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Distribution System Operation with Electric Vehicle Charging Schedules and Renewable Energy Resources

Author

Listed:
  • Gerardo J. Osório

    (C-MAST, University of Beira Interior, 6201-001 Covilhã, Portugal)

  • Miadreza Shafie-khah

    (INESC TEC, 4200-465 Porto, Portugal)

  • Pedro D. L. Coimbra

    (Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal)

  • Mohamed Lotfi

    (INESC TEC, 4200-465 Porto, Portugal
    Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal)

  • João P. S. Catalão

    (C-MAST, University of Beira Interior, 6201-001 Covilhã, Portugal
    INESC TEC, 4200-465 Porto, Portugal
    Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
    INESC-ID, Instituto Superior Técnico, University of Lisbon, 1049-001 Lisbon, Portugal)

Abstract

Electric vehicles (EVs) promote many advantages for distribution systems such as increasing efficiency and reliability, decreasing dependence on non-endogenous resources, and reducing pollutant emissions. Due to increased proliferation of EVs and their integration in power systems, management and operation of distribution systems (ODS) is becoming more important. Recent studies have shown that EV can increase power grid flexibility since EV owners do not use them for 93–96% of the daytime. Therefore, it is important to exploit parking time, during which EVs can act either as a load or distributed storage device, to maximize the benefit for the power system. Following a survey of the current state-of-the-art, this work studies the impact of EV charging on the load profile. Since renewable energy resources (RES) play a critical role in future distribution systems the current case study considered the presence of RES and their stochastic nature has been modeled. The study proceeds with analyzing EV owners’ driving habits, enabling prediction of the network load profile. The impact of: EV charging modes (i.e., controlled and uncontrolled charging), magnitude of wind and photovoltaic (PV) generation, number of EVs (penetration), and driving patterns on the ODS is analyzed.

Suggested Citation

  • Gerardo J. Osório & Miadreza Shafie-khah & Pedro D. L. Coimbra & Mohamed Lotfi & João P. S. Catalão, 2018. "Distribution System Operation with Electric Vehicle Charging Schedules and Renewable Energy Resources," Energies, MDPI, vol. 11(11), pages 1-20, November.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:3117-:d:182063
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    References listed on IDEAS

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    1. Khodakarami, Alireza & Farahani, Hassan Feshki & Aghaei, Jamshid, 2017. "Stochastic characterization of electricity energy markets including plug-in electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 112-122.
    2. Ekman, Claus Krog, 2011. "On the synergy between large electric vehicle fleet and high wind penetration – An analysis of the Danish case," Renewable Energy, Elsevier, vol. 36(2), pages 546-553.
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    Citations

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    Cited by:

    1. Feiqi Liu & Fuquan Zhao & Zongwei Liu & Han Hao, 2018. "China’s Electric Vehicle Deployment: Energy and Greenhouse Gas Emission Impacts," Energies, MDPI, vol. 11(12), pages 1-19, November.
    2. Giovanni Artale & Antonio Cataliotti & Valentina Cosentino & Dario Di Cara & Salvatore Guaiana & Enrico Telaretti & Nicola Panzavecchia & Giovanni Tinè, 2019. "Incremental Heuristic Approach for Meter Placement in Radial Distribution Systems," Energies, MDPI, vol. 12(20), pages 1-17, October.
    3. Julia Vopava & Ulrich Bergmann & Thomas Kienberger, 2020. "Synergies between e-Mobility and Photovoltaic Potentials—A Case Study on an Urban Medium Voltage Grid," Energies, MDPI, vol. 13(15), pages 1-29, July.
    4. Sunoh Kim & Jin Hur, 2020. "A Probabilistic Modeling Based on Monte Carlo Simulation of Wind Powered EV Charging Stations for Steady-States Security Analysis," Energies, MDPI, vol. 13(20), pages 1-13, October.
    5. Hak-Ju Lee & Byeong-Chan Oh & Seok-Woong Kim & Sung-Yul Kim, 2020. "V2G Strategy for Improvement of Distribution Network Reliability Considering Time Space Network of EVs," Energies, MDPI, vol. 13(17), pages 1-19, August.
    6. Luigi Rubino & Guido Rubino & Raffaele Esempio, 2023. "Linear Programming-Based Power Management for a Multi-Feeder Ultra-Fast DC Charging Station," Energies, MDPI, vol. 16(3), pages 1-17, January.
    7. Konstantinos Kotsalos & Ismael Miranda & Nuno Silva & Helder Leite, 2019. "A Horizon Optimization Control Framework for the Coordinated Operation of Multiple Distributed Energy Resources in Low Voltage Distribution Networks," Energies, MDPI, vol. 12(6), pages 1-27, March.
    8. Marija Zima-Bockarjova & Antans Sauhats & Lubov Petrichenko & Roman Petrichenko, 2020. "Charging and Discharging Scheduling for Electrical Vehicles Using a Shapley-Value Approach," Energies, MDPI, vol. 13(5), pages 1-21, March.
    9. Tianze Lan & Kittisak Jermsittiparsert & Sara T. Alrashood & Mostafa Rezaei & Loiy Al-Ghussain & Mohamed A. Mohamed, 2021. "An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand," Energies, MDPI, vol. 14(3), pages 1-25, January.

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