AI-Driven Sustainable Energy Systems: Smart Grids, Homeostatic Control, and Distributed Resource Optimization

Dear Colleagues,

The global transition toward sustainable energy systems is accelerating in response to climate change, increasing electrification, and the large-scale integration of distributed renewable energy resources. However, this transition introduces unprecedented operational complexity into modern power systems—essentially cyber–physical systems, and with this, it also increases the vulnerability risk of the smart grid. The proliferation of distributed energy resources (DERs), bidirectional power flows, prosumers, electric vehicles, microgrids, and storage systems challenges the traditional centralized grid paradigm. Artificial Intelligence (AI) has emerged as a transformative enabler for addressing these complexities. Advanced machine learning algorithms, deep learning architectures, reinforcement learning frameworks, and data-driven optimization techniques now allow power systems to operate in adaptive, predictive, and self-regulating modes. In particular, AI supports the development of homeostatic energy systems—grids capable of maintaining dynamic equilibrium through real-time monitoring, anomaly detection, autonomous decision-making, and distributed control actions. Thus, smart grids empowered by AI can enhance resilience, improve energy efficiency, enable predictive maintenance, and facilitate optimal coordination of distributed resources such as Virtual Power Plants (VPPs), microgrids, battery energy storage systems (BESS), and renewable generation units. Furthermore, AI-driven optimization supports economic dispatch, demand response management, voltage and frequency stability, and cyber–physical security in increasingly digitalized energy infrastructures.

This Special Issue seeks high-quality theoretical, methodological, and applied contributions that explore AI-based solutions for sustainable, intelligent, and resilient energy systems. Submissions may address foundational algorithms, system architectures, control strategies, optimization techniques, real-world case studies, or interdisciplinary approaches integrating engineering, economics, and sustainability science. Particular emphasis is placed on energy homeostasis and homeostatic control mechanisms, distributed intelligence, and scalable optimization frameworks capable of supporting the energy transition while ensuring reliability, affordability, and environmental sustainability.

Topics include, but are not limited to, the following:

• AI-based control and optimization in smart grids;
• Machine learning for anomaly detection and fault diagnosis;
• Homeostatic and self-healing grid architectures;
• Virtual Power Plants (VPPs) and distributed resource coordination;
• Reinforcement learning for energy dispatch and demand response;
• Cyber–physical systems and grid resilience;
• AI-driven forecasting for renewable integration;
• Edge computing and distributed intelligence in power systems;
• Multi-agent systems for energy management;
• Sustainable energy market design supported by AI.

Prof. Dr. Franco F. Yanine
Guest Editor

Manuscript Submission Information

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• artificial intelligence
• smart grids
• homeostatic control
• distributed energy resources (DERs)
• cyber-physical systems
• virtual power plants (VPPs)
• machine learning
• energy optimization
• grid resilience
• sustainable energy systems