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A Hybrid EV Charging Approach Based on MILP and a Genetic Algorithm

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  • Syed Abdullah Al Nahid

    (McComish Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, SD 57007, USA)

  • Junjian Qi

    (McComish Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, SD 57007, USA)

Abstract

Uncoordinated electric vehicle (EV) charging can significantly complicate power system operations. In this paper, we develop a hybrid EV charging method that seamlessly integrates centralized EV charging and distributed control schemes to address EV energy demand challenges. The proposed method includes (1) a centralized day-ahead optimal scheduling mechanism and EV shifting process based on mixed-integer linear programming (MILP) and (2) a distributed control strategy based on a genetic algorithm (GA) that dynamically adjusts the charging rate in real-time grid scenarios. The MILP minimizes energy imbalance at overloaded slots by reallocating EVs based on supply–demand mismatch. By combining full and minimum charging strategies with MILP-based shifting, the method significantly reduces network stress due to EV charging. The centralized model schedules time slots using valley-filling and EV-specific constraints, and the local GA-based distributed control adjusts charging currents based on minimum energy, system availability, waiting time, and a priority index (PI). This PI enables user prioritization in both the EV shifting process and power allocation decisions. The method is validated using demand data on a radial feeder with residential and commercial load profiles. Simulation results demonstrate that the proposed hybrid EV charging framework significantly improves grid-level efficiency and user satisfaction. Compared to the baseline without EV integration, the average-to-peak demand ratio is improved from 61% to 74% at Station-A, from 64% to 80% at Station-B, and from 51% to 63% at Station-C, highlighting enhanced load balancing. The framework also ensures that all EVs receive energy above their minimum needs, achieving user satisfaction scores of 88.0% at Stations A and B and 81.6% at Station C. This study underscores the potential of hybrid charging schemes in optimizing energy utilization while maintaining system reliability and user convenience.

Suggested Citation

  • Syed Abdullah Al Nahid & Junjian Qi, 2025. "A Hybrid EV Charging Approach Based on MILP and a Genetic Algorithm," Energies, MDPI, vol. 18(14), pages 1-30, July.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:14:p:3656-:d:1698948
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    References listed on IDEAS

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    1. González-Garrido, Amaia & González-Pérez, Mikel & Asensio, Francisco Javier & Cortes-Borray, Andrés Felipe & Santos-Mugica, Maider & Vicente-Figueirido, Ibon, 2024. "Hierarchical control for collaborative electric vehicle charging to alleviate network congestion and enhance EV hosting in constrained distribution networks," Renewable Energy, Elsevier, vol. 230(C).
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    5. An, Sihai & Qiu, Jing & Lin, Jiafeng & Yao, Zongyu & Liang, Qijun & Lu, Xin, 2025. "Planning of a multi-agent mobile robot-based adaptive charging network for enhancing power system resilience under extreme conditions," Applied Energy, Elsevier, vol. 395(C).
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