Impact of fast charging station for electric vehicles with grid integration: Forensic‐based investigation and Archimedes optimization algorithm approach

This manuscript proposes a novel technique for the precise model of electric vehicles (EVs) in the reliability and adequacy model of smart grids (SG). The proposed method combines forensic‐based investigation (FBI) and Archimedes optimization algorithm (AOA), named the FBIAOA technique. The objectiv...

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Veröffentlicht in:Optimal control applications & methods Jg. 45; H. 3; S. 1305 - 1326
Hauptverfasser: Singh, Abhishek Kumar, Kumar, Ashwani
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Glasgow Wiley Subscription Services, Inc 01.05.2024
Schlagworte:
Electric vehicle charging
Electric vehicles
Genetic algorithms
Optimization
Optimization algorithms
Photovoltaic cells
Reliability
Smart grid
Storage systems
ISSN:0143-2087, 1099-1514
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Zusammenfassung:This manuscript proposes a novel technique for the precise model of electric vehicles (EVs) in the reliability and adequacy model of smart grids (SG). The proposed method combines forensic‐based investigation (FBI) and Archimedes optimization algorithm (AOA), named the FBIAOA technique. The objective of the proposed method is to rise the profit of fast charging stations and lessen the rising energy demand on the grid that is made up of storage systems and renewable energy generation (wind and PV). The demand for EVs and renewable generation is calculated using the FBI algorithm method. The growth of the proposed method is to examine the reliability of SG depending on the aggregation of the state matrices of EV stochastic parameters. The proposed method can help accelerate the reliability calculations by determining the desired count of EV states. The proposed strategy is run in MATLAB and is evaluated in its performance with existing methods. The proposed method gives a lower cost than the existing genetic algorithm, cuttlefish algorithm, and tunicate swarm algorithm methods.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0143-2087
1099-1514
DOI:10.1002/oca.3100