Influence of Cell Temperature on Theoretical Properties of InGaP/
InGaAs/Ge Triple-Junction Concentrated Solar Cells

Zhiqiang      Song; Zilong      Wang; Hua      Zhang; Weidong      Wu; Binlin      Dou; Ziao      Tian; Changqing      Hu; Qian      Jin

doi:10.2174/0122127976281400231211113923

Abstract

Aims: This study aims to analyze the accuracy of single- and double-diode models in predicting the electrical parameters of InGaP/InGaAs/Ge triple-junction solar cells as described in relevant patents under various operating conditions.

Methods: This study obtained and analyzed experimental and theoretical values of the relevant electrical parameters of solar cells through a combination of experimental research and theoretical model calculations.

Results: The results indicated that the root mean square error of the short-circuit current decreased from 0.21 at 400 W/m² to 0.11 at 1000 W/m². The temperature of the two precision cut-off points for the open-circuit voltage in the single- and double-diode models increased from 34°C and 64°C at 400 W/m² to 39°C and 72°C at 1000 W/m². Additionally, for peak power and conversion efficiency, the precision cut-off temperatures of the single- and double-diode models were 56°C, 68°C, and 77°C at 400 W/m², 600 W/m², and 800 W/m², respectively.

Conclusion: The theoretical values of the short-circuit current exceeded the corresponding experimental values. The single- and double-diode models for open-circuit voltage exhibited two accuracy cut-off points, with the single-diode model demonstrating greater accuracy within this temperature range. Similarly, the peak power and conversion efficiency models for single- and double-diodes have an accuracy cut-off point, with the double-diode model performing better at higher temperatures.

[1]
Gregoris PP, Soteris KA, Savvas TA. PEM fuel cells for energy production in solar hydrogen systems. Recent Pat Mech Eng  2011; 3(3)

[2]
Kathirvel K, Rajasekar R, Shanmuharajan T, Pal SK, Sathish Kumar P, Saravana Kumar J. Development of calcium titanium oxide coated silicon solar cells for enhanced voltage generation capacity. Mater Sci Pol  2017; 35(1): 181-7.
 [http://dx.doi.org/10.1515/msp-2017-0036]

[3]
Basel II. Thermophotovoltaic energy conversion for direct generation of electricity as an alternative clean energy source technology. Recent Pat Mech Eng  2011; 4(2)

[4]
Palaniappan SK, Chinnasamy M, Rathanasamy R, Pal SK. Recycling of solar panels. In: Rajasekar R, Moganapriya C, Mohankumar A, Eds. Materials for Solar Energy Conversion.  2021.
 [http://dx.doi.org/10.1002/9781119752202.ch3]

[5]
Kaliyannan GV, Gandhi U, Rathanasamy R, et al. Effect of zinc oxide—aluminium oxide mechanical blends for boosting the polycrystalline silicon solar cell performance through antireflection properties.  Silicon 2023; pp. 1-12.

[6]
Ismail IB. Advanced electrical power generation technology using renewable & clean low-enthalpy geothermal energy sources. Recent Pat Mech Eng  2011; 4(2)
 [http://dx.doi.org/10.2174/2212797611104020168]

[7]
Pei K, Rubin L, Shuai W, et al.  Novel three-junction gallium arsenide solar battery TianjinCN102339890A 2012.

[8]
Tadas P, Vaidas P, Viktorija S, et al. Performance assessment of a triple-junction solar cell with 1.0 eV GaAsBi absorber Discover Nano 2023; 18(1)

[9]
Abo-Zahhad EM, Ookawara S, Radwan A, El-Shazly AH, El-Kady MF. Numerical analyses of high concentrator triple-junction solar cell under jet impingement cooling. Energy Procedia  2018; 152: 1051-6.
 [http://dx.doi.org/10.1016/j.egypro.2018.09.119]

[10]
Gao H, Yang R, Zhang Y. Improving radiation resistance of gainp/gainas/ge triple-junction solar cells using GaInP back-surface field in the middle subcell. Materials  2020; 13(8): 1958.
 [http://dx.doi.org/10.3390/ma13081958] [PMID:  32331238]

