Multi-objective optimization framework for electric vehicle charging and discharging scheduling in distribution networks using the red deer algorithm
Abstract This article addresses the optimization of the challenging electric vehicles (EVs) charging and discharging schedules in distribution networks, focusing on the needs of EV aggregators and household EV users. To contribute to this problem solving, a multi-objective framework for EV demands r...
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-97473-7 |
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| author | Sahbi Boubaker Habib Kraiem Nejib Ghazouani Souad Kamel Adel Mellit Faisal S. Alsubaei Farid Bourennani Walid Meskine Tariq Alqubaysi |
| author_facet | Sahbi Boubaker Habib Kraiem Nejib Ghazouani Souad Kamel Adel Mellit Faisal S. Alsubaei Farid Bourennani Walid Meskine Tariq Alqubaysi |
| author_sort | Sahbi Boubaker |
| collection | DOAJ |
| description | Abstract This article addresses the optimization of the challenging electric vehicles (EVs) charging and discharging schedules in distribution networks, focusing on the needs of EV aggregators and household EV users. To contribute to this problem solving, a multi-objective framework for EV demands response in power systems, optimizing charging and discharging schedules while considering maximum load-handling capacity and EV users’ state of charge (SoC) satisfaction as constraints are proposed. The framework employs a vehicle-to-grid (V2G) approach to achieve these goals. The proposed model, centered on aggregators and EV users, tackles issues such as power loss reduction, voltage profile enhancement, and optimal EV charging and discharging scheduling to maximize system performance. For this aim, we address this problem as a multi-objective optimization one using a linear weighted sum technique to simultaneously address the framework objectives. To tackle the optimization problem, a metaheuristic swarm intelligence algorithm, the Red Deer Algorithm (RDA), is utilized to determine the optimal EV charging and discharging timings. The efficiency of the proposed method on a IEEE 69-bus system is tested. The experiment simulates residential EV loads using two sets of different EVs; The first set included Tata Nexon, BYD Seal and Hyundai Ioniq and the second set included Nissan Leaf e+, MG ZS EV Long Range and Mercedes EQS AMG 53 4MATIC+. These vehicles, with different charging powers, are connected to the network via load or generator buses based on household demands. The aim was to determine the optimal power flow for charging and analyze the impact of EV integration during peak and off-peak hours. Simulation results demonstrated that the EV schedule management method significantly reduces average EV load demand without overloading the distribution network’s power flow, while maintaining an improved voltage profile. Furthermore, by integrating drone technology, EVs can transmit stored information back to the grid, enhancing the overall energy management beyond power consumption. |
| format | Article |
| id | doaj-art-55ffde44308248cfb732713ae409b811 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-55ffde44308248cfb732713ae409b8112025-08-20T02:17:54ZengNature PortfolioScientific Reports2045-23222025-04-0115112310.1038/s41598-025-97473-7Multi-objective optimization framework for electric vehicle charging and discharging scheduling in distribution networks using the red deer algorithmSahbi Boubaker0Habib Kraiem1Nejib Ghazouani2Souad Kamel3Adel Mellit4Faisal S. Alsubaei5Farid Bourennani6Walid Meskine7Tariq Alqubaysi8Department of Computer and Network Engineering, College of Computer Science and Engineering, University of JeddahCenter for Scientific Research and Entrepreneurship, Northern Border UniversityDepartment of Civil Engineering, College of Engineering, Northern Border UniversityDepartment of Computer and Network Engineering, College of Computer Science and Engineering, University of JeddahInternational Centre for Theoretical Physics (ICTP), Trieste University, 11-I-34151Department of Cybersecurity, College of Computer Science and Engineering, University of JeddahDepartment of Information Systems and Technology, College of Computer Science and Engineering, University of JeddahSAMATWAIQ for drones CompanyDepartment of Civil Engineering, College of Engineering, Northern Border UniversityAbstract This article addresses the optimization of the challenging electric vehicles (EVs) charging and discharging schedules in distribution networks, focusing on the needs of EV aggregators and household EV users. To contribute to this problem solving, a multi-objective framework for EV demands response in power systems, optimizing charging and discharging schedules while considering maximum load-handling capacity and EV users’ state of charge (SoC) satisfaction as constraints are proposed. The framework employs a vehicle-to-grid (V2G) approach to achieve these goals. The proposed model, centered on aggregators and EV users, tackles issues such as power loss reduction, voltage profile enhancement, and optimal EV charging and discharging scheduling to maximize system performance. For this aim, we address this problem as a multi-objective optimization one using a linear weighted sum technique to simultaneously address the framework objectives. To tackle the optimization problem, a metaheuristic swarm intelligence algorithm, the Red Deer Algorithm (RDA), is utilized to determine the optimal EV charging and discharging timings. The efficiency of the proposed method on a IEEE 69-bus system is tested. The experiment simulates residential EV loads using two sets of different EVs; The first set included Tata Nexon, BYD Seal and Hyundai Ioniq and the second set included Nissan Leaf e+, MG ZS EV Long Range and Mercedes EQS AMG 53 4MATIC+. These vehicles, with different charging powers, are connected to the network via load or generator buses based on household demands. The aim was to determine the optimal power flow for charging and analyze the impact of EV integration during peak and off-peak hours. Simulation results demonstrated that the EV schedule management method significantly reduces average EV load demand without overloading the distribution network’s power flow, while maintaining an improved voltage profile. Furthermore, by integrating drone technology, EVs can transmit stored information back to the grid, enhancing the overall energy management beyond power consumption.https://doi.org/10.1038/s41598-025-97473-7EVs charging schedulingVehicle-to-Grid (V2G)Multi-Objective optimizationDistribution network managementRed deer algorithm (RDA) |
| spellingShingle | Sahbi Boubaker Habib Kraiem Nejib Ghazouani Souad Kamel Adel Mellit Faisal S. Alsubaei Farid Bourennani Walid Meskine Tariq Alqubaysi Multi-objective optimization framework for electric vehicle charging and discharging scheduling in distribution networks using the red deer algorithm Scientific Reports EVs charging scheduling Vehicle-to-Grid (V2G) Multi-Objective optimization Distribution network management Red deer algorithm (RDA) |
| title | Multi-objective optimization framework for electric vehicle charging and discharging scheduling in distribution networks using the red deer algorithm |
| title_full | Multi-objective optimization framework for electric vehicle charging and discharging scheduling in distribution networks using the red deer algorithm |
| title_fullStr | Multi-objective optimization framework for electric vehicle charging and discharging scheduling in distribution networks using the red deer algorithm |
| title_full_unstemmed | Multi-objective optimization framework for electric vehicle charging and discharging scheduling in distribution networks using the red deer algorithm |
| title_short | Multi-objective optimization framework for electric vehicle charging and discharging scheduling in distribution networks using the red deer algorithm |
| title_sort | multi objective optimization framework for electric vehicle charging and discharging scheduling in distribution networks using the red deer algorithm |
| topic | EVs charging scheduling Vehicle-to-Grid (V2G) Multi-Objective optimization Distribution network management Red deer algorithm (RDA) |
| url | https://doi.org/10.1038/s41598-025-97473-7 |
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