# Edge Computing for IoT-Enabled Smart Grid: The Future of Energy
## 3 Survey of EC-IoT Smart Grids
### Micro-Grid Systems  
One of the most important drivers of the economic development of a country is energy resources. A stable energy source will improve the living quality of humans. To solve this problem, the government and energy suppliers are making efforts to build and deploy SG infrastructures. In terms of management, SGs consist of two main components: (1) power distribution grid systems which are operated by power suppliers; (2) micro-grid systems which are grid systems in a building, home or enterprise. Micro-grid systems need to be optimized for efficient power distribution and management. In this subsection, we present the state-of-the-art EC-IoT-based SG applications for smart homes and buildings and highlight related challenges to energy management in micro-grid systems.  
The smart grid is one of the factors that determine the success of a smart factory. Currently, smart factories often use battery energy storage systems to ensure supply stability and power quality. However, calculating the load, energy storage capacity, and timing are the main challenges of this solution. To solve this problem, [PERSON] et al. (2021) [41] introduced an adaptive evolution energy control algorithm based on edge computing and mutation strategy to trade-off between load, cost, timing, and other metrics aimed at optimal energy efficiency for smart factories. The experiment results demonstrated that the proposed solution enhances the effectiveness compared to the existing solutions.  
The smart home is emerging as an inevitable trend in modern cities. In these homes, energy optimization and load management are increasing concerns by homeowners. Nowadays, the smart grid uses the nonintrusive load monitoring (NILM) technique to monitor the energy consumption for individual electrical loads of households at a certain measurement point. In this technique, the accuracy of household electrical loads is a significant challenge. To address this problem, [PERSON] et al. (2021) [42] proposed the load feature extraction technique, improved from the NILM technique, based on the support of the current harmonic signature analysis and fuzzy rule-based intelligent identification method to monitor different electrical loads. The simulation results demonstrated that the proposed solution enhances the accuracy of household electrical loads compared to existing solutions.  
Obviously, energy plays a particularly important role in each household as well as for the whole society. According to the authors, buildings are the biggest electricity consumers. In order to highlight the green energy trend for smart homes and buildings, [PERSON] et al. (2021) [43] presented a full picture of smart grids for smart apartments and buildings. Specifically, they have introduced different solutions, techniques, approaches and challenges to save energy for households and buildings. In addition, they have also proposed the self-managing energy system model, called SES.  
Aiming to drive renewable energy solutions for households and provide smart transaction mechanisms for excess power from solar grids, [PERSON] et al. (2021) [44] have proposed an autonomous commercial architecture based on blockchain to transact excess power from solar micro-grids of households. Additionally, in this direction, [PERSON] et al. (2022) [45] have proposed an autonomous energy distribution mechanism to sell households' excess power based on private distributed ledgers. This solution allows automatic bidding, ensuring openness and transparency and real-time trading.  
The analysis results have shown an overview picture of micro-grid systems for smart homes and buildings as well as challenges and open issues.