Hybrid photovoltaic-thermoelectric system using a novel spectral splitting solar concentrator

Alnajideen, Mohammad; Min, Gao

doi:10.1016/j.enconman.2021.114981

Cited by 42 publications

(9 citation statements)

References 62 publications

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“…In addition, existing commercial reflectors can achieve a reflectivity of more than 90% in a wideband, and even up to 95%. [ 28 ] Therefore, curved reflectors can substantially increase the practical application potential of the RTPV.…”

Section: Resultsmentioning

confidence: 99%

An Efficient Photon Utilization Radioisotope Thermophotovoltaic Based on Curled Reflectors

et al. 2023

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For radioisotope thermophotovoltaic (RTPV) to produce higher output power, it is often required that the irradiance incident on the thermophotovoltaic (TPV) cell array be more uniform. However, the irradiance received by each cell of the TPV array is relatively different under the conventional cell array surround structure of RTPV, resulting in a serious overall electrical output mismatch loss and a lower output power utilization rate. Herein, an RTPV with curled reflectors optimized by finite‐element method is proposed. It is shown in the simulation results that the irradiation uniformity of the cell array can reach 87.5%, the total output power reaches 88.12 W, which is 39.8% higher than that prior to optimization, and the mismatch loss is reduced by 69.2%. In addition, the impact of the reflectivity of reflectors is also targeted from the perspective of practical applications, and it can still produce higher output power than that of the conventional structure even at reflectivity as low as 70%. Ultimately, the RTPV prototype with reflectors is developed, and the electrical performance is tested to verify the effect of irradiance improvement. Herein, reflectors with positive gain for RTPV are proposed, which provides a new idea for the development of high‐efficiency power supplies.

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Section: Resultsmentioning

confidence: 99%

An Efficient Photon Utilization Radioisotope Thermophotovoltaic Based on Curled Reflectors

et al. 2023

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“…2(a)]. [27][28][29][30] As a result, the temperature distributions within the PV and the TEG stages are fully independent, and each of them may be conveniently optimized. The use of external solar splitters, while enabling optimal temperatures for both stages, unavoidably makes available to the TEG only a fraction of the total heat, as that generated by hot pair relaxation, hole-electron pair recombination, and Joule effect within the PV cell never reaches the TEG.…”

Section: Pairing Schemes In Hybrid Harvestersmentioning

confidence: 99%

Hybrid thermoelectric-photovoltaic solar harvesters: technological and economic issues

Narducci

Lorenzi

2022

Jpn. J. Appl. Phys.

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The possibility of recovering heat released by photovoltaic (PV) solar cells into electric power by using thermoelectric generators has attracted a remarkable research effort over the last two decades. Views and results are not fully converging, however, ranging from overoptimistic estimates of power gains to fully negative opinions about the convenience of hybridization. Aim of this paper is to review both energetic and economic profitability of photovoltaic-thermoelectric hybridization, as both issues are to be considered in the design of hybrid solar harvesters. It will be shown how the PV material rules the total system efficiency and its economic competitiveness compared to standard PV modules. Marginal opportunities are reported for rooftop concentrated solar harvesters. Yet, hybridization may leverage novel PV materials, currently not considered due to their lower efficiency compared to polycrystalline silicon. Much more significant is instead the window of opportunity when thermoelectric generators are coupled to perovskite solar cells.

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“…Further efforts were made to optimize and assess the overall performance of solar photovoltaic-thermoelectric. Alnajideen and Min [28] carried out a practical demonstration of the higher performance of a spectrum splitting solar photovoltaic-thermoelectric system compared to a standalone solar photovoltaic module. The experimental results obtained showed that the stand-alone photovoltaic module efficiency was 15.9% while that of the hybrid system was 16.9%, indicating a 6.3% due to thermoelectric waste heat scavenging.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Performance Prediction of a Solar Photovoltaic-Thermoelectric System with Various Crystalline Silicon Cell Types

Alghamdi

Maduabuchi

Albaker

et al. 2023

International Journal of Energy Research

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Since the solar photovoltaic-thermoelectric (PV-TE) is an upcoming technology, the current literature on PV-TEs have failed to thoroughly investigate the effects of different solar cell types on the PV-TE’s performance. Such parametric study becomes necessary since the properties of the solar cell, characterized by the cell temperature coefficient and reference efficiency, determine the overall performance of the PV-TE. Further, the design information obtained from numerical solvers is minimal due to the high time and computational energy required to optimize the photovoltaic-thermoelectric (PV-TE) performance. This problem severely affects the ease with which useful design information can be drawn from current methods to design high-performance PVs. Additionally, to reduce the complexity of the existing numerical model, the previous models have introduced some principal assumptions that have significantly affected the accuracy of the numerical results. Finally, the numerical model has neglected several real-life operating conditions since they increase the model complexity. For the first time, deep neural networks are proposed to predict the photovoltaic-thermoelectric performance designed with 3 different crystalline solar cells as a perfect replacement for the inefficient numerical methods used to analyze the hybrid system. After that, the data generated by the numerical solver is fed to an optimum 3-layer deep neural network with 20 neurons per layer to forecast the hybrid system’s performance efficiently. The training of the neural network is governed by Bayesian regularization and the performance of the model is evaluated using the mean squared error, coefficient of determination, and training time. Results show that the deep neural network accurately learned the results that took the conventional numerical solver 1,600 mins to generate in just 12 min and 27 s, making the proposed network 128.51 times faster than the traditional numerical method. Furthermore, the accuracy of the network is demonstrated by the low mean squared error of 3.34 × 10 − 8 and high training and testing regression of 100% and 99.98%, respectively.

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Hybrid photovoltaic-thermoelectric system using a novel spectral splitting solar concentrator

Cited by 42 publications

References 62 publications

An Efficient Photon Utilization Radioisotope Thermophotovoltaic Based on Curled Reflectors

An Efficient Photon Utilization Radioisotope Thermophotovoltaic Based on Curled Reflectors

Hybrid thermoelectric-photovoltaic solar harvesters: technological and economic issues

Machine Learning Performance Prediction of a Solar Photovoltaic-Thermoelectric System with Various Crystalline Silicon Cell Types

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