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.
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First, a microgrid control structure with edge‐computing services based on hybrid control theory is proposed, which can exploit the hybrid characteristics of the microgrid control and...
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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 through the case
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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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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 energy has been
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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
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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.
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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 energy
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To realize the cost-optimal control decision of microgrids under the condition of load balance, this paper proposed a bilevel optimization model for microgrid users based on edge computing.
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Edge computing is a distributed computing model that brings computation and data storage closer to the sources of data. More broadly, it refers to any design that pushes computation physically closer to a user,
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