Publication: Designing an optimal multi-energy system with fast charging and hydrogen refueling station under uncertainties
| dc.contributor.author | Er, Gulfem | |
| dc.contributor.author | Soykan, Gurkan | |
| dc.contributor.author | Çanakoǧlu, Ethem | |
| dc.contributor.institution | Er, Gulfem, Department of Industrial Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey | |
| dc.contributor.institution | Soykan, Gurkan, Department of Energy Systems Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey | |
| dc.contributor.institution | Çanakoǧlu, Ethem, College of Engineering and Technology, American University of the Middle East, Al Ahmadi, Kuwait | |
| dc.date.accessioned | 2025-10-05T14:43:42Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | As the demand for battery-powered and hydrogen-based electric vehicles continues to rise, the need for integrated charging and refueling systems with energy systems becomes more critical. Therefore, it is crucial to consider the stations serving diverse vehicle users during the system design process. This paper presents an optimal sizing strategy for a multi-energy system that covers clean energy sources and two energy storage technologies under the presence of a fast-charging station and a hydrogen refueling station for various types of car users. For the considered system, the optimal sizing problem aims to minimize the loss of power supply probability (LPSP) and annualized total life cycle cost (TLCC) through a two-stage stochastic programming-based multi-objective optimization approach. Using a scenario-based approach, the study considers the uncertainties associated with renewables, homeowner's vehicle demands, and energy demands from homes and stations. Different cases are produced to evaluate the impacts of the different stations with the availability of various electric vehicle profiles under different levels of reliability. The TLCC decreases if LPSP increases in each case. Also, the cost of installing a fast charging station is significantly lower than that of a hydrogen refueling station for all LPSP limits. Moreover, the overall cost of the system design is at its lowest when homeowners’ vehicles provide vehicle-to-grid services. It is worth noting that when the station loads which come from the customers’ vehicle increase by 10%, the TLCC increases by 1.19%. Additionally, the revenue from hydrogen refueling station is approximately 70% less than the fast charging station. © 2024 Elsevier B.V., All rights reserved. | |
| dc.identifier.doi | 10.1016/j.segan.2024.101403 | |
| dc.identifier.issn | 23524677 | |
| dc.identifier.scopus | 2-s2.0-85192859841 | |
| dc.identifier.uri | https://doi.org/10.1016/j.segan.2024.101403 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14719/7015 | |
| dc.identifier.volume | 39 | |
| dc.language.iso | en | |
| dc.publisher | Elsevier Ltd | |
| dc.relation.source | Sustainable Energy, Grids and Networks | |
| dc.subject.authorkeywords | Electric Vehicle | |
| dc.subject.authorkeywords | Fast Charging | |
| dc.subject.authorkeywords | Hydrogen | |
| dc.subject.authorkeywords | Refueling Station | |
| dc.subject.authorkeywords | Sustainability | |
| dc.subject.authorkeywords | Charging (batteries) | |
| dc.subject.authorkeywords | Costs | |
| dc.subject.authorkeywords | Design | |
| dc.subject.authorkeywords | Electric Energy Storage | |
| dc.subject.authorkeywords | Electric Loads | |
| dc.subject.authorkeywords | Electric Vehicles | |
| dc.subject.authorkeywords | Life Cycle | |
| dc.subject.authorkeywords | Multiobjective Optimization | |
| dc.subject.authorkeywords | Stochastic Programming | |
| dc.subject.authorkeywords | Sustainable Development | |
| dc.subject.authorkeywords | Systems Analysis | |
| dc.subject.authorkeywords | Battery Powered | |
| dc.subject.authorkeywords | Fast Charging | |
| dc.subject.authorkeywords | Fast Charging Stations | |
| dc.subject.authorkeywords | Hydrogen Refueling Stations | |
| dc.subject.authorkeywords | Loss Of Power Supply Probability | |
| dc.subject.authorkeywords | Multi-energy Systems | |
| dc.subject.authorkeywords | Optimal Sizing | |
| dc.subject.authorkeywords | Refueling Station | |
| dc.subject.authorkeywords | Total Life Cycle Costs | |
| dc.subject.authorkeywords | Uncertainty | |
| dc.subject.authorkeywords | Stochastic Systems | |
| dc.subject.indexkeywords | Charging (batteries) | |
| dc.subject.indexkeywords | Costs | |
| dc.subject.indexkeywords | Design | |
| dc.subject.indexkeywords | Electric energy storage | |
| dc.subject.indexkeywords | Electric loads | |
| dc.subject.indexkeywords | Electric vehicles | |
| dc.subject.indexkeywords | Life cycle | |
| dc.subject.indexkeywords | Multiobjective optimization | |
| dc.subject.indexkeywords | Stochastic programming | |
| dc.subject.indexkeywords | Sustainable development | |
| dc.subject.indexkeywords | Systems analysis | |
| dc.subject.indexkeywords | Battery powered | |
| dc.subject.indexkeywords | Fast charging | |
| dc.subject.indexkeywords | Fast charging stations | |
| dc.subject.indexkeywords | Hydrogen refueling stations | |
| dc.subject.indexkeywords | Loss of power supply probability | |
| dc.subject.indexkeywords | Multi-energy systems | |
| dc.subject.indexkeywords | Optimal sizing | |
| dc.subject.indexkeywords | Refueling station | |
| dc.subject.indexkeywords | Total life cycle costs | |
| dc.subject.indexkeywords | Uncertainty | |
| dc.subject.indexkeywords | Stochastic systems | |
| dc.title | Designing an optimal multi-energy system with fast charging and hydrogen refueling station under uncertainties | |
| dc.type | Article | |
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| dspace.entity.type | Publication | |
| local.indexed.at | Scopus | |
| person.identifier.scopus-author-id | 57218847484 | |
| person.identifier.scopus-author-id | 8636421100 | |
| person.identifier.scopus-author-id | 15047450100 |
