# A Review of Edge Computing Technology and Its Applications in Power Systems
## 1 Introduction
Instead of transmitting data from each device at the edge of the network to the cloud center, the edge computing architecture enables the edge servers to collect, process, and store the data locally. By allowing data to be collected, processed, and stored on local edge servers, both the response speed and the safety of sensitive data can be improved. Furthermore, the management of the end devices across various geographical locations can rely on these local edge servers, helping eliminate the vision gaps between cloud servers and local edge devices. As shown in Figure 1, the edge computing architecture comprises three tiers: central tier, edge tier, and device tier [11]. The central tier aims to provide wide connectivity coverage using cloud computing services, where vast computational resources and storage capabilities are employed to process and manage large-scale data tasks. The edge tier serves as an intermediary layer that extends cloud capabilities closer to the data source by deploying edge servers between the cloud and end devices to handle intermediate data processing, reducing latency and bandwidth usage while still leveraging cloud resources for more intensive tasks. Edge computing further pushes data processing to the very edge of the network, right at or near the data sources. The device tier consists of various end devices with limited computing and storage resources, such as sensors and actuators, responsible for collecting raw data. The edge server layer receives raw data from the local end devices nearby and pre-processes the data. Subsequently, the pre-processed data is sent to the edge computing layer or cloud server for further processing and computing. As interconnected layers, cloud computing and edge computing collectively enhance the efficiency and effectiveness of data processing in today's digital infrastructures. The proximity reduces latency, conserves bandwidth [12], enhances security, reduces energy consumption of cloud centers, and improves the responsiveness of applications. Edge computing communication can be categorized into two layers: (i) access networks, which connect the device tier with the edge tier, and (ii) transport networks, which connect the edge tier with the central tier. In edge computing systems, communications are frequently between access points and end devices. The edge servers typically perform as small data centers that are co-located with the wireless access points. For example, the edge servers can be placed in the local base stations to eliminate the additional expenditure. Access points are also responsible for accessing large-scale cloud data centers. Different access technologies, including cellular wireless networks and fiber-wireless access networks, can be used to connect edge devices and edge servers. When edge devices are positioned at the wireless access point, they can directly access edge servers through the radio channels. In such scenarios, cooperative communication abilities of wireless channels can optimize task offloading through relaying via nearer mobile devices. In recent years, the 5 th generation mobile network (5G) has emerged as a cellular network technology capable of providing higher-quality communication and improving the quality of service. This feature enables 5G to support highly interactive applications with high throughput and low latency [13]. Edge computing can benefit from 5G by bringing computing resources from the cloud to the end users. Compared with the traditional cloud computing model, edge computing in 5G is more suitable for highly computational and interactive applications. Edge computing has been successfully applied in various areas. Recently, the increasing need for renewable energy integration and decarbonization has led to a rapid expansion in the deployment of distributed energy resources (DERs) and sensors. Power systems have become one of the areas that benefit significantly from edge computing.