Intelligent control of battery energy storage for microgrid energy management using ANN
TLDR
An intelligent control strategy for a microgrid system consisting of Photovoltaic panels, grid-connected, and li-ion battery energy storage systems proposed by integrating artificial neural network (ANN) for the estimation of the battery state of charge (SOC) and for the control of bidirectional converter.Abstract:
In this paper, an intelligent control strategy for a microgrid system consisting of Photovoltaic panels, grid-connected, and li-ion battery energy storage systems proposed. The energy management based on the managing of battery charging and discharging by integration of a smart controller for DC/DC bidirectional converter. The main novelty of this solution are the integration of artificial neural network (ANN) for the estimation of the battery state of charge (SOC) and for the control of bidirectional converter. The simulation results obtained in the MATLAB/Simulink environment explain the performance and the robust of the proposed control technique.read more
Citations
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References
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Supervisory Control of an Adaptive-Droop Regulated DC Microgrid With Battery Management Capability
TL;DR: In this paper, a double-layer hierarchical control strategy was proposed to overcome the control challenge associated with coordination of multiple batteries within one stand-alone microgrid, where the unit-level primary control layer was established by an adaptive voltage-droop method aimed to regulate the common bus voltage and to sustain the states of charge (SOCs) of batteries close to each other during moderate replenishment.
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Extended-Phase-Shift Control of Isolated Bidirectional DC–DC Converter for Power Distribution in Microgrid
TL;DR: In this paper, an extended phase-shift (EPSC) control of IBDC for power distribution in microgrid is proposed, which not only expands regulating range of transmission power and enhances regulating flexibility, but also reduces current stress and improves the system efficiency.
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