A comparative study of advanced evolutionary algorithms for optimizing microgrid performance under dynamic pricing conditions
Abstract The integration of microgrids into the existing power system framework enhances the reliability and efficiency of the utility grid. This manuscript presents an innovative mathematical paradigm designed for the optimization of both the structural and operational aspects of a grid-connected m...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-54829-9 |
| _version_ | 1797275123054542848 |
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| author | Rasha Elazab Ahmed T. Abdelnaby A.A. Ali |
| author_facet | Rasha Elazab Ahmed T. Abdelnaby A.A. Ali |
| author_sort | Rasha Elazab |
| collection | DOAJ |
| description | Abstract The integration of microgrids into the existing power system framework enhances the reliability and efficiency of the utility grid. This manuscript presents an innovative mathematical paradigm designed for the optimization of both the structural and operational aspects of a grid-connected microgrid, leveraging the principles of Demand-Side Management (DSM). The focus of this work lies in a comprehensive exploration of the implications brought about by the Renewable Generation-Based Dynamic Pricing Demand Response (RGDP-DR) mechanism, particularly in terms of its influence on the optimal microgrid configuration, considering perspectives from end-users and the utility entity. This inquiry is rooted in a holistic assessment that encompasses technical and economic performance benchmarks. The RGDP-induced DR framework adeptly addresses the needs of the consumer base, showcasing notable efficiency and economic feasibility. To address the intricate nonlinear optimization challenge at hand, we employ an evolutionary algorithm named the "Dandelion Algorithm" (DA). A rigorous comparative study is conducted to evaluate the efficacy of four optimization techniques, affirming the supremacy of the proposed DA. Within this discourse, the complexity of microgrid sizing is cast as a dual-objective optimization task. The twin objectives involve minimizing the aggregate annual outlay and reducing emissions. The results of this endeavor unequivocally endorse the superiority of the DA over its counterparts. The DA demonstrates exceptional proficiency in orchestrating the most cost-effective microgrid and consumer invoice, surpassing the performance of alternative optimization methodologies. |
| first_indexed | 2024-03-07T15:09:49Z |
| format | Article |
| id | doaj.art-18a7ada4e7d64fd694995e8701a9eea8 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-07T15:09:49Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-18a7ada4e7d64fd694995e8701a9eea82024-03-05T18:43:03ZengNature PortfolioScientific Reports2045-23222024-02-0114111910.1038/s41598-024-54829-9A comparative study of advanced evolutionary algorithms for optimizing microgrid performance under dynamic pricing conditionsRasha Elazab0Ahmed T. Abdelnaby1A.A. Ali2Faculty of Engineering, Helwan UniversityFaculty of Engineering, Helwan UniversityFaculty of Engineering, Helwan UniversityAbstract The integration of microgrids into the existing power system framework enhances the reliability and efficiency of the utility grid. This manuscript presents an innovative mathematical paradigm designed for the optimization of both the structural and operational aspects of a grid-connected microgrid, leveraging the principles of Demand-Side Management (DSM). The focus of this work lies in a comprehensive exploration of the implications brought about by the Renewable Generation-Based Dynamic Pricing Demand Response (RGDP-DR) mechanism, particularly in terms of its influence on the optimal microgrid configuration, considering perspectives from end-users and the utility entity. This inquiry is rooted in a holistic assessment that encompasses technical and economic performance benchmarks. The RGDP-induced DR framework adeptly addresses the needs of the consumer base, showcasing notable efficiency and economic feasibility. To address the intricate nonlinear optimization challenge at hand, we employ an evolutionary algorithm named the "Dandelion Algorithm" (DA). A rigorous comparative study is conducted to evaluate the efficacy of four optimization techniques, affirming the supremacy of the proposed DA. Within this discourse, the complexity of microgrid sizing is cast as a dual-objective optimization task. The twin objectives involve minimizing the aggregate annual outlay and reducing emissions. The results of this endeavor unequivocally endorse the superiority of the DA over its counterparts. The DA demonstrates exceptional proficiency in orchestrating the most cost-effective microgrid and consumer invoice, surpassing the performance of alternative optimization methodologies.https://doi.org/10.1038/s41598-024-54829-9 |
| spellingShingle | Rasha Elazab Ahmed T. Abdelnaby A.A. Ali A comparative study of advanced evolutionary algorithms for optimizing microgrid performance under dynamic pricing conditions Scientific Reports |
| title | A comparative study of advanced evolutionary algorithms for optimizing microgrid performance under dynamic pricing conditions |
| title_full | A comparative study of advanced evolutionary algorithms for optimizing microgrid performance under dynamic pricing conditions |
| title_fullStr | A comparative study of advanced evolutionary algorithms for optimizing microgrid performance under dynamic pricing conditions |
| title_full_unstemmed | A comparative study of advanced evolutionary algorithms for optimizing microgrid performance under dynamic pricing conditions |
| title_short | A comparative study of advanced evolutionary algorithms for optimizing microgrid performance under dynamic pricing conditions |
| title_sort | comparative study of advanced evolutionary algorithms for optimizing microgrid performance under dynamic pricing conditions |
| url | https://doi.org/10.1038/s41598-024-54829-9 |
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