The increasing complexity of conventional energy distribution systems, combined with the growing demand for efficient data processing, has necessitated the implementation of smart grid technologies and the integration of advanced computing paradigms such as edge computing. Traditional cloud-based solutions face significant challenges, including high latency, limited bandwidth, and cybersecurity
... [Show full abstract] vulnerabilities, rendering them less effective for real-time smart grid applications. Edge computing enables localized data processing, which significantly reduces latency and optimizes bandwidth usage. These capabilities enhance the resilience and intelligence of modern energy systems. This paper presents a systematic review of edge computing in energy distribution systems, examining its architectures, methodologies, and real-world applications. Key application areas consist of real-time data transmission, smart metering, microgrid management, anomaly and fault detection, state estimation, and energy management. The analysis shows how edge computing improves secure communication, supports decentralized intelligence, and facilitates scalable energy optimization. Beyond these advantages, the review also identifies critical challenges such as interoperability issues, resource constraints, and security vulnerabilities. By categorizing edge computing applications, the findings provide a comprehensive reference for both researchers and industry professionals working on the development of next-generation energy management systems.