Influence of laterally split spectral illumination on multi-junction CPV solar cell performance

Bunthof, L.A.A.; Haverkamp, E.J.; Woude, D. van der; Bauhuis, G.J.; Corbeek, W.H.M.; Veelenturf, S.; Vlieg, Elias; Schermer, J.J.

doi:10.1016/j.solener.2018.05.033

Cited by 6 publications

(3 citation statements)

References 28 publications

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“…Based on the simulation results, the paper proposed to increase the density of grid lines in the central area of the solar cells to reduce the decline in filling factor (FF) and conversion efficiency caused by non-uniformity of illumination. Bunthof et al [22] explored the influence of spectral mismatch on the photoelectric performance of CPV cells. They artificially constructed a spectral non-uniform state by placing different filters at both ends of the cell surface and found that the greater the distance between the two regions, the lower the filling factor and open-circuit voltage.…”

Section: Introductionmentioning

confidence: 99%

Effect of Optical–Electrical–Thermal Coupling on the Performance of High-Concentration Multijunction Solar Cells

et al. 2022

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In the process of high-concentration photovoltaic (HCPV) power generation, multijunction cells work in the conditions of high radiation and high current. Non-uniformity of focusing, the mismatch between the focusing spectrum caused by the dispersion effect and the spectrum of multijunction solar cell design and the increase in cell temperature are the key factors affecting the photoelectric performance of the multijunction solar cell. The coupling effect of three factors on the performance of multijunction solar cell intensifies its negative impact. Based on the previous research, the light intensity and spectral characteristics under Fresnel lens focusing are calculated through the optical model, and the optical–electrical–thermal coupling model under non-uniform illumination is established. The results show that obvious changes exist in the concentration spectrum distribution, energy and non-uniformity along different optical axis positions. These changes lead to serious current mismatch and transverse current in the multijunction solar cell placed near the focal plane which decreases the output power. The lost energy makes the cell temperature highest near the focal plane. In the condition of passive heat dissipation with 500 times geometric concentration ratio, the output power of the solar cell near the focal plane decreases by 35% and the temperature increases by 15%. Therefore, optimizing the placement position of the multijunction cell in the optical axis direction can alleviate the negative effects of optical–electrical–thermal coupling caused by focusing non-uniformity, spectral mismatch and rising cell temperature, and improve the output performance of the cell. This conclusion is verified by the experimental result.

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Section: Introductionmentioning

confidence: 99%

Effect of Optical–Electrical–Thermal Coupling on the Performance of High-Concentration Multijunction Solar Cells

et al. 2022

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“…Crystalline germanium (c-Ge), as the substrate and bottom cell of multijunction 2 of 12 solar cells, can effectively make use of photo-generated carriers and improve the efficiency of solar cells [8,9]. Ge-based multijunction solar cells are used in space and concentrator photovoltaics (CPV) applications [10,11]. Ge solar cells have also been used in thermal and photovoltaic applications [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Based on Ultrathin PEDOT:PSS/c-Ge Solar Cells Design and Their Photoelectric Performance

Yang

et al. 2021

Coatings

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In recent years, nanostructures have improved the performance of solar cells and are regarded as the most promising microstructures. The optical properties of PEDOT:PSS/c-Ge hybrid solar cells (HSCs) based on the octagon germanium nanoparticles (O-GNPs) were numerically analyzed using the finite-difference time-domain (FDTD) method. The optimal structure of the hybrid solar cell is determined by changing the thickness of the organic layer and structural parameters of nanoparticles to enhance the optical absorption and eventually achieve high broadband absorption. By changing the structure parameter of O-GNPs, we studied its effect on solar cells. The optimization of geometric parameters is based on maximum absorption. The light absorption of our optimized HSCs is basically above 90% between 200 and 1500 nm. PEDOT:PSS is placed on top of O-GNPs to transmit the holes better, allowing O-GNPs to capture a lot of photons, to increase absorbance value properties in the AM1.5 solar spectral irradiated region. The transmittance is increased by adding poly-methyl methacrylate (PMMA). At the same time, the electrical characteristics of Ge solar cells were simulated by DEVICE, and short-circuit current (Jsc), open-circuit voltage (Voc), maximum power (Pmax), filling coefficient (FF) and photoelectric conversion efficiency (PCE) were obtained. According to the optimization results after adjusting the structural parameters, the maximum short-circuit current is 44.32 mA/cm2; PCE is 7.84 mW/cm2; FF is 69%. The results show that the O-GNPs have a good light trapping effect, and the structure design has great potential for the absorption of HSCs; it is believed that the conversion efficiency will be further improved through further research.

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“…With the rise in energy demand, intelligent conversion of solar energy is becoming more of a necessity to be addressed fundamentally. Laterally arranged solar cells sys-tem has a strong potential in the generation of electricity and incorporate holographic phase plates to achieve spectral splitting of broadband light [22,23]. Unlike conventional diffractive optical elements that are generally designed for one task, SpliCons provide simultaneous spectral splitting and concentration of light [15].…”

Section: Introductionmentioning

confidence: 99%

Spectral splitting and concentration of broadband light using neural networks

Yolalmaz

Yüce

2021

APL Photonics

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Compact photonic elements that control both the diffraction and interference of light offer superior performance at ultra-compact dimensions. Unlike conventional optical structures, these diffractive optical elements can provide simultaneous control of spectral and spatial profile of light. However, the inverse-design of such a diffractive optical element is time-consuming with current algorithms, and the designs generally lack experimental validation. Here, we develop a neural network model to experimentally design and validate SpliCons; a special type of diffractive optical element that can achieve simultaneous concentration and spectral splitting of broadband light. We use neural networks to exploit nonlinear operations that result from wavefront reconstruction through a phase plate. Our results show that the neural network model yields enhanced spectral splitting performance for phase plates with quantitative assessment compared to phase plates that are optimized via local search optimization algorithm. The capabilities of the phase plates optimized via neural network are experimentally validated by comparing the intensity distribution at the output plane. Once the neural networks are trained, we manage to design SpliCons with 96.8% accuracy within 2 seconds, which is orders of magnitude faster than iterative search algorithms. We openly share the fast and efficient framework that we develop in order to contribute to the design and implementation of diffractive optical elements that can lead to transformative effects in microscopy, spectroscopy, and solar energy applications.

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Influence of laterally split spectral illumination on multi-junction CPV solar cell performance

Cited by 6 publications

References 28 publications

Effect of Optical–Electrical–Thermal Coupling on the Performance of High-Concentration Multijunction Solar Cells

Effect of Optical–Electrical–Thermal Coupling on the Performance of High-Concentration Multijunction Solar Cells

Based on Ultrathin PEDOT:PSS/c-Ge Solar Cells Design and Their Photoelectric Performance

Spectral splitting and concentration of broadband light using neural networks

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