Modelling and Allocation of Hydrogen-Fuel-Cell-Based Distributed Generation to Mitigate Electric Vehicle Charging Station Impact and Reliability Analysis on Electrical Distribution Systems

Thangaraj Yuvaraj, Thirukoilur Dhandapani Suresh, Arokiasamy Ananthi Christy, Thanikanti Sudhakar Babu and Benedetto Nastasi ()
Additional contact information
Thangaraj Yuvaraj: Centre for Computational Modeling, Chennai Institute of Technology, Chennai 600069, India
Thirukoilur Dhandapani Suresh: Department of Electrical and Electronics Engineering, Saveetha Engineering College, Chennai 602105, India
Arokiasamy Ananthi Christy: Department of Marine Engineering, AMET University, East Coast Road, Kanathur, Chennai 603112, India
Thanikanti Sudhakar Babu: Department of Electrical and Electronics Engineering, Chaitanya Bharathi Institute of Technology, Hyderabad 500075, India
Benedetto Nastasi: Department of Planning, Design and Technology of Architecture, Sapienza University of Rome, Via Flaminia 72, 00196 Rome, Italy

Energies, 2023, vol. 16, issue 19, 1-31

Abstract: The research presented in this article aims at the modelling and optimization of hydrogen-fuel-cell-based distributed generation (HFC-DG) to minimize the effect of electric vehicle charging stations (EVCSs) in a radial distribution system (RDS). The key objective of this work is to address various challenges that arise from the integration of EVCSs, including increased power demand, voltage fluctuations, and voltage stability. To accomplish this objective, the study utilizes a novel spotted hyena optimizer algorithm (SHOA) to simultaneously optimize the placement of HFC-DG units and EVCSs. The main goal is to mitigate real power loss resulting from the additional power demand of EVCSs in the IEEE 33-bus RDS. Furthermore, the research also investigates the influence of HFC-DG and EVCSs on the reliability of the power system. Reliability is crucial for all stakeholders, particularly electricity consumers. Therefore, the study thoroughly examines how the integration of HFC-DG and EVCSs influences system reliability. The optimized solutions obtained from the SHOA and other algorithms are carefully analyzed to assess their effectiveness in minimizing power loss and improving reliability indices. Comparative analysis is conducted with varying load factors to estimate the performance of the presented optimization approach. The results prove the benefits of the optimization methodology in terms of reducing power loss and improvising the reliability of the RDS. By utilizing HFC-DG and EVCSs, optimized through the SHOA and other algorithms, the research contributes to mitigating power loss caused by EVCS power demand and improving overall system reliability. Overall, this research addresses the challenges associated with integrating EVCSs into distribution systems and proposes a novel optimization approach using HFC-DG. The findings highlight the potential benefits of this approach in terms of minimizing power loss, enhancing reliability, and optimizing distribution system operations in the context of increasing EV adoption.

Keywords: electrical vehicles (EVs); spotted hyena optimizer algorithm (SHOA); bat algorithm (BA); African vulture optimization algorithm (AVOA); bald eagle search algorithm (BESA); hydrogen-fuel-cell-based distributed generation (HFC-DG); electric vehicle charging stations (EVCSs); reliability; radial distribution system (RDS) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/19/6869/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/19/6869/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:19:p:6869-:d:1250270

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().