Renewable Energy Integration in Power Grids: Trends, Challenges, and Stability Advances
A Special Issue of Electronics (ISSN 2079-9292) belonging to the section "Power Electronics".
Deadline for manuscript submissions: 15 January 2027 | Viewed by 831
Editor
Special Issue Information
Dear Colleagues,
The rapid integration of renewable energy sources, electric vehicles, and distributed energy resources (DERs) into modern power systems presents significant challenges for protection and control mechanisms. Traditional protection schemes, often based on fixed thresholds and centralized architectures, are increasingly inadequate in handling the complexity, variability, and cyber–physical interdependencies of today's smart grids. This Special Issue aims to explore the development and application of AI-driven multicriterial and adaptive systems that enhance the protection, monitoring, and control of modern power grids.
We welcome original research and comprehensive reviews that focus on machine learning, deep learning, and data-driven methods, offering robust, real-time, and scalable solutions. Topics of interest include, but are not limited to, multicriterial decision-making in protection relays, adaptive fault detection and localization, federated learning for privacy-preserving grid analytics, and AI-powered control strategies for DER integration. Contributions related to Hardware-in-the-Loop (HIL) testing, physics-informed learning models, and cyber-resilient system architectures are also encouraged.
This Special Issue serves as a platform for academic and industry researchers to share advances that ensure grid resilience, security, and intelligence through adaptive and AI-based protection and control systems.
Potential topics include (but are not limited to) the following:
- AI and machine learning algorithms for fault detection and localization;
- Multicriterial decision-making techniques in power system protection;
- Adaptive protection schemes for renewable and DER-integrated power systems;
- Federated and distributed learning approaches for grid monitoring and control;
- Cyber-resilient architectures for protection and control in smart grids;
- Physics-informed machine learning models for grid stability and security;
- Hardware-in-the-Loop (HIL) and CHIL-based testing of intelligent protection systems;
- Real-time data analytics for adaptive grid operation and protection;
- Protection and control strategies for microgrids and virtual power plants;
- Digital twin and edge AI applications in power system protection.
Dr. Arif Hussain
Guest Editor
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Keywords
- power system protection
- adaptive control systems
- artificial intelligence (AI)
- multicriteria decision-making
- distributed energy resources (DERs)
- machine learning and deep learning
- cyber–physical systems
- smart grids
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