Therefore, this paper proposes a bilevel optimization model for microgrid users based on edge computing, which is divided into an upper-level module and a lower-level module. The purpose of the
The superiority of edge-computing services based on hybrid control theory and event-triggered technology in reducing communication and improving control in real time is demonstrated
The concept of integrating a microgrid with an edge compute network presents a pioneering approach in the realm of sustainable technology and efficient data processing.
In this research the proposed model for VANET communication. IoT edge cloud computing module and the smart micro grid architecture is used for energy management in VANET. Vehicle
In this paper, we consider the problem of power adjustment and propose the framework of multi-agent deep reinforcement learning and edge computing for distributed power control in microgrids.
Microgrids are perfect for leveraging edge computing. Resources like solar inverters, batteries and EV chargers can network into a local microgrid edge system. This lets microgrids
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
Therefore, in this paper, we study about the microgrid-enabled MEC networks'' energy supply plan, where we first formulate an optimization problem and the objective is to minimize the
Additionally, the paper examines the application of cutting-edge technologies like machine learning, blockchain, reinforcement learning, neural networks, edge computing, and the
In this paper, an edge computing-based machine-learning study is conducted for solar inverter power forecasting and droop control in a remote microgrid.
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