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Optimal Distribution Grid Operation Using DLMP-Based Pricing for Electric Vehicle Charging Infrastructure in a Smart City

Author

Listed:
  • Bruno Canizes

    (GECAD—Knowledge Engineering and Decision Support Research Center—Polytechnic of Porto (IPP), R. Dr. António Bernardino de Almeida, 431, 4200-072 Porto, Portugal)

  • João Soares

    (GECAD—Knowledge Engineering and Decision Support Research Center—Polytechnic of Porto (IPP), R. Dr. António Bernardino de Almeida, 431, 4200-072 Porto, Portugal)

  • Zita Vale

    (GECAD—Knowledge Engineering and Decision Support Research Center—Polytechnic of Porto (IPP), R. Dr. António Bernardino de Almeida, 431, 4200-072 Porto, Portugal)

  • Juan M. Corchado

    (University of Salamanca, 37008 Salamanca, Spain
    Osaka Institute of Technology, 5 Chome-16-1 Omiya, Asahi Ward, Osaka 535-8585, Japan
    University of Technology Malaysia, Pusat Pentadbiran Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia)

Abstract

The use of electric vehicles (EVs) is growing in popularity each year, and as a result, considerable demand increase is expected in the distribution network (DN). Additionally, the uncertainty of EV user behavior is high, making it urgent to understand its impact on the network. Thus, this paper proposes an EV user behavior simulator, which operates in conjunction with an innovative smart distribution locational marginal pricing based on operation/reconfiguration, for the purpose of understanding the impact of the dynamic energy pricing on both sides: the grid and the user. The main goal, besides the distribution system operator (DSO) expenditure minimization, is to understand how and to what extent dynamic pricing of energy for EV charging can positively affect the operation of the smart grid and the EV charging cost. A smart city with a 13-bus DN and a high penetration of distributed energy resources is used to demonstrate the application of the proposed models. The results demonstrate that dynamic energy pricing for EV charging is an efficient approach that increases monetary savings considerably for both the DSO and EV users.

Suggested Citation

  • Bruno Canizes & João Soares & Zita Vale & Juan M. Corchado, 2019. "Optimal Distribution Grid Operation Using DLMP-Based Pricing for Electric Vehicle Charging Infrastructure in a Smart City," Energies, MDPI, vol. 12(4), pages 1-40, February.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:4:p:686-:d:207692
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    References listed on IDEAS

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    Cited by:

    1. Bruno Canizes & João Costa & Diego Bairrão & Zita Vale, 2023. "Local Renewable Energy Communities: Classification and Sizing," Energies, MDPI, vol. 16(5), pages 1-26, March.
    2. Bruno Canizes & João Soares & Angelo Costa & Tiago Pinto & Fernando Lezama & Paulo Novais & Zita Vale, 2019. "Electric Vehicles’ User Charging Behaviour Simulator for a Smart City," Energies, MDPI, vol. 12(8), pages 1-20, April.
    3. Christian Thiel & Andreea Julea & Beatriz Acosta Iborra & Nerea De Miguel Echevarria & Emanuela Peduzzi & Enrico Pisoni & Jonatan J. Gómez Vilchez & Jette Krause, 2019. "Assessing the Impacts of Electric Vehicle Recharging Infrastructure Deployment Efforts in the European Union," Energies, MDPI, vol. 12(12), pages 1-23, June.
    4. Almeida, José & Soares, Joao & Lezama, Fernando & Vale, Zita & Francois, Bruno, 2024. "Comparison of evolutionary algorithms for solving risk-based energy resource management considering conditional value-at-risk analysis," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 224(PB), pages 87-110.
    5. Luis B. Elvas & Joao C Ferreira, 2021. "Intelligent Transportation Systems for Electric Vehicles," Energies, MDPI, vol. 14(17), pages 1-9, September.

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