Real-Time Energy Management for DC Microgrids Using Artificial Intelligence
Microgrids are defined as an interconnection of several renewable energy sources in order to provide the load power demand at any time. Due to the intermittence of renewable energy sources, storage systems are necessary, and they are generally used as a backup system. Indeed, to manage the power flo...
Main Authors: | , , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-08-01
|
Series: | Energies |
Subjects: | |
Online Access: | https://www.mdpi.com/1996-1073/14/17/5307 |
id |
doaj-aa2b7c66044548e0bed548ed74968abc |
---|---|
record_format |
Article |
spelling |
doaj-aa2b7c66044548e0bed548ed74968abc2021-09-09T13:42:52ZengMDPI AGEnergies1996-10732021-08-01145307530710.3390/en14175307Real-Time Energy Management for DC Microgrids Using Artificial IntelligenceAiman J. Albarakati0Younes Boujoudar1Mohamed Azeroual2Reda Jabeur3Ayman Aljarbouh4Hassan El Moussaoui5Tijani Lamhamdi6Najat Ouaaline7Department of Computer Engineering, Faculty of Computer and Information Sciences, Majmaah University, Majmaah 11952, Saudi ArabiaDepartment of Electrical Engineering, Faculty of Sciences and Technology, Sidi Mohamed Ben Abdullah University, Fez BP 2626, MoroccoDepartment of Electrical Engineering, Faculty of Sciences and Technology, Sidi Mohamed Ben Abdullah University, Fez BP 2626, MoroccoDepartment of Electrical and Mechanical Engineering, Faculty of Science and Technology, Hassan 1 University, Settat BP 577, MoroccoDepartment of Computer Science, School of Arts and Sciences, University of Central Asia, Naryn 722918, KyrgyzstanDepartment of Electrical Engineering, Faculty of Sciences and Technology, Sidi Mohamed Ben Abdullah University, Fez BP 2626, MoroccoDepartment of Electrical Engineering, Faculty of Sciences and Technology, Sidi Mohamed Ben Abdullah University, Fez BP 2626, MoroccoDepartment of Electrical and Mechanical Engineering, Faculty of Science and Technology, Hassan 1 University, Settat BP 577, MoroccoMicrogrids are defined as an interconnection of several renewable energy sources in order to provide the load power demand at any time. Due to the intermittence of renewable energy sources, storage systems are necessary, and they are generally used as a backup system. Indeed, to manage the power flows along the entire microgrid, an energy management strategy (EMS) is necessary. This paper describes a microgrid energy management system, which is composed of solar panels and wind turbines as renewable sources, Li-ion batteries, electrical grids as backup sources, and AC/DC loads. The proposed EMS is based on the maximum extraction of energy from the renewable sources, by making them operate under Maximum Power Point Tracking (MPPT) mode; both of those MPPT algorithms are implemented with a multi-agent system (MAS). In addition, management of the stored energy is performed through the optimal control of battery charging and discharging using artificial neural network controllers (ANNCs). The main objective of this system is to maintain the power balance in the microgrid and to provide a configurable and a flexible control for the different scenarios of all kinds of variations. All the system’s components were modeled in MATLAB/Simulink, the MAS system was developed using Java Agent Development Framework (JADE), and Multi-Agent Control using Simulink with Jade extension (MACSIMJX) was used to insure the communication between Simulink and JADE.https://www.mdpi.com/1996-1073/14/17/5307energy managementmicrogridMASANNCbatterywind |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Aiman J. Albarakati Younes Boujoudar Mohamed Azeroual Reda Jabeur Ayman Aljarbouh Hassan El Moussaoui Tijani Lamhamdi Najat Ouaaline |
spellingShingle |
Aiman J. Albarakati Younes Boujoudar Mohamed Azeroual Reda Jabeur Ayman Aljarbouh Hassan El Moussaoui Tijani Lamhamdi Najat Ouaaline Real-Time Energy Management for DC Microgrids Using Artificial Intelligence Energies energy management microgrid MAS ANNC battery wind |
author_facet |
Aiman J. Albarakati Younes Boujoudar Mohamed Azeroual Reda Jabeur Ayman Aljarbouh Hassan El Moussaoui Tijani Lamhamdi Najat Ouaaline |
author_sort |
Aiman J. Albarakati |
title |
Real-Time Energy Management for DC Microgrids Using Artificial Intelligence |
title_short |
Real-Time Energy Management for DC Microgrids Using Artificial Intelligence |
title_full |
Real-Time Energy Management for DC Microgrids Using Artificial Intelligence |
title_fullStr |
Real-Time Energy Management for DC Microgrids Using Artificial Intelligence |
title_full_unstemmed |
Real-Time Energy Management for DC Microgrids Using Artificial Intelligence |
title_sort |
real-time energy management for dc microgrids using artificial intelligence |
publisher |
MDPI AG |
series |
Energies |
issn |
1996-1073 |
publishDate |
2021-08-01 |
description |
Microgrids are defined as an interconnection of several renewable energy sources in order to provide the load power demand at any time. Due to the intermittence of renewable energy sources, storage systems are necessary, and they are generally used as a backup system. Indeed, to manage the power flows along the entire microgrid, an energy management strategy (EMS) is necessary. This paper describes a microgrid energy management system, which is composed of solar panels and wind turbines as renewable sources, Li-ion batteries, electrical grids as backup sources, and AC/DC loads. The proposed EMS is based on the maximum extraction of energy from the renewable sources, by making them operate under Maximum Power Point Tracking (MPPT) mode; both of those MPPT algorithms are implemented with a multi-agent system (MAS). In addition, management of the stored energy is performed through the optimal control of battery charging and discharging using artificial neural network controllers (ANNCs). The main objective of this system is to maintain the power balance in the microgrid and to provide a configurable and a flexible control for the different scenarios of all kinds of variations. All the system’s components were modeled in MATLAB/Simulink, the MAS system was developed using Java Agent Development Framework (JADE), and Multi-Agent Control using Simulink with Jade extension (MACSIMJX) was used to insure the communication between Simulink and JADE. |
topic |
energy management microgrid MAS ANNC battery wind |
url |
https://www.mdpi.com/1996-1073/14/17/5307 |
work_keys_str_mv |
AT aimanjalbarakati realtimeenergymanagementfordcmicrogridsusingartificialintelligence AT younesboujoudar realtimeenergymanagementfordcmicrogridsusingartificialintelligence AT mohamedazeroual realtimeenergymanagementfordcmicrogridsusingartificialintelligence AT redajabeur realtimeenergymanagementfordcmicrogridsusingartificialintelligence AT aymanaljarbouh realtimeenergymanagementfordcmicrogridsusingartificialintelligence AT hassanelmoussaoui realtimeenergymanagementfordcmicrogridsusingartificialintelligence AT tijanilamhamdi realtimeenergymanagementfordcmicrogridsusingartificialintelligence AT najatouaaline realtimeenergymanagementfordcmicrogridsusingartificialintelligence |
_version_ |
1717760548854038528 |