

Ratio of polluted PV panel short-circuit current to clean PV panel short-circuit current versus corresponding dust density derived from measurements reported by different authors [16, 26, 28, 41, 71, 72, 73].

Fig. 9

Ratio of polluted PV panel power output to clean PV panel power output versus corresponding dust density derived from measurements reported by different authors [9, 41, 72, 74].

Fig. 10

Ratio of polluted PV panel open circuit voltage to clean PV panel open circuit voltage and corresponding dust density derived from measurements reported by different authors [16, 72].

Fig. 11

Ratio of polluted PV panel fill factor to clean PV panel fill factor and corresponding dust density derived and measurements reported by different authors [41, 72].

Fig. 12

Ratio of polluted PV panel efficiency to clean PV panel efficiency and corresponding dust density derived and measurements reported by different authors [15, 16, 48, 49, 71, 73, 75, 76].

Fig. 13

(a) Transmittance spectra of moderately and heavily soil layer . (b) Spectral responses of the c-Si at AM1.5G spectrum (up to 1300 nm) .

Fig. 14

Particle size distribution during normal and stormy days, adapted from .

Fig. 15

Geographical distribution of research on dust impact on photovoltaic performance. Red dots represent the distribution during 2010 and black dots represent the current distribution.