Deep Reinforcement Learning in Power Grids

Dear Colleagues,

Modern power systems are rapidly evolving into highly dynamic cyber–physical infrastructures, driven by increasing penetration of renewable generation, distributed energy resources, electric vehicles, and advanced communication networks. Smart grids, smart energy systems, and smart homes with home energy management systems (HEMS) introduce unprecedented flexibility but also create complex decision-making problems across multiple time scales and layers. Classical optimization and control techniques often struggle with the nonlinear, stochastic, and high-dimensional nature of these tasks. Deep Reinforcement Learning (DRL) has emerged as a powerful paradigm to address these challenges by enabling agents to learn near-optimal control and management strategies directly from interaction with detailed grid models and real-world data.

This Special Issue aims to present and disseminate the most recent advances in DRL for planning, operation, control, and protection in future power grids, including transmission networks, distribution systems, and resilient microgrids. We especially welcome contributions that combine DRL with domain knowledge in power engineering and telecommunications, ensure safety and stability, and demonstrate practical implementations through simulations, hardware-in-the-loop experiments, or field trials.

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

• Deep Reinforcement Learning for smart grids, smart energy systems, and resilient microgrids.
• DRL for smart homes and HEMS.
• DRL-based coordination of distributed energy resources and energy-on-demand services.
• DRL combined with optimization methods for power grids and optimal power flow.
• DRL for power flow control and power flow coloring.
• Stability-aware DRL for power system stability and control.
• DRL for demand response and flexible load management.
• DRL for energy storage scheduling and energy balancing.
• DRL for distributed sensing and control in large-scale networks.
• Deep Reinforcement Learning and telecommunications for communication-aware grid control and edge/fog deployments.

Dr. Iacovos Ioannou
Dr. Saher Javaid
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-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.