Intelligent Control and Energy Management in Smart Grids for the Energy Transition
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".
Deadline for manuscript submissions: 15 January 2026 | Viewed by 3
Special Issue Editor
Interests: Power-to-X; smart-grid; energy efficiency; energy management and control; renewable energy; energy storage; predictive control; fault detection and diagnosis; predictive maintenance; anomaly detection; condition monitoring
Special Issue Information
Dear Colleagues,
The actual global energy transition, motivated by the urgent need to reduce CO2 emissions, ensure energy security, and promote sustainability, requires a vital transformation of the power sector. Traditional power systems are progressing into smart grids, characterized by the integration of renewable energy sources, energy storage systems, and electric vehicles (EVs) with their challenges and limitations. This progress includes increased complexity, variability, and uncertainty in grid operations, necessitating more intelligent, adaptive, and decentralized control and energy management strategies.
In this context, the role of intelligent control and advanced energy management systems becomes crucial. Exploiting artificial intelligence, machine learning, multi-agent systems, Big Data, Internet of Things, and Real-Time Data Analytics, researchers and engineers are invited to develop solutions to optimize energy flows, enhance grid stability, enable demand-side flexibility, and support decision-making under uncertainty to support the ongoing energy transition. It provides a platform for interdisciplinary contributions from academia, industry, and policymakers, focusing on the development and deployment of novel algorithms, architectures, and systems that support the transition to a clean, digital, and decentralized energy future.
We invite original works that address the integration of renewable energy sources, distributed energy resources, demand response, predictive analytics, and advanced optimization techniques in smart grid operations. Emphasis is placed on artificial intelligence, machine learning, multi-agent systems, predictive/optimal control, and real-time control approaches that enhance grid flexibility, reliability, and sustainability.
Dr. Abdellatif Elmouatamid
Guest Editor
Manuscript Submission Information
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Keywords
- AI and ML-based forecasting, control, and decision-making
- energy management in microgrids and virtual power plants
- demand-side management and dynamic pricing mechanisms
- cyber-physical security and resilience in intelligent grids
- real-time optimization and control of energy resources
- multi-agent and distributed control architectures
- edge/fog/cloud computing for energy data analytics
- integration of electric vehicles (EVs) and energy storage
- grid-to-vehicle and vehicle-to-grid
- decision support systems for grid operators
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