# A Review of Edge Computing Technology and Its Applications in Power Systems
## 4 Review of Edge Computing in Power Systems
### Resource Management  
Since the grid edge is a comprehensive concept covering a series of resources at the customer side, behind-the-meter (BTM) resources at the grid edge have become a popular topic in recent years. In [115], [PERSON] illustrated that BTM resources are treated as a primary component of the power grid. These resources, such as DERs and flexible loads, are important during the traditional power systems' transition to net-zero emissions and 100% renewable generation. Therefore, monitoring and forecasting these resources are critical goals in power system management. This paper focuses on discussing the gaps and issues of grid edge visibility. The gap in this paper is considered as _\"the deficiency in the performance of a grid entity's responsibilities (e.g., efficiency or reliability) as a result of limitations in either the existing sensing and measurement technologies or the surrounding institutional frameworks or standards\"_. The gaps in grid edge visibility include four categories: weather forecasting, grid edge monitoring, data standards, and stakeholder obstacles. The first gap is weather sensing and forecasting. Accurate real-time forecasts and weather monitoring play an important role in forecasting grid edge DERs and the net load. The traditional method relies on third-party weather forecasting data, which are collected on a very large spatial and temporal scale. To improve the accuracy and reduce latency of the weather data, one solution is to develop local weather monitoring sensors. The local weather monitoring sensors at the grid edge can provide real-time weather data and benefit resource management. The second gap is the lack of real-time visibility of grid edge components. One avenue to address this issue is to sort the grid edge devices by type and by load. Different devices are suitable for using different monitors to collect data. The third gap is the lack of standard data formats among different applications. With the development of the grid edge resources, standard data can increase communication effectiveness. Last but not least, stakeholder capability and interest also harm the grid edge visibility. As distributed energy resources become the secondary side of the grid edge, distributed grid edge control has become a new challenge for utilities. For distributed utility management, volt and var are two primary parameters. However, objectives such as minimizing distribution loss, saving energy, and reducing peak demand are far from easy for existing controlling strategies to achieve. Therefore, [PERSON] [116] presented a distributed grid edge control by using autonomous fast-acting hybrid power electronics devices. The first contribution is that the presented distributed controlling strategy is an \"edge-up\" architecture instead of the traditional \"top-down\" approach. Autonomous devices called the Edge of Network Grid Optimization (ENGO), are introduced to realize the \"edge-up\" strategy. ENGO devices are managed by a software layer called the Grid Edge Management System (GEMS) [Wartsila Energy Storage--GEMS Digital Energy Platform Available online: [[https://storage.wartsila.com/technology/gems/](https://storage.wartsila.com/technology/gems/)]([https://storage.wartsila.com/technology/gems/](https://storage.wartsila.com/technology/gems/)) (accessed on 27 June 2024)]. The ENGO-GEMS system provides an effective solution for utilities to manage the distributed grid and achieve the objectives mentioned above. Furthermore, a control principle, implemented with the coordination of set points between all ENGO devices and the major assets, was presented in the ENGO-GEMS system. Due to the diversity of energy resources and increasing demand for energy, the way the power system produces and transmits energy is becoming more and more complex. The adoption of these new energy resources presents new grid management challenges. In recent years, artificial intelligence (AI) and machine learning have been introduced at the grid edge to ensure the reliability and resiliency of energy systems.