Using new control strategies to improve the effectiveness and efficiency of the hybrid power system based on the battery storage system
Abstract Hybrid energy systems (HESs) are integrated systems that have successfully addressed the problems of meeting the increasing demand for electrical power. Like all known power systems, the energy and stream quality are among the most important issues in addition to the durability of the HES....
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Nature Portfolio
2025-02-01
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| Online Access: | https://doi.org/10.1038/s41598-025-88804-9 |
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| author | Karima Boutaghane Habib Benbouhenni Nedjoua Bennecib Ilhami Colak Z. M. S. Elbarbary Saad F. Al-Gahtani |
| author_facet | Karima Boutaghane Habib Benbouhenni Nedjoua Bennecib Ilhami Colak Z. M. S. Elbarbary Saad F. Al-Gahtani |
| author_sort | Karima Boutaghane |
| collection | DOAJ |
| description | Abstract Hybrid energy systems (HESs) are integrated systems that have successfully addressed the problems of meeting the increasing demand for electrical power. Like all known power systems, the energy and stream quality are among the most important issues in addition to the durability of the HES. In this study, the battery-powered HES is presented, where this designed system consists of a wind system and a photovoltaic (PV) system. The strategy of maximum power point (MPP) tracking (MPPT) based on the adaptive neuro-fuzzy inference system (ANFIS) method is used to command the PV system and the wind system, and the MPPT based on the neural method is used. These proposed strategies do not need the mathematical model of the studied system and augment the robustness and stability, where the system performance is great. Also, the fractional-order proportional-integral regulator and the integral sliding mode control approach are combined to control the battery-based storage system, and the particle swarm optimization approach was used to estimate the gain values of the resulting controller. The HES was realized using MATLAB, where the competence is tested under different work scenarios. The results showed excellent efficacy of the designed control and were compared with conventional control. The simulation results showed that using the neural MPPT strategy in the case of the wind speed being 12 m/s, the values of rise time, response time, MPP, and steady-state error (SSE) are improved by rates estimated at 99.32%, 60%, 1.5%, and 60%, respectively compared to the perturbations and observations-based MPPT approach. Compared to the traditional strategy, the ANFIS-MPPT strategy improves the values of MPP, response time, SSE, and rise time in the case of irradiation, which takes the value of 1000 W/m2, by percentages estimated at 18%, 60%, 94.70%, and 69.23%, respectively. Also, the PSO-FOPI-ISMC strategy improves the harmonic distortion of the current value in the second test by 55.20% and 72.90% for mode 1 and mode 2, respectively, compared to the traditional approach. These results make the designed approach of great importance in the future in other industrial applications. |
| format | Article |
| id | doaj-art-d3450b3ccd9c48c8b3f82109384e007b |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-d3450b3ccd9c48c8b3f82109384e007b2025-02-09T12:37:15ZengNature PortfolioScientific Reports2045-23222025-02-0115113210.1038/s41598-025-88804-9Using new control strategies to improve the effectiveness and efficiency of the hybrid power system based on the battery storage systemKarima Boutaghane0Habib Benbouhenni1Nedjoua Bennecib2Ilhami Colak3Z. M. S. Elbarbary4Saad F. Al-Gahtani5Department of Electrical Engineering, Laboratory of Electrical Engineering of Constantine (LGEC), Brothers Mentouri UniversityLaboratoire d’Automatique Et d’Analyse Des Systèmes (LAAS), Ecole Nationale Polytechnique d’Oran (ENP d’Oran)Department of Electrical Engineering, Laboratory of Electrical Engineering of Constantine (LGEC), Brothers Mentouri UniversityDepartment of Electrical and Electronics Engineering, Istinye UniversityDepartment of Electrical Engineering, College of Engineering, King Khalid University, KSADepartment of Electrical Engineering, College of Engineering, King Khalid University, KSAAbstract Hybrid energy systems (HESs) are integrated systems that have successfully addressed the problems of meeting the increasing demand for electrical power. Like all known power systems, the energy and stream quality are among the most important issues in addition to the durability of the HES. In this study, the battery-powered HES is presented, where this designed system consists of a wind system and a photovoltaic (PV) system. The strategy of maximum power point (MPP) tracking (MPPT) based on the adaptive neuro-fuzzy inference system (ANFIS) method is used to command the PV system and the wind system, and the MPPT based on the neural method is used. These proposed strategies do not need the mathematical model of the studied system and augment the robustness and stability, where the system performance is great. Also, the fractional-order proportional-integral regulator and the integral sliding mode control approach are combined to control the battery-based storage system, and the particle swarm optimization approach was used to estimate the gain values of the resulting controller. The HES was realized using MATLAB, where the competence is tested under different work scenarios. The results showed excellent efficacy of the designed control and were compared with conventional control. The simulation results showed that using the neural MPPT strategy in the case of the wind speed being 12 m/s, the values of rise time, response time, MPP, and steady-state error (SSE) are improved by rates estimated at 99.32%, 60%, 1.5%, and 60%, respectively compared to the perturbations and observations-based MPPT approach. Compared to the traditional strategy, the ANFIS-MPPT strategy improves the values of MPP, response time, SSE, and rise time in the case of irradiation, which takes the value of 1000 W/m2, by percentages estimated at 18%, 60%, 94.70%, and 69.23%, respectively. Also, the PSO-FOPI-ISMC strategy improves the harmonic distortion of the current value in the second test by 55.20% and 72.90% for mode 1 and mode 2, respectively, compared to the traditional approach. These results make the designed approach of great importance in the future in other industrial applications.https://doi.org/10.1038/s41598-025-88804-9Hybrid energy systemsFractional-order proportional-integral regulatorAdaptive neuro-fuzzy inference system methodPhotovoltaic systemIntegral sliding mode control |
| spellingShingle | Karima Boutaghane Habib Benbouhenni Nedjoua Bennecib Ilhami Colak Z. M. S. Elbarbary Saad F. Al-Gahtani Using new control strategies to improve the effectiveness and efficiency of the hybrid power system based on the battery storage system Scientific Reports Hybrid energy systems Fractional-order proportional-integral regulator Adaptive neuro-fuzzy inference system method Photovoltaic system Integral sliding mode control |
| title | Using new control strategies to improve the effectiveness and efficiency of the hybrid power system based on the battery storage system |
| title_full | Using new control strategies to improve the effectiveness and efficiency of the hybrid power system based on the battery storage system |
| title_fullStr | Using new control strategies to improve the effectiveness and efficiency of the hybrid power system based on the battery storage system |
| title_full_unstemmed | Using new control strategies to improve the effectiveness and efficiency of the hybrid power system based on the battery storage system |
| title_short | Using new control strategies to improve the effectiveness and efficiency of the hybrid power system based on the battery storage system |
| title_sort | using new control strategies to improve the effectiveness and efficiency of the hybrid power system based on the battery storage system |
| topic | Hybrid energy systems Fractional-order proportional-integral regulator Adaptive neuro-fuzzy inference system method Photovoltaic system Integral sliding mode control |
| url | https://doi.org/10.1038/s41598-025-88804-9 |
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