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A Framework for Determining a Prediction-Of-Use Tariff Aimed at Coordinating Aggregators of Plug-In Electric Vehicles

Author

Listed:
  • Gustavo E. Coria

    (Instituto de Energía Eléctrica, Universidad Nacional de San Juan-CONICET, Avenida Libertador General San Martín 1109, San Juan 5400, Argentina)

  • Angel M. Sanchez

    (Instituto de Energía Eléctrica, Universidad Nacional de San Juan-CONICET, Avenida Libertador General San Martín 1109, San Juan 5400, Argentina)

  • Ameena S. Al-Sumaiti

    (Advanced Power and Energy Center, Electrical Engineering and Computer Science Department, Khalifa University, Abu Dhabi 127788, UAE)

  • Guiseppe A. Rattá

    (Instituto de Energía Eléctrica, Universidad Nacional de San Juan-CONICET, Avenida Libertador General San Martín 1109, San Juan 5400, Argentina)

  • Sergio R. Rivera

    (Universidad Nacional de Colombia, Cra 45, Bogotá 111321, Colombia)

  • Andrés A. Romero

    (Instituto de Energía Eléctrica, Universidad Nacional de San Juan-CONICET, Avenida Libertador General San Martín 1109, San Juan 5400, Argentina)

Abstract

The objective of this article is to propose a framework for defining a day-ahead prediction-of-use tariff (POU) that promotes aggregators of the plug-in electric vehicles (PEVs) to operate as closely as possible to an optimal charging curve previously calculated by the distribution system operator (DSO). The DSO calculates this optimal charging curve to flatten the load curve of the distribution transformers as much as possible by coordinating the daily recharging of PEVs. The objective is to establish the optimal power profile of the PEV aggregators needed to flatten the power curve supplied by the transformer, so that PEV customers’ needs can be met throughout the day. The proposed framework is applied in a case study accounting for uncertainties associated with charging through Monte Carlo simulation, in order to find the POU tariff. The results demonstrate that applying the POU tariff determines the transformer’s minimum power limit necessary to meet all PEV users’ needs. Additionally, the day-ahead POU tariff does not generate new demand peaks, since it does not concentrate the energy supply of flexible loads in pre-established time bands. Finally, simulation reflects the significant effect of the PEV charging on the distribution system in terms of enhancing the voltage profile, maximizing the transformer life, and reducing the power/energy losses.

Suggested Citation

  • Gustavo E. Coria & Angel M. Sanchez & Ameena S. Al-Sumaiti & Guiseppe A. Rattá & Sergio R. Rivera & Andrés A. Romero, 2019. "A Framework for Determining a Prediction-Of-Use Tariff Aimed at Coordinating Aggregators of Plug-In Electric Vehicles," Energies, MDPI, vol. 12(23), pages 1-18, November.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:23:p:4487-:d:290695
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    References listed on IDEAS

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    Cited by:

    1. Prince Aduama & Zhibo Zhang & Ameena S. Al-Sumaiti, 2023. "Multi-Feature Data Fusion-Based Load Forecasting of Electric Vehicle Charging Stations Using a Deep Learning Model," Energies, MDPI, vol. 16(3), pages 1-14, January.
    2. Alya AlHammadi & Nasser Al-Saif & Ameena Saad Al-Sumaiti & Mousa Marzband & Tareefa Alsumaiti & Ehsan Heydarian-Forushani, 2022. "Techno-Economic Analysis of Hybrid Renewable Energy Systems Designed for Electric Vehicle Charging: A Case Study from the United Arab Emirates," Energies, MDPI, vol. 15(18), pages 1-20, September.
    3. Daniel Losada & Ameena Al-Sumaiti & Sergio Rivera, 2021. "Uncertainty Cost Functions in Climate-Dependent Controllable Loads in Commercial Environments," Energies, MDPI, vol. 14(10), pages 1-22, May.
    4. Ibrahim Alsaidan & Mohd Bilal & Muhannad Alaraj & Mohammad Rizwan & Fahad M. Almasoudi, 2023. "A Novel EA-Based Techno–Economic Analysis of Charging System for Electric Vehicles: A Case Study of Qassim Region, Saudi Arabia," Mathematics, MDPI, vol. 11(9), pages 1-31, April.

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