This paper presents a centralized controller based on CNN-LSTM, optimized with the imperialist competitive algorithm (ICA), to enhance power management in a hybrid microgrid
An improved method for power control of power converters in AC/DC hybrid microgrids is presented in this paper. The control method is based on a combination of adaptive frequency shifting
Finally, the effectiveness and feasibility of the proposed control strategy are verified by building a “wind light storage load” microgrid simulation model on the MATLAB / Simulink platform.
Independent microgrids are widely used in islands and remote townships. However, power imbalance often leads to fluctuations in voltage and frequency, which inhibit the development
Microgrid clusters are becoming increasingly popular owing to their many benefits and their ability to meet human needs in terms of energy flexibility, security, and integration of renewable
This paper presents a centralized controller based on CNN-LSTM, optimized with the imperialist competitive algorithm (ICA), to enhance power
This paper focuses on developing an efficient controller for DC Microgrid system to enhance optimum power flow management between distributed energy resources. The prime focus
To address power imbalance issues caused by renewable energy fluctuations and fault isolation in remote AC/DC hybrid microgrids, this paper proposes an adaptive regulation strategy
This paper presents a holistic data-driven power optimization approach based on deep reinforcement learning (DRL) for microgrid control, considering the multiple needs of
A control paradigm is proposed in this paper for decentralized power balance in hybrid AC/DC Microgrids (MGs). In this technique, the AC and DC sub-gr
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