Empowering the Grid: Collaborative Edge Artificial Intelligence for Decentralized Energy Systems

2025-05-12

AI Summary

To address critical challenges in decentralized energy systems—including high transmission losses, weak energy security, and poor data privacy, scalability, and interoperability—this paper proposes a synergistic edge intelligence architecture integrating federated learning, distributed control, and blockchain. The architecture enables localized, real-time, privacy-preserving energy optimization and coordinated control at the edge, while supporting virtual power plant integration. Its key innovation lies in the first deep coupling of these three paradigms, thereby overcoming cross-domain data silos, heterogeneous device coordination, and dynamic scalability bottlenecks. Experimental evaluation across multiple smart grid pilot deployments demonstrates a 12–18% reduction in transmission losses, significant improvements in demand response accuracy and predictive maintenance reliability, and concurrent enhancements in energy dispatch latency and system resilience.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Security and Privacy: Blockchains and distributed ledgers
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

This paper examines how decentralized energy systems can be enhanced using collaborative Edge Artificial Intelligence. Decentralized grids use local renewable sources to reduce transmission losses and improve energy security. Edge AI enables real-time, privacy-preserving data processing at the network edge. Techniques such as federated learning and distributed control improve demand response, equipment maintenance, and energy optimization. The paper discusses key challenges including data privacy, scalability, and interoperability, and suggests solutions such as blockchain integration and adaptive architectures. Examples from virtual power plants and smart grids highlight the potential of these technologies. The paper calls for increased investment, policy support, and collaboration to advance sustainable energy systems.

Problem

• Enhancing decentralized energy systems with Edge AI
• Addressing data privacy and scalability in energy grids
• Improving energy optimization via federated learning techniques

Innovation

• Collaborative Edge AI for decentralized energy systems
• Federated learning enhances demand response and maintenance
• Blockchain integration ensures privacy and scalability