Reconfiguration of Distribution System Network to Minimize Transmission & Distribution (T&D) Losses Using Advanced Technologies

Transmission and Distribution (T&D) losses significantly impact the efficiency and reliability of power systems, leading to economic losses and increased operational costs. Network reconfiguration has emerged as a key strategy to minimize these losses by optimizing the topology of distribution networks. With the advent of smart grid technologies, artificial intelligence (AI), and advanced optimization techniques, reconfiguration methods have evolved from traditional heuristic approaches to dynamic, real-time solutions. This review paper presents a comprehensive analysis of distribution system reconfiguration techniques, focusing on their effectiveness in T&D loss reduction. We examine classical optimization methods (e.g., Genetic Algorithms, Particle Swarm Optimization), machine learning (ML)-based approaches, and hybrid models, along with their integration with IoT, smart sensors, and distributed energy resources (DERs). Additionally, we discuss real-world case studies, challenges in implementation, and future trends, such as digital twin applications and quantum computing for large-scale systems. The findings highlight that AI-driven adaptive reconfiguration offers superior performance in loss minimization compared to conventional methods, though computational complexity and cybersecurity remain key concerns. This review serves as a valuable resource for researchers and power system engineers seeking to enhance grid efficiency through advanced reconfiguration strategies.