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Combined Framework with Heuristic Programming and Rule-Based Strategies for Scheduling and Real Time Operation in Electric Vehicle Charging Stations

Author

Listed:
  • Héricles Eduardo Oliveira Farias

    (Department of Electrical Engineering , Federal University of Santa Maria—UFSM, Santa Maria 97105-900, Brazil)

  • Camilo Alberto Sepulveda Rangel

    (Department of Electrical Engineering , Federal University of Santa Maria—UFSM, Santa Maria 97105-900, Brazil)

  • Leonardo Weber Stringini

    (Department of Electrical Engineering , Federal University of Santa Maria—UFSM, Santa Maria 97105-900, Brazil)

  • Luciane Neves Canha

    (Department of Electrical Engineering , Federal University of Santa Maria—UFSM, Santa Maria 97105-900, Brazil)

  • Daniel Pegoraro Bertineti

    (Department of Electrical Engineering , Federal University of Santa Maria—UFSM, Santa Maria 97105-900, Brazil)

  • Wagner da Silva Brignol

    (Department of Electrical Engineering , Federal University of Santa Maria—UFSM, Santa Maria 97105-900, Brazil)

  • Zeno Iensen Nadal

    (Electric Energy Paranaense Company—COPEL-DIS, Curitiba 81200-240, Brazil)

Abstract

This paper proposes a flexible framework for scheduling and real time operation of electric vehicle charging stations (EVCS). The methodology applies a multi-objective evolutionary particle swarm optimization algorithm (EPSO) for electric vehicles (EVs) scheduling based on a day-ahead scenario. Then, real time operation is managed based on a rule-based (RB) approach. Two types of consumer were considered: EV owners with a day-ahead request for charging (scheduled consumers, SCh) and non-scheduling users (NSCh). EPSO has two main objectives: cost reduction and reduce overloading for high demand in grid. The EVCS has support by photovoltaic generation (PV), battery energy storage systems (BESS), and the distribution grid. The method allows the selection between three types of charging, distributing it according to EV demand. The model estimates SC remaining state of charge (SoC) for arriving to EVCS and then adjusts the actual difference by the RB. The results showed a profit for EVCS by the proposed technique. The proposed EPSO and RB have a fast solution to the problem that allows practical implementation.

Suggested Citation

  • Héricles Eduardo Oliveira Farias & Camilo Alberto Sepulveda Rangel & Leonardo Weber Stringini & Luciane Neves Canha & Daniel Pegoraro Bertineti & Wagner da Silva Brignol & Zeno Iensen Nadal, 2021. "Combined Framework with Heuristic Programming and Rule-Based Strategies for Scheduling and Real Time Operation in Electric Vehicle Charging Stations," Energies, MDPI, vol. 14(5), pages 1-27, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:5:p:1370-:d:509310
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    References listed on IDEAS

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    1. Bharatiraja Chokkalingam & Sanjeevikumar Padmanaban & Pierluigi Siano & Ramesh Krishnamoorthy & Raghu Selvaraj, 2017. "Real-Time Forecasting of EV Charging Station Scheduling for Smart Energy Systems," Energies, MDPI, vol. 10(3), pages 1-16, March.
    2. Monica Alonso & Hortensia Amaris & Jean Gardy Germain & Juan Manuel Galan, 2014. "Optimal Charging Scheduling of Electric Vehicles in Smart Grids by Heuristic Algorithms," Energies, MDPI, vol. 7(4), pages 1-27, April.
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    12. Teguh Kurniawan & Craig A. Baguley & Udaya K. Madawala & Suwarno & Nanang Hariyanto & Yuana Adianto, 2020. "An Electric Vehicle Charge Scheduling Approach Suited to Local and Supplying Distribution Transformers," Energies, MDPI, vol. 13(13), pages 1-13, July.
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    Keywords

    EVCS; EPSO; rule-based; EV scheduling;
    All these keywords.

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