AI-Driven and IoT-Enabled Energy Management for Resilient Decentralized Power Systems

Modern power systems are undergoing rapid transformation driven by the integration of renewable energy sources, distributed generation, and increasingly complex control requirements. Virtual Power Plants (VPPs), microgrids, and IoT-enabled smart grids are emerging as central pillars for balancing supply and demand while maintaining stability under variable and decentralized conditions. Artificial intelligence and homeostatic control principles offer a promising path toward self-regulating, resilient, and self-healing energy infrastructures that adapt to disturbances without centralized intervention.This Research Topic invites original research, reviews, and case studies exploring how AI, machine learning, and IoT technologies enhance the stability, resilience, and efficiency of decentralized power systems and smart grids. Particular emphasis is placed on homeostatic control strategies for VPPs, as well as broader applications in smart grid optimization, distributed energy resource integration, renewable energy forecasting, anomaly detection, and cyber-physical grid resilience. Given that the operating feasibility of VPPs is closely tied to electricity market mechanisms, this Research Topic also welcomes contributions addressing market-responsive control, price-based dispatch, and techno-economic evaluation of VPP operation — approached from technical and system-design perspectives rather than from policy or socio-economic analyses.The themes of this Research Topic include, but are not limited to:• AI-based control and optimization for smart grids and virtual power plants• Homeostatic and self-healing control architectures for decentralized power systems• Machine learning for fault diagnosis and anomaly detection in grid infrastructure• Cyber-physical system resilience in smart grids and microgrids• Distributed control and power system automation for renewable-dominated grids• AI-driven forecasting for renewable energy integration and grid connection• IoT-based monitoring and smart energy management for photovoltaic systems• Real-time optimal load scheduling and decision algorithms for smart energy management• Distributed energy resource optimization within smart grid architectures• Case studies on VPP deployment and distributed generation (PMGD) integration in real grids• Cloud-based and IoT-enabled monitoring systems and communication infrastructure for distributed renewable energy generation• Market-driven and price-responsive control strategies for VPP operation and dispatch• AI-based bidding and scheduling algorithms for VPP participation in electricity markets• Techno-economic assessment of VPP operating feasibility under market and grid constraints• Real-time market signal integration for distributed energy resource optimization• Technical and economic conflicts between distribution grid infrastructure and renewable-based distributed generation: market-driven challenges to grid integration Topic editors: Franco Fernando Yanine, Prof. Sarat Kumar Sahoo, CHALLA KRISHNA RAO Source: https://www.frontiersin.org/research-topics/84884/ai-driven-and-iot-enabled-energy-management-for-resilient-decentralized-power-systems