# A Review of Edge Computing Technology and Its Applications in Power Systems
## 4 Review of Edge Computing in Power Systems
### Power Grid Distribution Monitoring  
Power grid monitoring is one of the most important research topics in smart grids today. With advancements in power grid technology, power sources have diversified. Devices for renewable energy, such as solar energy and wind energy, have a varied geographical distribution. Furthermore, the emerging smart end devices used in smart grids have made traditional manual inspection inadequate for ensuring safety and efficiency. Smart grid monitoring and control have become increasingly complex, necessitating real-timeresponses. Many researchers have focused on this issue and proposed various applications to address these challenges.  
In 2018, [PERSON] et al. [91] emphasized the importance of power grid monitoring in preventing severe safety accidents. Recognizing that traditional manual detection cannot meet the needs of modern power grids, the authors proposed an edge computing framework for real-time monitoring. They developed this framework by moving part of the computation from the central cloud to the edge servers. Their experiments showed that the detection delay was reduced by up to 85% compared to cloud-based solutions.  
Monitoring real-time smart meters is another popular topic. In 2020, [PERSON] et al. [92] focused on AMI in smart grids. Given that smart grid infrastructures are geographically distributed, there is a demand for effective monitoring and control systems. A good routing protocol is crucial for the metering infrastructure to communicate effectively with the fog router. A standard routing protocol for IPv6 over Low Power Personal Area Network (6 LoWPAN) is the routing protocol for Low Power Lossy Area Network (RPL). Smart meters within the 6 LoWPAN network communicate with fog routers, which results in nodes far from the fog routers having low packet delivery ratios and high delays. The authors proposed an advanced RPL scheme to address this issue. Instead of communicating with the fog router directly, aggregator-based RPL nodes were set between edge nodes and the fog router. Performance evaluations showed that this scheme effectively increased packet delivery ratios and reduced end-to-end delays.  
[PERSON] et al. [93] proposed an edge computing node planning model for real-time transmission line monitoring. Monitoring transmission lines is crucial to ensure the stable operation of the power grid. The emergence of real-time online monitoring services enhances the safety of the transmission lines, but it also introduces a high latency requirement. Edge computing can bring the edge nodes closer to the terminal, thereby reducing latency. In their paper, an edge computing node placement model was established, and a genetic algorithm based on a predator search strategy was proposed to overcome the shortcomings of the original genetic algorithm. Simulation experiments demonstrated that this improved genetic algorithm performed better in planning the placement of edge nodes.  
The work presented in [94] focuses on the communication system models used for voltage profile monitoring and power loss estimation in smart grids. The author implemented and compared two different architectures: cloud-based and cloud-fog-based. Experimental results showed that the cloud-fog-based approach significantly reduced the total simulation time and the data size transferred to the cloud.