Fuzzy logic-based particle swarm optimization for integrated energy management system considering battery storage degradation
Considering the rapidly evolving microgrid technology and the increasing complexity associated with integrating renewable energy sources, innovative approaches to energy management are crucial for ensuring sustainability and efficiency. This paper presents a novel Fuzzy Logic-Based Particle Swarm Op...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English English |
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Elsevier B.V.
2024
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/42548/ http://umpir.ump.edu.my/id/eprint/42548/1/Fuzzy%20logic-based%20particle%20swarm%20optimization%20for%20integrated%20energy%20management%20system.pdf http://umpir.ump.edu.my/id/eprint/42548/7/Fuzzy%20logic-based%20particle%20swarm%20optimization%20for%20integrated%20energy.pdf |
| _version_ | 1848826640744841216 |
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| author | Ibrahim, Oladimeji Mohd Junaidi, Abdul Aziz Razman, Ayop Dahiru, Ahmed Tijjani Low, Wen Yao Mohd Herwan, Sulaiman Amosa, Temitope Ibrahim |
| author_facet | Ibrahim, Oladimeji Mohd Junaidi, Abdul Aziz Razman, Ayop Dahiru, Ahmed Tijjani Low, Wen Yao Mohd Herwan, Sulaiman Amosa, Temitope Ibrahim |
| author_sort | Ibrahim, Oladimeji |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Considering the rapidly evolving microgrid technology and the increasing complexity associated with integrating renewable energy sources, innovative approaches to energy management are crucial for ensuring sustainability and efficiency. This paper presents a novel Fuzzy Logic-Based Particle Swarm Optimization (FLB-PSO) technique to enhance the performance of hybrid energy management systems. The proposed FLB-PSO algorithm effectively addresses the challenge of balancing exploration and exploitation in optimization problems, thereby enhancing convergence speed and solution accuracy with robustness across diverse and complex scenarios. By leveraging the adaptability of fuzzy logic to adjust PSO parameters dynamically, the method optimizes the allocation and utilization of diverse energy resources within a grid-connected microgrid. Under fixed grid tariffs, the investigation demonstrates that FLB-PSO achieves grid power purchase and battery degradation costs of $1935.07 and $49.93, respectively, compared to $2159.67 and $61.43 for the traditional PSO. This results in an optimal cost of $1985.00 for FLB-PSO, leading to a cost saving of $236.09 compared to the $2221.10 of PSO. Furthermore, under dynamic grid tariffs, FLB-PSO incurs grid power purchase and battery degradation costs of $2359.20 and $64.66, respectively, in contrast to $2606.47 and $54.61 for PSO. The optimal cost for FLB-PSO is $2423.86, representing a cost reduction of $237.23 compared to the $2661.08 of PSO. The FLB-PSO algorithm proficiently manages energy sources while addressing complexities associated with battery storage degradation. Overall, the FLB-PSO algorithm outperforms traditional PSO in terms of robustness to system dynamics, convergence rate, operational cost reduction, and improved energy efficiency. |
| first_indexed | 2025-11-15T03:48:02Z |
| format | Article |
| id | ump-42548 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:48:02Z |
| publishDate | 2024 |
| publisher | Elsevier B.V. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-425482024-09-23T02:41:45Z http://umpir.ump.edu.my/id/eprint/42548/ Fuzzy logic-based particle swarm optimization for integrated energy management system considering battery storage degradation Ibrahim, Oladimeji Mohd Junaidi, Abdul Aziz Razman, Ayop Dahiru, Ahmed Tijjani Low, Wen Yao Mohd Herwan, Sulaiman Amosa, Temitope Ibrahim TK Electrical engineering. Electronics Nuclear engineering Considering the rapidly evolving microgrid technology and the increasing complexity associated with integrating renewable energy sources, innovative approaches to energy management are crucial for ensuring sustainability