Electric Ferry Fleet Peak Charging Power Schedule Optimization Considering the Timetable and Daily Energy Profile
Decarbonization of shipping is a legal obligation imposed by the International Maritime Organization (IMO). The ferry port and daily operations located near or in urban zones negatively impact the nearby environment. The electrification of ferries contributes to reducing the negative environmental i...
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MDPI AG
2024-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/1/235 |
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| author | Tomislav Peša Maja Krčum Grgo Kero Joško Šoda |
| author_facet | Tomislav Peša Maja Krčum Grgo Kero Joško Šoda |
| author_sort | Tomislav Peša |
| collection | DOAJ |
| description | Decarbonization of shipping is a legal obligation imposed by the International Maritime Organization (IMO). The ferry port and daily operations located near or in urban zones negatively impact the nearby environment. The electrification of ferries contributes to reducing the negative environmental impact. The available electrical infrastructure in ports often does not meet daily needs. The ferry fleet’s sailing schedule creates a non-periodic daily energy profile to determine the energy needs of the shore connection. The proposed research aims to optimize the daily electric ferry fleet peak charging power schedule process using particle swarm optimization and a greedy algorithm. A four-stage model has been proposed, consisting of the initialization of the ferry fleet’s daily energy profile, initial population generation with input constraints, optimization, and the creation of the modified daily energy load diagram. Robustness and validation of the proposed model were investigated and proven for energy profiles with and without optimization. For the proposed charging schedule, the study results show a reduction in peak power of 24%. By optimizing the charging process, peak charging power has been reduced without needing an additional energy storage system. |
| format | Article |
| id | doaj-art-5ce510b5065c4695b60854cc2dcdd353 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-5ce510b5065c4695b60854cc2dcdd3532025-01-10T13:14:53ZengMDPI AGApplied Sciences2076-34172024-12-0115123510.3390/app15010235Electric Ferry Fleet Peak Charging Power Schedule Optimization Considering the Timetable and Daily Energy ProfileTomislav Peša0Maja Krčum1Grgo Kero2Joško Šoda3Faculty of Maritime Studies, University of Split, Ulica Ruđera Boškovića 31, 21000 Split, CroatiaFaculty of Maritime Studies, University of Split, Ulica Ruđera Boškovića 31, 21000 Split, CroatiaNaval Studies, University of Split, Ulica Ruđera Boškovića 31, 21000 Split, CroatiaFaculty of Maritime Studies, University of Split, Ulica Ruđera Boškovića 31, 21000 Split, CroatiaDecarbonization of shipping is a legal obligation imposed by the International Maritime Organization (IMO). The ferry port and daily operations located near or in urban zones negatively impact the nearby environment. The electrification of ferries contributes to reducing the negative environmental impact. The available electrical infrastructure in ports often does not meet daily needs. The ferry fleet’s sailing schedule creates a non-periodic daily energy profile to determine the energy needs of the shore connection. The proposed research aims to optimize the daily electric ferry fleet peak charging power schedule process using particle swarm optimization and a greedy algorithm. A four-stage model has been proposed, consisting of the initialization of the ferry fleet’s daily energy profile, initial population generation with input constraints, optimization, and the creation of the modified daily energy load diagram. Robustness and validation of the proposed model were investigated and proven for energy profiles with and without optimization. For the proposed charging schedule, the study results show a reduction in peak power of 24%. By optimizing the charging process, peak charging power has been reduced without needing an additional energy storage system.https://www.mdpi.com/2076-3417/15/1/235charging optimizationferry electrificationparticle swarm optimizationrenewable energy sources |
| spellingShingle | Tomislav Peša Maja Krčum Grgo Kero Joško Šoda Electric Ferry Fleet Peak Charging Power Schedule Optimization Considering the Timetable and Daily Energy Profile Applied Sciences charging optimization ferry electrification particle swarm optimization renewable energy sources |
| title | Electric Ferry Fleet Peak Charging Power Schedule Optimization Considering the Timetable and Daily Energy Profile |
| title_full | Electric Ferry Fleet Peak Charging Power Schedule Optimization Considering the Timetable and Daily Energy Profile |
| title_fullStr | Electric Ferry Fleet Peak Charging Power Schedule Optimization Considering the Timetable and Daily Energy Profile |
| title_full_unstemmed | Electric Ferry Fleet Peak Charging Power Schedule Optimization Considering the Timetable and Daily Energy Profile |
| title_short | Electric Ferry Fleet Peak Charging Power Schedule Optimization Considering the Timetable and Daily Energy Profile |
| title_sort | electric ferry fleet peak charging power schedule optimization considering the timetable and daily energy profile |
| topic | charging optimization ferry electrification particle swarm optimization renewable energy sources |
| url | https://www.mdpi.com/2076-3417/15/1/235 |
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