Advancing Power System Intelligence: AI-Based Forecasting, Operation, and Control

Dear Colleagues,

Modern power systems are undergoing a fundamental transformation toward decentralization, decarbonization, and digitalization. As inverter-based resources (IBRs), distributed energy systems, and renewable generation become increasingly prevalent, traditional paradigms for forecasting, operation, protection, and control face new challenges in terms of complexity, uncertainty, and real-time responsiveness. At the same time, artificial intelligence (AI), machine learning (ML), and advanced optimization techniques are offering powerful tools to enhance the intelligence, adaptability, and resilience of future power systems.

We are pleased to invite you to contribute to this Special Issue, “Advancing Power System Intelligence: AI-based Forecasting, Operation, and Control”, in the Power Electronics section of Electronics. The aim of this Special Issue is to showcase cutting-edge research and practical developments at the intersection of AI and power systems, with a focus on forecasting, real-time control, optimization, protection, and decision support under complex and dynamic operating conditions.

This Special Issue will bring together original contributions and comprehensive reviews that address the theoretical, computational, and application-focused aspects of AI-driven methods in modern power systems. The scope will cover the integration of data-driven and model-based techniques in inverter-dominated grids, cyber-physical energy networks, and coordinated energy management environments.

For this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• AI-based transient stability analysis and control in inverter-dominated power systems;
• Data-driven protection schemes for modern distribution and transmission networks;
• Machine learning techniques for short-term and long-term power system forecasting;
• Intelligent control strategies for grid-forming/grid-following inverters;
• The coordinated operation and optimization of integrated energy systems;
• Resilience assessment and enhancement via AI-enabled decision-making;
• Demand response optimization and real-time load-side management;
• Cyber-physical security and risk-aware control in intelligent grids;
• Market forecasting and bidding strategies using deep learning and reinforcement learning;
• Hybrid model-driven and data-driven approaches in power system dynamics and operation;
• Explainable AI and trustworthy ML for critical energy infrastructure applications;
• Edge and federated learning applications in distributed control and monitoring.

We look forward to receiving your contributions and advancing this exciting interdisciplinary field together.

Dr. Guoqing Gao
Prof. Dr. Yue Zhou
Dr. Chenhui Lin
Prof. Dr. Yixun Xue
Dr. Qi Wang
Guest Editors

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 100 words) can be sent to the Editorial Office for announcement on this website.

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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics 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 2400 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.

• power forecasting
• artificial intelligence
• machine learning
• renewable energy prediction
• load forecasting
• smart grid
• deep learning