Multi-objective optimization and algorithmic evaluation for EMS in a HRES integrating PV, wind, and backup storage
Abstract This manuscript focuses on optimizing a Hybrid Renewable Energy System (HRES) that integrates photovoltaic (PV) panels, wind turbines (WT), and various energy storage systems (ESS), including batteries, supercapacitors (SCs), and hydrogen storage. The system uses a multi-objective optimizat...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-84227-0 |
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| author | Ahmed A. Shaier Mahmoud M. Elymany Mohamed A. Enany Nadia A. Elsonbaty |
| author_facet | Ahmed A. Shaier Mahmoud M. Elymany Mohamed A. Enany Nadia A. Elsonbaty |
| author_sort | Ahmed A. Shaier |
| collection | DOAJ |
| description | Abstract This manuscript focuses on optimizing a Hybrid Renewable Energy System (HRES) that integrates photovoltaic (PV) panels, wind turbines (WT), and various energy storage systems (ESS), including batteries, supercapacitors (SCs), and hydrogen storage. The system uses a multi-objective optimization strategy to balance power management, aiming to minimize costs and reduce the likelihood of loss of power supply probability (LPSP). Seven different algorithms are assessed to identify the most efficient one for achieving these objectives, with the goal of selecting the algorithm that best balances cost efficiency and system performance. The system is assessed across three operational scenarios: (1) when energy supply meets demand with help from backup systems, (2) when demand exceeds supply and energy storage systems are depleted, and (3) when energy generation surpasses demand and storage systems are full. The HBA-based optimization effectively manages energy flow and storage, ensuring grid stability and minimizing overcharging risks. This system offers a reliable and sustainable power supply for isolated microgrids, effectively managing energy production, storage, and distribution. The research sets a new benchmark for future studies in decentralized energy systems, particularly in balancing technical efficiency and economic feasibility. |
| format | Article |
| id | doaj-art-5bc41b3afcbc4ee0bed8bc166c90905d |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-5bc41b3afcbc4ee0bed8bc166c90905d2025-01-12T12:15:59ZengNature PortfolioScientific Reports2045-23222025-01-0115113410.1038/s41598-024-84227-0Multi-objective optimization and algorithmic evaluation for EMS in a HRES integrating PV, wind, and backup storageAhmed A. Shaier0Mahmoud M. Elymany1Mohamed A. Enany2Nadia A. Elsonbaty3Electrical Power and Machines Department, Faculty of Engineering, Zagazig UniversityElectrical Power and Machines Department, Faculty of Engineering, Zagazig UniversityElectrical Power and Machines Department, Faculty of Engineering, Zagazig UniversityElectrical Power and Machines Department, Faculty of Engineering, Zagazig UniversityAbstract This manuscript focuses on optimizing a Hybrid Renewable Energy System (HRES) that integrates photovoltaic (PV) panels, wind turbines (WT), and various energy storage systems (ESS), including batteries, supercapacitors (SCs), and hydrogen storage. The system uses a multi-objective optimization strategy to balance power management, aiming to minimize costs and reduce the likelihood of loss of power supply probability (LPSP). Seven different algorithms are assessed to identify the most efficient one for achieving these objectives, with the goal of selecting the algorithm that best balances cost efficiency and system performance. The system is assessed across three operational scenarios: (1) when energy supply meets demand with help from backup systems, (2) when demand exceeds supply and energy storage systems are depleted, and (3) when energy generation surpasses demand and storage systems are full. The HBA-based optimization effectively manages energy flow and storage, ensuring grid stability and minimizing overcharging risks. This system offers a reliable and sustainable power supply for isolated microgrids, effectively managing energy production, storage, and distribution. The research sets a new benchmark for future studies in decentralized energy systems, particularly in balancing technical efficiency and economic feasibility.https://doi.org/10.1038/s41598-024-84227-0Hybrid backup systemSmart power flow managementHoney badger algorithm (HBA)SupercapacitorsHybrid renewable energy system |
| spellingShingle | Ahmed A. Shaier Mahmoud M. Elymany Mohamed A. Enany Nadia A. Elsonbaty Multi-objective optimization and algorithmic evaluation for EMS in a HRES integrating PV, wind, and backup storage Scientific Reports Hybrid backup system Smart power flow management Honey badger algorithm (HBA) Supercapacitors Hybrid renewable energy system |
| title | Multi-objective optimization and algorithmic evaluation for EMS in a HRES integrating PV, wind, and backup storage |
| title_full | Multi-objective optimization and algorithmic evaluation for EMS in a HRES integrating PV, wind, and backup storage |
| title_fullStr | Multi-objective optimization and algorithmic evaluation for EMS in a HRES integrating PV, wind, and backup storage |
| title_full_unstemmed | Multi-objective optimization and algorithmic evaluation for EMS in a HRES integrating PV, wind, and backup storage |
| title_short | Multi-objective optimization and algorithmic evaluation for EMS in a HRES integrating PV, wind, and backup storage |
| title_sort | multi objective optimization and algorithmic evaluation for ems in a hres integrating pv wind and backup storage |
| topic | Hybrid backup system Smart power flow management Honey badger algorithm (HBA) Supercapacitors Hybrid renewable energy system |
| url | https://doi.org/10.1038/s41598-024-84227-0 |
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