Transforming Power Systems and Smart Grids with Deep Learning—2nd Edition

Dear Colleagues,

The rapid evolution of artificial intelligence (AI), digital technologies, renewable energy, and electrification is transforming modern power and energy systems. At the same time, the rapid growth of AI-driven computing and data centers is creating new challenges and opportunities for electricity generation, transmission, distribution, power conversion, energy management, and grid planning. These developments are increasing the need for intelligent, adaptive, resilient, and sustainable power and energy systems.

Artificial intelligence (AI), machine learning (ML), deep learning, and other data-driven technologies are increasingly being applied across the power system, from renewable energy forecasting and grid optimization to intelligent control, predictive maintenance, fault diagnosis, cybersecurity, and distributed energy resource management. Advanced AI/ML techniques also offer significant opportunities for improving the control and optimization of power electronic converters, microgrids, energy storage systems, and complex cyber–physical energy infrastructures. In parallel, the rapid expansion of AI and high-performance computing data centers is introducing large, dynamic, and geographically concentrated electrical loads. These emerging energy-intensive facilities require innovative solutions for power system integration, energy-efficient operation, reliability, resilience, renewable energy utilization, energy storage, advanced power conversion, and intelligent energy management. AI and ML can play an important role in addressing these challenges through intelligent forecasting, control, optimization, digital twins, predictive operation, and coordinated management of data-center and grid resources.

This Special Issue, “Transforming Power Systems and Smart Grids with Deep Learning—2nd Edition,” aims to present recent advances in the application of AI, ML, deep learning, and intelligent optimization techniques to modern power systems, smart grids, power electronics, renewable energy systems, and emerging energy-intensive infrastructures. Contributions involving theoretical developments, numerical and experimental studies, practical implementations, digital twins, and interdisciplinary approaches are highly encouraged.

Topics of interest include, but are not limited to, the following:

• AI- and ML-based load forecasting and demand prediction;
• Deep learning applications in power systems and smart grids;
• Renewable energy forecasting, integration, and optimization using AI/ML;
• AI/ML-based control and optimization of power systems;
• Intelligent control of power electronic converters and inverter-dominated systems;
• AI-enabled optimization of AC, DC, and hybrid energy systems;
• Predictive maintenance, asset management, and remaining useful life estimation;
• Intelligent fault detection, diagnosis, and system restoration;
• AI-based grid resilience and reliability enhancement;
• Cybersecurity, anomaly detection, and cyber–physical security for intelligent energy systems;
• Intelligent microgrid control and distributed energy resource management;
• AI-enabled energy storage management and optimization;
• Digital twins and real-time simulation for power and energy systems;
• Data-driven energy management systems;
• Explainable, trustworthy, and secure AI for critical energy infrastructure;
• Federated learning and decentralized AI for smart grids;
• AI and intelligent optimization for power system planning and operation;
• AI-enabled grid integration and energy management of data centers;
• Power system impacts of large-scale AI and data-center loads;
• Intelligent control and optimization of data-center power and energy systems;
• Renewable energy, energy storage, and grid-interactive solutions for sustainable data centers;
• Data-center power system reliability, resilience, and power quality;
• Advanced power architectures, power conversion, and energy management for AI and high-performance computing infrastructure.

We invite researchers and practitioners from academia, industry, and government to contribute original research articles, review articles, and communications addressing these important and rapidly evolving topics.

Dr. Gajendra Singh Chawda
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• artificial intelligence and machine learning
• intelligent power systems and smart grids
• AI-enabled control and optimization
• data center power and energy systems
• renewable energy and distributed energy resources

• Transforming Power Systems and Smart Grids with Deep Learning in Energies (5 articles)