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...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Published: |
Multidisciplinary Digital Publishing Institute
2022
|
| Online Access: | http://psasir.upm.edu.my/id/eprint/101253/ |
| _version_ | 1848863523456679936 |
|---|---|
| author | Al Sumarmad, Khaizaran Abdulhussein Sulaiman, Nasri Abdul Wahab, Noor Izzri Hizam, Hashim |
| author_facet | Al Sumarmad, Khaizaran Abdulhussein Sulaiman, Nasri Abdul Wahab, Noor Izzri Hizam, Hashim |
| author_sort | Al Sumarmad, Khaizaran Abdulhussein |
| building | UPM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T13:34:16Z |
| format | Article |
| id | upm-101253 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T13:34:16Z |
| publishDate | 2022 |
| publisher | Multidisciplinary Digital Publishing Institute |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-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 |
| 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 |
| title | 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_short | 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 |
| url | http://psasir.upm.edu.my/id/eprint/101253/ http://psasir.upm.edu.my/id/eprint/101253/ http://psasir.upm.edu.my/id/eprint/101253/ |