A modified multi-objective particle swarm optimization (M-MOPSO) for optimal sizing of a solar–wind–battery hybrid renewable energy system
This study proposes and utilizes a modified multi-objective particle swarm optimization (M-MOPSO) algorithm for the optimal sizing of a solar-wind-battery hybrid renewable energy system for a rural community in Rivers State, Nigeria. Unlike previous studies that primarily focused on minimizing total...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Solar Compass |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S277294002400016X |
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| author | Ukoima Kelvin Nkalo Okoro Ogbonnaya Inya Obi, Patrick Ifeanyi Akuru Udochukwu Bola Davidson Innocent Ewean |
| author_facet | Ukoima Kelvin Nkalo Okoro Ogbonnaya Inya Obi, Patrick Ifeanyi Akuru Udochukwu Bola Davidson Innocent Ewean |
| author_sort | Ukoima Kelvin Nkalo |
| collection | DOAJ |
| description | This study proposes and utilizes a modified multi-objective particle swarm optimization (M-MOPSO) algorithm for the optimal sizing of a solar-wind-battery hybrid renewable energy system for a rural community in Rivers State, Nigeria. Unlike previous studies that primarily focused on minimizing total economic cost (TEC) and total annual cost (TAC), this research emphasizes minimizing the loss of power supply probability (LPSP) and levelized cost of energy (LCOE). The M-MOPSO algorithm introduces a dynamic inertia weight, a unique repository update mechanism, and a dominance-based personal best update strategy, which collectively enhance its performance. Comparative analysis with PSO, NSGA-II, MOPSO and hybrid GA-PSO demonstrates that M-MOPSO consistently achieves a lower LPSP, although its LCOE remains higher. The M-MOPSO optimal configuration when simulated under various climatic scenarios was able to meet the energy needs of the community irrespective of ambient condition. |
| format | Article |
| id | doaj-art-3edff76fdd7445c5be81e20f4c2cb637 |
| institution | Kabale University |
| issn | 2772-9400 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Solar Compass |
| spelling | doaj-art-3edff76fdd7445c5be81e20f4c2cb6372024-12-12T05:24:36ZengElsevierSolar Compass2772-94002024-12-0112100082A modified multi-objective particle swarm optimization (M-MOPSO) for optimal sizing of a solar–wind–battery hybrid renewable energy systemUkoima Kelvin Nkalo0Okoro Ogbonnaya Inya1Obi, Patrick Ifeanyi2Akuru Udochukwu Bola3Davidson Innocent Ewean4Department of Electrical Electronic Engineering, Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria; Corresponding author.Department of Electrical Electronic Engineering, Michael Okpara University of Agriculture, Umudike, Abia State, NigeriaDepartment of Electrical Electronic Engineering, Michael Okpara University of Agriculture, Umudike, Abia State, NigeriaDepartment of Electrical Engineering, Tshwane University of Technology, Pretoria, South AfricaDept of Electrical Electronic and Computer Engineering French-South Africa Institute of Technology (F'SATI)/Africa Space Innovation Centre (ASIC) Cape Peninsula University of Tech., Bellville, South AfricaThis study proposes and utilizes a modified multi-objective particle swarm optimization (M-MOPSO) algorithm for the optimal sizing of a solar-wind-battery hybrid renewable energy system for a rural community in Rivers State, Nigeria. Unlike previous studies that primarily focused on minimizing total economic cost (TEC) and total annual cost (TAC), this research emphasizes minimizing the loss of power supply probability (LPSP) and levelized cost of energy (LCOE). The M-MOPSO algorithm introduces a dynamic inertia weight, a unique repository update mechanism, and a dominance-based personal best update strategy, which collectively enhance its performance. Comparative analysis with PSO, NSGA-II, MOPSO and hybrid GA-PSO demonstrates that M-MOPSO consistently achieves a lower LPSP, although its LCOE remains higher. The M-MOPSO optimal configuration when simulated under various climatic scenarios was able to meet the energy needs of the community irrespective of ambient condition.http://www.sciencedirect.com/science/article/pii/S277294002400016XMOPSOPSONGSA-IIHybrid GA-PSO |
| spellingShingle | Ukoima Kelvin Nkalo Okoro Ogbonnaya Inya Obi, Patrick Ifeanyi Akuru Udochukwu Bola Davidson Innocent Ewean A modified multi-objective particle swarm optimization (M-MOPSO) for optimal sizing of a solar–wind–battery hybrid renewable energy system Solar Compass MOPSO PSO NGSA-II Hybrid GA-PSO |
| title | A modified multi-objective particle swarm optimization (M-MOPSO) for optimal sizing of a solar–wind–battery hybrid renewable energy system |
| title_full | A modified multi-objective particle swarm optimization (M-MOPSO) for optimal sizing of a solar–wind–battery hybrid renewable energy system |
| title_fullStr | A modified multi-objective particle swarm optimization (M-MOPSO) for optimal sizing of a solar–wind–battery hybrid renewable energy system |
| title_full_unstemmed | A modified multi-objective particle swarm optimization (M-MOPSO) for optimal sizing of a solar–wind–battery hybrid renewable energy system |
| title_short | A modified multi-objective particle swarm optimization (M-MOPSO) for optimal sizing of a solar–wind–battery hybrid renewable energy system |
| title_sort | modified multi objective particle swarm optimization m mopso for optimal sizing of a solar wind battery hybrid renewable energy system |
| topic | MOPSO PSO NGSA-II Hybrid GA-PSO |
| url | http://www.sciencedirect.com/science/article/pii/S277294002400016X |
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