Novel Design for Thermal Management of PV Cells in Harsh Environmental Conditions

Ahmad, Nasser; Khandakar, Amith; El-Tayeb, Amir; Benhmed, Kamel; Iqbal, Atif; Touati, Farid

doi:10.3390/en11113231

Cited by 28 publications

(13 citation statements)

References 15 publications

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“…Generally speaking, PV cooling systems can be grouped as (i) consolidated technologies, and (ii) in-development techniques. Passive and active cooling methods are the consolidated and commercially available technologies, while liquid immersion cooling, cooling using Phase Change Material (PCM), colorless and lucid silicon coating, microporous evaporation foils, and thermoelectric cooling systems based on the Peltier effects, are in-development technologies [34][35][36].…”

mentioning

confidence: 99%

An Experimental Investigation of a Novel Low-Cost Photovoltaic Panel Active Cooling System

Benato

Stoppato

2019

Energies

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Renewable energy sources are the most useful way to generate clean energy and guide the transition toward green power generation and a low-carbon economy. Among renewables, the best alternative to electricity generation from fossil fuels is solar energy because it is the most abundant and does not release pollutants during conversion processes. Despite the photovoltaic (PV) module ability to produce electricity in an eco-friendly way, PV cells are extremely sensitive to temperature increments. This can result in efficiency drop of 0.25%/ ∘ C to 0.5%/ ∘ C. To overcome this issue, manufacturers and researchers are devoted to the improvement of PV cell efficiency by decreasing operating temperature. For this purpose, the authors have developed a low-cost and high-performance PV cooling system that can drastically reduce module operating temperature. In the present work, the authors present a set of experimental measurements devoted to selecting the PV cooling arrangement that guarantees the best compromise of water-film uniformity, module temperature reduction, water-consumption minimization, and module power production maximization. Results show that a cooling system equipped with 3 nozzles characterized by a spraying angle of 90 ∘ , working with an inlet pressure of 1.5 bar, and which remains active for 30 s and is switched off for 120 s, can reduce module temperature by 28 ∘ C and improve the module efficiency by about 14%. In addition, cost per single module of the cooling system is only 15 €.

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mentioning

confidence: 99%

An Experimental Investigation of a Novel Low-Cost Photovoltaic Panel Active Cooling System

Benato

Stoppato

2019

Energies

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“…More PV and environmental data are being acquired for training a more accurate predictive model using the approach described in this work and will be compared with CNN-based approach in the future. Moreover, the concept of cooling the PV using the techniques discussed in Reference [5] are going to combine with the existing PV system, which can potentially help in increasing the efficiency and also avoid the accumulation of dust, which can affect the PV performance. Future works can open new horizons in this domain regarding different bias correction algorithm, with five years' system data, and more parameters for prediction-including cooling and cleaning effect on the PV system.…”

Section: Discussionmentioning

confidence: 99%

“…The PV output power is directly related to the solar irradiance on the PV panels, which is a well-known fact. However, other meteorological parameters (e.g., ambient temperature, relative humidity, wind speed and dust accumulation) have been reported to influence the PV efficiency as well [2][3][4][5]. This association substantially increases in the harsh environment of the Gulf region.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Based Photovoltaics (PV) Power Prediction Using Different Environmental Parameters of Qatar

et al. 2019

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Photovoltaics (PV) output power is highly sensitive to many environmental parameters and the power produced by the PV systems is significantly affected by the harsh environments. The annual PV power density of around 2000 kWh/m2 in the Arabian Peninsula is an exploitable wealth of energy source. These countries plan to increase the contribution of power from renewable energy (RE) over the years. Due to its abundance, the focus of RE is on solar energy. Evaluation and analysis of PV performance in terms of predicting the output PV power with less error demands investigation of the effects of relevant environmental parameters on its performance. In this paper, the authors have studied the effects of the relevant environmental parameters, such as irradiance, relative humidity, ambient temperature, wind speed, PV surface temperature and accumulated dust on the output power of the PV panel. Calibration of several sensors for an in-house built PV system was described. Several multiple regression models and artificial neural network (ANN)-based prediction models were trained and tested to forecast the hourly power output of the PV system. The ANN models with all the features and features selected using correlation feature selection (CFS) and relief feature selection (ReliefF) techniques were found to successfully predict PV output power with Root Mean Square Error (RMSE) of 2.1436, 6.1555, and 5.5351, respectively. Two different bias calculation techniques were used to evaluate the instances of biased prediction, which can be utilized to reduce bias to improve accuracy. The ANN model outperforms other regression models, such as a linear regression model, M5P decision tree and gaussian process regression (GPR) model. This will have a noteworthy contribution in scaling the PV deployment in countries like Qatar and increase the share of PV power in the national power production.

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“…82 Accordingly, new approaches were proposed for soiling mitigation by preventing condensation through active and passive surface heating 17,27 (see Figure 4D). This includes heat generation by controlled current supply to solar cells, adapted application of photovoltaic thermal hybrid solar collectors 83 or using latent heat from phase-change materials (PCM), typically proposed for PV cooling during the day. 84 In addition, low-emissive (low-ε) coatings could significantly reduce the radiative cooling and therefore the occurrence of dew.…”

Section: Heating Of Surfaces Preventing Water Condensationmentioning

confidence: 99%

Techno-Economic Assessment of Soiling Losses and Mitigation Strategies for Solar Power Generation

Ilse

Micheli

Figgis

et al. 2019

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278

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Soiling consists of the deposition of contaminants onto photovoltaic (PV) modules or mirrors and tubes of concentrated solar power systems (CSPs). It often results in a drastic reduction of power generation, which potentially renders an installation economically unviable and therefore must be mitigated. On the other hand, the corresponding costs for cleaning can significantly increase the price of energy generated. In this work, the importance of soiling is assessed for the global PV and CSP key markets. Even in optimized cleaning scenarios, soiling reduces the current global solar power production by at least 3%-4%, with at least 3-5 billion V annual revenue losses, which could rise to 4%-7%, and more than 4-7 billion V losses, in 2023. Therefore, taking into account the underlying physics of natural soiling processes and the regional cleaning costs, a techno-economic assessment of current and proposed soiling mitigation strategies such as innovative coating materials is presented. Accordingly, the research and development needs and challenges in addressing soiling are discussed.

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Novel Design for Thermal Management of PV Cells in Harsh Environmental Conditions

Cited by 28 publications

References 15 publications

An Experimental Investigation of a Novel Low-Cost Photovoltaic Panel Active Cooling System

An Experimental Investigation of a Novel Low-Cost Photovoltaic Panel Active Cooling System

Machine Learning Based Photovoltaics (PV) Power Prediction Using Different Environmental Parameters of Qatar

Techno-Economic Assessment of Soiling Losses and Mitigation Strategies for Solar Power Generation

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