[11]
Legan MD, Pchelyakov PO, Preobrazhenskii VV. Optimization of the In 0.3 Ga 0.7 as-layer thickness in a triple-junction in 0.3 ga 0.7 As/GaAs/In 0.5 Ga 0.5 P solar cell. Semiconductors  2020; 54(1)

[12]
Mintairov M, Evstropov V, Mintairov S, Shvarts M, Kalyuzhnyy N. GaAs subcell of triple-junction solar cells with hybrid quantum objects: Temperature photovoltaic characteristics. AIP Conf Proc.   2019; 2149: p. (1)080005.
 [http://dx.doi.org/10.1063/1.5124215]

[13]
Lei Z, Pingjuan N, Yuqiang L, et al. Investigation on high-efficiency Ga0.51In0.49P/In0.01Ga0.99As/Ge triple-junction solar cells for space applications. AIP Adv  2017; 7(12)

[14]
Jiehui W. Gallium arsenide concentrating photovoltaic power generation system. Shanghai CN104993772B  2020; -09-01.

[15]
XinYu P, MengDi Y, Xing J. A switchable concentrating photovoltaic/concentrating solar power (CPV/CSP) hybrid system for flexible electricity/thermal generation. Sci China Technol Sci  2023; 66(8)

[16]
Jin SO, Hyungchan K, Youngsun H. Monte carlo ray-tracing simulation of a cassegrain solar concentrator module for CPV. Front Energy Res  2021; 9.

[17]
Liang S, Zheng H, Ma X, Cui D. Design and experimental investigation on a solar concentrating photovoltaic underwater. Energy  2020; 204: 117958.
 [http://dx.doi.org/10.1016/j.energy.2020.117958]

[18]
Ustaoglu A, Ozbey U. Torlaklı H. Numerical investigation of concentrating photovoltaic/thermal (CPV/T) system using compound hyperbolic –trumpet, V-trough and compound parabolic concentrators. Renew Energy  2020; 152: 1192-208.
 [http://dx.doi.org/10.1016/j.renene.2020.01.094]

[19]
Han X, Sun Y, Huang J, Zheng J. Design and analysis of a CPV/T solar receiver with volumetric absorption combined spectral splitter. Int J Energy Res  2020; 44(6): 4837-50.
 [http://dx.doi.org/10.1002/er.5277]

[20]
Aslan G, Mohammad A, Majid Z, et al. Impact of harsh weather conditions on solar photovoltaic cell temperature: Experimental analysis and thermal-optical modeling. Sol Energy  2023; 252.

[21]
 G O D, Anishkumar S, U D K. Investigation into fill factor and open-circuit voltage degradations in silicon heterojunction solar cells under accelerated life testing at elevated temperatures. Sol Energy Mater Sol Cells  2023; 263.

[22]
Vakeel A, Thamraa A, Salem AB, et al. Effect of temperature on the performance of Cesium formamidinium lead mixed halide perovskite solar cells. Curr Appl Phys  2023; 53.

[23]
Z N M, Tawfik I, F S I. Effect of thickness and temperature on flexible organic P3HT:PCBM solar cell performance. Mater Res Express  2021; 8(9)

[24]
Shadidi A A AZ. The effect of environmental factors (temperatures & humidity) on the solar cell performance / matlab model. Int j appl math theo phys  2020; 6(4)

[25]
Tatiane C. Review on photovoltaic power and solar resource forecasting: Current status and trends. J Sol Energy Eng 2021.

