RESUMO
The low bandgap antimony selenide (Sb2Se3) and wide bandgap organic solar cell (OSC) can be considered suitable bottom and top subcells for use in tandem solar cells. Some properties of these complementary candidates are their non-toxicity and cost-affordability. In this current simulation study, a two-terminal organic/Sb2Se3 thin-film tandem is proposed and designed through TCAD device simulations. To validate the device simulator platform, two solar cells were selected for tandem design, and their experimental data were chosen for calibrating the models and parameters utilized in the simulations. The initial OSC has an active blend layer, whose optical bandgap is 1.72 eV, while the initial Sb2Se3 cell has a bandgap energy of 1.23 eV. The structures of the initial standalone top and bottom cells are ITO/PEDOT:PSS/DR3TSBDT:PC71BM/PFN/Al, and FTO/CdS/Sb2Se3/Spiro-OMeTAD/Au, while the recorded efficiencies of these individual cells are about 9.45% and 7.89%, respectively. The selected OSC employs polymer-based carrier transport layers, specifically PEDOT:PSS, an inherently conductive polymer, as an HTL, and PFN, a semiconducting polymer, as an ETL. The simulation is performed on the connected initial cells for two cases. The first case is for inverted (p-i-n)/(p-i-n) cells and the second is for the conventional (n-i-p)/(n-i-p) configuration. Both tandems are investigated in terms of the most important layer materials and parameters. After designing the current matching condition, the tandem PCEs are boosted to 21.52% and 19.14% for the inverted and conventional tandem cells, respectively. All TCAD device simulations are made by employing the Atlas device simulator given an illumination of AM1.5G (100 mW/cm2). This present study can offer design principles and valuable suggestions for eco-friendly solar cells made entirely of thin films, which can achieve flexibility for prospective use in wearable electronics.
RESUMO
This paper proposes a new intelligent algorithm named improved transient search optimization algorithm (ITSOA) integrated with multiobjective optimization for determining the optimal configuration of an unbalanced distribution network. The conventional transient search optimization algorithm (TSOA) is improved with opposition learning and nonlinearly decreasing strategies for enhancing the convergence to find the global solution and obtain a desirable balance between local and global search. The multiobjective function includes different objectives such as power loss reduction, enhancement of voltage sag and unbalance, and network energy not supplied minimization. The decision variables of the reconfiguration problem including opened switches or identification of optimal network configuration are determined using ITSOA and satisfying operational and radiality constraints. The proposed methodology is implemented on unbalanced 13-bus and 118-bus networks. The results showed that the proposed ITSOA is capable to find the optimal network configuration for enhancing the different objectives in loading conditions. The results cleared the proposed methodology's good effectiveness, especially in power quality and reliability enhancement, without compromising the different objectives. Comparing ITSOA to conventional TSOA, particle swarm optimization (PSO), gray wolf optimization (GWO), bat algorithm (BA), manta ray foraging optimization (MRFO), and ant lion Optimizer (ALO), and previous approaches, it is concluded that ITSOA in improving the different objectives.
