Mitigating the Effect of Electric Vehicle integration in Distribution Grid using Slime Mould Algorithm
The effects of electric vehicle (EV) integration in three different urban residential neighborhoods of Bangladesh are assessed in this work. A Monte Carlo simulation-based stochastic load flow algorithm is developed to analyze system parameters such as aggregated power demand, voltage profile, energ...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-02-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016822006160 |
| _version_ | 1797961143699898368 |
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| author | Md. Shadman Abid Hasan Jamil Apon Abdullah Alavi Md. Arif Hossain Fahim Abid |
| author_facet | Md. Shadman Abid Hasan Jamil Apon Abdullah Alavi Md. Arif Hossain Fahim Abid |
| author_sort | Md. Shadman Abid |
| collection | DOAJ |
| description | The effects of electric vehicle (EV) integration in three different urban residential neighborhoods of Bangladesh are assessed in this work. A Monte Carlo simulation-based stochastic load flow algorithm is developed to analyze system parameters such as aggregated power demand, voltage profile, energy losses, and voltage stability margin (VSMsys). Two scenarios for EV penetration (20% and 30%) were investigated, and the implications of seasonal load variation were assessed. The Monte Carlo results indicate that one of the areas is susceptible to sustaining EVs in the present system due to the potential of voltage collapse. The results also suggest that the other two areas would be able to accommodate EVs in the future. Finally, a slime mould algorithm (SMA)-based optimization approach is developed and applied to the relevant networks to identify their optimal charging strategies based on the outcomes of the Monte-Carlo simulation. A constrained objective function with an optimal amount of load, (VSMsys) index, and total active power loss was formulated to complete the assessment. The results indicate that the proposed optimal charging approach improves the grid’s capacity to accommodate a more significant proportion of the EV load while maintaining ideal system voltage and reducing power loss. |
| first_indexed | 2024-04-11T00:54:46Z |
| format | Article |
| id | doaj.art-c6fa60b2dd854ba5a3ef05ec0b785172 |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-04-11T00:54:46Z |
| publishDate | 2023-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-c6fa60b2dd854ba5a3ef05ec0b7851722023-01-05T06:46:17ZengElsevierAlexandria Engineering Journal1110-01682023-02-0164785800Mitigating the Effect of Electric Vehicle integration in Distribution Grid using Slime Mould AlgorithmMd. Shadman Abid0Hasan Jamil Apon1Abdullah Alavi2Md. Arif Hossain3Fahim Abid4Corresponding author.; Department of Electrical and Electronic Engineering, Islamic University of Technology, Gazipur 1704, BangladeshDepartment of Electrical and Electronic Engineering, Islamic University of Technology, Gazipur 1704, BangladeshDepartment of Electrical and Electronic Engineering, Islamic University of Technology, Gazipur 1704, BangladeshDepartment of Electrical and Electronic Engineering, Islamic University of Technology, Gazipur 1704, BangladeshDepartment of Electrical and Electronic Engineering, Islamic University of Technology, Gazipur 1704, BangladeshThe effects of electric vehicle (EV) integration in three different urban residential neighborhoods of Bangladesh are assessed in this work. A Monte Carlo simulation-based stochastic load flow algorithm is developed to analyze system parameters such as aggregated power demand, voltage profile, energy losses, and voltage stability margin (VSMsys). Two scenarios for EV penetration (20% and 30%) were investigated, and the implications of seasonal load variation were assessed. The Monte Carlo results indicate that one of the areas is susceptible to sustaining EVs in the present system due to the potential of voltage collapse. The results also suggest that the other two areas would be able to accommodate EVs in the future. Finally, a slime mould algorithm (SMA)-based optimization approach is developed and applied to the relevant networks to identify their optimal charging strategies based on the outcomes of the Monte-Carlo simulation. A constrained objective function with an optimal amount of load, (VSMsys) index, and total active power loss was formulated to complete the assessment. The results indicate that the proposed optimal charging approach improves the grid’s capacity to accommodate a more significant proportion of the EV load while maintaining ideal system voltage and reducing power loss.http://www.sciencedirect.com/science/article/pii/S1110016822006160Electric vehicleMonte Carlo simulationSlime mould algorithmOptimal chargingOptimization |
| spellingShingle | Md. Shadman Abid Hasan Jamil Apon Abdullah Alavi Md. Arif Hossain Fahim Abid Mitigating the Effect of Electric Vehicle integration in Distribution Grid using Slime Mould Algorithm Alexandria Engineering Journal Electric vehicle Monte Carlo simulation Slime mould algorithm Optimal charging Optimization |
| title | Mitigating the Effect of Electric Vehicle integration in Distribution Grid using Slime Mould Algorithm |
| title_full | Mitigating the Effect of Electric Vehicle integration in Distribution Grid using Slime Mould Algorithm |
| title_fullStr | Mitigating the Effect of Electric Vehicle integration in Distribution Grid using Slime Mould Algorithm |
| title_full_unstemmed | Mitigating the Effect of Electric Vehicle integration in Distribution Grid using Slime Mould Algorithm |
| title_short | Mitigating the Effect of Electric Vehicle integration in Distribution Grid using Slime Mould Algorithm |
| title_sort | mitigating the effect of electric vehicle integration in distribution grid using slime mould algorithm |
| topic | Electric vehicle Monte Carlo simulation Slime mould algorithm Optimal charging Optimization |
| url | http://www.sciencedirect.com/science/article/pii/S1110016822006160 |
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