Optimized Cleaning Cost and Schedule Based on Observed Soiling Conditions for Photovoltaic Plants in Central Saudi Arabia

Jones, Russell K.; Baras, Abdulaziz; Saeeri, Abdullah Al; Qahtani, Ayman Al; Amoudi, Ahmed O. Al; Shaya, Yousef Al; Alodan, Maher A.; Al-Hsaien, Shafi Ali

doi:10.1109/jphotov.2016.2535308

Cited by 136 publications

(78 citation statements)

References 16 publications

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“…Reported annual losses for the United States have ranged from 0 to 6% [1][2][3][4], while annual losses are not readily available for some of the dustiest regions of the world, like the Middle East, India, or China. In these locations, peak losses have been reported ranging between 20 and 70% [5][6][7][8][9][10], but these values do not directly translate to annual losses, which are typically much lower. Annual PV soiling losses are generally the result of considering the soiling rate (typically the increase in loss per day) for a site in combination with rainfall patterns or other cleaning events.…”

Section: Introductionmentioning

confidence: 99%

An investigation of the key parameters for predicting PV soiling losses

Micheli

Muller

2017

Progress in Photovoltaics

128

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One hundred and two environmental and meteorological parameters have been investigated and compared with the performance of 20 soiling stations installed in the USA, in order to determine their ability to predict the soiling losses occurring on PV systems. The results of this investigation showed that the annual average of the daily mean particulate matter values recorded by monitoring stations deployed near the PV systems are the best soiling predictors, with coefficients of determination (R 2 ) as high as 0.82. The precipitation pattern was also found to be relevant: among the different meteorological parameters, the average length of dry periods had the best correlation with the soiling ratio. A preliminary investigation of two-variable regressions was attempted and resulted in an adjusted R 2 of 0.90 when a combination of PM 2.5 and a binary classification for the average length of the dry period was introduced.

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Section: Introductionmentioning

confidence: 99%

An investigation of the key parameters for predicting PV soiling losses

Micheli

Muller

2017

Progress in Photovoltaics

128

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“…Manual and machine-assisted methods are available for solar PV cleaning. The machineassisted method had a cost per cleaning event 80% lower than the manual method [24]. For simplicity, the cleaning cost is considered based on an aggregated value in terms of unit electricity generation.…”

Section: Economic Analysismentioning

confidence: 99%

“…For simplicity, the cleaning cost is considered based on an aggregated value in terms of unit electricity generation. Due to the lack of relevant data for cleaning costs for the considered cities, we apply the ones reported by [24] in the case studies: 0.19 USD/kW for manual cleaning and 0.032 USD/kW for machine-assisted cleaning (Exchange rate: 1 SAR=0.27 USD). To account for the variation of the efficiency loss with respect to time, a Monte Carlo simulation analysis is used to decide the starting time of cleaning based on which the average efficiency loss is calculated.…”

Section: Economic Analysismentioning

confidence: 99%

“…On the other hand, to develop an economically sustainable solar PV cleaning protocol, it is critical to predict the optimum cleaning interval or frequency from a system perspective. Existing studies estimated the optimum cleaning interval by matching the cleaning-related cost with the energy output loss by soiling [24]. This method, however, did not consider system-level economics and the time value of money.…”

Section: Introductionmentioning

confidence: 99%

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On the temporal modelling of solar photovoltaic soiling: Energy and economic impacts in seven cities

et al. 2018

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This work developed a framework to predict the energy and economic impacts of solar photovoltaic soiling. This framework includes the effects of relative humidity, precipitation and tilt angle on solar photovoltaic soiling. A concept of relative net-present value change was introduced to determine the optimal cleaning interval. The uncertainties in the economic analysis were accounted for using a Monte Carlo simulation method. The framework was used to study the soiling-induced efficiency and economic losses of solar photovoltaic modules in seven cities (i.e. Taichung, Tokyo, Hami, Malibu, Sanlucar la Mayor, Doha, and Walkaway). Overall, the efficiency loss (in ascending order) for Tokyo/Walkaway < Taichung < Sanlucar la Mayor < Malibu/Hami < Doha for a one-year study period. Doha experiences an efficiency loss over 80% for a 140-day exposure, while Tokyo has an efficiency loss less than 4% for a one-year exposure. Malibu has longest optimal cleaning intervals (70 days for manual cleaning and 49 days for machine-assisted cleaning) that leads to the relative net-present value changes of 1.7% and 1.1%. Doha has the shortest optimal cleaning intervals (23 days for manual cleaning and 17 days for machine-assisted cleaning) that leads to the relative net-present value changes of 21% and 19%. The work serves as an effective tool for designing optimal cleaning protocols for solar photovoltaic systems.

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“…Additionally, it was shown in the work presented in [29] that out of more than 17 pollutants, six had a significant effect on PV performance. A mathematical formulation for the optimum cleaning interval depending on the dust accumulation and the cost of cleaning operation in central Saudi Arabia was presented in [30]: they concluded that the highest loss rates occur in the spring season due to the dust storms. The authors emphasize that this conclusion is exclusive for this site and corrections to the model should be taken into consideration for other geographical and climatic conditions.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Analysis Framework for Investigating the Impact of Dust and Temperature on PV Systems’ Performance and Optimum Cleaning Frequency

et al. 2019

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This paper proposes computational models to investigate the effects of dust and ambient temperature on the performance of a photovoltaic system built at the Hashemite University, Jordan. The system is connected on-grid with an azimuth angle of 0° and a tilt angle of 26°. The models have been developed employing optimized architectures of artificial neural network (ANN) and extreme learning machine (ELM) models to estimate conversion efficiency based on experimental data. The methodology of building the models is demonstrated and validated for its accuracy using different metrics. The effect of each parameter was found to be in agreement with the well-known relationship between each parameter and the predicted efficiency. It is found that the optimized ELM model predicts conversion efficiency with the best accuracy, yielding an R2 of 91.4%. Moreover, a recommendation for cleaning frequency of every two weeks is proposed. Finally, different scenarios of electricity tariffs with their sensitivity analyses are illustrated.

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Optimized Cleaning Cost and Schedule Based on Observed Soiling Conditions for Photovoltaic Plants in Central Saudi Arabia

Cited by 136 publications

References 16 publications

An investigation of the key parameters for predicting PV soiling losses

An investigation of the key parameters for predicting PV soiling losses

On the temporal modelling of solar photovoltaic soiling: Energy and economic impacts in seven cities

Modeling and Analysis Framework for Investigating the Impact of Dust and Temperature on PV Systems’ Performance and Optimum Cleaning Frequency

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