Control and Implementation of an Energy Management Strategy for a PV–Wind–Battery Microgrid Based on an Intelligent Prediction Algorithm of Energy Production
This paper describes an energy management strategy for a DC microgrid that utilizes a hybrid renewable energy system (HRES) composed of a photovoltaic (PV) module, a wind turbine based on a permanent magnetic synchronous generator (PMSG), and a battery energy storage system (BESS). The strategy is d...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/4/1883 |
| _version_ | 1797621255137918976 |
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| author | Sameh Mahjoub Larbi Chrifi-Alaoui Saïd Drid Nabil Derbel |
| author_facet | Sameh Mahjoub Larbi Chrifi-Alaoui Saïd Drid Nabil Derbel |
| author_sort | Sameh Mahjoub |
| collection | DOAJ |
| description | This paper describes an energy management strategy for a DC microgrid that utilizes a hybrid renewable energy system (HRES) composed of a photovoltaic (PV) module, a wind turbine based on a permanent magnetic synchronous generator (PMSG), and a battery energy storage system (BESS). The strategy is designed to provide a flexible and reliable system architecture that ensures continuous power supply to loads under all conditions. The control scheme is based on the generation of reference source currents and the management of power flux. To optimize the supply–demand balance and ensure optimal power sharing, the strategy employs artificial intelligence algorithms that use previous data, constantly updated forecasts (such as weather forecasts and local production data), and other factors to control all system components in an optimal manner. A double-input single-output DC–DC converter is used to extract the maximum power point tracking (MPPT) from each source. This allows the converter to still transfer power from one source to another even if one of the sources is unable to generate power. In this HRES configuration, all the sources are connected in parallel through the common DC–DC converter. The strategy also includes a long short-term memory (LSTM) network-based forecasting approach to predict the available energy production and the battery state of charge (SOC). The system is tested using Matlab/Simulink and validated experimentally in a laboratory setting. |
| first_indexed | 2024-03-11T08:53:10Z |
| format | Article |
| id | doaj.art-c439d58a86074cf6b8bbadcfe188bee5 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T08:53:10Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-c439d58a86074cf6b8bbadcfe188bee52023-11-16T20:19:08ZengMDPI AGEnergies1996-10732023-02-01164188310.3390/en16041883Control and Implementation of an Energy Management Strategy for a PV–Wind–Battery Microgrid Based on an Intelligent Prediction Algorithm of Energy ProductionSameh Mahjoub0Larbi Chrifi-Alaoui1Saïd Drid2Nabil Derbel3Laboratory of Innovative Technology (LTI, UR-UPJV 3899), University of Picardie Jules Verne, 80000 Amiens, FranceLaboratory of Innovative Technology (LTI, UR-UPJV 3899), University of Picardie Jules Verne, 80000 Amiens, FranceL.S.P.I.E Laboratory, Electrical Engineering Department, Batna 2, Batna 05000, AlgeriaNational Engineering School of Sfax, Sfax 3038, TunisiaThis paper describes an energy management strategy for a DC microgrid that utilizes a hybrid renewable energy system (HRES) composed of a photovoltaic (PV) module, a wind turbine based on a permanent magnetic synchronous generator (PMSG), and a battery energy storage system (BESS). The strategy is designed to provide a flexible and reliable system architecture that ensures continuous power supply to loads under all conditions. The control scheme is based on the generation of reference source currents and the management of power flux. To optimize the supply–demand balance and ensure optimal power sharing, the strategy employs artificial intelligence algorithms that use previous data, constantly updated forecasts (such as weather forecasts and local production data), and other factors to control all system components in an optimal manner. A double-input single-output DC–DC converter is used to extract the maximum power point tracking (MPPT) from each source. This allows the converter to still transfer power from one source to another even if one of the sources is unable to generate power. In this HRES configuration, all the sources are connected in parallel through the common DC–DC converter. The strategy also includes a long short-term memory (LSTM) network-based forecasting approach to predict the available energy production and the battery state of charge (SOC). The system is tested using Matlab/Simulink and validated experimentally in a laboratory setting.https://www.mdpi.com/1996-1073/16/4/1883double-input single-output converterenergy managementexperimental validationhybrid renewable energy system (HRES)prediction |
| spellingShingle | Sameh Mahjoub Larbi Chrifi-Alaoui Saïd Drid Nabil Derbel Control and Implementation of an Energy Management Strategy for a PV–Wind–Battery Microgrid Based on an Intelligent Prediction Algorithm of Energy Production Energies double-input single-output converter energy management experimental validation hybrid renewable energy system (HRES) prediction |
| title | Control and Implementation of an Energy Management Strategy for a PV–Wind–Battery Microgrid Based on an Intelligent Prediction Algorithm of Energy Production |
| title_full | Control and Implementation of an Energy Management Strategy for a PV–Wind–Battery Microgrid Based on an Intelligent Prediction Algorithm of Energy Production |
| title_fullStr | Control and Implementation of an Energy Management Strategy for a PV–Wind–Battery Microgrid Based on an Intelligent Prediction Algorithm of Energy Production |
| title_full_unstemmed | Control and Implementation of an Energy Management Strategy for a PV–Wind–Battery Microgrid Based on an Intelligent Prediction Algorithm of Energy Production |
| title_short | Control and Implementation of an Energy Management Strategy for a PV–Wind–Battery Microgrid Based on an Intelligent Prediction Algorithm of Energy Production |
| title_sort | control and implementation of an energy management strategy for a pv wind battery microgrid based on an intelligent prediction algorithm of energy production |
| topic | double-input single-output converter energy management experimental validation hybrid renewable energy system (HRES) prediction |
| url | https://www.mdpi.com/1996-1073/16/4/1883 |
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