[26]
Lee H. Towards convergence in federated learning via non-iid analysis in a distributed solar energy grid. Electronics  2023; 12(7): 1580.
 [http://dx.doi.org/10.3390/electronics12071580]

[27]
Harpreet S, Sachin M. Optimization of solar grid-based virtual power plant using distributed energy resources customer adoption model: A case study of indian power sector. Arab J Sci Eng  2021; 47(3)

[28]
I M A A, Soliman S E S A. A different visions for uninterruptible load using hybrid solar-grid energy. Int J Power Electron Drive Syst  2019; 10(1)

[29]
S D R, N Y G S. Extreme power point tracking to yield maximum power in solar grid system Int J Eng Technol  2018; 7(2.32)

[30]
Rolf K. A straightforward approach to drawing temperature-dependent i–v curves of solar cell models Solar 2022; 2(4)

[31]
Kapim A K DR M ,NM T, et al. Prediction of band gap and optimum electrical parameters of a thin homojunction perovskite solar cell based on FA1–xCsxSnyPb1–yI3 through a combination of SCAPS-1D and machine learning based modelling. Mater Today Commun  2023; 37.

[32]
Grzibovskis R, Polaks A, Vembris A. Intrinsic photoconductivity spectral dependence as a tool for prediction of open-circuit voltage in organic solar cells. Energies  2023; 16(18): 6728.
 [http://dx.doi.org/10.3390/en16186728]

[33]
Reisya I, Stephen P. Using machine learning for prediction of spray coated perovskite solar cells efficiency: From experimental to theoretical models. Mater Des  2023; 233.

[34]
Emad G. Performance prediction of aluminum and polycarbonate solar stills with air cavity using an optimized neural network model by golden jackal optimizer. Case Stud Therm Eng  2023; 47.

[35]
Rakesh STA, Punit S, et al. Machine learning framework for the analysis and prediction of energy loss for non-fullerene organic solar cells. Sol Energy  2023; 250.

[36]
Neeraj T, Geeta R, Singh VD, et al. Role of artificial neural networks in predicting design and efficiency of dye sensitized solar cells. Int J Energy Res  2022; 46(9)

[37]
Abhishek K. A novel analytical method for determination of diode parameters from the dark forward I–V characteristics of a silicon solar cell. Phys Scr  2023; 98(9)

[38]
Yuheng Z, Zetao D, Zunke L, et al. Efficiency-loss analysis of monolithic perovskite/silicon tandem solar cells by identifying the patterns of a dual two-diode model’s current-voltage curves. J Semicond  2023; 44(8)

[39]
Brendel R. Analytic efficiency optimization of solar cells under light concentration in the framework of the single-diode model. Sol RRL  2023; 7(17): 2300279.
 [http://dx.doi.org/10.1002/solr.202300279]

[40]
Li J, Qin C, Yang C, Ai B, Zhou Y. Extraction of single diode model parameters of solar cells and pv modules by combining an intelligent optimization algorithm with simplified explicit equation based on lambert W function. Energies  2023; 16(14): 5425.
 [http://dx.doi.org/10.3390/en16145425]

[41]
A comprehensive and critical review of bio-inspired metaheuristic frameworks for extracting parameters of solar cell single and double diode models. Energy Rep  2022; 8.

[42]
Rawa M, Calasan M, Abusorrah A, et al. Single diode solar cells—improved model and exact current–voltage analytical solution based on lambert’s W function. Sensors  2022; 22(11): 4173.
 [http://dx.doi.org/10.3390/s22114173] [PMID:  35684794]

[43]
Abdelkader A, Rached MB, Yemna B, et al. Improved arithmetic optimization algorithm for parameters extraction of photovoltaic solar cell single-diode model. Arab J Sci Eng  2022; 47(8)

[44]
Pillai SD, Sahoo B, Ram PJ, et al. Modelling of organic photovoltaic cells based on an improved reverse double diode model. Energy Procedia  2017; 117.

[45]
Abdelkader A, Rached MB, Bilel T, et al. Parameterization of photovoltaic solar cell double-diode model based on improved arithmetic optimization algorithm. Optik   2022; 253.

[46]
Amer M. Parameter identification of photovoltaic cell model using modified elephant herding optimization-based algorithms. Appl Sci   2021; 11(24)

[47]
An analytical model for organic bulk heterojunction solar cells based on Saha equation for exciton dissociation. Phys Chem Chem Phys  2023; 25(40)

[48]
Memon ZA, Akbari MA, Zare M. An improved cheetah optimizer for accurate and reliable estimation of unknown parameters in photovoltaic cell and module models. Appl Sci   2023; 13(18): 9997.
 [http://dx.doi.org/10.3390/app13189997]

[49]
Pramita N, Abhijit B. Model development of lattice-matched p-GaInP/i-GaAs/n-GaInP hetero-junction solar cell and its performance optimization. Opt Mater  2023; 143.

