Guest Editors
Dr. Leila Bagherzadeh
Email: leila.bagherzadeh.1@ulaval.ca
Affiliation: Department of Electrical Engineering, Laval University, Québec, G1V 0A6, Canada
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Research Interests: modeling, optimization, control of smart grids, integration of renewable energy sources, energy storage systems, demand response, decentralized and community-based energy systems, applying AI, data-driven techniques for efficient and resilient power system operation
Summary
The transformation of conventional power systems into smart grids is crucial for addressing the increasing integration of renewable energy sources, electrification of transportation, and growing demands for flexibility and resilience. Smart grids facilitate bidirectional power flows, advanced monitoring, and intelligent control, enabling power systems to operate more efficiently, reliably, and sustainably under high variability and uncertainty. As energy systems evolve toward decentralized and community-based architectures, new challenges emerge in planning, operation, protection, and coordination across transmission and distribution levels.
This Special Issue aims to showcase recent theoretical, methodological, and practical advances in smart grid technologies and applications. It highlights innovative approaches in modeling, optimization, control, and data-driven techniques that enhance system efficiency, flexibility, resilience, and sustainability. Both centralized and decentralized paradigms, real-world case studies, and emerging technologies are welcome.
Suggested themes include, but are not limited to:
- integration of renewable energy and energy storage systems
- demand response and flexible load management
- smart grid optimization and control strategies
- energy hubs and community-based energy systems
- electric vehicles and sector coupling; grid resilience and reliability enhancement
- advanced forecasting and data analytics
- artificial intelligence and machine learning applications in smart grids
Keywords
smart grids; renewable energy integration; energy storage systems; demand response; grid resilience; artificial intelligence; machine learning
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