Optimizing dynamic economic dispatch through an enhanced Cheetah-inspired algorithm for integrated renewable energy and demand-side management
Abstract This study presents the Enhanced Cheetah Optimizer Algorithm (ECOA) designed to tackle the intricate real-world challenges of dynamic economic dispatch (DED). These complexities encompass demand-side management (DSM), integration of non-conventional energy sources, and the utilization of pu...
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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/s41598-024-53688-8 |
| _version_ | 1827327451640889344 |
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| author | Karthik Nagarajan Arul Rajagopalan Mohit Bajaj R. Sitharthan Shir Ahmad Dost Mohammadi Vojtech Blazek |
| author_facet | Karthik Nagarajan Arul Rajagopalan Mohit Bajaj R. Sitharthan Shir Ahmad Dost Mohammadi Vojtech Blazek |
| author_sort | Karthik Nagarajan |
| collection | DOAJ |
| description | Abstract This study presents the Enhanced Cheetah Optimizer Algorithm (ECOA) designed to tackle the intricate real-world challenges of dynamic economic dispatch (DED). These complexities encompass demand-side management (DSM), integration of non-conventional energy sources, and the utilization of pumped-storage hydroelectric units. Acknowledging the variability of solar and wind energy sources and the existence of a pumped-storage hydroelectric system, this study integrates a solar-wind-thermal energy system. The DSM program not only enhances power grid security but also lowers operational costs. The research addresses the DED problem with and without DSM implementation to analyze its impact. Demonstrating effectiveness on two test systems, the suggested method's efficacy is showcased. The recommended method's simulation results have been compared to those obtained using Cheetah Optimizer Algorithm (COA) and Grey Wolf Optimizer. The optimization results indicate that, for both the 10-unit and 20-unit systems, the proposed ECOA algorithm achieves savings of 0.24% and 0.43%, respectively, in operation costs when Dynamic Economic Dispatch is conducted with Demand-Side Management (DSM). This underscores the advantageous capability of DSM in minimizing costs and enhancing the economic efficiency of the power systems. Our ECOA has greater adaptability and reliability, making it a promising solution for addressing multi-objective energy management difficulties within microgrids, particularly when demand response mechanisms are incorporated. Furthermore, the suggested ECOA has the ability to elucidate the multi-objective dynamic optimal power flow problem in IEEE standard test systems, particularly when electric vehicles and renewable energy sources are integrated. |
| first_indexed | 2024-03-07T15:01:25Z |
| format | Article |
| id | doaj.art-7a4967ffc5f94ae0851627ddf4df6367 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-07T15:01:25Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-7a4967ffc5f94ae0851627ddf4df63672024-03-05T19:07:19ZengNature PortfolioScientific Reports2045-23222024-02-0114112210.1038/s41598-024-53688-8Optimizing dynamic economic dispatch through an enhanced Cheetah-inspired algorithm for integrated renewable energy and demand-side managementKarthik Nagarajan0Arul Rajagopalan1Mohit Bajaj2R. Sitharthan3Shir Ahmad Dost Mohammadi4Vojtech Blazek5Department of Electrical and Electronics Engineering, Hindustan Institute of Technology and ScienceCentre for Smart Grid Technologies, School of Electrical Engineering, Vellore Institute of TechnologyElectrical Engineering Department, Graphic Era (Deemed to be University)Centre for Smart Grid Technologies, School of Electrical Engineering, Vellore Institute of TechnologyDepartment of Electrical and Electronics, Faculty of Engineering, Alberoni UniversityENET Centre, VSB—Technical University of OstravaAbstract This study presents the Enhanced Cheetah Optimizer Algorithm (ECOA) designed to tackle the intricate real-world challenges of dynamic economic dispatch (DED). These complexities encompass demand-side management (DSM), integration of non-conventional energy sources, and the utilization of pumped-storage hydroelectric units. Acknowledging the variability of solar and wind energy sources and the existence of a pumped-storage hydroelectric system, this study integrates a solar-wind-thermal energy system. The DSM program not only enhances power grid security but also lowers operational costs. The research addresses the DED problem with and without DSM implementation to analyze its impact. Demonstrating effectiveness on two test systems, the suggested method's efficacy is showcased. The recommended method's simulation results have been compared to those obtained using Cheetah Optimizer Algorithm (COA) and Grey Wolf Optimizer. The optimization results indicate that, for both the 10-unit and 20-unit systems, the proposed ECOA algorithm achieves savings of 0.24% and 0.43%, respectively, in operation costs when Dynamic Economic Dispatch is conducted with Demand-Side Management (DSM). This underscores the advantageous capability of DSM in minimizing costs and enhancing the economic efficiency of the power systems. Our ECOA has greater adaptability and reliability, making it a promising solution for addressing multi-objective energy management difficulties within microgrids, particularly when demand response mechanisms are incorporated. Furthermore, the suggested ECOA has the ability to elucidate the multi-objective dynamic optimal power flow problem in IEEE standard test systems, particularly when electric vehicles and renewable energy sources are integrated.https://doi.org/10.1038/s41598-024-53688-8 |
| spellingShingle | Karthik Nagarajan Arul Rajagopalan Mohit Bajaj R. Sitharthan Shir Ahmad Dost Mohammadi Vojtech Blazek Optimizing dynamic economic dispatch through an enhanced Cheetah-inspired algorithm for integrated renewable energy and demand-side management Scientific Reports |
| title | Optimizing dynamic economic dispatch through an enhanced Cheetah-inspired algorithm for integrated renewable energy and demand-side management |
| title_full | Optimizing dynamic economic dispatch through an enhanced Cheetah-inspired algorithm for integrated renewable energy and demand-side management |
| title_fullStr | Optimizing dynamic economic dispatch through an enhanced Cheetah-inspired algorithm for integrated renewable energy and demand-side management |
| title_full_unstemmed | Optimizing dynamic economic dispatch through an enhanced Cheetah-inspired algorithm for integrated renewable energy and demand-side management |
| title_short | Optimizing dynamic economic dispatch through an enhanced Cheetah-inspired algorithm for integrated renewable energy and demand-side management |
| title_sort | optimizing dynamic economic dispatch through an enhanced cheetah inspired algorithm for integrated renewable energy and demand side management |
| url | https://doi.org/10.1038/s41598-024-53688-8 |
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