[50]
Anvarhaghighi N, Habibzadeh-Sharif A. Modified transmission line model for grating solar cells. Opt Express  2023; 31(10): 16315-29.
 [http://dx.doi.org/10.1364/OE.486511] [PMID:  37157713]

[51]
Sánchez PA, Esteban O, Elshorbagy MH, Cuadrado A, Alda J. Effective index model as a reliable tool for the design of nanostructured thin-film solar cells. Sci Rep  2023; 13(1): 6227.
 [http://dx.doi.org/10.1038/s41598-023-33085-3] [PMID:  37069230]

[52]
Aisha S, Muhammad B, Qian C, et al. An artificial neural network-based performance model of triple-junction InGaP/InGaAs/Ge cells for the production estimation of concentrated photovoltaic systems. Front Energy Res 2023.

[53]
Nishioka K, Takamoto T, Agui T, Kaneiwa M, Uraoka Y, Fuyuki T. Annual output estimation of concentrator photovoltaic systems using high-efficiency InGaP/InGaAs/Ge triple-junction solar cells based on experimental solar cell’s characteristics and field-test meteorological data. Sol Energy Mater Sol Cells  2006; 90(1): 57-67.
 [http://dx.doi.org/10.1016/j.solmat.2005.01.011]

[54]
Zilong W, Hua Z, Wei Z, et al. The effect of concentrated light intensity on temperature coefficient ofthe ingap/ingaas/ge triple-junction solar cell. Open Fuels Energy Sci J  2015; 8(1)

[55]
Zhang YQ, Cao WJ, Liu AM. Analysis of temperature characteristics of an algaas/gaas/ge triple-junction solar cell by is. Adv Mat Res  2013; 756-759(756-759): 101-4.
 [http://dx.doi.org/10.4028/www.scientific.net/AMR.756-759.101]

[56]
Gholami A, Ameri M, Zandi M, Gavagsaz Ghoachani R. A single-diode model for photovoltaic panels in variable environmental conditions: Investigating dust impacts with experimental evaluation. Sustain Energy Technol Assess  2021; 47: 101392.
 [http://dx.doi.org/10.1016/j.seta.2021.101392]

[57]
Chaibi Y, Allouhi A, Malvoni M, Salhi M, Saadani R. Solar irradiance and temperature influence on the photovoltaic cell equivalent-circuit models. Sol Energy  2019; 188: 1102-10.
 [http://dx.doi.org/10.1016/j.solener.2019.07.005]

[58]
Segev G, Mittelman G, Kribus A. Equivalent circuit models for triple-junction concentrator solar cells. Sol Energy Mater Sol Cells  2012; 98: 57-65.
 [http://dx.doi.org/10.1016/j.solmat.2011.10.013]

[59]
Neufeld CJ, Cruz SC, Farrell RM, et al. Effect of doping and polarization on carrier collection in InGaN quantum well solar cells. Appl Phys Lett  2011; 98(24): 243507.
 [http://dx.doi.org/10.1063/1.3595487]

[60]
Rezk H, Fathy A. A novel optimal parameters identification of triple-junction solar cell based on a recently meta-heuristic water cycle algorithm. Sol Energy  2017; 157: 778-91.
 [http://dx.doi.org/10.1016/j.solener.2017.08.084]

[61]
Sarniak MT, Wernik J, Krzysztof J. Wołosz. Application of the double diode model of photovoltaic cells for simulation studies on the impact of partial shading of silicon photovoltaic modules on the waveforms of their current–voltage characteristic. Energies  2019; 12(12): 2421.
 [http://dx.doi.org/10.3390/en12122421]

[62]
Amjadian S, Esmaeilzadeh M, Ajeian R, Riazi G, Ramakrishna S. Efficiency enhancement of biophotovoltaic solid-state solar cells. Energy Technol  2023; 11(10): 2300337.
 [http://dx.doi.org/10.1002/ente.202300337]

[63]
Tianhuan H, Zheling Z, Qiaogan L, et al. Achieved 18.9% efficiency by fine-tuning non-fullerene acceptor content to simultaneously increase the short-circuit current and fill factor of organic solar cells. In: Small. Germany: Weinheim on Bergstrasse 2023.

