Recently, hybrid organic-inorganic perovskites have been extensively studied due to their promising optical properties with relatively low-cost and simple processing. However, the perovskite solar cells have some low optical absorption in the visible spectrum, especially around the red region. In this paper, an improvement of perovskite solar cell efficiency is studied via simulations through adding plasmonic nanoparticles (NPs) at the rear side of the solar cell. The plasmonic resonance wavelength is selected to be very close to the spectrum range of lower absorption of the perovskite: around 600 nm. Both gold and silver nanoparticles (Au and Ag NPs) are selected to introduce the plasmonic effect with diameters above 40 nm, to get an overlap between the plasmonic resonance spectrum and the requested lower absorption spectrum of the perovskite layer. Simulations show the increase in the short circuit current density (Jsc) as a result of adding Au and Ag NPs, respectively. Enhancement in Jsc is observed as the diameter of both Au and Ag NPs is increased beyond 40 nm. Furthermore, there is a slight increase in the reflection loss as the thickness of the plasmonic nanoparticles at the rear side of the solar cell is increased. A significant decrease in the current loss due to transmission is achieved as the size of the nanoparticles increases. As a comparison, slightly higher enhancement in external quantum efficiency (EQE) can be achieved in case of adding Ag NPs rather than Au NPs.
This study represents the investigation of earth-abundant and non-toxic CZTSSe absorber materials in kesterite solar cell by using the Finite Element Method (FEM) with (1) electrical, and (2) optical approaches. The simulated results have been validated with the experimental results to define guidelines for boosting the cell performance. For improving the cell efficiency, potential barrier variations in the front contact, and the effect of different lattice defects in the CZTSSe absorber layer have been examined. Controlling the defects and the secondary phases of absorber layer have significant influence on the cell performance improvement. Previous studies have demonstrated that, synthesis of CZTSSe:Na nanocrystals and controlling the S/(S + Se), Cu/(Zn + Sn), and Zn/Sn ratios (stoichiometry) have significant effects on the reduction of trap-assisted recombination (Shockley–Read–Hall recombination model). In this work, a screening-based approach has been employed to study the cell efficiency over a wide range of defect densities. Two categorized defect types including benign defects ($${N}_{t}<{10}^{16}$$ N t < 10 16 cm−3 , Nt defines trap density) and harmful defects $${(N}_{t}>{10}^{16}$$ ( N t > 10 16 cm−3) in the absorber bandgap in the CZTSSe solar cell, by analyzing their position changes with respect to the electron Fermi level (Efn) and the Valence Band Maximum positions have been identified. It is realized that, the harmful defects are the dominant reason for the low efficiency of the kesterite solar cells, therefore, reducing the number of harmful defects and also total defect densities lead to the power conversion efficiency record of 19.06%. This increment makes the CZTSSe solar cells as a promising candidate for industrial and commercial applications.
Optimal scheduling of reconfigurable interconnected microgrids is a precious and critical task for the residential consumers especially with the integration of renewable energy sources, dispatchable units and energy storage systems. In this regard, not only the optimal scheduling of the microgrids in a realistic and correlated environment is a necessity, but also the guarantied security and the prevention of cyber-attacks are mandatory tasks for the operators. This article first addresses these issues by developing a novel framework based on blockchain for secured data transaction from the individual microgrids' components to the central control unit and then tries to find the optimal scheduling plan using stochastic programming based on point estimate method (PEM). Through such a hybrid PEM-blockchain based framework, the interconnected microgrids can supply the residential loads in a fully reliable, economic and secured structure. We also consider a social-economic framework to not only minimize the total operating cost of the microgrids, but also benefit the customers by enhancing the social factors through the optimal switching. Considering the complex and nonlinear nature of the problem, an effective corrected crow search (CCS) algorithm is deployed to find the most optimal operating point for the microgrids. The quality and capabilities of the proposed model are investigated using a practical residential interconnected microgrid. The results show that the optimal switching could reduce the total operation cost from $22,716 to $21,935 (3.56% reduction). Also, the average energy not supplied (AENS) has reduced from 1.4115 to 1.352 kWh/customer.yr (4.40% reduction), which are notable values. The results advocate the quality and functionality of the proposed framework.
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