Intelligent Deep-Q-Network-Based Energy Management for an Isolated Microgrid
The development of hybrid renewable energy systems (HRESs) can be the most feasible solution for a stable, environment-friendly, and cost-effective power generation, especially in rural and island territories. In this studied HRES, solar and wind energy are used as the major resources. Moreover, the...
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| Format: | Article |
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MDPI AG
2022-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/17/8721 |
| _version_ | 1797496330050863104 |
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| author | Bao Chau Phan Meng-Tse Lee Ying-Chih Lai |
| author_facet | Bao Chau Phan Meng-Tse Lee Ying-Chih Lai |
| author_sort | Bao Chau Phan |
| collection | DOAJ |
| description | The development of hybrid renewable energy systems (HRESs) can be the most feasible solution for a stable, environment-friendly, and cost-effective power generation, especially in rural and island territories. In this studied HRES, solar and wind energy are used as the major resources. Moreover, the electrolyzed hydrogen is utilized to store energy for the operation of a fuel cell. In case of insufficiency, battery and fuel cell are storage systems that supply energy, while a diesel generator adds a backup system to meet the load demand under bad weather conditions. An isolated HRES energy management system (EMS) based on a Deep Q Network (DQN) is introduced to ensure the reliable and efficient operation of the system. A DQN can deal with the problem of continuous state spaces and manage the dynamic behavior of hybrid systems without exact mathematical models. Following the power consumption data from Basco island of the Philippines, HOMER software is used to calculate the capacity of each component in the proposed power plant. In MATLAB/Simulink, the plant and its DQN-based EMS are simulated. Under different load profile scenarios, the proposed method is compared to the convectional dispatch (CD) control for a validation. Based on the outstanding performances with fewer fuel consumption, DQN is a very powerful and potential method for energy management. |
| first_indexed | 2024-03-10T03:02:10Z |
| format | Article |
| id | doaj.art-d13e8268356c48ea8b977dae15929ea6 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T03:02:10Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-d13e8268356c48ea8b977dae15929ea62023-11-23T12:45:50ZengMDPI AGApplied Sciences2076-34172022-08-011217872110.3390/app12178721Intelligent Deep-Q-Network-Based Energy Management for an Isolated MicrogridBao Chau Phan0Meng-Tse Lee1Ying-Chih Lai2Department of Aeronautics and Astronautics, College of Engineering, National Cheng Kung University, Tainan 701, TaiwanDepartment of Automation Engineering, College of Engineering, National Formosa University, Yunlin 632, TaiwanDepartment of Aeronautics and Astronautics, College of Engineering, National Cheng Kung University, Tainan 701, TaiwanThe development of hybrid renewable energy systems (HRESs) can be the most feasible solution for a stable, environment-friendly, and cost-effective power generation, especially in rural and island territories. In this studied HRES, solar and wind energy are used as the major resources. Moreover, the electrolyzed hydrogen is utilized to store energy for the operation of a fuel cell. In case of insufficiency, battery and fuel cell are storage systems that supply energy, while a diesel generator adds a backup system to meet the load demand under bad weather conditions. An isolated HRES energy management system (EMS) based on a Deep Q Network (DQN) is introduced to ensure the reliable and efficient operation of the system. A DQN can deal with the problem of continuous state spaces and manage the dynamic behavior of hybrid systems without exact mathematical models. Following the power consumption data from Basco island of the Philippines, HOMER software is used to calculate the capacity of each component in the proposed power plant. In MATLAB/Simulink, the plant and its DQN-based EMS are simulated. Under different load profile scenarios, the proposed method is compared to the convectional dispatch (CD) control for a validation. Based on the outstanding performances with fewer fuel consumption, DQN is a very powerful and potential method for energy management.https://www.mdpi.com/2076-3417/12/17/8721hybrid renewable energy system (HRES)isolated microgridenergy management system (EMS)Deep Q Network (DQN)HOMER software |
| spellingShingle | Bao Chau Phan Meng-Tse Lee Ying-Chih Lai Intelligent Deep-Q-Network-Based Energy Management for an Isolated Microgrid Applied Sciences hybrid renewable energy system (HRES) isolated microgrid energy management system (EMS) Deep Q Network (DQN) HOMER software |
| title | Intelligent Deep-Q-Network-Based Energy Management for an Isolated Microgrid |
| title_full | Intelligent Deep-Q-Network-Based Energy Management for an Isolated Microgrid |
| title_fullStr | Intelligent Deep-Q-Network-Based Energy Management for an Isolated Microgrid |
| title_full_unstemmed | Intelligent Deep-Q-Network-Based Energy Management for an Isolated Microgrid |
| title_short | Intelligent Deep-Q-Network-Based Energy Management for an Isolated Microgrid |
| title_sort | intelligent deep q network based energy management for an isolated microgrid |
| topic | hybrid renewable energy system (HRES) isolated microgrid energy management system (EMS) Deep Q Network (DQN) HOMER software |
| url | https://www.mdpi.com/2076-3417/12/17/8721 |
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