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Blockchain and Machine Learning for Future Smart Grids: A Review
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0002-7921-8568
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0001-8561-7963
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0002-8681-9572
2023 (English)In: Energies, E-ISSN 1996-1073, Vol. 16, no 1, article id 528Article, review/survey (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
MDPI , 2023. Vol. 16, no 1, article id 528
Keywords [en]
blockchain, demand response management, electric vehicles, energy trading, machine learning, security, smart grids
National Category
Computer Sciences Energy Systems Computer Systems
Research subject
Pervasive Mobile Computing
Identifiers
URN: urn:nbn:se:ltu:diva-95274DOI: 10.3390/en16010528ISI: 000909000800001Scopus ID: 2-s2.0-85145775833OAI: oai:DiVA.org:ltu-95274DiVA, id: diva2:1727334
Note

Validerad;2023;Nivå 2;2023-01-16 (hanlid);

Funder: Stiftelsen Rönnbäret

Available from: 2023-01-16 Created: 2023-01-16 Last updated: 2023-08-28Bibliographically approved

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Mololoth, Vidya KrishnanSaguna, SagunaÅhlund, Christer

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