Novel deep deterministic policy gradient technique for automated micro-grid energy management in rural and islanded areas
The microgrid enhances power grid reliability, resiliency, and sustainability, particularly in rural and islanded areas with limited manual network management. However, microgrid energy management systems (EMS), especially in islanded mode, require precise and reliable techniques to prevent severe b...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-11-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S111001682300861X |
| _version_ | 1797640308288126976 |
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| author | Lilia Tightiz L. Minh Dang Joon Yoo |
| author_facet | Lilia Tightiz L. Minh Dang Joon Yoo |
| author_sort | Lilia Tightiz |
| collection | DOAJ |
| description | The microgrid enhances power grid reliability, resiliency, and sustainability, particularly in rural and islanded areas with limited manual network management. However, microgrid energy management systems (EMS), especially in islanded mode, require precise and reliable techniques to prevent severe blackouts/brownouts. This paper presents a novel deep deterministic policy gradient (DDPG) algorithm to schedule EMS for the autonomous microgrid in real-time. Our solution utilizes deep reinforcement learning (DRL) to converge model-free, sequential, random, and continuous characteristics of the microgrid. Additionally, we use reward shaping and transfer learning attachment to DDPG to support microgrid performance restrictions and minimize load shedding during peak hours. This solution offers an efficient training process comparable to other DRL techniques in simplicity, less computation, and supporting future system extension. Residential Gasa Island microgrid profile characteristics have been selected and tested to examine the proposed approach. Results demonstrate the high efficiency and accuracy of the proposed technique compared to existing methods. |
| first_indexed | 2024-03-11T13:30:03Z |
| format | Article |
| id | doaj.art-ffe3767140ce444aae45edf011f4666a |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-03-11T13:30:03Z |
| publishDate | 2023-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-ffe3767140ce444aae45edf011f4666a2023-11-03T04:14:56ZengElsevierAlexandria Engineering Journal1110-01682023-11-0182145153Novel deep deterministic policy gradient technique for automated micro-grid energy management in rural and islanded areasLilia Tightiz0L. Minh Dang1Joon Yoo2School of Computing, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si, 13120, Gyeonggi-do, KoreaThe Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam; Faculty of Information Technology, Duy Tan University, Da Nang, 550000, Viet NamSchool of Computing, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si, 13120, Gyeonggi-do, Korea; Corresponding author.The microgrid enhances power grid reliability, resiliency, and sustainability, particularly in rural and islanded areas with limited manual network management. However, microgrid energy management systems (EMS), especially in islanded mode, require precise and reliable techniques to prevent severe blackouts/brownouts. This paper presents a novel deep deterministic policy gradient (DDPG) algorithm to schedule EMS for the autonomous microgrid in real-time. Our solution utilizes deep reinforcement learning (DRL) to converge model-free, sequential, random, and continuous characteristics of the microgrid. Additionally, we use reward shaping and transfer learning attachment to DDPG to support microgrid performance restrictions and minimize load shedding during peak hours. This solution offers an efficient training process comparable to other DRL techniques in simplicity, less computation, and supporting future system extension. Residential Gasa Island microgrid profile characteristics have been selected and tested to examine the proposed approach. Results demonstrate the high efficiency and accuracy of the proposed technique compared to existing methods.http://www.sciencedirect.com/science/article/pii/S111001682300861XDeep deterministic policy gradientEnergy management systemMicrogridResponsive loadsTransfer learning |
| spellingShingle | Lilia Tightiz L. Minh Dang Joon Yoo Novel deep deterministic policy gradient technique for automated micro-grid energy management in rural and islanded areas Alexandria Engineering Journal Deep deterministic policy gradient Energy management system Microgrid Responsive loads Transfer learning |
| title | Novel deep deterministic policy gradient technique for automated micro-grid energy management in rural and islanded areas |
| title_full | Novel deep deterministic policy gradient technique for automated micro-grid energy management in rural and islanded areas |
| title_fullStr | Novel deep deterministic policy gradient technique for automated micro-grid energy management in rural and islanded areas |
| title_full_unstemmed | Novel deep deterministic policy gradient technique for automated micro-grid energy management in rural and islanded areas |
| title_short | Novel deep deterministic policy gradient technique for automated micro-grid energy management in rural and islanded areas |
| title_sort | novel deep deterministic policy gradient technique for automated micro grid energy management in rural and islanded areas |
| topic | Deep deterministic policy gradient Energy management system Microgrid Responsive loads Transfer learning |
| url | http://www.sciencedirect.com/science/article/pii/S111001682300861X |
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