Artificial Intelligence and Machine Learning Applications in Electric Power and Energy Systems

Dear Colleagues,

The rapid advancement of variable renewable energy (VRE) sources—such as wind and solar photovoltaics—coupled with the widespread integration of energy storage systems (ESS), demand response (DR) programs, hydrogen-based power-to-X (P2X) technologies, and multi-energy microgrids, has profoundly transformed the production, distribution, and consumption paradigms of modern power and energy systems. Concurrently, the deployment of advanced sensing networks, 5G-enabled communication infrastructure, and digital twin technologies has enhanced the feasibility of economically efficient and resilient operation of these complex systems, even amid the inherent volatility of VRE penetration. This transformation has led to an explosion of multi-dimensional data across the generation, transmission, distribution, and end-user sectors. Effectively harnessing this big data to ensure energy security, supply adequacy, grid stability, and carbon neutrality compliance remains a pivotal challenge for researchers and practitioners in the power and energy domain.

Meanwhile, the adoption of cutting-edge machine learning (ML) and artificial intelligence (AI) techniques is indispensable for addressing the intricate planning, scheduling, and control challenges spanning the entire energy supply-demand chain. Data-driven methodologies—particularly physics-informed neural networks, reinforcement learning, and distributionally robust optimization—have been increasingly developed and deployed to tackle complex tasks such as high-resolution forecasting, multi-objective optimal dispatch, and real-time situational awareness. These tasks, which often involve non-linear dynamics, multi-energy coupling, and significant uncertainty, are typically intractable with traditional model-based approaches, underscoring the need for advanced big data analytics, ML, and AI solutions.

For this Special Issue, we welcome original research and review articles focusing on state-of-the-art data-driven methods and their applications in power and energy systems. Target audiences include academic researchers, industry engineers, and policymakers engaged in energy system modernization. The goal is to establish a platform for showcasing innovative research findings and fostering interdisciplinary collaboration in related fields. All submissions must be original works, written in rigorous academic English, and must not have been previously published or currently be under review by any other journal or conference.

Topics of interest for publication include, but are not limited to, data-driven techniques applied to the following areas:

• High-resolution forecasting of renewable energy generation, multi-energy load profiles, energy prices, and carbon emissions;
• Demand response and flexible load management in smart buildings and industrial sectors;
• Energy storage system optimization (e.g., ESS sizing, scheduling, and integration with VRE);
• Integrated energy systems (IES) with hydrogen–electric-heat coupling (e.g., P2H, fuel cell applications);
• Smart grid and microgrid operation, including inverter-based resource control and stability enhancement;
• Digital twin-enabled grid monitoring, predictive maintenance, and fault diagnosis;
• Cyber-physical security and anomaly detection for power electronics-dominated grids;
• Data-driven planning, operation, protection, and control of power and energy systems;
• Multi-objective economic dispatch considering energy efficiency and carbon footprint minimization;
• Electricity market design, peer-to-peer trading, and demand-side participation in energy markets.

Prof. Dr. Jun Liu
Dr. Xiaoming Liu
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 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-blind 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.

• data-driven methods
• power and energy systems
• renewable energy integration
• integrated energy systems
• smart grid
• microgrid
• demand response
• energy storage systems
• economic dispatch
• cyber–physical system security