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Energy management and voltage control in microgrids using artificial neural networks, PID, and fuzzy logic controllers

Microgrids, comprising distributed generation, energy storage systems, and loads, have recently piqued users’ interest as a potentially viable renewable energy solution for combating climate change. According to the upstream electricity grid conditions, microgrid can operate in grid-connected and is...

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Main Authors: Al Sumarmad, Khaizaran Abdulhussein, Sulaiman, Nasri, Abdul Wahab, Noor Izzri, Hizam, Hashim
Format: Article
Published: Multidisciplinary Digital Publishing Institute 2022
Online Access:http://psasir.upm.edu.my/id/eprint/101253/
https://www.mdpi.com/1996-1073/15/1/303
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spelling my.upm.eprints.1012532023-06-15T21:39:24Z http://psasir.upm.edu.my/id/eprint/101253/ Energy management and voltage control in microgrids using artificial neural networks, PID, and fuzzy logic controllers Al Sumarmad, Khaizaran Abdulhussein Sulaiman, Nasri Abdul Wahab, Noor Izzri Hizam, Hashim Microgrids, comprising distributed generation, energy storage systems, and loads, have recently piqued users’ interest as a potentially viable renewable energy solution for combating climate change. According to the upstream electricity grid conditions, microgrid can operate in grid-connected and islanded modes. Energy storage systems play a critical role in maintaining the frequency and voltage stability of an islanded microgrid. As a result, several energy management systems techniques have been proposed. This paper introduces a microgrid system, an overview of local control in a microgrid, and an efficient EMS for effective microgrid operations using three smart controllers for optimal microgrid stability. We designed a microgrid consisting of renewable sources, Li-ion batteries, the main grid as a backup system, and AC/DC loads. The proposed system control was based on supplying loads as efficiently as possible using renewable energy sources and monitoring the battery’s state of charge. The simulation results using MATLAB Simulink demonstrate the performance of the three proposed microgrid stability strategies (PID, artificial neural network, and fuzzy logic). The comparison results confirmed the viability and effectiveness of the proposed technique for energy management in a microgrid which is based on fuzzy logic controllers. Multidisciplinary Digital Publishing Institute 2022-01-03 Article PeerReviewed Al Sumarmad, Khaizaran Abdulhussein and Sulaiman, Nasri and Abdul Wahab, Noor Izzri and Hizam, Hashim (2022) Energy management and voltage control in microgrids using artificial neural networks, PID, and fuzzy logic controllers. Energies, 15 (1). art. no. 303. pp. 1-22. ISSN 1996-1073 https://www.mdpi.com/1996-1073/15/1/303 10.3390/en15010303
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description Microgrids, comprising distributed generation, energy storage systems, and loads, have recently piqued users’ interest as a potentially viable renewable energy solution for combating climate change. According to the upstream electricity grid conditions, microgrid can operate in grid-connected and islanded modes. Energy storage systems play a critical role in maintaining the frequency and voltage stability of an islanded microgrid. As a result, several energy management systems techniques have been proposed. This paper introduces a microgrid system, an overview of local control in a microgrid, and an efficient EMS for effective microgrid operations using three smart controllers for optimal microgrid stability. We designed a microgrid consisting of renewable sources, Li-ion batteries, the main grid as a backup system, and AC/DC loads. The proposed system control was based on supplying loads as efficiently as possible using renewable energy sources and monitoring the battery’s state of charge. The simulation results using MATLAB Simulink demonstrate the performance of the three proposed microgrid stability strategies (PID, artificial neural network, and fuzzy logic). The comparison results confirmed the viability and effectiveness of the proposed technique for energy management in a microgrid which is based on fuzzy logic controllers.
format Article
author Al Sumarmad, Khaizaran Abdulhussein
Sulaiman, Nasri
Abdul Wahab, Noor Izzri
Hizam, Hashim
spellingShingle Al Sumarmad, Khaizaran Abdulhussein
Sulaiman, Nasri
Abdul Wahab, Noor Izzri
Hizam, Hashim
Energy management and voltage control in microgrids using artificial neural networks, PID, and fuzzy logic controllers
author_facet Al Sumarmad, Khaizaran Abdulhussein
Sulaiman, Nasri
Abdul Wahab, Noor Izzri
Hizam, Hashim
author_sort Al Sumarmad, Khaizaran Abdulhussein
title Energy management and voltage control in microgrids using artificial neural networks, PID, and fuzzy logic controllers
title_short Energy management and voltage control in microgrids using artificial neural networks, PID, and fuzzy logic controllers
title_full Energy management and voltage control in microgrids using artificial neural networks, PID, and fuzzy logic controllers
title_fullStr Energy management and voltage control in microgrids using artificial neural networks, PID, and fuzzy logic controllers
title_full_unstemmed Energy management and voltage control in microgrids using artificial neural networks, PID, and fuzzy logic controllers
title_sort energy management and voltage control in microgrids using artificial neural networks, pid, and fuzzy logic controllers
publisher Multidisciplinary Digital Publishing Institute
publishDate 2022
url http://psasir.upm.edu.my/id/eprint/101253/
https://www.mdpi.com/1996-1073/15/1/303
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score 13.211869

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