Hybrid Energy Microgrids: A Comparative Study of Optimization Techniques
This study examines the relative effectiveness of Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Linear Programming (LP) in optimizing hybrid energy microgrids. Drawing upon empirical data derived from the study, the research explores many facets, including...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2024-01-01
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| Series: | E3S Web of Conferences |
| Subjects: | |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/41/e3sconf_amgse2024_01018.pdf |
| _version_ | 1797213092754489344 |
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| author | Nemova Darya Viktorovna Rao D. Siva Naga Malleswara Singh Rajat Bhardwaj Rishabh Sharma Sorabh |
| author_facet | Nemova Darya Viktorovna Rao D. Siva Naga Malleswara Singh Rajat Bhardwaj Rishabh Sharma Sorabh |
| author_sort | Nemova Darya Viktorovna |
| collection | DOAJ |
| description | This study examines the relative effectiveness of Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Linear Programming (LP) in optimizing hybrid energy microgrids. Drawing upon empirical data derived from the study, the research explores many facets, including economic efficacy, environmental viability, and microgrid robustness. The use of GA showcases a significant 10% decrease in overall system expenses, highlighting its efficacy in augmenting economic feasibility. PSO diligently tracks, attaining an 8% decrease, while SA and LP make significant contributions but provide somewhat lesser cost reductions at 7% and 6%, correspondingly. Within the domain of renewable energy integration, GA and PSO have emerged as frontrunners, with remarkable advancements of 12% and 10%, respectively. SA and LP provide commendable contributions, demonstrating their effectiveness in optimizing the usage of renewable energy sources inside the microgrid, as seen by their respective increases of 8% and 7%. The environmental factor, as quantified by the decrease of carbon emissions, highlights the commendable efficacy of GA and PSO, resulting in significant reductions of 15% and 12% respectively. SA and LP provide praiseworthy environmental efforts, achieving reductions of 10% and 8% respectively. The resilience index highlights the strength of GA and PSO in assessing the resilience of the microgrid, with GA showing an increase of 0.05 and PSO showing an increase of 0.04. SA and LP make a significant contribution, with increments of 0.03 and 0.02, underscoring the potential of evolutionary and swarm-based methodologies to bolster the microgrid’s resilience against disturbances. Scenario analysis effectively brings unpredictability into the operational environment of the microgrid, continually showcasing the remarkable flexibility of GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) over a wide range of situations. SA and LP demonstrate consistent efficacy but with somewhat reduced flexibility. Statistical evaluations provide compelling evidence confirming the exceptional efficacy of GA and PSO in improving microgrid metrics. Ultimately, this research provides valuable perspectives on the intricate trade-offs between various optimization techniques, empowering decision-makers to choose strategies that align with specific microgrid objectives. Moreover, it contributes to the wider discussion on resilient, sustainable, and economically feasible energy infrastructures. |
| first_indexed | 2024-04-24T10:52:47Z |
| format | Article |
| id | doaj.art-094d7a148d2c4f9a95fdf710fceefa90 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-04-24T10:52:47Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-094d7a148d2c4f9a95fdf710fceefa902024-04-12T07:41:36ZengEDP SciencesE3S Web of Conferences2267-12422024-01-015110101810.1051/e3sconf/202451101018e3sconf_amgse2024_01018Hybrid Energy Microgrids: A Comparative Study of Optimization TechniquesNemova Darya Viktorovna0Rao D. Siva Naga Malleswara1Singh Rajat2Bhardwaj Rishabh3Sharma Sorabh4Peter the Great St. Petersburg Polytechnic UniversityDepartment of EEE, GRIET, BachupallyUttaranchal UniversityChitkara Centre for Research and Development, Chitkara UniversityCentre of Research Impact and Outcome, Chitkara UniversityThis study examines the relative effectiveness of Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Linear Programming (LP) in optimizing hybrid energy microgrids. Drawing upon empirical data derived from the study, the research explores many facets, including economic efficacy, environmental viability, and microgrid robustness. The use of GA showcases a significant 10% decrease in overall system expenses, highlighting its efficacy in augmenting economic feasibility. PSO diligently tracks, attaining an 8% decrease, while SA and LP make significant contributions but provide somewhat lesser cost reductions at 7% and 6%, correspondingly. Within the domain of renewable energy integration, GA and PSO have emerged as frontrunners, with remarkable advancements of 12% and 10%, respectively. SA and LP provide commendable contributions, demonstrating their effectiveness in optimizing the usage of renewable energy sources inside the microgrid, as seen by their respective increases of 8% and 7%. The environmental factor, as quantified by the decrease of carbon emissions, highlights the commendable efficacy of GA and PSO, resulting in significant reductions of 15% and 12% respectively. SA and LP provide praiseworthy environmental efforts, achieving reductions of 10% and 8% respectively. The resilience index highlights the strength of GA and PSO in assessing the resilience of the microgrid, with GA showing an increase of 0.05 and PSO showing an increase of 0.04. SA and LP make a significant contribution, with increments of 0.03 and 0.02, underscoring the potential of evolutionary and swarm-based methodologies to bolster the microgrid’s resilience against disturbances. Scenario analysis effectively brings unpredictability into the operational environment of the microgrid, continually showcasing the remarkable flexibility of GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) over a wide range of situations. SA and LP demonstrate consistent efficacy but with somewhat reduced flexibility. Statistical evaluations provide compelling evidence confirming the exceptional efficacy of GA and PSO in improving microgrid metrics. Ultimately, this research provides valuable perspectives on the intricate trade-offs between various optimization techniques, empowering decision-makers to choose strategies that align with specific microgrid objectives. Moreover, it contributes to the wider discussion on resilient, sustainable, and economically feasible energy infrastructures.https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/41/e3sconf_amgse2024_01018.pdfhybrid energy microgridsoptimization techniquesgenetic algorithmsrenewable energy integrationmicrogrid resilience |
| spellingShingle | Nemova Darya Viktorovna Rao D. Siva Naga Malleswara Singh Rajat Bhardwaj Rishabh Sharma Sorabh Hybrid Energy Microgrids: A Comparative Study of Optimization Techniques E3S Web of Conferences hybrid energy microgrids optimization techniques genetic algorithms renewable energy integration microgrid resilience |
| title | Hybrid Energy Microgrids: A Comparative Study of Optimization Techniques |
| title_full | Hybrid Energy Microgrids: A Comparative Study of Optimization Techniques |
| title_fullStr | Hybrid Energy Microgrids: A Comparative Study of Optimization Techniques |
| title_full_unstemmed | Hybrid Energy Microgrids: A Comparative Study of Optimization Techniques |
| title_short | Hybrid Energy Microgrids: A Comparative Study of Optimization Techniques |
| title_sort | hybrid energy microgrids a comparative study of optimization techniques |
| topic | hybrid energy microgrids optimization techniques genetic algorithms renewable energy integration microgrid resilience |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/41/e3sconf_amgse2024_01018.pdf |
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