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Grid-Constrained Online Scheduling of Flexible Electric Vehicle Charging

Author

Listed:
  • Emily van Huffelen

    (Department of Information and Computing Sciences, Utrecht University, Princetonplein 5, 3584 CC Utrecht, The Netherlands)

  • Roel Brouwer

    (Department of Information and Computing Sciences, Utrecht University, Princetonplein 5, 3584 CC Utrecht, The Netherlands)

  • Marjan van den Akker

    (Department of Information and Computing Sciences, Utrecht University, Princetonplein 5, 3584 CC Utrecht, The Netherlands)

Abstract

The rapid growth of Electric Vehicles (EVs) risks causing grid congestion. Smart charging strategies can help to prevent overload while ensuring timely charging, thereby reducing the need for costly infrastructure upgrades. We study EV charging from a scheduling perspective, assuming an aggregator manages charging while respecting network cable capacities. In our model, vehicles depart only after charging is complete, so delays are possible. Our aim is to minimize these delays. We consider a network of parking lots, some of which are equipped with solar panels, where the demand that can be served is limited by the cables connecting them to the grid. We propose novel scheduling strategies that combine an online variant of well-known schedule generation schemes with a destroy-and-repair heuristic. We evaluate their effectiveness in a case study with data from the city of Utrecht. Without control, network cables would be overloaded 60–70% of the time. Our strategies completely eliminate overload, introducing an average delay of just over 1.5 min per EV in high-occupancy scenarios. This demonstrates that scheduling can significantly increase the number of EVs charged without compromising grid safety at the cost of a rather small delay. We also highlight the importance of accounting for grid topology and show the benefits of using flexible charging rates.

Suggested Citation

  • Emily van Huffelen & Roel Brouwer & Marjan van den Akker, 2025. "Grid-Constrained Online Scheduling of Flexible Electric Vehicle Charging," Energies, MDPI, vol. 18(19), pages 1-20, September.
  • Handle: RePEc:gam:jeners:v:18:y:2025:i:19:p:5063-:d:1756471
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    References listed on IDEAS

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    1. 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).
    2. Kolisch, Rainer, 1996. "Serial and parallel resource-constrained project scheduling methods revisited: Theory and computation," European Journal of Operational Research, Elsevier, vol. 90(2), pages 320-333, April.
    3. Dorokhova, Marina & Martinson, Yann & Ballif, Christophe & Wyrsch, Nicolas, 2021. "Deep reinforcement learning control of electric vehicle charging in the presence of photovoltaic generation," Applied Energy, Elsevier, vol. 301(C).
    4. Hartmann, Sönke & Briskorn, Dirk, 2022. "An updated survey of variants and extensions of the resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 297(1), pages 1-14.
    5. Park, Keonwoo & Moon, Ilkyeong, 2022. "Multi-agent deep reinforcement learning approach for EV charging scheduling in a smart grid," Applied Energy, Elsevier, vol. 328(C).
    6. Jia, Chunchun & Liu, Wei & He, Hongwen & Chau, K.T., 2025. "Health-conscious energy management for fuel cell vehicles: An integrated thermal management strategy for cabin and energy source systems," Energy, Elsevier, vol. 333(C).
    7. Hartmann, Sönke & Briskorn, Dirk, 2010. "A survey of variants and extensions of the resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 207(1), pages 1-14, November.
    8. Brinkel, N.B.G. & Schram, W.L. & AlSkaif, T.A. & Lampropoulos, I. & van Sark, W.G.J.H.M., 2020. "Should we reinforce the grid? Cost and emission optimization of electric vehicle charging under different transformer limits," Applied Energy, Elsevier, vol. 276(C).
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