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Blockchain and Machine Learning for Future Smart Grids: A Review

Author

Listed:
  • Vidya Krishnan Mololoth

    (Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, 971 87 Luleå, Sweden)

  • Saguna Saguna

    (Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, 971 87 Luleå, Sweden)

  • Christer Åhlund

    (Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, 971 87 Luleå, Sweden)

Abstract

Developments such as the increasing electrical energy demand, growth of renewable energy sources, cyber–physical security threats, increased penetration of electric vehicles (EVs), and unpredictable behavior of prosumers and EV users pose a range of challenges to the electric power system. To address these challenges, a decentralized system using blockchain technology and machine learning techniques for secure communication, distributed energy management and decentralized energy trading between prosumers is required. Blockchain enables secure distributed trust platforms, addresses optimization and reliability challenges, and allows P2P distributed energy exchange as well as flexibility services between customers. On the other hand, machine learning techniques enable intelligent smart grid operations by using prediction models and big data analysis. Motivated from these facts, in this review, we examine the potential of combining blockchain technology and machine learning techniques in the development of smart grid and investigate the benefits achieved by using both techniques for the future smart grid scenario. Further, we discuss research challenges and future research directions of applying blockchain and machine learning techniques for smart grids both individually as well as combining them together. The identified areas that require significant research are demand management in power grids, improving the security of grids with better consensus mechanisms, electric vehicle charging systems, scheduling of the entire grid system, designing secure microgrids, and the interconnection of different blockchain networks.

Suggested Citation

  • Vidya Krishnan Mololoth & Saguna Saguna & Christer Åhlund, 2023. "Blockchain and Machine Learning for Future Smart Grids: A Review," Energies, MDPI, vol. 16(1), pages 1-39, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:1:p:528-:d:1023384
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    References listed on IDEAS

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    2. Mikołaj Gwiazdowicz & Marek Natkaniec, 2023. "Feature Selection and Model Evaluation for Threat Detection in Smart Grids," Energies, MDPI, vol. 16(12), pages 1-25, June.
    3. Vitor Monteiro & Joao L. Afonso, 2023. "The Future of Electrical Power Grids: A Direction Rooted in Power Electronics," Energies, MDPI, vol. 16(13), pages 1-10, June.
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    5. Daniel Sousa-Dias & Daniel Amyot & Ashkan Rahimi-Kian & John Mylopoulos, 2023. "A Review of Cybersecurity Concerns for Transactive Energy Markets," Energies, MDPI, vol. 16(13), pages 1-32, June.
    6. Wenbing Zhao & Quan Qi & Jiong Zhou & Xiong Luo, 2023. "Blockchain-Based Applications for Smart Grids: An Umbrella Review," Energies, MDPI, vol. 16(17), pages 1-35, August.
    7. Tehseen Mazhar & Hafiz Muhammad Irfan & Sunawar Khan & Inayatul Haq & Inam Ullah & Muhammad Iqbal & Habib Hamam, 2023. "Analysis of Cyber Security Attacks and Its Solutions for the Smart grid Using Machine Learning and Blockchain Methods," Future Internet, MDPI, vol. 15(2), pages 1-37, February.
    8. Qian Wang & Xiaolong Yang & Xiaoyu Yu & Jingwen Yun & Jinbo Zhang, 2023. "Electric Vehicle Participation in Regional Grid Demand Response: Potential Analysis Model and Architecture Planning," Sustainability, MDPI, vol. 15(3), pages 1-22, February.
    9. Casey Watters, 2023. "When Criminals Abuse the Blockchain: Establishing Personal Jurisdiction in a Decentralised Environment," Laws, MDPI, vol. 12(2), pages 1-16, April.

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