[64]
Marziyeh A. Saffron dye-sensitized solar cells with polyvinyl alcohol based gel polymer electrolytes. Opt Quantum Electron  2023; 55(9)

[65]
Yuqi C, Jiangjian S, Fanqi M, et al. A versatile molten-salt induction strategy to achieve efficient cspbi3 perovskite solar cells with a high open-circuit voltage >1.2 V In: Advanced materials Fla: Deerfield Beach.   2022; 34.(45)

[66]
JuHyeon K, Ryun YK, Juae K. Efficient and stable perovskite solar cells with a high open-circuit voltage over 1.2 V achieved by a dual- side passivation layer In: Advanced materials.  Fla: Deerfield Beach 2022; 34.(41)

[67]
V K C , Sung J J, Dhananjay K. Combating open circuit voltage loss in Sb2Se3 solar cell with an application of SnS as a back surface field layer. Sol Energy  2022; 233.

[68]
Weili M, Chao D, Jing S, et al. Reduced graphene oxide-Mn3O4 composites as effective electron acceptors for hybrid polymer-based solar cells. Mater Sci Semicond Process  2022; 145.

[69]
Kumar N, Hussain I, Singh B, Panigrahi BK. Peak power detection of PS solar PV panel by using WPSCO. IET Renew Power Gener  2017; 11(4): 480-9.
 [http://dx.doi.org/10.1049/iet-rpg.2016.0733]

[70]
Zaimi M, El Achouby H, Ibral A, Assaid EM. Determining combined effects of solar radiation and panel junction temperature on all model-parameters to forecast peak power and photovoltaic yield of solar panel under non-standard conditions. Sol Energy  2019; 191: 341-59.
 [http://dx.doi.org/10.1016/j.solener.2019.09.007]

[71]
Hoji Y, Oshima R, Makita K, et al. 28.3% efficient III–V tandem solar cells fabricated using a triple-chamber hydride vapor phase epitaxy system. Sol RRL 2021.
 [http://dx.doi.org/10.1002/solr.202100948]

[72]
Velu Kaliyannan G, Palanisamy SV, Rathanasamy R, Palanisamy M, Palaniappan SK, Chinnasamy M. Influence of ultrathin gahnite anti-reflection coating on the power conversion efficiency of polycrystalline silicon solar cell. J Mater Sci Mater Electron  2020; 31(3): 2308-19.
 [http://dx.doi.org/10.1007/s10854-019-02763-2]

[73]
Gobinath VK, Rajasekar R, Moganapriya C, et al. Surface engineering of zinc sulphide film for augmenting the performance of polycrystalline silicon solar cells. Chalcogenide Lett  2021; 18(7): 375-84.
 [http://dx.doi.org/10.15251/CL.2021.187.375]

[74]
Seungyoon S, Gyuho S, Hyojung J, et al. Mechanistic insights into the effect of newly developed dye anchor counter electrode in enhancing the efficiency of dye sensitized solar cells. Sol Energy  2023; 253.

[75]
Fernandes S A RV. F G M O D K, Guerra B R M V. Forecasting the performance of a photovoltaic solar system installed in other locations using artificial neural networks. Electr Power Compon Syst  2020; 48(1-2)

[76]
Faiz A, Adil S, Farhad BI, et al. Parameter extraction of photovoltaic models using atomic orbital search algorithm on a decent basis for novel accurate RMSE calculation. Energy Convers Manage  2023; 277.

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DOI https://dx.doi.org/10.2174/0122127976281400231211113923	Print ISSN 2212-7976
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