## 1 Introduction
The main contributions of this study are as follows:  
* Systematic Literature Review: A systematic review is conducted to analyze the role of EC in energy distribution systems, addressing its potential advantages and limitations in comparison to other computing paradigms. * Technical Comparison of Computing Architectures: EC is critically compared with CC and FC in terms of essential criteria such as latency, computational efficiency, data transmission, and security, emphasizing their contextual applicability in power systems. * Real-Time Data Processing and Optimization Techniques: The integration of EC with communication protocols such as Minimum Message Queuing Telemetry Transport (MQTT) and Advanced Message Queuing Protocol (AMQP), container technologies like Docker and Kubernetes, and AI-driven predictive maintenance models is explored. The study also analyzes the role of EC in sensor data processing to enhance fault prediction accuracy and optimize demand-side management in energy systems. * Technological Components and Infrastructure: The role of hardware and software architectures in EC-enabled energy systems is analyzed. The study explores computational frameworks, edge devices, virtualization technologies, and distributed resource allocation strategies that contribute to the efficient deployment of EC. * Applications in Power Systems: The advantages of EC in various power system applications, including data transmission, real-time monitoring and control, microgridsystems, smart metering, anomaly detection, state estimation, energy management, and fault detection, are assessed in terms of their contributions to grid stability, operational efficiency, and system reliability. The study evaluates how EC-based solutions improve energy consumption modeling, enable decentralized energy management, optimize grid stability, and enhance system reliability. * Security and Privacy Solutions: By identifying major vulnerabilities in EC-powered energy systems, the paper proposes mitigation strategies including decentralized authentication, hybrid encryption, and secure transmission protocols to strengthen system resilience. * Future Research Directions: Key areas requiring further investigation are highlighted, including scalable EC architectures, energy-efficient edge processors, advanced data management techniques, and the integration of distributed AI applications to enhance the resilience and efficiency of energy systems. The structure of this paper is illustrated in Figure 1 and organized as follows. Section 2 presents an overview of various computing paradigms utilized in power systems, including CC, EC, FC, Mobile Cloud Computing (MCC), and Mobile Edge Computing (MEC). It also presents a comparative analysis of these paradigms, highlighting their respective strengths and limitations in the context of energy distribution systems. Section 3 examines the core technologies, hardware, and software tools employed in EC, with subsections dedicated to data processing techniques, container technologies, communication protocols, and the software platforms and hardware tools that facilitate EC deployment. Section 4 discusses the diverse applications of EC systems, such as data transmission, monitoring and control, microgrid systems, smart metering, anomaly detection, state estimation, energy management, and fault detection. In Section 5, security and privacy concerns associated with EC are addressed, and potential solutions are proposed. Section 6 identifies the challenges faced by EC systems and outlines potential future research directions to advance the field. Finally, Section 7 concludes the paper by summarizing the key findings and contributions. Figure 1: Structure of this paper.