Special lssues

Emerging Technologies for Future Smart Grids

Submission Deadline: 01 May 2025 Submit to Special Issue

Guest Editors

Name: Nicu BIZON
Affiliation: The National University of Science and Technology POLITEHNICA Bucharest, Romania
E-mail: nicubizon@yahoo.com
Research Interests: power electronics; renewable energy; fuel cell; hybrid power systems; control; optimization

Name: Bhargav Appasani
Affiliation: School of Electronics Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India
E-mail:  bhargav.appasanifet@kiit.ac.in
Research Interests:  smart grid communication networks, ocean wave energy control, second generation current conveyors, terahertz metamaterial absorbers, optimization techniques, artificial intelligence


This is a special issue based on the 16th INTERNATIONAL CONFERENCE on Electronics, Computers and Artificial Intelligence (https://ecai.ro/). Prof. Nicu is the conference chair and the selected outstanding papers will be recommended to this special issue.


This Special Issue addresses the state-of-the-art research related to emerging technologies in the smart grid. With the increased proliferation of renewable energy resources, and the devices in grid becoming capable to exchanging both power and data, the complexity of the grid has increased enormously. The smart grid is itself an evolved version of the power grid, with enhanced monitoring and control capabilities to meet the complex data and energy requirements of various devices. Emerging technologies like Artificial Intelligence, Edge Computing, Quantum computing, Digital twins, Blockchain, Bigdata, etc., are poised to further revolutionize smart grids, unlocking unprecedented capabilities and efficiencies.


These technologies are still in nascent stages and thus, this special issue aims to highlight these emerging technologies, with original research and review papers on the following topics related to emerging technologies in smarts grids.


Topics of interest of this Special Issue include, but are not limited to:

Artificial Intelligence for smart grid applications

Machine learning and deep learning for smart grids

AI for energy management in smart grid

AI for electric vehicles charging behaviour prediction

Predictive maintenance using AI

Blockchain for decentralised applications in smart grid

Blockchain for energy management in smart grid

Edge computing for smart grid

Bigdata analytics for smart grid

Quantum computing for smart grid optimization

Quantum encryption for enhaced cybersecurity in smart grids.

Quantum machine learning for smart grid

Quantum computing for efficient energy management

Real-time digital twins for effective situational awareness in smart grids

Digital twins for optimal grid planning.

Digital twins for real-time energy management.

Digital twins for fault diagnosis.

Generative AI for large synthetic data creation

Generative AI for enhanced cybersecurity in smart grids.

Generative AI for anomaly detection in smart grid.

Digital twins for smart grid equipment: metering infrastructure, and phasor measurement units.

Improved smart grid control.


Artificial intelligence for smart grids
Blockchain for smart grids
Edge computing for smart grids
Quantum computing for smart grid
Quantum machine learning for smart grid
Quantum encryption algorithms for smart grid
Digital twins for smart grid networks
Digital twins for micro grids
Advanced metering infrastructure
Phasor measurement units
Generative AIin smart grid
Energy management
Optimal power flow
Load frequency control
Synthesizing data for low frequency events

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