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Optimal Strategy to Exploit the Flexibility of an Electric Vehicle Charging Station

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  • Cesar Diaz-Londono

    (Departamento de Electrónica, Pontificia Universidad Javeriana, Bogotá 110231, Colombia
    Dipartimento Energia “Galileo Ferraris”, Politecnico di Torino, 10129 Torino, Italy)

  • Luigi Colangelo

    (Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino, 10129 Torino, Italy)

  • Fredy Ruiz

    (Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino, 10129 Torino, Italy
    Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy)

  • Diego Patino

    (Departamento de Electrónica, Pontificia Universidad Javeriana, Bogotá 110231, Colombia)

  • Carlo Novara

    (Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino, 10129 Torino, Italy)

  • Gianfranco Chicco

    (Dipartimento Energia “Galileo Ferraris”, Politecnico di Torino, 10129 Torino, Italy)

Abstract

The increasing use of electric vehicles connected to the power grid gives rise to challenges in the vehicle charging coordination, cost management, and provision of potential services to the grid. Scheduling of the power in an electric vehicle charging station is a quite challenging task, considering time-variant prices, customers with different charging time preferences, and the impact on the grid operations. The latter aspect can be addressed by exploiting the vehicle charging flexibility. In this article, a specific definition of flexibility to be used for an electric vehicle charging station is provided. Two optimal charging strategies are then proposed and evaluated, with the purpose of determining which strategy can offer spinning reserve services to the electrical grid, reducing at the same time the operation costs of the charging station. These strategies are based on a novel formulation of an economic model predictive control algorithm, aimed at minimising the charging station operation cost, and on a novel formulation of the flexibility capacity maximisation, while reducing the operation costs. These formulations incorporate the uncertainty in the arrival time and state of charge of the electric vehicles at their arrival. Both strategies lead to a considerable reduction of the costs with respect to a simple minimum time charging strategy, taken as the benchmark. In particular, the strategy that also accounts for flexibility maximisation emerges as a new tool for maintaining the grid balance giving cost savings to the charging stations.

Suggested Citation

  • Cesar Diaz-Londono & Luigi Colangelo & Fredy Ruiz & Diego Patino & Carlo Novara & Gianfranco Chicco, 2019. "Optimal Strategy to Exploit the Flexibility of an Electric Vehicle Charging Station," Energies, MDPI, vol. 12(20), pages 1-29, October.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:20:p:3834-:d:275064
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    References listed on IDEAS

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    Cited by:

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    3. Edgar Sokolovskij & Arkadiusz Małek & Jacek Caban & Agnieszka Dudziak & Jonas Matijošius & Andrzej Marciniak, 2023. "Selection of a Photovoltaic Carport Power for an Electric Vehicle," Energies, MDPI, vol. 16(7), pages 1-16, March.
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    5. Andrea Stabile & Michela Longo & Wahiba Yaïci & Federica Foiadelli, 2020. "An Algorithm for Optimization of Recharging Stops: A Case Study of Electric Vehicle Charging Stations on Canadian’s Ontario Highway 401," Energies, MDPI, vol. 13(8), pages 1-19, April.
    6. Zhao, Jing & Yang, Zilan & Shi, Linyu & Liu, Dehan & Li, Haonan & Mi, Yumiao & Wang, Hongbin & Feng, Meili & Hutagaol, Timothy Joseph, 2024. "Photovoltaic capacity dynamic tracking model predictive control strategy of air-conditioning systems with consideration of flexible loads," Applied Energy, Elsevier, vol. 356(C).
    7. Cesar Diaz-Londono & José Vuelvas & Giambattista Gruosso & Carlos Adrian Correa-Florez, 2022. "Remuneration Sensitivity Analysis in Prosumer and Aggregator Strategies by Controlling Electric Vehicle Chargers," Energies, MDPI, vol. 15(19), pages 1-24, September.
    8. Marco Badami & Gabriele Fambri & Salvatore Mancò & Mariapia Martino & Ioannis G. Damousis & Dimitrios Agtzidis & Dimitrios Tzovaras, 2019. "A Decision Support System Tool to Manage the Flexibility in Renewable Energy-Based Power Systems," Energies, MDPI, vol. 13(1), pages 1-16, December.
    9. Wanhao Yang & Hong Wang & Zhijie Wang & Xiaolin Fu & Pengchi Ma & Zhengchen Deng & Zihao Yang, 2020. "Optimization Strategy of Electric Vehicles Charging Path Based on “Traffic-Price-Distribution” Mode," Energies, MDPI, vol. 13(12), pages 1-26, June.

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