Model Predictive Control of Multi-input Solar-Wind Hybrid System in DC Community with Battery Back-up

This paper proposes a multi-input hybrid DC microgrid system to combine renewable energy sources like photovoltaics (PV), wind with storage by applying fast adaptive controls based on the Model Predictive Control (MPC) method. The proposed MPC approach will be based on Maximum Power Point Tracking (...

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Bibliographic Details
Published in2021 IEEE 12th International Symposium on Power Electronics for Distributed Generation Systems (PEDG) pp. 1 - 8
Main Authors Ghosh, Sumana, Barman, Jitesh Chandra, Batarseh, Issa
Format Conference Proceeding
LanguageEnglish
Published IEEE 28.06.2021
Subjects
Batteries
DC Microgrid
Droop
Hybrid
Maximum power point trackers
Maximum Power Point Tracking
Microgrids
Model Predictive Controller
Photovoltaic systems
Predictive models
Renewable energy sources
Solar
Wind
Wind energy
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Summary:This paper proposes a multi-input hybrid DC microgrid system to combine renewable energy sources like photovoltaics (PV), wind with storage by applying fast adaptive controls based on the Model Predictive Control (MPC) method. The proposed MPC approach will be based on Maximum Power Point Tracking (MPPT) approach, which is used to regulate the DC-DC converters connecting Photovoltaic (PV) sources. Whereas, the wind energy module uses tip-speed ratio based MPPT method for switching control of converter. A back-to-back converter is added to improve the current sharing between the PV panels and wind farm. Also, to enhance the system reliability due to current sharing under varying and unpredictable weather conditions, droop based MPC is implemented to fix the dc bus voltage. The state of charge (SOC) controlled storage unit gives reliability to sustain peak load demands in the proposed DC microgrid system.
ISSN:2329-5767
DOI:10.1109/PEDG51384.2021.9494234