## 4 The Application of Edge Computing System
### Microgrid System  
EC architecture in microgrid systems represents an innovative approach that enhances energy management and data processing. This architecture enables the efficient processing, storage, and management of data from energy equipment, thereby enhancing real-time decision-making and operational efficiency. As shown in Figure 11, the EC architecture for microgrids follows a three-tier structure comprising device, edge, and cloud layers. The device layer includes of microgrid energy equipment, the edge layer incorporates edge platforms that provide computing, storage, and application functionalities, while the cloud layer hosts various cloud-based services. In microgrid applications, the edge layer plays a crucial role in data collection, transmission, and device control by utilizing various network communication protocols such as MQTT and CoAP, as described in Edge Computing Software Protocols. Each edge gateway device collects real-time operational data from energy equipment and transmits it to the edge server, enabling local data processing and efficient network management. This decentralized approach alleviates the processing burden on the cloud while enhancing  
Figure 11: Architectural overview of EC integration in microgrid systems. Figure 10: Design principles and functional structure of EC architecture in smart grid applications. system responsiveness. The cloud server subsequently analyzes the aggregated data to support decision-making processes. For instance, Figure 12 illustrates a microgrid system implemented in a rural community in central China, where EC-based energy management techniques enable real-time forecasting of renewable energy generation and load demand [171]. Equipment scheduling microservices further optimize energy usage by managing generator outputs and battery charge-discharge cycles in real-time. Several studies have explored the integration of EC into microgrid energy management. Ref. [172] investigates energy supply scheduling in multi-access edge computing (MEC)-enabled microgrids, proposing an optimization framework that considers uncertainties and delay constraints in energy production and consumption. Addressing similar challenges, refs. [173; 174] introduce a risk-sensitive energy profiling approach for MEC networks, utilizing multi-agent deep reinforcement learning (MADRL) to enhance decision-making under dynamic conditions. Additionally, ref. [175] presents a power control framework that integrates EC with reinforcement learning, enabling real-time network state detection and power flow optimization through edge intelligence. Figure 13 illustrates the system model designed in [172; 173] general terms. The use of machine learning (ML) in EC-based microgrid management has gained significant attention. Ref. [176] proposes an ML-driven EC architecture for energy management, utilizing Kafka Streams and open-source IoT platforms to predict energy consumption and production. In this model, edge devices collect data from individual prosumers and apply AI algorithms for real-time energy forecasting. Similarly, ref. [177] examines a microgrid operating in islanding mode, focusing on dynamic response mechanisms to maintain voltage and frequency stability. Further, ref. [178] explores power supply management, power balance, and malicious behavior detection in IoT-enabled smart grids, introducing EC-based prediction strategies and task rating-based privacy protection mechanisms. To enhance microgrid efficiency, ref. [179] presents a hybrid EC framework that leverages short-term electricity generation and consumption forecasting, incorporating  
Figure 12: Edge computing-based microgrid architecture in central China [171]. wind power and smart meter data. In addition, ref. [180] integrates AI-driven algorithms into MEC-based microgrid optimization, proposing a neural network-based identification method that employs online adaptive dynamic programming to eliminate dependency on predefined system models. Finally, ref.