Real-Time Thermoelectrical Model of PV Panels for Degradation Assessment

Nehme, Bechara; Akiki, Tilda; Naamane, Aziz; M'Sirdi, Kouider

doi:10.1109/jphotov.2017.2711430

Cited by 16 publications

(7 citation statements)

References 27 publications

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“…In our previous works we modeled all degradations as function of time and of external environmental conditions [17], [18]. We also enhanced our model for taking into consideration the fact that external conditions vary during the day and during the year [19].…”

Section: Imentioning

confidence: 99%

Assessing the Effect of Temperature on Degradation Modes of PV Panels

Nehme

M'Sirdi

Akiki

et al. 2020

2020 5th International Conference on Renewable Energies for Developing Countries (REDEC)

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PV panels are affected by many degradation modes. We list the Potential Induced Degradation, the Light Induced Degradation, the UltraViolet Light Degradation, the Moisture Induced Degradation, and the Cell Cracks. These degradation modes are affected by external environmental conditions as irradiance, temperature and humidity. The common factor that affects the degradation modes is temperature. The degradation process follows an Arrhenius equation; it is exponentially related to temperature. In this paper we evaluate precisely the effect of temperature on the degradation process of PV panels.

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Section: Imentioning

confidence: 99%

Assessing the Effect of Temperature on Degradation Modes of PV Panels

Nehme

M'Sirdi

Akiki

et al. 2020

2020 5th International Conference on Renewable Energies for Developing Countries (REDEC)

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“…In fact, according to the modeling assumed in Equation (3), the daily cost of the PV System C pv remains for all days of the year, even when the condition st pv = 0 is valid (for some days of the year). According to the authors in [35], solar panels suffer degradation mainly due to climatic and environmental conditions. Therefore, in this modeling, the degradation of the PV system is assumed to be a continuous process that occurs even if it is turned off.…”

Section: Pv Systemmentioning

confidence: 99%

“…As an example, consider a LiFePO 4 BESS of 140 kW/280 kWh, SOH tsh of 0.8, 6000 cycle life at DOD r of 0.90, η bess of 0.92 (according to Table 6), where the cost of capital C cap,bess is 91,000 USD (real market data). Thus, using Equations ( 32) and (35), and assuming k e = 0.55, the result is L E,bess ≥ 2, 681, 776 kWh, and p bess ≤ 0.0339 USD/kWh, respectively. As p bess represents the cost of charging and discharging each kWh at the battery cells node, then the BESS powers in the objective function, whose reference is the main (bus) node, must be weighted by the efficiency of BESS, which is similar to consider cost of charging: ∆tP sto p bess = ∆tP chr η bess p bess = ∆tP chr p chr , ⇒ p chr = η bess p bess (36a) cost of discharging: ∆tP sto p bess = ∆t P dch η bess p bess = ∆tP dch p dch , ⇒ p dch = 1…”

Section: Bess Costsmentioning

confidence: 99%

Optimal Day-Ahead Scheduling of Microgrids with Battery Energy Storage System

2020

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Optimal scheduling is a requirement for microgrids to participate in current and future energy markets. Although the number of research articles on this subject is on the rise, there is a shortage of papers containing detailed mathematical modeling of the distributed energy resources available in a microgrid. To address this gap, this paper presents in detail how to mathematically model resources such as battery energy storage systems, solar generation systems, directly controllable loads, load shedding, scheduled intentional islanding, and generation curtailment in the microgrid optimal scheduling problem. The proposed modeling also includes a methodology to determine the availability cost of battery and solar systems assets. Simulations were carried out considering energy prices from an actual time-of-use tariff, costs based on real market data, and scenarios with scheduled islanding. Simulation results provide support to validate the proposed model. Data illustrate how energy arbitrage can reduce microgrid costs in a time-of-use tariff. Results also show how the microgrid’s self-sufficiency and the storage system’s capacity can impact the microgrid’s energy bill. The findings also bring out the need to consider the scheduled islanding event in the day-ahead optimization for microgrids.

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“…Boron-oxygen defects formation in a bulk region, aging of cell material affects I 01 , n 1 parameters. 10,11 Encapsulant discoloration mainly reduces the light reaching to cell, consequently decreasing cell I ph . 9 Economically, a PV module is expected to serve for 25-30 years with an annual degradation rate of 0.5-0.8%.…”

mentioning

confidence: 99%

Generalized quantitative electroluminescence method for the performance evaluation of defective and unevenly degraded crystalline silicon photovoltaic module

Puranik

Kumar

Gupta

2022

Progress in Photovoltaics

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Photovoltaic (PV) module experiences multiple cell defects and degradation in a field operation, which significantly reduces module output. Hence, quantitative assessment of a PV module is essential to ensure module operation within a desirable limit. This article proposes the generalized quantitative electroluminescence (g‐QUEL) method to evaluate the performance of a defective and unevenly degraded PV module from the EL images. Four module classes are considered from the view of field applications, such as new, PID‐s affected, field aged shunted, and nonshunted modules. The effects of defects and degradation on fundamental cell electroluminescence (EL) characteristics and constants are analyzed for each class. Based on this analysis, the g‐QUEL method is developed, which utilizes module multiple EL images, datasheet information, and terminal voltage measurements for quantification. First, a test module is qualitatively assessed to identify the present defects and classified into one of four classes. In the second part, the g‐QUEL algorithm is applied to a test module in a customized manner to extract five or seven parameters of a PV cell. Subsequently, a single or two diode model simulation is carried out at a module, submodule, or cell level to estimate module output by generating I‐V curve. Experimental verification of the proposed method was conducted on five test modules of each class. Results demonstrate the accuracy and effectiveness of the g‐QUEL method as it evaluates the output power of new to field‐aged modules with a relative error ≤±3%.

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Real-Time Thermoelectrical Model of PV Panels for Degradation Assessment

Cited by 16 publications

References 27 publications

Assessing the Effect of Temperature on Degradation Modes of PV Panels

Assessing the Effect of Temperature on Degradation Modes of PV Panels

Optimal Day-Ahead Scheduling of Microgrids with Battery Energy Storage System

Generalized quantitative electroluminescence method for the performance evaluation of defective and unevenly degraded crystalline silicon photovoltaic module

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