A multi-objective optimization framework for EV-integrated distribution grids using the hiking optimization algorithm

Electric vehicle (EV) integration into distribution grids introduces significant challenges in maintaining grid stability, minimizing operational costs, and ensuring overall system efficiency. In response to these challenges, a novel multi-objective optimization model is proposed that concurrently m...

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Veröffentlicht in:Scientific reports Jg. 15; H. 1; S. 13324 - 25
Hauptverfasser: Samiei Moghaddam, Mahmoud, Azadikhouy, Masoumeh, Salehi, Nasrin, Hosseina, Majid
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 17.04.2025
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Algorithms
Alternative energy
Costs
Distribution grid optimization
Electric vehicle (EV) integration
Electric vehicles
Energy
Energy resources
Energy sources
Hiking
Hiking optimization algorithm (HOA)
Humanities and Social Sciences
Integration
multidisciplinary
Optimization algorithms
Optimization techniques
Photovoltaics
Renewable energy
Renewable energy management
Renewable resources
Science
Science (multidisciplinary)
Voltage
Electric vehicle (EV) integration
Hiking optimization algorithm (HOA)
Distribution grid optimization
Renewable energy management
ISSN:2045-2322, 2045-2322
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Zusammenfassung:Electric vehicle (EV) integration into distribution grids introduces significant challenges in maintaining grid stability, minimizing operational costs, and ensuring overall system efficiency. In response to these challenges, a novel multi-objective optimization model is proposed that concurrently minimizes energy losses, energy procurement costs, load shedding, and voltage deviations over a 24-hour period, while also accounting for the operational costs associated with EV and battery management. The model is optimized using the Hiking Optimization Algorithm (HOA), which leverages an adaptive search mechanism based on Tobler’s Hiking Function. This mechanism enhances the exploration of the solution space and effectively avoids local optima, resulting in superior performance compared to conventional methods. Simulation results on a 33-bus distribution grid demonstrated that, with EV integration, operational costs were reduced by 19.3%, energy losses decreased by 59.7%, load shedding was minimized by 75.4%, and voltage deviations improved by 43.5% relative to a scenario without EVs. Additionally, the model eliminated photovoltaic (PV) curtailment, thereby ensuring optimal utilization of renewable energy resources. When benchmarked against alternative optimization techniques, the HOA achieved a 4.4% lower total cost than the Komodo Mlipir Algorithm (KMA) and reduced energy losses by 24.5% compared to Particle Swarm Optimization (PSO). These results clearly demonstrate the model’s effectiveness in enhancing grid stability, optimizing costs, and improving operational efficiency. The proposed approach offers a scalable and reliable solution for modern grid management in the context of increasing EV penetration and renewable energy integration.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-97271-1