Transforming Power Systems and Smart Grids with Deep Learning

Dear Colleagues,

The rapid evolution of modern society has placed unprecedented demands on energy systems, pushing traditional power infrastructures to their limits. In response, the integration of intelligent, data-driven technologies, including artificial intelligence (AI) and machine learning (ML), into power systems and smart grids is revolutionizing how electricity is generated, transmitted, distributed, and consumed. These advancements are enabling energy networks to become more adaptive, autonomous, resilient, and sustainable.

The application of intelligent technologies spans numerous facets of modern power systems, including dynamic load forecasting, real-time grid optimization, predictive asset maintenance, enhanced fault detection, renewable energy integration, and cyber–physical security. These innovations not only address the challenges posed by the increasing penetration of renewable energy sources and distributed generation but also unlock new opportunities for improved efficiency, higher reliability, and greater sustainability.

This Special Issue aims to present and disseminate the latest research, methodologies, and technological advancements related to the intelligent transformation of power systems and smart grids. Contributions exploring theoretical developments, practical implementations, emerging applications, and interdisciplinary approaches are highly encouraged.

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

• AI- and ML-based load forecasting and demand prediction;
• Renewable energy integration and grid optimization with intelligent algorithms;
• Predictive maintenance and asset management using intelligent systems;
• Intelligent fault diagnosis and grid resilience enhancement;
• Machine-intelligence-based control for AC and DC converters enabling high renewable energy (RE) penetration;
• Cybersecurity solutions and anomaly detection in intelligent grids;
• Edge computing and real-time decision-making in smart grid environments;
• Digital twins and simulation platforms for advanced energy networks;
• Data-driven energy management and optimization systems;
• Intelligent microgrid control and distributed energy resource (DER) management;
• Explainable AI (XAI) and trustworthy intelligent systems for energy applications;
• Federated learning and decentralized AI solutions for smart grids;
• Advanced optimization algorithms for smart grid planning and operation.

We invite researchers and practitioners from academia, industry, and government sectors to contribute their original research articles, reviews, and case studies to this Special Issue.

Dr. Gajendra Singh Chawda
Guest Editor

Manuscript Submission Information

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