Distributed Intelligence for smart grid management: Architectures, applications, and future

The modern energy landscape is undergoing a significant transformation with the advent of smart grids, characterized by enhanced monitoring, control, and integration of renewable energy sources. This article explores a comprehensive exploration of intelligent smart grid management, emphasizing the crucial role of distributed systems and Artificial Intelligence. It delves into the foundational architecture of distributed systems, including sensor networks, communication infrastructure, and the synergistic integration of edge and cloud computing, which enables real-time data exchange and processing. The article explores the transformative applications of AI algorithms in predicting energy demand, detecting faults proactively, optimizing energy distribution, and enhancing cybersecurity and asset management. Recognizing the sensitive nature of energy consumption data, various privacy-preserving architectures and techniques are discussed to ensure data security while enabling advanced analytics. Furthermore, key challenges in the implementation of these technologies are identified, alongside the significant opportunities they offer for improved energy efficiency, grid reliability, and the integration of renewable resources. Finally, emerging trends like blockchain integration, advanced AI models, autonomous grid management, and digital twins are explored, highlighting the transformative potential of distributed intelligence in shaping a more efficient, resilient, and sustainable energy future.