BMOTSM: design of a hybrid bioinspired model to determine optimal turbine sizing for capacity maximisation in environment-and-economy aware deployments
Multiparametric optimisation determines optimal turbine size for eco-friendly wind farm repowering. This involves identifying turbine ratings, repowering locations, and wind zone analysis for maximum economic efficiency and low environmental impacts. Existing models that perform these tasks are eith...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | International Journal of Sustainable Energy |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/14786451.2023.2168001 |
| _version_ | 1797680276356202496 |
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| author | Radharaman Shaha Lata Gidwani Komaragiri Venkata Subba Rao |
| author_facet | Radharaman Shaha Lata Gidwani Komaragiri Venkata Subba Rao |
| author_sort | Radharaman Shaha |
| collection | DOAJ |
| description | Multiparametric optimisation determines optimal turbine size for eco-friendly wind farm repowering. This involves identifying turbine ratings, repowering locations, and wind zone analysis for maximum economic efficiency and low environmental impacts. Existing models that perform these tasks are either highly complex or cannot be scaled due to deployment-specific characteristics. Most of these models do not consider economic or environmental impacts when repowering wind farms. This text discusses the design of a novel hybrid bioinspired model to determine optimal turbine sizing in environment- and economy-aware deployments. The model combines GWO, PSO, and GA to optimise turbine ratings, economic impacts, and environmental impacts during the repowering process. GA model optimises new turbine locations, while PSO maximises turbine efficiency. Both these models are internally optimised via GWO due to economic and environmental effects. The GWO model continuously tunes GA and PSO to find the best multiobjective repowering solution. The integrated model was validated on real-time wind farms to evaluate power conversion efficiency, deployment cost, soil fragmentation percentage, and cost-to-power ratios. The proposed BMOTSM model achieved 6.5% higher conversion efficiency, 8.5% lower deployment cost, 15.4% lower soil fragmentation, and 3.5% lower cost-to-power ratio than state-of-the-art models, making it useful for a variety of real-time wind farm repowering scenarios. |
| first_indexed | 2024-03-11T23:26:36Z |
| format | Article |
| id | doaj.art-6e7af2dedb5d40029342793b269cd06b |
| institution | Directory Open Access Journal |
| issn | 1478-6451 1478-646X |
| language | English |
| last_indexed | 2024-03-11T23:26:36Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Sustainable Energy |
| spelling | doaj.art-6e7af2dedb5d40029342793b269cd06b2023-09-20T10:45:15ZengTaylor & Francis GroupInternational Journal of Sustainable Energy1478-64511478-646X2023-12-0142111610.1080/14786451.2023.21680012168001BMOTSM: design of a hybrid bioinspired model to determine optimal turbine sizing for capacity maximisation in environment-and-economy aware deploymentsRadharaman Shaha0Lata Gidwani1Komaragiri Venkata Subba Rao2Department of Electrical Engineering, Tulsiramji Gaikwad-Patil College of Engineering and TechnologyRajasthan Technical UniversityRajasthan Technical UniversityMultiparametric optimisation determines optimal turbine size for eco-friendly wind farm repowering. This involves identifying turbine ratings, repowering locations, and wind zone analysis for maximum economic efficiency and low environmental impacts. Existing models that perform these tasks are either highly complex or cannot be scaled due to deployment-specific characteristics. Most of these models do not consider economic or environmental impacts when repowering wind farms. This text discusses the design of a novel hybrid bioinspired model to determine optimal turbine sizing in environment- and economy-aware deployments. The model combines GWO, PSO, and GA to optimise turbine ratings, economic impacts, and environmental impacts during the repowering process. GA model optimises new turbine locations, while PSO maximises turbine efficiency. Both these models are internally optimised via GWO due to economic and environmental effects. The GWO model continuously tunes GA and PSO to find the best multiobjective repowering solution. The integrated model was validated on real-time wind farms to evaluate power conversion efficiency, deployment cost, soil fragmentation percentage, and cost-to-power ratios. The proposed BMOTSM model achieved 6.5% higher conversion efficiency, 8.5% lower deployment cost, 15.4% lower soil fragmentation, and 3.5% lower cost-to-power ratio than state-of-the-art models, making it useful for a variety of real-time wind farm repowering scenarios.http://dx.doi.org/10.1080/14786451.2023.2168001gapsogwofragmentationpowerrepowering |
| spellingShingle | Radharaman Shaha Lata Gidwani Komaragiri Venkata Subba Rao BMOTSM: design of a hybrid bioinspired model to determine optimal turbine sizing for capacity maximisation in environment-and-economy aware deployments International Journal of Sustainable Energy ga pso gwo fragmentation power repowering |
| title | BMOTSM: design of a hybrid bioinspired model to determine optimal turbine sizing for capacity maximisation in environment-and-economy aware deployments |
| title_full | BMOTSM: design of a hybrid bioinspired model to determine optimal turbine sizing for capacity maximisation in environment-and-economy aware deployments |
| title_fullStr | BMOTSM: design of a hybrid bioinspired model to determine optimal turbine sizing for capacity maximisation in environment-and-economy aware deployments |
| title_full_unstemmed | BMOTSM: design of a hybrid bioinspired model to determine optimal turbine sizing for capacity maximisation in environment-and-economy aware deployments |
| title_short | BMOTSM: design of a hybrid bioinspired model to determine optimal turbine sizing for capacity maximisation in environment-and-economy aware deployments |
| title_sort | bmotsm design of a hybrid bioinspired model to determine optimal turbine sizing for capacity maximisation in environment and economy aware deployments |
| topic | ga pso gwo fragmentation power repowering |
| url | http://dx.doi.org/10.1080/14786451.2023.2168001 |
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