Division methods and selection principles for the ideal optical window of spectral beam splitting photovoltaic/thermal systems

Li, Wenzhi; El-Samie, Mostafa M. Abd; Zhao, Shuang; Lin, Jianqing; Jü, Xing; Xu, Chao

doi:10.1016/j.enconman.2021.114736

Cited by 19 publications

(2 citation statements)

References 32 publications

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“…The fill factor can be expressed as follows:

FF = \frac{V_{m}}{V_{oc}} (1 - \frac{\exp (\frac{e V_{m}}{k_{B} T_{pv}}) - 1}{\exp (\frac{e V_{oc}}{k_{B} T_{pv}}) - 1})

where

V_{m}

is the voltage output at maximum power point, which can determined as follows:

V_{m} = k \times V_{italicoc}

The PV/T system's electrical efficiency can be determined by using Equation () 51 :

η_{italicel} = \frac{J_{italicsc} V_{italicoc} FF}{Q_{inc}}

Considering the PV's empirical parameters, the PV module's short circuit current is employed to determine its corresponding open‐circuit voltage and fill factor 40 . The electrical efficiency of the system varying with temperature can be defined as follows:

η_{italicel} = η_{italicref} (1 - β (T_{italicpv} - T_{a}))

where

η_{italicref}

is the reference efficiency,

T_{italicpv}

and

T_{a}

are the PV panel's surface and the ambient temperatures.…”

Section: Mathematical Modelmentioning

confidence: 99%

Performance enhancement study of ag@ SiO ₂ core‐shell nanoplate plasmonic hybrid spectral splitting nanofluid based photovoltaic thermal system in a temperate region

Meraje

Huang

Ujihara

et al. 2022

Intl J of Energy Research

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Summary Nanofluid spectral splitting photovoltaic thermal (PV/T) systems have become immensely common for the utilization of full‐spectrum. The performance enhancement investigation of the plasmonic hybrid nanofluid spectral filtering PV/T system in dynamical modeling and a practical validation was carried out in this study. The Ag@SiO2 core‐shell nanoplate/ethylene glycol‐CoSO4 plasmonic hybrid nanofluid was synthesized to selectively absorb incident natural sunlight. We used H2O2 as a basic shape‐selective oxidant to synthesize the silver nanoplate using a one‐step method. This approach simplifies seed formation and transformation of spherical nanoparticles to different‐sized nanoplates. A practical study of the plasmonic hybrid nanofluid optical filtering PV/T system was conducted outdoors on sunny days to represent the temperate region and evaluate the dynamic modeling and the system's applicability in real situations. It was found that the plasmonic beam splitter serves as an efficient heat transfer medium and maintains the PV module around the ambient temperature. The highest temperature recorded on the typical day of our experiment was 51.62, 54.34, and 57.56°C for the ethylene‐CoSO4 base fluid and nanofluids with concentrations of 18.3 and 28.3 ppm, respectively, and 33.16°C for the PV panel. The highest overall performance of up to 87.6%, 86.4%, and 85.3% was attained by the spectral filtering plasmonic hybrid nanofluid PV/T system using a 27.3 and 18.3 ppm concentration ratio and hybrid base fluid, respectively. The study shows the nanofluid reduces the electricity output but produces higher merit function values than the PV standalone. This shows that the proposed nanofluid is a potential candidate for PV/T systems targeting utilization of full‐spectrum sunlight and enhancing the overall performance.

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“…The fill factor can be expressed as follows:

FF = \frac{V_{m}}{V_{oc}} (1 - \frac{\exp (\frac{e V_{m}}{k_{B} T_{pv}}) - 1}{\exp (\frac{e V_{oc}}{k_{B} T_{pv}}) - 1})

where

V_{m}

is the voltage output at maximum power point, which can determined as follows:

V_{m} = k \times V_{italicoc}

The PV/T system's electrical efficiency can be determined by using Equation () 51 :

η_{italicel} = \frac{J_{italicsc} V_{italicoc} FF}{Q_{inc}}

η_{italicel} = η_{italicref} (1 - β (T_{italicpv} - T_{a}))

where

η_{italicref}

is the reference efficiency,

T_{italicpv}

and

T_{a}

are the PV panel's surface and the ambient temperatures.…”

Section: Mathematical Modelmentioning

confidence: 99%

Performance enhancement study of ag@ SiO ₂ core‐shell nanoplate plasmonic hybrid spectral splitting nanofluid based photovoltaic thermal system in a temperate region

