A comparison of different metaheuristic optimization algorithms on hydrogen storage-based microgrid sizing
Microgrids (MGs) with a high penetration of renewable energy are becoming increasingly popular, mainly due to the need for a sustainable and environmentally friendly power system. However, the stochastic characteristic of renewable energy sources makes it a considerable challenge when designing a mi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-10-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484723009010 |
| _version_ | 1827582113917960192 |
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| author | Long Phan-Van Hirotaka Takano Tuyen Nguyen Duc |
| author_facet | Long Phan-Van Hirotaka Takano Tuyen Nguyen Duc |
| author_sort | Long Phan-Van |
| collection | DOAJ |
| description | Microgrids (MGs) with a high penetration of renewable energy are becoming increasingly popular, mainly due to the need for a sustainable and environmentally friendly power system. However, the stochastic characteristic of renewable energy sources makes it a considerable challenge when designing a microgrid. Appropriate installation of energy storage systems (ESSs) such as battery and hydrogen storage systems are needed to counter the intermittent nature of energy sources. This study presents a comparison and evaluation of eight different metaheuristic approaches for optimizing the size of a hydrogen storage-based microgrid, with the aims of minimizing the microgrid’s cost and ensuring the ability to regulate the energy flow within the system. In addition, the optimization algorithm considers the power of the photovoltaic (PV) system, electrolyzer, fuel cell, and the capacity of the battery and hydrogen tank as decision variables. Results of numerical simulations proved that, under the above problem framework, the particle swarm optimization algorithm outperforms the rest. The algorithm is able to produce an optimized microgrid with a 25.3% lower annual system cost compared to the worst-performing algorithm. Its ability to escape the local optimum solution is also showcased. |
| first_indexed | 2024-03-08T22:45:54Z |
| format | Article |
| id | doaj.art-062e598502ad48599ec6995d917bf31a |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-03-08T22:45:54Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-062e598502ad48599ec6995d917bf31a2023-12-17T06:39:06ZengElsevierEnergy Reports2352-48472023-10-019542549A comparison of different metaheuristic optimization algorithms on hydrogen storage-based microgrid sizingLong Phan-Van0Hirotaka Takano1Tuyen Nguyen Duc2School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, Viet NamDepartment of Electrical, Electronic and Computer Engineering, Gifu University, JapanSchool of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, Viet Nam; Department of Electrical Engineering, Shibaura Institute of Technology, Tokyo, Japan; Corresponding author at: School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, Viet Nam.Microgrids (MGs) with a high penetration of renewable energy are becoming increasingly popular, mainly due to the need for a sustainable and environmentally friendly power system. However, the stochastic characteristic of renewable energy sources makes it a considerable challenge when designing a microgrid. Appropriate installation of energy storage systems (ESSs) such as battery and hydrogen storage systems are needed to counter the intermittent nature of energy sources. This study presents a comparison and evaluation of eight different metaheuristic approaches for optimizing the size of a hydrogen storage-based microgrid, with the aims of minimizing the microgrid’s cost and ensuring the ability to regulate the energy flow within the system. In addition, the optimization algorithm considers the power of the photovoltaic (PV) system, electrolyzer, fuel cell, and the capacity of the battery and hydrogen tank as decision variables. Results of numerical simulations proved that, under the above problem framework, the particle swarm optimization algorithm outperforms the rest. The algorithm is able to produce an optimized microgrid with a 25.3% lower annual system cost compared to the worst-performing algorithm. Its ability to escape the local optimum solution is also showcased.http://www.sciencedirect.com/science/article/pii/S2352484723009010Hydrogen storage systemHybrid renewable energy systemMicrogridMetaheuristic algorithmSystem optimizing |
| spellingShingle | Long Phan-Van Hirotaka Takano Tuyen Nguyen Duc A comparison of different metaheuristic optimization algorithms on hydrogen storage-based microgrid sizing Energy Reports Hydrogen storage system Hybrid renewable energy system Microgrid Metaheuristic algorithm System optimizing |
| title | A comparison of different metaheuristic optimization algorithms on hydrogen storage-based microgrid sizing |
| title_full | A comparison of different metaheuristic optimization algorithms on hydrogen storage-based microgrid sizing |
| title_fullStr | A comparison of different metaheuristic optimization algorithms on hydrogen storage-based microgrid sizing |
| title_full_unstemmed | A comparison of different metaheuristic optimization algorithms on hydrogen storage-based microgrid sizing |
| title_short | A comparison of different metaheuristic optimization algorithms on hydrogen storage-based microgrid sizing |
| title_sort | comparison of different metaheuristic optimization algorithms on hydrogen storage based microgrid sizing |
| topic | Hydrogen storage system Hybrid renewable energy system Microgrid Metaheuristic algorithm System optimizing |
| url | http://www.sciencedirect.com/science/article/pii/S2352484723009010 |
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