Power Management Control of an Autonomous Photovoltaic/Wind Turbine/Battery System

The study presents an optimal control approach for managing a hybrid Photovoltaic/Wind Turbine/Battery system in an isolated area. The system includes multiple energy sources connected to a DC bus through DC/DC converters for maximum power point tracking. The proposed hybrid MPPT approach (HMPPT) ma...

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Main Authors: Rekioua D., Rekioua T., Elsanabary A., Mekhilef S.
Other Authors: 6506639323
Format: Article
Published: MDPI 2024
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spelling my.uniten.dspace-343272024-10-14T11:19:04Z Power Management Control of an Autonomous Photovoltaic/Wind Turbine/Battery System Rekioua D. Rekioua T. Elsanabary A. Mekhilef S. 6506639323 6506051950 57221120034 57928298500 design hybrid MPPT optimization panels solar battery storage wind turbine Battery storage DC-DC converters Electric load flow Maximum power point trackers Power control Power management Renewable energy resources Secondary batteries Solar panels Solar power generation Thermoelectricity Battery storage Battery systems Energy productions Hybrid MPPT Optimal controls Optimisations Panel Power flows Solar battery storage System efficiency Wind turbines The study presents an optimal control approach for managing a hybrid Photovoltaic/Wind Turbine/Battery system in an isolated area. The system includes multiple energy sources connected to a DC bus through DC/DC converters for maximum power point tracking. The proposed hybrid MPPT approach (HMPPT) manages the energy production from different sources, while the power flow method is used to balance the load and renewable power. The study shows that integrating the HMPPT algorithm and power flow approach results in improved system performance, including increased power generation and reduced stress on the batteries. The study also proposes an accurate sizing method to further improve system efficiency. The study demonstrates the effectiveness of the proposed approach by presenting results for twelve different days with varying weather conditions. The results show that the proposed approach effectively manages the energy production and load, resulting in optimal system performance. This study provides valuable insights into the optimal control of hybrid renewable energy systems, and highlights the importance of considering different energy sources and optimal sizing for maximizing system efficiency. � 2023 by the authors. Final 2024-10-14T03:19:04Z 2024-10-14T03:19:04Z 2023 Article 10.3390/en16052286 2-s2.0-85149758666 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149758666&doi=10.3390%2fen16052286&partnerID=40&md5=6ecf4e4a3d361eab5f501619b62a0463 https://irepository.uniten.edu.my/handle/123456789/34327 16 5 2286 All Open Access Gold Open Access MDPI Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic design
hybrid MPPT
optimization
panels
solar battery storage
wind turbine
Battery storage
DC-DC converters
Electric load flow
Maximum power point trackers
Power control
Power management
Renewable energy resources
Secondary batteries
Solar panels
Solar power generation
Thermoelectricity
Battery storage
Battery systems
Energy productions
Hybrid MPPT
Optimal controls
Optimisations
Panel
Power flows
Solar battery storage
System efficiency
Wind turbines
spellingShingle design
hybrid MPPT
optimization
panels
solar battery storage
wind turbine
Battery storage
DC-DC converters
Electric load flow
Maximum power point trackers
Power control
Power management
Renewable energy resources
Secondary batteries
Solar panels
Solar power generation
Thermoelectricity
Battery storage
Battery systems
Energy productions
Hybrid MPPT
Optimal controls
Optimisations
Panel
Power flows
Solar battery storage
System efficiency
Wind turbines
Rekioua D.
Rekioua T.
Elsanabary A.
Mekhilef S.
Power Management Control of an Autonomous Photovoltaic/Wind Turbine/Battery System
description The study presents an optimal control approach for managing a hybrid Photovoltaic/Wind Turbine/Battery system in an isolated area. The system includes multiple energy sources connected to a DC bus through DC/DC converters for maximum power point tracking. The proposed hybrid MPPT approach (HMPPT) manages the energy production from different sources, while the power flow method is used to balance the load and renewable power. The study shows that integrating the HMPPT algorithm and power flow approach results in improved system performance, including increased power generation and reduced stress on the batteries. The study also proposes an accurate sizing method to further improve system efficiency. The study demonstrates the effectiveness of the proposed approach by presenting results for twelve different days with varying weather conditions. The results show that the proposed approach effectively manages the energy production and load, resulting in optimal system performance. This study provides valuable insights into the optimal control of hybrid renewable energy systems, and highlights the importance of considering different energy sources and optimal sizing for maximizing system efficiency. � 2023 by the authors.
author2 6506639323
author_facet 6506639323
Rekioua D.
Rekioua T.
Elsanabary A.
Mekhilef S.
format Article
author Rekioua D.
Rekioua T.
Elsanabary A.
Mekhilef S.
author_sort Rekioua D.
title Power Management Control of an Autonomous Photovoltaic/Wind Turbine/Battery System
title_short Power Management Control of an Autonomous Photovoltaic/Wind Turbine/Battery System
title_full Power Management Control of an Autonomous Photovoltaic/Wind Turbine/Battery System
title_fullStr Power Management Control of an Autonomous Photovoltaic/Wind Turbine/Battery System
title_full_unstemmed Power Management Control of an Autonomous Photovoltaic/Wind Turbine/Battery System
title_sort power management control of an autonomous photovoltaic/wind turbine/battery system
publisher MDPI
publishDate 2024
_version_ 1814061050462470144
score 13.209306