Photovoltaic mono and bifacial module/string electrical model parameters identification and validation based on a new differential evolution bee colony optimizer

Hachana, Oussama; Aoufi, Belkacem; Tina, Giuseppe Marco; Sid, Mohamed Amine

doi:10.1016/j.enconman.2021.114667

Cited by 10 publications

(4 citation statements)

References 68 publications

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“…The statistical analysis confirms our algorithm's precision, establishing it as a highly effective solution for optimization in the energy sector. 8.0e-04 ABC [58][59][60][61] 9.0e-04 DE [41,[62][63][64] 8.0e-04…”

Section: Discussionmentioning

confidence: 99%

Parameters optimization of solar PV cell using genetic-iterative algorithm

Ben Said,

Nouri,

Sakhi

et al. 2024

Phys. Scr.

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Accurately determining optimal parameters from current-voltage (IV) data in solar photovoltaic (PV) models is crucial for effective system simulation and control. In this study, we propose a novel approach that combines genetic algorithm and iterative techniques maximizing their strengths, and exploiting the influence of each parameter on the IV curve to categorize them into groups. This adaptable method can adjust the interval of each parameter to different scenarios during optimization. We evaluated the method across various solar cell models including both the ’SDM’ and ’SDM-based PMM’, achieving notable accuracy and reliability compared to other advanced meta-heuristic algorithms. The results indicate a value of 7.3870e-5 for the SDM and 9.3365e-4 for the PMM (STM6-40/36). The proposed algorithm demonstrates notable accuracy and reliability, highlighting its usefulness in accurately determining parameters in solar PV models.

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Section: Discussionmentioning

confidence: 99%

Parameters optimization of solar PV cell using genetic-iterative algorithm

Ben Said,

Nouri,

Sakhi

et al. 2024

Phys. Scr.

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“…In curve fitting, the parameter determination involves fitting of theoretical I ‐ V curves to measures curves. Different meta‐heuristic techniques such as Genetic Algorithm, Artificial Bee Swarm, Particle Swarm optimization etc., have been extensively reported in earlier works 38,45‐47 . Contrary to such techniques, analytical and numerical methods make use of the curve parameters I SC , V OC , I MPP , and V MPP for extraction of the five parameters at any reference condition.…”

Section: Methodsmentioning

confidence: 99%

“…Different meta-heuristic techniques such as Genetic Algorithm, Artificial Bee Swarm, Particle Swarm optimization etc., have been extensively reported in earlier works. 38,[45][46][47] Contrary to such techniques, analytical and numerical methods make use of the curve parameters I SC , V OC , I MPP , and V MPP for extraction of the five parameters at any reference condition. This method involves solving a set of non-linear equations derived at the reference condition.…”

Section: Simulink Modelmentioning

confidence: 99%

“…36 In case of bifacial modules, while the effects of front-side shading may be similar to the effects on a monofacial type module, addition of the rear side in the power generation process brings along with it the effects of non-uniform illumination on the rear side. 37,38 The non-uniformity of irradiance on the rear side can be a result of; either differences in incident irradiance due to ground reflections, or due to variation in reflected radiation due to modules in adjacent rows. Non-uniformity may also arise due to self-shade from a module, that is, the shade of the module on the ground may reduce the area for direct reflection of incident irradiance on the ground.…”

Section: Introductionmentioning

confidence: 99%

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Behavior of bifacial and monofacial photovoltaic modules under partial shading scenarios

Raina

Mathur

Sinha

2022

Intl J of Energy Research

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The current industrial scenario highlights the increased attention being awarded to bifacial photovoltaics (PV) technology due to its property of flux absorption from the rear side. But akin to monofacial modules, bifacial modules are also subjected to shading scenarios due to non-uniform illumination.A case study for comparative analysis on performance of bifacial and monofacial PV system is presented for Jaipur, India, which falls in the composite zone of Indian climate. An attempt has been made to quantify the performance of bifacial PV module under various shading scenarios and compared with monofacial module under similar condition. The work is done using a threephase approach, wherein initially the performance is studied at a module level and simulations performed to generate voltage profiles of individual cells under shade. This is followed by determining optimum inter-row spacing through experiment and RADIANCE simulations in an attempt to mitigate unwanted shade due to adjacent rows in an array. Finally, a dynamic approach to reconfigure the array under shade, combining electrical and physical reconfiguration methods to mitigate shading is studied. Based on the results of the study, bifacial technology always emerges as the superior in terms of power output over conventional monofacial technology for a wide range of partial shading scenarios. Combined physical and electrical array reconfiguration for a 3 Â 3 array shows fill factor reduction can be limited to 18.9% compared to non-uniform shade reduction of over 40%.

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