# Edge Computing for IoT-Enabled Smart Grid: The Future of Energy
## 3 Survey of EC-IoT Smart Grids
### Power Distribution Monitor Systems  
The power distribution monitor system is one of the most important applications of smart grids. Today, with the robust development of science, and the open support policies of governments, the power sources are increasingly diverse. Some emerging renewable energy sources such as solar power and wind power are increasingly high, distributed in a variety of locations, from the rooftop of households to manufacturing enterprises, from small-scale power sources to large-scale power generation corporations. The monitoring and control of power grids to operate safe, stable and efficient is increasingly complex and requires real-time responsive services. Many solutions have been proposed to address these challenges. In the power distribution monitor sector, one of the key challenges is monitoring real-time smart meter nodes. [PERSON] et al. (2020) [36] proposed an efficient aggregation RPL scheme based on fog computing and 6 LoWPAN routing protocol to improve system  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref. No** & **Scenarios** & **Key Technologies** & **Case Study: Key Focus** \\ \hline
[36] & Power Distribution & Edge computing \& & \\  & 6 LoWPAN protocol  
performance for low power lossy area networks. Specifically, in a power distribution system, all the smart meters form the 6 LoWPAN network. This led to some nodes far from the fog/edge servers having high delay and low packet delivery ratio. To solve this problem, the author used the aggregator to support communication between these smart meters to fog/edge servers. The results have demonstrated that the proposed solution improved over 35.6% of packet delivery ratio and 13.24% of delay compared to existing RPL-based solutions. The development of science and technology leads to the birth of renewable energy sources for humanity. Nowadays, energy sources such as solar power and wind power are ubiquitous and connected to power distribution systems. This has led to Volt/VAR control (VVC) becoming increasingly complex and a significant challenge in distribution systems. The existing solutions use DER inverters. However, this led to DER inverters conflicting with each other when autonomously operating under grid voltage control. To solve this problem, [PERSON] et al. (2019) [PERSON] et al. (2019) [PERSON] et al. (2019) proposed a novel VVC strategy based on an artificial neural network at the edge layer. Simulation results indicated that the proposed solution could control DER inverters to achieve VVC goals at the grid edge. Additionally, in this direction, the diversity of renewable energy sources leads to an increasingly rapid proliferation of distributed generators (DGs). Consequently, Volt/VAR control in power distribution systems is increasingly complex. To solve this problem, [PERSON] et al. (2020) [PERSON] et al. (2020) proposed a hierarchical hybrid architecture for Volt/VAR control of power distribution grids. Specifically, the authors proposed a three-layer architecture in which the top layer performs central optimization based on an optimal scheduling schema with a 15 min cycle to minimize power losses. The middle layer controls voltage for DGs based on the distributed approach to reduce service response time. The bottom layer aims for real-time exact decision making based on edge computing. Simulation results demonstrated that the proposed architecture improved coordination at 80% penetration of DGs. In order to stabilize the voltage on the power distribution grids, smart inverters are used to control the voltage of grids fluctuations within a predefined range. However, controlling these inverters requires an intelligent, flexible and real-time control scheme. To address these challenges, [PERSON] et al. (2021) [PERSON] et al. (2021) [PERSON] et al. (2021) proposed a multi-time scale three-tiered voltage control schema for dispersed smart inverters at the grid edge to stabilize the voltage fluctuations.