# A Review of Edge Computing Technology and Its Applications in Power Systems
## 5 Discussion and Future Opportunities  
The major objective of this review paper is to comprehensively summarize and study edge computing and its advantages that can benefit various industries, especially the energy industry. In Sections 2 and 3, edge computing architectures and relative technologies are explored. The practical examples in Sections 2 and 3 demonstrated that edge computing can solve various issues by modifying specific edge computing frameworks based on basic edge computing architectures. These examples can also prove that edge computing can be used in a wide range of fields, such as smart cities and smart health. In the future, edge computing can contribute to more traditional fields and help with digital transformation. In Section 4, various edge computing technologies in the smart grid edge are discussed and illustrate that with the development of the smart grids and grid edge, the usage of distributed smart devices and energy resources is increasing. Edge computing can be an  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**References** & **Edge Nodes** & **Edge Servers** \\ \hline
[91] & IoT Devices for monitoring the smart grids & Wi-Fi access points or 5G base stations \\ \hline
[93] & The sensors installed on the tower and the wires & Edge computing nodes \\ \hline
[92; 94; 95; 96; 105] & Smart information devices such as smart meters & Concentrators \\ \hline
[97] & Smart meters & Long Range (LoRa) nodes \\ \hline
[101; 106] & Smart meters & Edge transformers \\ \hline
[109; 112] & Power Quality Meter (PQM) and Phasor Measurement Unit (PMU) & Co-located embedded personal computer (ePC) \\ \hline
[113] & Intelligent electronic devices (IEDs) & The IEDs that are elected as leader \\ \hline
[116] & Distributed energy resources & Edge of Network Grid Optimization (DERs) at grid edge & Edge of Network Grid Optimization (ENGO) devices \\ \hline
[117; 119] & Distributed energy resources & \multirow{2}{*}{DER clients} \\  & (DERs) & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Table of Edge Computing Architecture Detailed Settings in Smart Grids.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Research Topics** & **References** \\ \hline Power Grid Distribution Monitoring & [91; 92; 93; 94] \\ Smart Meters Management & [95; 96; 97; 100] \\ Data Collection and Analysis & [101; 102] \\ Anomaly Detection & [103; 104; 105; 106; 107] \\ Measurement Placement & [108; 109] \\ State Estimation & [112; 113] \\ Energy Storage & [114] \\ Resource Management & [115; 116; 117] \\ Security of Grid Edge Management System & [118; 119] \\ Renewable Energy Forecasting & [120; 121; 122; 123] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Reference Table of Edge Computing in Smart Grids.  
ideal solution to fulfill different requirements in the energy industry, such as forecasting the generation of electricity with renewable energy resources. Figure 6 also illustrates that edge computing has become more and more important for energy systems and has been widely discussed in recent years.  
The emergence of the latest technologies has also proved that there are still numerous opportunities and topics to explore. In this section, we discuss several future opportunities for edge computing at the grid edge, including grid edge intelligence, 5G-based grid edge application, and grid edge workflow management.