Optimization of wind-solar hybrid microgrids using swarm intelligence algorithms
The study explores the enhancement of wind-solar hybrid microgrids via the use of Swarm Intelligence Algorithms (SIAs). It assesses the efficacy of these algorithms in efficiently managing renewable energy sources, load demands, and battery storage inside the microgrid system. An examination of actu...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2024-01-01
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| Series: | MATEC Web of Conferences |
| Subjects: | |
| Online Access: | https://www.matec-conferences.org/articles/matecconf/pdf/2024/04/matecconf_icmed2024_01187.pdf |
| _version_ | 1797248889289441280 |
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| author | Mittal Aman Karuna G. |
| author_facet | Mittal Aman Karuna G. |
| author_sort | Mittal Aman |
| collection | DOAJ |
| description | The study explores the enhancement of wind-solar hybrid microgrids via the use of Swarm Intelligence Algorithms (SIAs). It assesses the efficacy of these algorithms in efficiently managing renewable energy sources, load demands, and battery storage inside the microgrid system. An examination of actual data highlights the influence of environmental elements on the production of electricity, as seen by the diverse wind speeds resulting in power outputs that range from 15 kW at 4 m/s to 30 kW at 7 m/s. This underscores the clear and direct relationship between wind speed and the amount of power created. Likewise, solar irradiance levels demonstrate oscillations ranging from 500 W/m² to 800 W/m², therefore yielding power outputs that include a range of 15 kW to 24 kW, so illuminating the profound impact of solar irradiance on energy capture. The dynamic energy consumption patterns are exposed by the varying load demands, whereby the demand levels oscillate between 20 kW and 28 kW. This highlights the crucial significance of demand variability in determining energy needs. In addition, the data on battery storage reveals a range of charge levels, ranging from 25 kWh to 40 kWh, which underscores its pivotal function in the equilibrium of energy supply and consumption. When evaluating SIAs, it becomes evident that Particle Swarm Optimization (PSO) surpasses both Ant Colony Optimization (ACO) and Genetic Algorithms (GA) in obtaining an impressive 80% renewable energy penetration rate. PSO effectively reduces operating costs by 15%, demonstrating its exceptional proficiency in optimizing microgrid operations. This study provides valuable insights into the intricate interplay among environmental conditions, load demands, battery storage, and algorithmic optimization in wind-solar hybrid microgrids. |
| first_indexed | 2024-04-24T20:21:45Z |
| format | Article |
| id | doaj.art-764b332c4c42469b9d0eb930ee65894c |
| institution | Directory Open Access Journal |
| issn | 2261-236X |
| language | English |
| last_indexed | 2024-04-24T20:21:45Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | MATEC Web of Conferences |
| spelling | doaj.art-764b332c4c42469b9d0eb930ee65894c2024-03-22T08:05:26ZengEDP SciencesMATEC Web of Conferences2261-236X2024-01-013920118710.1051/matecconf/202439201187matecconf_icmed2024_01187Optimization of wind-solar hybrid microgrids using swarm intelligence algorithmsMittal Aman0Karuna G.1Lovely Professional UniversityDepartment of AIMLE, GRIETThe study explores the enhancement of wind-solar hybrid microgrids via the use of Swarm Intelligence Algorithms (SIAs). It assesses the efficacy of these algorithms in efficiently managing renewable energy sources, load demands, and battery storage inside the microgrid system. An examination of actual data highlights the influence of environmental elements on the production of electricity, as seen by the diverse wind speeds resulting in power outputs that range from 15 kW at 4 m/s to 30 kW at 7 m/s. This underscores the clear and direct relationship between wind speed and the amount of power created. Likewise, solar irradiance levels demonstrate oscillations ranging from 500 W/m² to 800 W/m², therefore yielding power outputs that include a range of 15 kW to 24 kW, so illuminating the profound impact of solar irradiance on energy capture. The dynamic energy consumption patterns are exposed by the varying load demands, whereby the demand levels oscillate between 20 kW and 28 kW. This highlights the crucial significance of demand variability in determining energy needs. In addition, the data on battery storage reveals a range of charge levels, ranging from 25 kWh to 40 kWh, which underscores its pivotal function in the equilibrium of energy supply and consumption. When evaluating SIAs, it becomes evident that Particle Swarm Optimization (PSO) surpasses both Ant Colony Optimization (ACO) and Genetic Algorithms (GA) in obtaining an impressive 80% renewable energy penetration rate. PSO effectively reduces operating costs by 15%, demonstrating its exceptional proficiency in optimizing microgrid operations. This study provides valuable insights into the intricate interplay among environmental conditions, load demands, battery storage, and algorithmic optimization in wind-solar hybrid microgrids.https://www.matec-conferences.org/articles/matecconf/pdf/2024/04/matecconf_icmed2024_01187.pdfwind-solar hybrid microgridsswarm intelligence algorithmsrenewable energy optimizationmicrogrid operationsenergy management strategies |
| spellingShingle | Mittal Aman Karuna G. Optimization of wind-solar hybrid microgrids using swarm intelligence algorithms MATEC Web of Conferences wind-solar hybrid microgrids swarm intelligence algorithms renewable energy optimization microgrid operations energy management strategies |
| title | Optimization of wind-solar hybrid microgrids using swarm intelligence algorithms |
| title_full | Optimization of wind-solar hybrid microgrids using swarm intelligence algorithms |
| title_fullStr | Optimization of wind-solar hybrid microgrids using swarm intelligence algorithms |
| title_full_unstemmed | Optimization of wind-solar hybrid microgrids using swarm intelligence algorithms |
| title_short | Optimization of wind-solar hybrid microgrids using swarm intelligence algorithms |
| title_sort | optimization of wind solar hybrid microgrids using swarm intelligence algorithms |
| topic | wind-solar hybrid microgrids swarm intelligence algorithms renewable energy optimization microgrid operations energy management strategies |
| url | https://www.matec-conferences.org/articles/matecconf/pdf/2024/04/matecconf_icmed2024_01187.pdf |
| work_keys_str_mv | AT mittalaman optimizationofwindsolarhybridmicrogridsusingswarmintelligencealgorithms AT karunag optimizationofwindsolarhybridmicrogridsusingswarmintelligencealgorithms |