ArticleScientific reports2025
Multi-objective optimization framework for electric vehicle charging and discharging scheduling in distribution networks using the red deer algorithm.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Quantum-inspired NSGA-II for multi-objective optimization of electric vehicle charging stations.Scientific reports · 2026Article
- A similarity-based predictive scheduling method for dynamic electric vehicle charging load management.Scientific reports · 2026Article
- A bi-decision model for electric vehicle dispatch and battery swapping station charging schedule problem.Scientific reports · 2025Article
- Novel control strategies for electric vehicle charging stations using stochastic modeling and queueing analysis.Scientific reports · 2025Article
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9 authors.
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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.
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