Optimizing energy and reserve minimization in a sustainable microgrid with electric vehicle integration: dynamic and adjustable manta ray foraging algorithm
The growing presence of EVs in regional microgrids introduces increased variability and uncertainty in the areas’ load profiles. This paper presents a novel approach for optimizing energy and reserve minimization in a sustainable integrated microgrid with electric vehicles (EVs) by the use of the dy...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI
2023
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| Subjects: | |
| Online Access: | https://umpir.ump.edu.my/id/eprint/44258/ |
| _version_ | 1848827319952605184 |
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| author | Abed, Adnan Ajam Suwaed, Mahmood Sh. Al-Rubaye, Ameer H. Awad, Omar I. Mohammed, M.N Hai, Tao Kadirgama, Kumaran Karah Bash, Ali A. H. |
| author_facet | Abed, Adnan Ajam Suwaed, Mahmood Sh. Al-Rubaye, Ameer H. Awad, Omar I. Mohammed, M.N Hai, Tao Kadirgama, Kumaran Karah Bash, Ali A. H. |
| author_sort | Abed, Adnan Ajam |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | The growing presence of EVs in regional microgrids introduces increased variability and uncertainty in the areas’ load profiles. This paper presents a novel approach for optimizing energy and reserve minimization in a sustainable integrated microgrid with electric vehicles (EVs) by the use of the dynamic and adjustable Manta Ray Foraging (DAMRF) algorithm. The DAMRF algorithm harnesses the inherent flexibility of EVs as controllable loads and develops a comprehensive dispatch model for a large-scale EV response. The model takes into account the management, operational, and environmental costs associated with load fluctuations in the microgrid. Simulation evaluations conducted based on a practical microgrid environment validate the effectiveness of our wind–solar energy storage and management strategy. The results showcase significant improvements in energy and reserve minimization, highlighting the potential advantages of integrating EVs into sustainable microgrid systems. In addition, the DAMRF algorithm achieves lower environmental pollution control costs (USD 8000) compared to the costs associated with the Genetic Algorithm (GA) (USD 8654.639) and PSO (USD 8579.546), emphasizing its ability to effectively control and minimize environmental pollution. In addition, the DAMRF algorithm offers a more cost-effective solution for managing the power grid, and the shorter solution running time of the DAMRF is almost the same as PSO’s quicker decision-making and response times, enhancing the overall responsiveness and adaptability of the power grid management system. |
| first_indexed | 2025-11-15T03:58:50Z |
| format | Article |
| id | ump-44258 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:58:50Z |
| publishDate | 2023 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-442582025-09-23T03:45:16Z https://umpir.ump.edu.my/id/eprint/44258/ Optimizing energy and reserve minimization in a sustainable microgrid with electric vehicle integration: dynamic and adjustable manta ray foraging algorithm Abed, Adnan Ajam Suwaed, Mahmood Sh. Al-Rubaye, Ameer H. Awad, Omar I. Mohammed, M.N Hai, Tao Kadirgama, Kumaran Karah Bash, Ali A. H. TJ Mechanical engineering and machinery TL Motor vehicles. Aeronautics. Astronautics The growing presence of EVs in regional microgrids introduces increased variability and uncertainty in the areas’ load profiles. This paper presents a novel approach for optimizing energy and reserve minimization in a sustainable integrated microgrid with electric vehicles (EVs) by the use of the dynamic and adjustable Manta Ray Foraging (DAMRF) algorithm. The DAMRF algorithm harnesses the inherent flexibility of EVs as controllable loads and develops a comprehensive dispatch model for a large-scale EV response. The model takes into account the management, operational, and environmental costs associated with load fluctuations in the microgrid. Simulation evaluations conducted based on a practical microgrid environment validate the effectiveness of our wind–solar energy storage and management strategy. The results showcase significant improvements in energy and reserve minimization, highlighting the potential advantages of integrating EVs into sustainable microgrid systems. In addition, the DAMRF algorithm achieves lower environmental pollution control costs (USD 8000) compared to the costs associated with the Genetic Algorithm (GA) (USD 8654.639) and PSO (USD 8579.546), emphasizing its ability to effectively control and minimize environmental pollution. In addition, the DAMRF algorithm offers a more cost-effective solution for managing the power grid, and the shorter solution running time of the DAMRF is almost the same as PSO’s quicker decision-making and response times, enhancing the overall responsiveness and adaptability of the power grid management system. MDPI 2023-10 Article PeerReviewed pdf en cc_by_4 https://umpir.ump.edu.my/id/eprint/44258/2/Optimizing%20energy%20and%20reserve%20minimization%20in%20a%20sustainable%20microgrid.pdf Abed, Adnan Ajam and Suwaed, Mahmood Sh. and Al-Rubaye, Ameer H. and Awad, Omar I. and Mohammed, M.N and Hai, Tao and Kadirgama, Kumaran and Karah Bash, Ali A. H. (2023) Optimizing energy and reserve minimization in a sustainable microgrid with electric vehicle integration: dynamic and adjustable manta ray foraging algorithm. Processes, 11 (10). pp. 1-17. ISSN 2227-9717. (Published) https://doi.org/10.3390/pr11102848 https://doi.org/10.3390/pr11102848 https://doi.org/10.3390/pr11102848 |
| spellingShingle | TJ Mechanical engineering and machinery TL Motor vehicles. Aeronautics. Astronautics Abed, Adnan Ajam Suwaed, Mahmood Sh. Al-Rubaye, Ameer H. Awad, Omar I. Mohammed, M.N Hai, Tao Kadirgama, Kumaran Karah Bash, Ali A. H. Optimizing energy and reserve minimization in a sustainable microgrid with electric vehicle integration: dynamic and adjustable manta ray foraging algorithm |
| title | Optimizing energy and reserve minimization in a sustainable microgrid with electric vehicle integration: dynamic and adjustable manta ray foraging algorithm |
| title_full | Optimizing energy and reserve minimization in a sustainable microgrid with electric vehicle integration: dynamic and adjustable manta ray foraging algorithm |
| title_fullStr | Optimizing energy and reserve minimization in a sustainable microgrid with electric vehicle integration: dynamic and adjustable manta ray foraging algorithm |
| title_full_unstemmed | Optimizing energy and reserve minimization in a sustainable microgrid with electric vehicle integration: dynamic and adjustable manta ray foraging algorithm |
| title_short | Optimizing energy and reserve minimization in a sustainable microgrid with electric vehicle integration: dynamic and adjustable manta ray foraging algorithm |
| title_sort | optimizing energy and reserve minimization in a sustainable microgrid with electric vehicle integration: dynamic and adjustable manta ray foraging algorithm |
| topic | TJ Mechanical engineering and machinery TL Motor vehicles. Aeronautics. Astronautics |
| url | https://umpir.ump.edu.my/id/eprint/44258/ https://umpir.ump.edu.my/id/eprint/44258/ https://umpir.ump.edu.my/id/eprint/44258/ |