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Multi-objective optimization of concentrated Photovoltaic-Thermoelectric hybrid system via non-dominated sorting genetic algorithm (NSGA II)
Istanbul Univ Cerrahpasa, Dept Elect & Elect Engn, TR-34320 Avcilar, Turkey..
Istanbul Univ Cerrahpasa, Dept Elect & Elect Engn, TR-34320 Avcilar, Turkey..ORCID iD: 0000-0002-7497-2377
Istanbul Univ Cerrahpasa, Dept Elect & Elect Engn, TR-34320 Avcilar, Turkey..
KTH, School of Engineering Sciences (SCI), Applied Physics, Biomedical and X-ray Physics.ORCID iD: 0000-0002-5672-5727
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2021 (English)In: Energy Conversion and Management, ISSN 0196-8904, E-ISSN 1879-2227, Vol. 236, article id 114065Article in journal (Refereed) Published
Abstract [en]

Thermoelectric generators harvest additional electrical power when used in combination with concentrated photovoltaic cells given rise to a hybrid system. Overall cost of the system is high; therefore, the parameters of the system need to be optimized to obtain high output performance. This study determines the output performances of four sets of equations (models) used in the hybrid system, using the performance of recently developed nanostructured thermoelectric materials. Seven parameters of the system were optimized through these models using non-dominated genetic algorithm. Models 1 and 2 have the highest performance chosen by TOPSIS decision-making method. The power output and conversion efficiencies of the hybrid system in models 1 and 2 are 426.5 W, 11.45% and 461.12 W, 10.77%, respectively. Likewise, the highest TOPSIS solution for power output of one TEG module operating in the hybrid system and its corresponding efficiency is obtained in model 4 and are 1.97 W and 0.078%, respectively. This validates the fact that TEG operating in a hybrid system has optimum performance at a point when the load resistance is less than its internal resistance.

Place, publisher, year, edition, pages
Elsevier BV , 2021. Vol. 236, article id 114065
Keywords [en]
Concentrated photovoltaic, Thermoelectric generator, NSGA-II, Optimization, TOPSIS, Hybrid system
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:kth:diva-296384DOI: 10.1016/j.enconman.2021.114065ISI: 000647766600005Scopus ID: 2-s2.0-85104988128OAI: oai:DiVA.org:kth-296384DiVA, id: diva2:1574289
Note

QC 20210628

Available from: 2021-06-28 Created: 2021-06-28 Last updated: 2022-06-25Bibliographically approved

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Hamawandi, BejanToprak, Muhammet

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