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A Novel Data-Energy Management Algorithm for Smart Transformers to Optimize the Total Load Demand in Smart Homes

Author

Listed:
  • Claude Ziad El-Bayeh

    (Canada Excellence Research Chair Team, Concordia University, Montreal, QC H3G 1M8, Canada)

  • Ursula Eicker

    (Canada Excellence Research Chair Team, Concordia University, Montreal, QC H3G 1M8, Canada)

  • Khaled Alzaareer

    (Department of Electrical Engineering, Ecole de Technologie Superieure, Montreal, QC H3C 1K3, Canada)

  • Brahim Brahmi

    (Mechanical Engineering Department, McGill University, Montreal, QC H3A 0G4, Canada)

  • Mohamed Zellagui

    (Department of Electrical Engineering, Faculty of Technology, University of Batna 2, Fesdis 05078, Batna, Algeria)

Abstract

The increased integration of Electric Vehicles (EVs) into the distribution network can create severe issues—especially when demand response programs and time-varying electricity prices are applied, EVs tend to charge during the off-peak time to minimize the electricity cost. Hence, another peak demand might be created, and other solutions are required. Many researchers tried to solve the problem; however, limitations exist because of the decentralized topology of the network. The system operator is not allowed to control the end-users’ load due to security and privacy issues. To overcome this situation, we propose a novel data-energy management algorithm on the transformer’s level that controls the power demand profiles of the householders and exchange energy between them without violating their privacy and security. Our method is compared to an existing one in the literature based on a decentralized control strategy. Simulations show that our approach has reduced the electricity cost of the end-users by 3%, increased the revenue of the system operator, and reduced techno-economic losses by 50% and 42%, respectively. Our strategy shows better performance even with a 100% penetration level of EVs on the network, in which it respects the network’s constraints and maintains the voltage within the recommended limits.

Suggested Citation

  • Claude Ziad El-Bayeh & Ursula Eicker & Khaled Alzaareer & Brahim Brahmi & Mohamed Zellagui, 2020. "A Novel Data-Energy Management Algorithm for Smart Transformers to Optimize the Total Load Demand in Smart Homes," Energies, MDPI, vol. 13(18), pages 1-22, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4984-:d:417546
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    Citations

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    Cited by:

    1. Claude Ziad El-Bayeh & Mohamed Zellagui & Navid Shirzadi & Ursula Eicker, 2021. "A Novel Optimization Algorithm for Solar Panels Selection towards a Self-Powered EV Parking Lot and Its Impact on the Distribution System," Energies, MDPI, vol. 14(15), pages 1-28, July.
    2. Oleh Lukianykhin & Tetiana Bogodorova, 2021. "Voltage Control-Based Ancillary Service Using Deep Reinforcement Learning," Energies, MDPI, vol. 14(8), pages 1-22, April.
    3. Claude Ziad El-Bayeh & Mohamed Zellagui & Brahim Brahmi & Walid Alqaisi & Ursula Eicker, 2021. "Impact of Charging Electric Vehicles under Different State of Charge Levels and Extreme Conditions," Energies, MDPI, vol. 14(20), pages 1-19, October.
    4. Mohammad Mehdi Davari & Hossein Ameli & Mohammad Taghi Ameli & Goran Strbac, 2022. "Impact of Local Emergency Demand Response Programs on the Operation of Electricity and Gas Systems," Energies, MDPI, vol. 15(6), pages 1-20, March.
    5. Krishnan Sakthidasan Sankaran & Claude Ziad El-Bayeh & Ursula Eicker, 2022. "Design of Multi-Renewable Energy Storage and Management System Using RL-ICSO Based MPPT Scheme for Electric Vehicles," Sustainability, MDPI, vol. 14(8), pages 1-21, April.

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