Advances in Machine Learning Applications in Stability Analysis and Optimal Operation of Power Systems
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F1: Electrical Power System".
Deadline for manuscript submissions: 25 March 2026 | Viewed by 21
Special Issue Editors
Interests: power system voltage stability; power grid reconfiguration; optimal power flow; application of machine learning in power systems
Interests: power system stability; optimal power flow; active distribution network; voltage control
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The increasing penetration of renewable energy sources (RESs), such as solar and wind, presents significant challenges to the stability and optimal operation of modern power systems. The inherent intermittency and stochasticity of RESs lead to critical challenges, such as increased frequency deviations, voltage instabilities, and operational constraints. Furthermore, the distributed and decentralized characteristics of these resources demand novel, scalable methodologies for grid management that can maintain system stability and optimize performance in real-time. Machine Learning (ML) approaches offer a promising, data-driven paradigm to address these challenges. By leveraging advanced ML techniques—such as deep neural networks, reinforcement learning, and generative adversarial networks—grid operators can move beyond traditional model-based methods. ML-based approaches enable sophisticated real-time monitoring, high-fidelity forecasting, and advanced control strategies. This empowers operators to proactively manage grid imbalances, mitigate operational risks, and optimize system performance in the dynamic environment of high RES integration.
This Special Issue seeks to consolidate cutting-edge research and innovative applications of machine learning for enhancing the stability and operational efficiency of power systems.
Topics of interest for publication include, but are not limited to:
- Renewable generation/load forecasting;
- Power system dynamics modeling and identification;
- Power system stability/security assessment;
- Power system fault/event detection;
- Optimal power flow;
- Economic dispatch;
- Network planning;
- Distribution network reconfiguration;
- Preventive and corrective cyber-defense;
- Cyber-resilient control;
- Cybersecurity enhancement.
Dr. Wanjun Huang
Prof. Dr. Lipeng Zhu
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. 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.
Keywords
- stability assessment
- security assessment
- fault detection
- reconfiguration
- planning
- optimal power flow
- forecast
- cyber security
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