and efficiency. This paper presents a novel Fuzzy Logic-Based Particle Swarm Optimization (FLB-PSO) technique to enhance the performance of hybrid energy management systems. The proposed FLB-PSO algorithm effectively addresses the challenge of balancing exploration and exploitation in optimization problems, thereby enhancing convergence speed and solution accuracy with robustness across diverse and complex scenarios. By leveraging the adaptability of fuzzy logic to adjust PSO parameters dynamically, the method optimizes the allocation and utilization of diverse energy resources within a grid-connected microgrid. Under fixed grid tariffs, the investigation demonstrates that FLB-PSO achieves grid power purchase and battery degradation costs of $1935.07 and $49.93, respectively, compared to $2159.67 and $61.43 for the traditional PSO. This results in an optimal cost of $1985.00 for FLB-PSO, leading to a cost saving of $236.09 compared to the $2221.10 of PSO. Furthermore, under dynamic grid tariffs, FLB-PSO incurs grid power purchase and battery degradation costs of $2359.20 and $64.66, respectively, in contrast to $2606.47 and $54.61 for PSO. The optimal cost for FLB-PSO is $2423.86, representing a cost reduction of $237.23 compared to the $2661.08 of PSO. The FLB-PSO algorithm proficiently manages energy sources while addressing complexities associated with battery storage degradation. Overall, the FLB-PSO algorithm outperforms traditional PSO in terms of robustness to system dynamics, convergence rate, operational cost reduction, and improved energy efficiency. Elsevier B.V. 2024-08-30 Article PeerReviewed pdf en cc_by_nc_nd_4 http://umpir.ump.edu.my/id/eprint/42548/1/Fuzzy%20logic-based%20particle%20swarm%20optimization%20for%20integrated%20energy%20management%20system.pdf pdf en cc_by_nc_nd_4 http://umpir.ump.edu.my/id/eprint/42548/7/Fuzzy%20logic-based%20particle%20swarm%20optimization%20for%20integrated%20energy.pdf Ibrahim, Oladimeji and Mohd Junaidi, Abdul Aziz and Razman, Ayop and Dahiru, Ahmed Tijjani and Low, Wen Yao and Mohd Herwan, Sulaiman and Amosa, Temitope Ibrahim (2024) Fuzzy logic-based particle swarm optimization for integrated energy management system considering battery storage degradation. Results in Engineering (RINENG), 24 (102816). pp. 1-33. ISSN 2590-1230. (In Press / Online First) (In Press / Online First) https://doi.org/10.1016/j.rineng.2024.102816 https://doi.org/10.1016/j.rineng.2024.102816 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Ibrahim, Oladimeji Mohd Junaidi, Abdul Aziz Razman, Ayop Dahiru, Ahmed Tijjani Low, Wen Yao Mohd Herwan, Sulaiman Amosa, Temitope Ibrahim Fuzzy logic-based particle swarm optimization for integrated energy management system considering battery storage degradation |
| title | Fuzzy logic-based particle swarm optimization for integrated energy management system considering battery storage degradation |
| title_full | Fuzzy logic-based particle swarm optimization for integrated energy management system considering battery storage degradation |
| title_fullStr | Fuzzy logic-based particle swarm optimization for integrated energy management system considering battery storage degradation |
| title_full_unstemmed | Fuzzy logic-based particle swarm optimization for integrated energy management system considering battery storage degradation |
| title_short | Fuzzy logic-based particle swarm optimization for integrated energy management system considering battery storage degradation |
| title_sort | fuzzy logic-based particle swarm optimization for integrated energy management system considering battery storage degradation |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/42548/ http://umpir.ump.edu.my/id/eprint/42548/ http://umpir.ump.edu.my/id/eprint/42548/ http://umpir.ump.edu.my/id/eprint/42548/1/Fuzzy%20logic-based%20particle%20swarm%20optimization%20for%20integrated%20energy%20management%20system.pdf http://umpir.ump.edu.my/id/eprint/42548/7/Fuzzy%20logic-based%20particle%20swarm%20optimization%20for%20integrated%20energy.pdf |