# A Review of Edge Computing Technology and Its Applications in Power Systems
## 4 Review of Edge Computing in Power Systems
### Renewable Energy Monitoring and Forecasting  
In recent years, the shortage of energy resources has become a popular topic. It is urgent to solve this issue by improving the efficiency of energy usage and developing renewable energy resources. This subsection focuses on monitoring and forecasting the generation and consumption of power produced by renewable energy resources. In recent years, the power facilities of renewable energy, such as photovoltaic systems, have been involved in a digital revolution. With the increasing scale of the distributed renewable energy systems, it is essential to develop a new computing paradigm in monitoring. In [120], [PERSON] et al. proposed a framework for distributed photovoltaic systems monitoring and fault detection in a smart city. This edge computing framework enables the data to be processed at the edge of the network, reducing the time and bandwidth consumption. Four light machine learning models are tested and selected as the best performers and implemented in the edge nodes. The experiment results show that this framework effectively improves the accuracy of fault detection in photovoltaic systems. In [121], [PERSON] et al. focused on forecasting short-term electricity generation and consumption. In this paper, smart grid 2.0 is mentioned as the basic knowledge that refers to the next generation of power grid, including distributed renewable energy resources, bi-directional energy, and modern digital energy management systems. Smart microgrid is one of the techniques that can help with realizing smart grid 2.0. It can provide automated management of distributed renewable energy resources. To realize smart microgrids, integrating edge computing and AI (edge-AI) plays a key role. An edge-AI-based forecasting method is proposed in this article. This method includes a wind generation forecasting algorithm implemented in edge devices based on convolutional neural network (CNN) and gated recurrent unit (GRU) machine learning models, and an electric load prediction algorithm in end users' devices. Experiment results on real-world datasets from China and Belgium show that the proposed algorithms improve the prediction accuracy of wind power generation and consumption forecasting. In [122], [PERSON] et al. discussed the energy management of microgrids. This paper aims to address the microgrid monitoring issue. The proposed edge-AI framework integrates machine learning models for forecasting power supply and demand at the edge of the smart microgrids. Experiments on real-time data under different scenarios demonstrate that the proposed framework can fit different types of microgrids and improve the flexibility and performance of microgrids. In recent years, hydrogen has become a popular renewable energy carrier and has gained great attention from enterprises and governments. [PERSON] et al. [123] proposed a sustainable energy platform integrating plugin electric vehicles and fuel cell electric vehicles with hydrogen as energy carriers. Furthermore, a decentralized model deployed in a mobile edge computing system is implemented for this integrated hydrogen microgrid to interact in a local energy market. According to the experiment results, the proposed platform can reduce the total cost. In summary, this section focuses on nine primary topics in edge computing-enabled smart grids, as shown in Table 3. Power grid distribution monitoring is one of the most important research topics aimed at handling the geographical diversity in renewable energy areas. With the development of the smart grid, smart meters play a crucial role in smart power systems. Research on smart grid detection can provide a safer and more stable power supply. The advanced metering system is another significant component of the smart grid, with several applications discussed in this subsection. Furthermore, anomaly and fault detection have been receiving increasing attention from the smart grid community recently. Therefore, its subsection includes three popular methods of anomaly detection.