## 1 Introduction
To overcome these challenges, EC has emerged as a more effective approach by enabling localized data processing at the source. Unlike CC, EC minimizes communication latency by reducing the distance data must travel before being analyzed, resulting in significantly lower response times [13]. At this point, EC significantly reduces communication latency compared to the cloud by processing data at the source. Time-sensitive data analysis tasks can be processed at the edge to reduce the latency caused by data transfer [14]. Still, EC not only provides the advantage of low latency but also has the potential to solve energy consumption, bandwidth overhead, and security issues. This localized processing reduces the latency associated with data transfer and offers distinct advantages for time-sensitive applications. EC not only provides the benefit of low latency but also has the potential to address issues related to energy consumption, bandwidth overhead, and security [15]. Each of the existing works in the literature focuses on different aspects of the applications of EC in smart grids, often delving into specific areas. For instance, ref. [16] provides an overview of the potential of EC in the SG, noting that only a limited number of studies have directly applied EC to SG. Ref. [17] provides a theoretical framework, concentratingon the hardware and software components of the EC-CC information architecture, but with limited coverage of practical application scenarios. Ref. [18] provides an overview of IoT and edge computing-based smart grid systems, addressing their architecture, integration with SCADA systems, and key challenges such as scalability, sustainability, security, and resilience. In this context, it focuses on key application areas such as power distribution monitoring, microgrids, and advanced metering systems. Similarly, ref. [19] proposes solutions for IoT-based smart grids where cloud computing is insufficient, such as real-time data processing, terminal management, and privacy protection. In the reviews on fog computing and related paradigms (including EC) [20], research topics have been categorized and classified. Ref. [21] introduces the emergence and capabilities of EC, examines state-of-the-art architectural developments, and explores communication techniques. This paper synthesizes the fragmented nature of the existing literature, systematically analyzing the relationships between EC, FC, and CC paradigms in power systems, both theoretically and practically. It spans all layers of power systems, from generation to consumption, from transmission to load management, from security to data privacy, and from field applications to energy optimization. The role of information technology (IT) paradigms, previously addressed in a fragmented manner in the literature, is integrated with an energy systems perspective. Therefore, this paper provides a unique and in-depth contribution to the literature by offering a comprehensive, multi-layered, and comparative overview, including application scenarios, advantage-disadvantage analyses, and future research directions. By performing so, it establishes a structured framework for evaluating the impact of EC on modern power systems, considering various computational methodologies, technological advancements, hardware and software components, architectural approaches, and application domains. A systematic literature review was conducted, encompassing over 500 publications. From these, 237 relevant and cohesive studies were selected for detailed analysis. These included peer-reviewed research articles, conference proceedings, and technical reports. The sources were drawn from a variety of applied science journals, including those focused on energy systems, computer science, and electrical engineering. The review also incorporated publications from prominent energy systems conferences related to smart grids, power distribution, and renewable energy technologies.