Meraje

Huang

Ujihara

et al. 2022

Intl J of Energy Research

View full text Add to dashboard Cite

show abstract

“…However, the training sample data set is determined in the stage of variable selection, which results in the explicit probability distribution for each variable. During variable selection, the absence of change in the correlation between wavelengths and properties may be unreasonable because the indicator of wavelength importance cannot accurately reflect the dynamic process of wavelength selection . The prediction accuracy and generalization of final NIR property models can be improved if the wavelengths selected in each iteration can contribute to the property prediction of samples.…”

Section: Introductionmentioning

confidence: 99%

Modified Hybrid Strategy Integrating Online Adjustable Oil Property Characterization and Data-Driven Robust Optimization under Uncertainty: Application in Gasoline Blending

et al. 2022

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With the increasing challenges in environmental protection, product quality, and market profit, the development of a feasible optimization approach coupling accurate oil property online prediction has become crucial to the intelligent production of refineries. However, traditional property modeling with near-infrared (NIR) spectroscopy cannot dynamically extract the synergistic effect between wavelengths and cause inaccurate property prediction. The success rate of the data-driven robust optimization (DDRO) of the refining process is influenced by process uncertainty. Thus, we proposed a modified hybrid strategy on online NIR prediction modeling with adjustable characteristic wavelength selection and DDRO under uncertainty for the refining process. An adjustable feature space variable extraction (AFSVE) method was first proposed to dynamically select the characteristic wavelengths for constructing accurate property prediction models. A data-driven robust optimization of gasoline blending model was rendered through dual transformation using the uncertainty set of oil property derived from principal component analysis combined with robust kernel density estimation. An industrial application showed the best prediction accuracy of property models with the AFSVE method, and a high blending success rate was obtained by the proposed modified hybrid strategy, which hedges against uncertainties, including measuring error and blending effect, and boosts environmentally friendly and intelligent process operation.

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Analysis of Optical Window for Constant Cooling Heat Flux‐Based Spectral Splitting in Concentrator Photovoltaic System

Han,

Hou,

Xiao

et al. 2024

Energy Tech

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The solar cells cooled to constant temperature at different concentration ratios (CR) and spectral bands (SB) require the same cooling heat flux (CHF), but the output power varies significantly. Thus, it is necessary to clarify the relationship of CHF‐CR‐SB to provide a reference for photovoltaic systems to select the output power generation of the spectral band under constant cooling heat flux. In this article, a selecting spectra model of a centralized photovoltaic (CPV) system is established and the selection of the spectrum based on CHF and the CR is analyzed. Theory shows that the wider the spectral division of the same CR, the larger the cooling heat flux consumed. The narrower the spectrum division, the higher the CR that the cell receiver can withstand. The experiments show that the higher the photoelectric conversion efficiency (PCE), the lower the cooling heat flux to be consumed. The cooling heat flux of 650 filter consumes 22.74% more than the UV700 filter, which means the demand for CHF is more severe when the heat spectrum distribution is wide. The spectral division should be carried out according to the requirements of high‐energy flux but a small thermal energy proportion to achieve efficient PCE.

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Division methods and selection principles for the ideal optical window of spectral beam splitting photovoltaic/thermal systems

Cited by 19 publications

References 32 publications

Performance enhancement study of ag@ SiO ₂ core‐shell nanoplate plasmonic hybrid spectral splitting nanofluid based photovoltaic thermal system in a temperate region

Performance enhancement study of ag@ SiO ₂ core‐shell nanoplate plasmonic hybrid spectral splitting nanofluid based photovoltaic thermal system in a temperate region

Modified Hybrid Strategy Integrating Online Adjustable Oil Property Characterization and Data-Driven Robust Optimization under Uncertainty: Application in Gasoline Blending

Analysis of Optical Window for Constant Cooling Heat Flux‐Based Spectral Splitting in Concentrator Photovoltaic System

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Division methods and selection principles for the ideal optical window of spectral beam splitting photovoltaic/thermal systems

Cited by 19 publications

References 32 publications

Performance enhancement study of ag@ SiO 2 core‐shell nanoplate plasmonic hybrid spectral splitting nanofluid based photovoltaic thermal system in a temperate region

Performance enhancement study of ag@ SiO 2 core‐shell nanoplate plasmonic hybrid spectral splitting nanofluid based photovoltaic thermal system in a temperate region

Modified Hybrid Strategy Integrating Online Adjustable Oil Property Characterization and Data-Driven Robust Optimization under Uncertainty: Application in Gasoline Blending

Analysis of Optical Window for Constant Cooling Heat Flux‐Based Spectral Splitting in Concentrator Photovoltaic System

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Performance enhancement study of ag@ SiO ₂ core‐shell nanoplate plasmonic hybrid spectral splitting nanofluid based photovoltaic thermal system in a temperate region

Performance enhancement study of ag@ SiO ₂ core‐shell nanoplate plasmonic hybrid spectral splitting nanofluid based photovoltaic thermal system in a temperate region