Optimal multiobjective design of an autonomous hybrid renewable energy system in the Adrar Region, Algeria
Abstract Extended power outages are not only a nuisance but a critical problem in the modern world, which demands a continuous supply of decent quality electricity. Hybrid renewable energy systems (HRES) within a microgrid (MG) play an important role in delivering energy to rural and off-grid areas...
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Nature Portfolio
2025-02-01
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| Online Access: | https://doi.org/10.1038/s41598-025-88438-x |
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| author | Abderrahmane Mehallou Benalia M’hamdi Abderrahmane Amari Madjid Teguar Abdelaziz Rabehi Mawloud Guermoui Amal H. Alharbi El-Sayed M. El-kenawy Doaa Sami Khafaga |
| author_facet | Abderrahmane Mehallou Benalia M’hamdi Abderrahmane Amari Madjid Teguar Abdelaziz Rabehi Mawloud Guermoui Amal H. Alharbi El-Sayed M. El-kenawy Doaa Sami Khafaga |
| author_sort | Abderrahmane Mehallou |
| collection | DOAJ |
| description | Abstract Extended power outages are not only a nuisance but a critical problem in the modern world, which demands a continuous supply of decent quality electricity. Hybrid renewable energy systems (HRES) within a microgrid (MG) play an important role in delivering energy to rural and off-grid areas and avoiding potential power outages. This research describes an in-depth study of the three phases, design, optimization, and performance analysis of a stand-alone hybrid microgrid for a residential area in a remote area in the province of Adrar in southern Algeria. The system is composed of photovoltaic (PV) modules and a wind turbine, a set of batteries as an energy storage unit, a diesel generator as a backup energy source, and an inverter. This paper investigates four recent methodologies based on Multi-objective Particle Swarm Optimization (MOPSO), Multi-objective Ant Lion Optimizer (MOALO), Multi-objective Dragonfly Algorithm (MODA), and Multi-objective Evolutionary Algorithm (MOGA) to identify the optimal sizing of a microgrid (MG) integrated with hybrid renewable energy sources (RES). The proposed methods are carried out to select the optimal system size, which is a multi-objective problem involving the minimization of the annual cost of electricity (COE), and the loss of power supply probability (LPSP) simultaneously. To achieve this, the proposed methods are combined with energy management strategy (EMS) rules that coordinate energy flows between the various system components. The findings reveal that the MOPSO method has the most efficient hybrid renewable configuration with an annual generation cost of electricity (COE) of 0.2520 $/kWh and loss of power supply probability (LPSP) of 9.164%, which dominates the performance of MOALO (COE of 0.1625$/kWh and LPSP of 8.4872%), MOGA (COE of 0.1577$/kWh and LPSP of 10%), and MODA (COE of 0.02425$/kWh and LPSP of 7.8649%). Furthermore, a sensitivity analysis is performed for the effect that COE variants may have on the design variables. |
| format | Article |
| id | doaj-art-719083be606743c2bc2d2ea87f6dfca7 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
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| spelling | doaj-art-719083be606743c2bc2d2ea87f6dfca72025-02-09T12:36:00ZengNature PortfolioScientific Reports2045-23222025-02-0115112410.1038/s41598-025-88438-xOptimal multiobjective design of an autonomous hybrid renewable energy system in the Adrar Region, AlgeriaAbderrahmane Mehallou0Benalia M’hamdi1Abderrahmane Amari2Madjid Teguar3Abdelaziz Rabehi4Mawloud Guermoui5Amal H. Alharbi6El-Sayed M. El-kenawy7Doaa Sami Khafaga8Applied Automation and Industrial Diagnostics Laboratory (LAADI), Ziane Achour University of DjelfaApplied Automation and Industrial Diagnostics Laboratory (LAADI), Ziane Achour University of DjelfaZiane Achour University of DjelfaLaboratoire de Recherche en Electrotechnique, Ecole Nationale PolytechniqueLaboratory of Telecommunications and Smart Systems Faculty of Sciences and Technologies, University of DjelfaLaboratory of Telecommunications and Smart Systems Faculty of Sciences and Technologies, University of DjelfaDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman UniversitySchool of ICT, Faculty of Engineering, Design and Information & Communications Technology (EDICT), Bahrain PolytechnicDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman UniversityAbstract Extended power outages are not only a nuisance but a critical problem in the modern world, which demands a continuous supply of decent quality electricity. Hybrid renewable energy systems (HRES) within a microgrid (MG) play an important role in delivering energy to rural and off-grid areas and avoiding potential power outages. This research describes an in-depth study of the three phases, design, optimization, and performance analysis of a stand-alone hybrid microgrid for a residential area in a remote area in the province of Adrar in southern Algeria. The system is composed of photovoltaic (PV) modules and a wind turbine, a set of batteries as an energy storage unit, a diesel generator as a backup energy source, and an inverter. This paper investigates four recent methodologies based on Multi-objective Particle Swarm Optimization (MOPSO), Multi-objective Ant Lion Optimizer (MOALO), Multi-objective Dragonfly Algorithm (MODA), and Multi-objective Evolutionary Algorithm (MOGA) to identify the optimal sizing of a microgrid (MG) integrated with hybrid renewable energy sources (RES). The proposed methods are carried out to select the optimal system size, which is a multi-objective problem involving the minimization of the annual cost of electricity (COE), and the loss of power supply probability (LPSP) simultaneously. To achieve this, the proposed methods are combined with energy management strategy (EMS) rules that coordinate energy flows between the various system components. The findings reveal that the MOPSO method has the most efficient hybrid renewable configuration with an annual generation cost of electricity (COE) of 0.2520 $/kWh and loss of power supply probability (LPSP) of 9.164%, which dominates the performance of MOALO (COE of 0.1625$/kWh and LPSP of 8.4872%), MOGA (COE of 0.1577$/kWh and LPSP of 10%), and MODA (COE of 0.02425$/kWh and LPSP of 7.8649%). Furthermore, a sensitivity analysis is performed for the effect that COE variants may have on the design variables.https://doi.org/10.1038/s41598-025-88438-xMult objective optimizationRenewable energyHybrid energy systemMicrogridEnergy management strategy |
| spellingShingle | Abderrahmane Mehallou Benalia M’hamdi Abderrahmane Amari Madjid Teguar Abdelaziz Rabehi Mawloud Guermoui Amal H. Alharbi El-Sayed M. El-kenawy Doaa Sami Khafaga Optimal multiobjective design of an autonomous hybrid renewable energy system in the Adrar Region, Algeria Scientific Reports Mult objective optimization Renewable energy Hybrid energy system Microgrid Energy management strategy |
| title | Optimal multiobjective design of an autonomous hybrid renewable energy system in the Adrar Region, Algeria |
| title_full | Optimal multiobjective design of an autonomous hybrid renewable energy system in the Adrar Region, Algeria |
| title_fullStr | Optimal multiobjective design of an autonomous hybrid renewable energy system in the Adrar Region, Algeria |
| title_full_unstemmed | Optimal multiobjective design of an autonomous hybrid renewable energy system in the Adrar Region, Algeria |
| title_short | Optimal multiobjective design of an autonomous hybrid renewable energy system in the Adrar Region, Algeria |
| title_sort | optimal multiobjective design of an autonomous hybrid renewable energy system in the adrar region algeria |
| topic | Mult objective optimization Renewable energy Hybrid energy system Microgrid Energy management strategy |
| url | https://doi.org/10.1038/s41598-025-88438-x |
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