Optimal configuration of photovoltaic power plant using grey wolf optimizer: A comparative analysis considering CdTe and c-Si PV modules

Zidane, Tekai Eddine Khalil; Adzman, Mohd Rafi; Tajuddin, Mohammad Faridun Naim; Zali, Samila Mat; Durusu, Ali

doi:10.1016/j.solener.2019.06.002

Cited by 35 publications

(17 citation statements)

References 25 publications

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“… 2 − 4 Given that device architectures and large-scale manufacturing procedures have been highly optimized for this technology—a result of several decades of intensive research 2 , 5 —further reductions in cost will be heavily dependent on improvements in photoconversion efficiency. 1 , 2 , 6 Indeed, under the idealized detailed balance model, CdTe has an upper limit of 32% single-junction PV efficiency (based on its electronic bandgap), 7 indicating that there is still room for improvement. 6 , 8 − 11 …”

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confidence: 99%

Rapid Recombination by Cadmium Vacancies in CdTe

2021

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CdTe is currently the largest thin-film photovoltaic technology. Non-radiative electron–hole recombination reduces the solar conversion efficiency from an ideal value of 32% to a current champion performance of 22%. The cadmium vacancy (V Cd ) is a prominent acceptor species in p -type CdTe; however, debate continues regarding its structural and electronic behavior. Using ab initio defect techniques, we calculate a negative-U double-acceptor level for V Cd , while reproducing the V Cd 1– hole–polaron, reconciling theoretical predictions with experimental observations. We find the cadmium vacancy facilitates rapid charge-carrier recombination, reducing maximum power-conversion efficiency by over 5% for untreated CdTe—a consequence of tellurium dimerization, metastable structural arrangements, and anharmonic potential energy surfaces for carrier capture.

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confidence: 99%

Rapid Recombination by Cadmium Vacancies in CdTe

2021

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“…For the mathematical simulation of grey wolves hunting, alpha (best possible candidate solution), beta (next to best), and delta wolves know better where the prey is, so we save the top solutions that have been found so far and we use other search agents (omegas) to adjust their positions according to the top search agents' locations. In this context, we propose the following formulas [23,24]:…”

Section: Types Of Algorithms 41 Grey Wolf Search Algorithmmentioning

confidence: 99%

Algorithms to Model and Optimize a Stand-Alone Photovoltaic-Diesel-Battery System: An Application in Rural Libya

Elbaz

Guneser

2021

Teh. vjesn.

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This paper introduces a new optimum calculation technique for a stand-alone hybrid photovoltaic-diesel-battery system (PDBS), which meets the energy requirements of a small village in southern Libya. The bat algorithm design strategy is applied to reduce the annual cost of the system, taking into consideration the controlled electricity restriction and the optimal numbers of PV panels, diesel generators, and batteries. Comparative tests are performed using MATLAB for the bat algorithm with the grey wolf search algorithm and particle swarm optimization, demonstrating that the bat algorithm determines the optimum size of the PDBS effectively at a lower expense. Results then indicate that, taking into account the reliability characteristics, this has a significant effect on optimum capacity, load supply, and cost.

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“…However, this article does not tackle the variation in power loss due to the selection of different inverters and LCOE are not included. Zidane et al presented a study that proposes a methodology for the optimal configuration of large‐scale PV power plants with the implementation of grey wolf optimization, whale optimization algorithm, salp swarm algorithm, and particle swarm optimization meta‐heuristic optimization algorithms. The optimization process aims to minimize the LCOE and suggest the optimal sizing of PV modules and inverters but does not focus on partial shading (commonly found in rooftop PV systems).…”

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confidence: 99%

Analysis on inverter selection for domestic rooftop solar photovoltaic system deployment

Ali

Khan

2020

Int Trans Electr Energ Syst

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Summary Partial shading is a common occurrence in residential and commercial photovoltaic (PV) installations. It causes mismatch losses, particularly in string and central inverter‐based systems, leading to output power loss and in turn lower performance ratio under partial shading conditions. The choice of string configuration itself is critical in lowering mismatch losses and the levelized energy cost. While a range of commercial inverters with different string configurations and prices are available, many standard string topologies are not optimum for common settings under various shading scenarios. Therefore, in this article, we evaluate various string configurations attached to state‐of‐the‐art commercial inverters widely used in the industry from both mismatch loss and cost perspective. We quantify the impact of various shading scenarios on multiple inverter configurations to ascertain the relative performance of PV systems under symmetrical shading as well as random shading in field settings. For multiple PV system configurations (on a single rooftop), the mismatch loss varied up to 2.3% and 6% under symmetrical shading and field settings, respectively. The levelized cost of electricity also varies from 0.062$/kWh to 0.041$/kWh and is dependent on the type of inverter and string configurations.

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Optimal configuration of photovoltaic power plant using grey wolf optimizer: A comparative analysis considering CdTe and c-Si PV modules

Cited by 35 publications

References 25 publications

Rapid Recombination by Cadmium Vacancies in CdTe

Rapid Recombination by Cadmium Vacancies in CdTe

Algorithms to Model and Optimize a Stand-Alone Photovoltaic-Diesel-Battery System: An Application in Rural Libya

Analysis on inverter selection for domestic rooftop solar photovoltaic system deployment

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