Optimizing Hybrid Renewable Energy Systems for Isolated Applications: A Modified Smell Agent Approach
This paper presents the optimal sizing of a hybrid renewable energy system (HRES) for an isolated residential building using modified smell agent optimization (mSAO). The paper introduces a time-dependent approach that adapts the selection of the original SAO control parameters as the algorithm prog...
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MDPI AG
2025-06-01
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| author | Manal Drici Mourad Houabes Ahmed Tijani Salawudeen Mebarek Bahri |
| author_facet | Manal Drici Mourad Houabes Ahmed Tijani Salawudeen Mebarek Bahri |
| author_sort | Manal Drici |
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| description | This paper presents the optimal sizing of a hybrid renewable energy system (HRES) for an isolated residential building using modified smell agent optimization (mSAO). The paper introduces a time-dependent approach that adapts the selection of the original SAO control parameters as the algorithm progresses through the optimization hyperspace. This modification addresses issues of poor convergence and suboptimal search in the original algorithm. Both the modified and standard algorithms were employed to design an HRES system comprising photovoltaic panels, wind turbines, fuel cells, batteries, and hydrogen storage, all connected via a DC-bus microgrid. The components were integrated with the microgrid using DC-DC power converters and supplied a designated load through a DC-AC inverter. Multiple operational scenarios and multi-objective criteria, including techno-economic metrics such as levelized cost of energy (LCOE) and loss of power supply probability (LPSP), were evaluated. Comparative analysis demonstrated that mSAO outperforms the standard SAO and the honey badger algorithm (HBA) used for the purpose of comparison only. Our simulation results highlighted that the PV–wind turbine–battery system achieved the best economic performance. In this case, the mSAO reduced the LPSP by approximately 38.89% and 87.50% over SAO and the HBA, respectively. Similarly, the mSAO also recorded LCOE performance superiority of 4.05% and 28.44% over SAO and the HBA, respectively. These results underscore the superiority of the mSAO in solving optimization problems. |
| format | Article |
| id | doaj-art-d7931fa8558c467b9460efa38d32f51c |
| institution | Kabale University |
| issn | 2673-4117 |
| language | English |
| publishDate | 2025-06-01 |
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| spelling | doaj-art-d7931fa8558c467b9460efa38d32f51c2025-08-20T03:27:26ZengMDPI AGEng2673-41172025-06-016612010.3390/eng6060120Optimizing Hybrid Renewable Energy Systems for Isolated Applications: A Modified Smell Agent ApproachManal Drici0Mourad Houabes1Ahmed Tijani Salawudeen2Mebarek Bahri3LEA Laboratory, Department of Electrical Engineering, University of Badji Mokhtar, Sidi Amar, Annaba 23000, AlgeriaDepartment of Electrical Engineering, ENSTI, Annaba 23003, AlgeriaDepartment of Electrical and Electronics Engineering, University of Jos, Jos 930001, NigeriaDepartment of Electrical Engineering, University of Biskra, Biskra 07000, AlgeriaThis paper presents the optimal sizing of a hybrid renewable energy system (HRES) for an isolated residential building using modified smell agent optimization (mSAO). The paper introduces a time-dependent approach that adapts the selection of the original SAO control parameters as the algorithm progresses through the optimization hyperspace. This modification addresses issues of poor convergence and suboptimal search in the original algorithm. Both the modified and standard algorithms were employed to design an HRES system comprising photovoltaic panels, wind turbines, fuel cells, batteries, and hydrogen storage, all connected via a DC-bus microgrid. The components were integrated with the microgrid using DC-DC power converters and supplied a designated load through a DC-AC inverter. Multiple operational scenarios and multi-objective criteria, including techno-economic metrics such as levelized cost of energy (LCOE) and loss of power supply probability (LPSP), were evaluated. Comparative analysis demonstrated that mSAO outperforms the standard SAO and the honey badger algorithm (HBA) used for the purpose of comparison only. Our simulation results highlighted that the PV–wind turbine–battery system achieved the best economic performance. In this case, the mSAO reduced the LPSP by approximately 38.89% and 87.50% over SAO and the HBA, respectively. Similarly, the mSAO also recorded LCOE performance superiority of 4.05% and 28.44% over SAO and the HBA, respectively. These results underscore the superiority of the mSAO in solving optimization problems.https://www.mdpi.com/2673-4117/6/6/120energy managementhybrid renewable energy system (HRES)meta-heuristicalgorithmsmultisourceoptimal sizing |
| spellingShingle | Manal Drici Mourad Houabes Ahmed Tijani Salawudeen Mebarek Bahri Optimizing Hybrid Renewable Energy Systems for Isolated Applications: A Modified Smell Agent Approach Eng energy management hybrid renewable energy system (HRES) meta-heuristic algorithms multisource optimal sizing |
| title | Optimizing Hybrid Renewable Energy Systems for Isolated Applications: A Modified Smell Agent Approach |
| title_full | Optimizing Hybrid Renewable Energy Systems for Isolated Applications: A Modified Smell Agent Approach |
| title_fullStr | Optimizing Hybrid Renewable Energy Systems for Isolated Applications: A Modified Smell Agent Approach |
| title_full_unstemmed | Optimizing Hybrid Renewable Energy Systems for Isolated Applications: A Modified Smell Agent Approach |
| title_short | Optimizing Hybrid Renewable Energy Systems for Isolated Applications: A Modified Smell Agent Approach |
| title_sort | optimizing hybrid renewable energy systems for isolated applications a modified smell agent approach |
| topic | energy management hybrid renewable energy system (HRES) meta-heuristic algorithms multisource optimal sizing |
| url | https://www.mdpi.com/2673-4117/6/6/120 |
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