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...

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Main Authors: Ibrahim, Oladimeji, Mohd Junaidi, Abdul Aziz, Razman, Ayop, Dahiru, Ahmed Tijjani, Low, Wen Yao, Mohd Herwan, Sulaiman, Amosa, Temitope Ibrahim
Format: Article
Language:English
English
Published: Elsevier B.V. 2024
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Online Access: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
http://umpir.ump.edu.my/id/eprint/42548/
https://doi.org/10.1016/j.rineng.2024.102816
https://doi.org/10.1016/j.rineng.2024.102816
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spelling my.ump.umpir.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
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic TK Electrical engineering. Electronics Nuclear engineering
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
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.
format Article
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
title 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_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_sort fuzzy logic-based particle swarm optimization for integrated energy management system considering battery storage degradation
publisher Elsevier B.V.
publishDate 2024
url 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
http://umpir.ump.edu.my/id/eprint/42548/
https://doi.org/10.1016/j.rineng.2024.102816
https://doi.org/10.1016/j.rineng.2024.102816
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