Enhanced grey wolf optimization for maximum power point tracking in photovoltaic systems with hybrid battery-supercapacitor storage
This paper proposes an enhanced Grey Wolf Optimization algorithm integrated with a stochastic Cauchy-Gaussian mutation to improve maximum power point tracking in standalone photovoltaic systems. The system incorporates a hybrid energy storage solution combining a lithium-ion battery and a supercapac...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | International Journal of Sustainable Energy |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/14786451.2025.2460029 |
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| author | Chirine Benzazah Najoua Mrabet Ahmed ElAkkary Fathallah Rerhrhaye |
| author_facet | Chirine Benzazah Najoua Mrabet Ahmed ElAkkary Fathallah Rerhrhaye |
| author_sort | Chirine Benzazah |
| collection | DOAJ |
| description | This paper proposes an enhanced Grey Wolf Optimization algorithm integrated with a stochastic Cauchy-Gaussian mutation to improve maximum power point tracking in standalone photovoltaic systems. The system incorporates a hybrid energy storage solution combining a lithium-ion battery and a supercapacitor to address energy fluctuations and ensure stable power management. The proposed method was compared with conventional and metaheuristic optimisation techniques, including Particle Swarm Optimization, Ant Colony Optimization, and the standard Grey Wolf Optimization algorithm. Simulation results demonstrate faster convergence, higher tracking efficiency, and improved stability under varying solar and load conditions. The enhanced approach significantly improves energy extraction and reduces tracking time compared to traditional methods. Additionally, the hybrid storage system ensures a balanced and stable operation between the battery and supercapacitor, extending the lifespan of the energy storage components. These findings underscore the algorithm’s reliability and effectiveness in optimising and enhancing system performance, making it suitable for real-world applications. |
| format | Article |
| id | doaj-art-ad2cad2fc28a401fbb571dc881c7f38a |
| institution | Kabale University |
| issn | 1478-6451 1478-646X |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Sustainable Energy |
| spelling | doaj-art-ad2cad2fc28a401fbb571dc881c7f38a2025-02-04T19:51:51ZengTaylor & Francis GroupInternational Journal of Sustainable Energy1478-64511478-646X2025-12-0144110.1080/14786451.2025.2460029Enhanced grey wolf optimization for maximum power point tracking in photovoltaic systems with hybrid battery-supercapacitor storageChirine Benzazah0Najoua Mrabet1Ahmed ElAkkary2Fathallah Rerhrhaye3Laboratory of Fundamental and Applied Physics, Polydisciplinary Faculty of Safi, Cadi Ayyad University, Marrakech, MoroccoLASTIMI Laboratory Systems Analysis Information Processing and Industrial Management, Higher School of Technology of Salé, Mohammed V University, Rabat, MoroccoLASTIMI Laboratory Systems Analysis Information Processing and Industrial Management, Higher School of Technology of Salé, Mohammed V University, Rabat, MoroccoLASTIMI Laboratory Systems Analysis Information Processing and Industrial Management, Higher School of Technology of Salé, Mohammed V University, Rabat, MoroccoThis paper proposes an enhanced Grey Wolf Optimization algorithm integrated with a stochastic Cauchy-Gaussian mutation to improve maximum power point tracking in standalone photovoltaic systems. The system incorporates a hybrid energy storage solution combining a lithium-ion battery and a supercapacitor to address energy fluctuations and ensure stable power management. The proposed method was compared with conventional and metaheuristic optimisation techniques, including Particle Swarm Optimization, Ant Colony Optimization, and the standard Grey Wolf Optimization algorithm. Simulation results demonstrate faster convergence, higher tracking efficiency, and improved stability under varying solar and load conditions. The enhanced approach significantly improves energy extraction and reduces tracking time compared to traditional methods. Additionally, the hybrid storage system ensures a balanced and stable operation between the battery and supercapacitor, extending the lifespan of the energy storage components. These findings underscore the algorithm’s reliability and effectiveness in optimising and enhancing system performance, making it suitable for real-world applications.https://www.tandfonline.com/doi/10.1080/14786451.2025.2460029Grey Wolf Optimization (GWO)mutation (Gaussian, Cauchy)greedy selectionphotovoltaic systems (PV)optimisation algorithms (PSO, ACO)hybrid storage systems (battery-supercapacitor) |
| spellingShingle | Chirine Benzazah Najoua Mrabet Ahmed ElAkkary Fathallah Rerhrhaye Enhanced grey wolf optimization for maximum power point tracking in photovoltaic systems with hybrid battery-supercapacitor storage International Journal of Sustainable Energy Grey Wolf Optimization (GWO) mutation (Gaussian, Cauchy) greedy selection photovoltaic systems (PV) optimisation algorithms (PSO, ACO) hybrid storage systems (battery-supercapacitor) |
| title | Enhanced grey wolf optimization for maximum power point tracking in photovoltaic systems with hybrid battery-supercapacitor storage |
| title_full | Enhanced grey wolf optimization for maximum power point tracking in photovoltaic systems with hybrid battery-supercapacitor storage |
| title_fullStr | Enhanced grey wolf optimization for maximum power point tracking in photovoltaic systems with hybrid battery-supercapacitor storage |
| title_full_unstemmed | Enhanced grey wolf optimization for maximum power point tracking in photovoltaic systems with hybrid battery-supercapacitor storage |
| title_short | Enhanced grey wolf optimization for maximum power point tracking in photovoltaic systems with hybrid battery-supercapacitor storage |
| title_sort | enhanced grey wolf optimization for maximum power point tracking in photovoltaic systems with hybrid battery supercapacitor storage |
| topic | Grey Wolf Optimization (GWO) mutation (Gaussian, Cauchy) greedy selection photovoltaic systems (PV) optimisation algorithms (PSO, ACO) hybrid storage systems (battery-supercapacitor) |
| url | https://www.tandfonline.com/doi/10.1080/14786451.2025.2460029 |
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