Particle Swarm Optimization for an Optimal Hybrid Renewable Energy Microgrid System under Uncertainty
Microgrids can assist in managing power supply and demand, increase grid resilience to adverse weather, increase the deployment of zero-emission energy sources, utilise waste heat, and reduce energy wasted through transmission lines. To ensure that the full benefits of microgrid use are realised, hy...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/2/422 |
| _version_ | 1797344162846081024 |
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| author | Manduleli Alfred Mquqwana Senthil Krishnamurthy |
| author_facet | Manduleli Alfred Mquqwana Senthil Krishnamurthy |
| author_sort | Manduleli Alfred Mquqwana |
| collection | DOAJ |
| description | Microgrids can assist in managing power supply and demand, increase grid resilience to adverse weather, increase the deployment of zero-emission energy sources, utilise waste heat, and reduce energy wasted through transmission lines. To ensure that the full benefits of microgrid use are realised, hybrid renewable energy-based microgrids must operate at peak efficiency. To offer an optimal solution for managing microgrids with hybrid renewable energy sources (HRESs) while taking microgrid reserve margins into account, the particle swarm optimisation (PSO) method is suggested. The suggested approach demonstrated good performance in terms of charging and discharging BESS and maintaining the necessary reserve margins to supply critical loads if the grid and renewable energy sources are unavailable. On a clear day, the amount of electricity sold to the grid increased by 58%, while on a partially overcast day, it increased by 153%. Microgrids provide a good return on investment for their operators when they are run at peak efficiency. This is because the BESS is largely charged during off-peak hours or with excess renewable energy, and power is only purchased during less expensive off-peak hours. |
| first_indexed | 2024-03-08T10:58:21Z |
| format | Article |
| id | doaj.art-8e11e1adb5bf40a7abd08d7c7e17a704 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-08T10:58:21Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-8e11e1adb5bf40a7abd08d7c7e17a7042024-01-26T16:19:17ZengMDPI AGEnergies1996-10732024-01-0117242210.3390/en17020422Particle Swarm Optimization for an Optimal Hybrid Renewable Energy Microgrid System under UncertaintyManduleli Alfred Mquqwana0Senthil Krishnamurthy1Department of Electrical, Electronics and Computer Engineering, Centre for Substation, Automation, and Energy Management Systems, Cape Peninsula University of Technology, Bellville P.O. Box 1906, South AfricaDepartment of Electrical, Electronics and Computer Engineering, Centre for Substation, Automation, and Energy Management Systems, Cape Peninsula University of Technology, Bellville P.O. Box 1906, South AfricaMicrogrids can assist in managing power supply and demand, increase grid resilience to adverse weather, increase the deployment of zero-emission energy sources, utilise waste heat, and reduce energy wasted through transmission lines. To ensure that the full benefits of microgrid use are realised, hybrid renewable energy-based microgrids must operate at peak efficiency. To offer an optimal solution for managing microgrids with hybrid renewable energy sources (HRESs) while taking microgrid reserve margins into account, the particle swarm optimisation (PSO) method is suggested. The suggested approach demonstrated good performance in terms of charging and discharging BESS and maintaining the necessary reserve margins to supply critical loads if the grid and renewable energy sources are unavailable. On a clear day, the amount of electricity sold to the grid increased by 58%, while on a partially overcast day, it increased by 153%. Microgrids provide a good return on investment for their operators when they are run at peak efficiency. This is because the BESS is largely charged during off-peak hours or with excess renewable energy, and power is only purchased during less expensive off-peak hours.https://www.mdpi.com/1996-1073/17/2/422particle swarm optimisationhybrid renewable energy resourcesmicrogridsreserve margins |
| spellingShingle | Manduleli Alfred Mquqwana Senthil Krishnamurthy Particle Swarm Optimization for an Optimal Hybrid Renewable Energy Microgrid System under Uncertainty Energies particle swarm optimisation hybrid renewable energy resources microgrids reserve margins |
| title | Particle Swarm Optimization for an Optimal Hybrid Renewable Energy Microgrid System under Uncertainty |
| title_full | Particle Swarm Optimization for an Optimal Hybrid Renewable Energy Microgrid System under Uncertainty |
| title_fullStr | Particle Swarm Optimization for an Optimal Hybrid Renewable Energy Microgrid System under Uncertainty |
| title_full_unstemmed | Particle Swarm Optimization for an Optimal Hybrid Renewable Energy Microgrid System under Uncertainty |
| title_short | Particle Swarm Optimization for an Optimal Hybrid Renewable Energy Microgrid System under Uncertainty |
| title_sort | particle swarm optimization for an optimal hybrid renewable energy microgrid system under uncertainty |
| topic | particle swarm optimisation hybrid renewable energy resources microgrids reserve margins |
| url | https://www.mdpi.com/1996-1073/17/2/422 |
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