Surface Dust and Aerosol Effects on the Performance of Grid-Connected Photovoltaic Systems

Abstract

A large number of grid-connected Photovoltaic parks of different scales have been operating worldwide for more than two decades. Systems’ performance varies with time, and an important factor that influences PV performance is dust and ambient aerosols. Dust accumulation has significant effects depending the region, and—on the other hand—understanding the role of absorption or scattering in particular wavelengths from aerosols is a challenging task. This paper focuses on performance analysis of a grid-connected PV system in Central Greece, aiming to study these effects. The methodology of analysis follows three directions, namely, PR computations, use of mathematical model’s prediction as reference value, and normalized efficiency calculation. These metrics are correlated with the levels of dust accumulation on PV panels’ surfaces and the ambient aerosol mass concentration. The results show that only heavily soiled surfaces have significant impact on PV performance and, particularly, a decrease of 5.6%. On the other hand, light or medium soiling have negligible impact on PV performance. On the other hand, the impact of ambient aerosol concentration levels on PV efficiency is more complex and requires further study. Aerosol scattering of different wavelengths can possibly affect PV efficiency, however, this fact may be related to the specific spectral response of PV cells.

Prediction of a Grid-Connected Photovoltaic Park’s Output with Artificial Neural Networks Trained by Actual Performance Data

“…Massi Pavan et al studied the effect of soiling on large-scale photovoltaic plants using two different techniques: Four Bayesian neural network (BNN) models have been developed in order to calculate the performance at standard test conditions (STCs) of two plants installed in Southern Italy and compare against the results of a regression model. The results indicate that the losses due to dust accumulation on poly-crystalline Si PV modules' surfaces range from roughly 1 to 5% after 1 year of operation [26], which agrees with observations from other researchers [27].…”

“…Furthermore, the effect of heavy soiling is another important factor that may decrease energy production by up to 6.5% on an annual basis [49]. Heavy soiling was observed during the spring of 2019 in Central Greece which resulted in an average decrease of 5.6% [27] in energy generation. This fact may be related to the observed deterioration in the models' performance for 2019.…”

A Local Training Strategy-Based Artificial Neural Network for Predicting the Power Production of Solar Photovoltaic Systems

“…This can be justified by the fact that at those time hours, the variability of the weather conditions is small that makes the intuitive justification of the Persistence model valid (i.e., the PV power production at time ℎ, ℎ ∈ [1,24], of the next day will be the same as the present PV power production collected at the same time ℎ). However, the performance of the Persistence model starts to notably decrease (the RMSE, MAE, and WMAE start to increase) at the middle days' hours (i.e., ℎ ∈ [11,17]) with respect to the performances obtained by using both the ANN and the ELM. This is due to the large variability of the weather conditions experienced by the ASU PV plant at those time hours;  The proposed (local) training strategy-based ANN allows obtaining more accurate power predictions throughout the whole time hours than the proposed (local) training strategy-based ELM; Whereas looking at Fig.…”

“…The main difficulty in the PV system is the complexity, parasitic capacitance, harmonic distortion, and sophistication of the equation of current-voltage and power-voltage characteristics [10]. The relationship among PV current and voltage is both implicit and complex depending on certain variables, among them are the ambient temperature, solar irradiation, wind speed, and dust accumulation [11], [12]. On hot days, the cell module temperature can quickly be attained 70°C, where power energy output can drop significantly below nominal values [13].…”

Soiling Comparison of Mirror Film and Glass Concentrating Solar Power Reflectors in Southwest Louisiana

“…Many factors affect the rate and nature of soiling, including location, time, mirror orientation, rainfall, wind, temperature, relative humidity, and the substances in the atmosphere [1,2]. Dust can be a significant problem in dry regions with little precipitation, whereas rainfall, wind, and gravity help self-cleaning in wet climate regions [3]. The most influential parameters on surface erosion phenomenon were found to be wind speed and direction [4].…”