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Electric Vehicles Energy Management with V2G/G2V Multifactor Optimization of Smart Grids

Author

Listed:
  • Gabriel Antonio Salvatti

    (Universidade Tecnológica Federal do Paraná-UTFPR, Pato Branco-PR 85503-390, Brazil)

  • Emerson Giovani Carati

    (Universidade Tecnológica Federal do Paraná-UTFPR, Pato Branco-PR 85503-390, Brazil)

  • Rafael Cardoso

    (Universidade Tecnológica Federal do Paraná-UTFPR, Pato Branco-PR 85503-390, Brazil)

  • Jean Patric da Costa

    (Universidade Tecnológica Federal do Paraná-UTFPR, Pato Branco-PR 85503-390, Brazil)

  • Carlos Marcelo de Oliveira Stein

    (Universidade Tecnológica Federal do Paraná-UTFPR, Pato Branco-PR 85503-390, Brazil)

Abstract

Energy Storage Systems (ESS) and Distributed Generation (DG) are topics in a large number of recent research works. Moreover, given the increasing adoption of EVs, high capacity EV batteries can be used as ESS, as most vehicles remain idle for long periods during work or home parking. However, the high EV penetration introduces some issues related to the charging power requirements, thereby increasing the peak demand for microgrids where EV chargers are installed. In addition, photovoltaic distributed generation is becoming another issue to deal with in EV charging microgrids. Therefore, this new scenario requires an Energy Management System (EMS) able to deal with charging demand, as well as with generation intermittency. This paper presents an EMS strategy for Microgrids that contain an EV parking lot (EVM), Photovoltaic (PV) arrays, and dynamic loads connected to the grid considering a Point of Common Coupling (PCC). The EVM-EMS utilizes the projections of future PV generation and future demand to accomplish a dynamic programming technique that optimizes the EVs’ charging (G2V) or discharging (V2G) profiles. This algorithm attends to user preferences while reducing the demand grid dependences and improves the microgrid efficiency.

Suggested Citation

  • Gabriel Antonio Salvatti & Emerson Giovani Carati & Rafael Cardoso & Jean Patric da Costa & Carlos Marcelo de Oliveira Stein, 2020. "Electric Vehicles Energy Management with V2G/G2V Multifactor Optimization of Smart Grids," Energies, MDPI, vol. 13(5), pages 1-22, March.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:5:p:1191-:d:328715
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    References listed on IDEAS

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    1. Shaukat, N. & Khan, B. & Ali, S.M. & Mehmood, C.A. & Khan, J. & Farid, U. & Majid, M. & Anwar, S.M. & Jawad, M. & Ullah, Z., 2018. "A survey on electric vehicle transportation within smart grid system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1329-1349.
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    Cited by:

    1. Lucas V. Bellinaso & Edivan L. Carvalho & Rafael Cardoso & Leandro Michels, 2021. "Price-Response Matrices Design Methodology for Electrical Energy Management Systems Based on DC Bus Signalling," Energies, MDPI, vol. 14(6), pages 1-19, March.
    2. Boris V. Malozyomov & Nikita V. Martyushev & Vladimir Yu. Konyukhov & Tatiana A. Oparina & Nikolay A. Zagorodnii & Egor A. Efremenkov & Mengxu Qi, 2023. "Mathematical Analysis of the Reliability of Modern Trolleybuses and Electric Buses," Mathematics, MDPI, vol. 11(15), pages 1-25, July.
    3. 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.
    4. Nandan Gopinathan & Prabhakar Karthikeyan Shanmugam, 2022. "Energy Anxiety in Decentralized Electricity Markets: A Critical Review on EV Models," Energies, MDPI, vol. 15(14), pages 1-40, July.

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