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Machine Learning and Game-Theoretic Model for Advanced Wind Energy Management Protocol (AWEMP)

Author

Listed:
  • Imed Khabbouchi

    (Thermal and Energy Systems Study Laboratory (LESTE), National Engineering School of Monastir, University of Monastir, Monastir 5000, Tunisia)

  • Dhaou Said

    (Electrical Engineering and Computer Sciences, University of Sherbrooke, Sherbrooke, QC J1K 2R1, Canada)

  • Aziz Oukaira

    (Department of Electrical and Computer Engineering, Universite du Quebec en Outaouais, Gatineau, QC J8X 3X7, Canada)

  • Idir Mellal

    (Department of Electrical Engineering and Computer Science, University of Toronto, Toronto, ON M5S 3G8, Canada)

  • Lyes Khoukhi

    (ENSI CAEN, GREYC Laboratory, 14000 Caen, France)

Abstract

To meet the target of carbon neutrality by the year 2050 and decrease the dependence on fossil fuels, renewable energy sources (RESs), specifically wind power, and Electric Vehicles (EVs) have to be massively deployed. Nevertheless, the integration of a large amount of wind power, with an intermittent nature, into the grid and the variability of the load on the demand side require an efficient and reliable energy management system (EMS) for operation, scheduling, maintenance and energy trading in the modern power system. This article proposes a new Energy Management Protocol (EMP) based on the combination of Machine Learning (ML) and Game-Theoretic (GT) algorithms to manage the operation of the charging/discharging of EVs from an energy storage system (ESS) via EV supply equipment (EVSE) when the main source of energy is wind power. The ESS can be linked to the grid to overcome downtimes of wind power production. Case study results of wind power forecasting using an ML algorithm and 10 min wind measurements, combined with a GT optimization model, showed good performance in the forecasting and management of power dispatching between EVs to ensure the efficient and accurate operation of the power system.

Suggested Citation

  • Imed Khabbouchi & Dhaou Said & Aziz Oukaira & Idir Mellal & Lyes Khoukhi, 2023. "Machine Learning and Game-Theoretic Model for Advanced Wind Energy Management Protocol (AWEMP)," Energies, MDPI, vol. 16(5), pages 1-15, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2179-:d:1078891
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    References listed on IDEAS

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