A HYBRID MVO-BMO TECHNIQUE FOR PLUG-IN ELECTRIC VEHICLE CHARGING OPTIMIZATION
The electric vehicle (EV) market is expanding rapidly around the world due to technological advancements, decreasing cost of batteries, and supportive government regulations. It is both a challenge and an opportunity for distribution utilities to manage the additional power demand from EVs. Effectiv...
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Penerbit UTM Press
2025
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| author | Razali N.M. Mohamad H. Abidin A.F. Ali Z. |
| author2 | 53980259500 |
| author_facet | 53980259500 Razali N.M. Mohamad H. Abidin A.F. Ali Z. |
| author_sort | Razali N.M. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | The electric vehicle (EV) market is expanding rapidly around the world due to technological advancements, decreasing cost of batteries, and supportive government regulations. It is both a challenge and an opportunity for distribution utilities to manage the additional power demand from EVs. Effective and optimal EV charging scheduling strategies are essential to avoid the adverse effects of large EV penetration in the power grid system. This paper proposes an optimal plug-in electric vehicle (PEV) charging scheduling in a distribution grid system using a hybrid algorithm approach that combines a multiverse optimizer (MVO) and also a barnacle mating optimizer (BMO) termed as HMVO-BMO. The optimization model is developed with the objective to minimize the grid power loss, considering overnight home charging. Random arrival times of PEVs are considered and charging is scheduled based on available power demand on the distribution grid. The proposed methodology is demonstrated on the IEEE 33-bus system with different PEV penetration levels. Comparisons are made between three optimization algorithm approaches, namely the standard MVO and BMO, and the proposed HMVO-BMO algorithms. The simulation results demonstrated that the proposed hybrid technique can achieve better and efficient results in terms of system power loss. ? 2024 Penerbit UTM Press. All rights reserved. |
| format | Article |
| id | my.uniten.dspace-36311 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2025 |
| publisher | Penerbit UTM Press |
| record_format | dspace |
| spelling | my.uniten.dspace-363112025-03-03T15:41:53Z A HYBRID MVO-BMO TECHNIQUE FOR PLUG-IN ELECTRIC VEHICLE CHARGING OPTIMIZATION Razali N.M. Mohamad H. Abidin A.F. Ali Z. 53980259500 36809989400 26666522700 25824589000 The electric vehicle (EV) market is expanding rapidly around the world due to technological advancements, decreasing cost of batteries, and supportive government regulations. It is both a challenge and an opportunity for distribution utilities to manage the additional power demand from EVs. Effective and optimal EV charging scheduling strategies are essential to avoid the adverse effects of large EV penetration in the power grid system. This paper proposes an optimal plug-in electric vehicle (PEV) charging scheduling in a distribution grid system using a hybrid algorithm approach that combines a multiverse optimizer (MVO) and also a barnacle mating optimizer (BMO) termed as HMVO-BMO. The optimization model is developed with the objective to minimize the grid power loss, considering overnight home charging. Random arrival times of PEVs are considered and charging is scheduled based on available power demand on the distribution grid. The proposed methodology is demonstrated on the IEEE 33-bus system with different PEV penetration levels. Comparisons are made between three optimization algorithm approaches, namely the standard MVO and BMO, and the proposed HMVO-BMO algorithms. The simulation results demonstrated that the proposed hybrid technique can achieve better and efficient results in terms of system power loss. ? 2024 Penerbit UTM Press. All rights reserved. Final 2025-03-03T07:41:53Z 2025-03-03T07:41:53Z 2024 Article 10.11113/jurnalteknologi.v86.20625 2-s2.0-85202542091 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202542091&doi=10.11113%2fjurnalteknologi.v86.20625&partnerID=40&md5=09d85e059424d17be7f00e20b4aa01c0 https://irepository.uniten.edu.my/handle/123456789/36311 86 5 23 34 All Open Access; Gold Open Access Penerbit UTM Press Scopus |
| spellingShingle | Razali N.M. Mohamad H. Abidin A.F. Ali Z. A HYBRID MVO-BMO TECHNIQUE FOR PLUG-IN ELECTRIC VEHICLE CHARGING OPTIMIZATION |
| title | A HYBRID MVO-BMO TECHNIQUE FOR PLUG-IN ELECTRIC VEHICLE CHARGING OPTIMIZATION |
| title_full | A HYBRID MVO-BMO TECHNIQUE FOR PLUG-IN ELECTRIC VEHICLE CHARGING OPTIMIZATION |
| title_fullStr | A HYBRID MVO-BMO TECHNIQUE FOR PLUG-IN ELECTRIC VEHICLE CHARGING OPTIMIZATION |
| title_full_unstemmed | A HYBRID MVO-BMO TECHNIQUE FOR PLUG-IN ELECTRIC VEHICLE CHARGING OPTIMIZATION |
| title_short | A HYBRID MVO-BMO TECHNIQUE FOR PLUG-IN ELECTRIC VEHICLE CHARGING OPTIMIZATION |
| title_sort | hybrid mvo-bmo technique for plug-in electric vehicle charging optimization |
| url_provider | http://dspace.uniten.edu.my/ |