Energy Management in Isolated DC Microgrid Systems Using ARO-NN for PV, Battery, and Supercapacitor Integration

This article describes how to utilise a battery and supercapacitor to control the energy in a stand-alone DC microgrid. This strategy makes use of an artificial rabbits optimised neural network (ARONN) control mechanism. This power management strategy's key objectives are to balance production and consumption, fulfil power demand, and maintain a steady DC bus voltage. By adjusting the real power available on the shared common bus, this method has the amazing advantage of correcting for losses in power modulator design. ARONN control, which regulates the power modulator in the storage system, and the incremental conductance maximum power point tracking (MPPT) approach, which maximises power extraction from PV sources, are integrated in the isolated DC microgrid regulator. The bus DC voltage is kept constant with little deviation from the reference voltage by efficiently managing the power flow on the shared DC bus. This strategy also lessens stress on the battery by assigning higher-frequency current control to the supercapacitor and lower-frequency current control to the battery. The simulation's outcomes confirm that the suggested energy management and regulator strategies are effective.

Authors

Publication Details

Journal
Communications on Applied Nonlinear Analysis
Published
2024-11-29
DOI
https://doi.org/10.52783/cana.v32.2551
Citations
1
Primary Topic
Microgrid Control and Optimization
Type
article
Field-Weighted Citation Impact
0.33
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article

Energy Management in Isolated DC Microgrid Systems Using ARO-NN for PV, Battery, and Supercapacitor Integration

Saurabh V Kumar
1 citations
Communications on Applied Nonlinear Analysis
Microgrid Control and Optimization
0.33
article

Energy Management in Isolated DC Microgrid Systems Using ARO-NN for PV, Battery, and Supercapacitor Integration

Saurabh V Kumar
article en
1 citations

Abstract

This article describes how to utilise a battery and supercapacitor to control the energy in a stand-alone DC microgrid. This strategy makes use of an artificial rabbits optimised neural network (ARONN) control mechanism. This power management strategy's key objectives are to balance production and consumption, fulfil power demand, and maintain a steady DC bus voltage. By adjusting the real power available on the shared common bus, this method has the amazing advantage of correcting for losses in power modulator design. ARONN control, which regulates the power modulator in the storage system, and the incremental conductance maximum power point tracking (MPPT) approach, which maximises power extraction from PV sources, are integrated in the isolated DC microgrid regulator. The bus DC voltage is kept constant with little deviation from the reference voltage by efficiently managing the power flow on the shared DC bus. This strategy also lessens stress on the battery by assigning higher-frequency current control to the supercapacitor and lower-frequency current control to the battery. The simulation's outcomes confirm that the suggested energy management and regulator strategies are effective.

Communications on Applied Nonlinear AnalysisVol. 32(3s)
Openalex Percentile: Top 37%
Microgrid Control and Optimization
0.33
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