A data‐driven scheduling approach for integrated electricity‐hydrogen system based on improved DDPG
Abstract The involvement of hydrogen energy systems has been recognised as a promising way to mitigate climate problems. As a kind of efficient multi‐energy complementary system, the hydropower‐photovoltaic‐hydrogen (HPH) system could be an ideal approach to combining hydrogen with an installed rene...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-02-01
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| Series: | IET Renewable Power Generation |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/rpg2.12693 |
| _version_ | 1797295093577678848 |
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| author | Yaping Zhao Jingsi Huang Endong Xu Jianxiao Wang Xiaoyun Xu |
| author_facet | Yaping Zhao Jingsi Huang Endong Xu Jianxiao Wang Xiaoyun Xu |
| author_sort | Yaping Zhao |
| collection | DOAJ |
| description | Abstract The involvement of hydrogen energy systems has been recognised as a promising way to mitigate climate problems. As a kind of efficient multi‐energy complementary system, the hydropower‐photovoltaic‐hydrogen (HPH) system could be an ideal approach to combining hydrogen with an installed renewable energy system to improve the flexibility of energy management and reduce power curtailment. However, the intra‐day scheduling of HPH system brings challenges due to the time‐related nonlinear hydropower generation process, the complex energy conversion process and the uncertain natural resource supply. Faced with these challenges, an improved deep deterministic policy gradient (DDPG)‐based data‐driven scheduling algorithm is proposed. In contrast to the prevalent DDPG, two sets of actor‐critic networks are properly designed based on prior knowledge‐based deep neural networks for the considered complex uncertain system to search for near‐optimal policies and approximate actor‐value functions. In addition, customized reward functions are proposed with the consideration of interactions among different energy supplies, which helps to improve convergence speed and stability. Finally, the case study results demonstrate that the proposed system model and the optimal energy management strategy based on the improved DDPG algorithm can guide the electricity‐hydrogen system to achieve rapid response and more reasonable energy management. |
| first_indexed | 2024-03-07T21:41:47Z |
| format | Article |
| id | doaj.art-9927128f128b47f6ad01f2f1504882fb |
| institution | Directory Open Access Journal |
| issn | 1752-1416 1752-1424 |
| language | English |
| last_indexed | 2024-03-07T21:41:47Z |
| publishDate | 2024-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Renewable Power Generation |
| spelling | doaj.art-9927128f128b47f6ad01f2f1504882fb2024-02-26T08:05:21ZengWileyIET Renewable Power Generation1752-14161752-14242024-02-0118344245510.1049/rpg2.12693A data‐driven scheduling approach for integrated electricity‐hydrogen system based on improved DDPGYaping Zhao0Jingsi Huang1Endong Xu2Jianxiao Wang3Xiaoyun Xu4Department of Transportation Economics and Logistics Management College of Economics Shenzhen University Shenzhen ChinaDepartment of Industrial Engineering and Management College of Engineering Peking University Beijing ChinaDepartment of Transportation Economics and Logistics Management College of Economics Shenzhen University Shenzhen ChinaNational Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing ChinaDepartment of Operations and Information Technology Graduate School of Business Ateneo de Manila University Quezon City Metro Manila PhilippinesAbstract The involvement of hydrogen energy systems has been recognised as a promising way to mitigate climate problems. As a kind of efficient multi‐energy complementary system, the hydropower‐photovoltaic‐hydrogen (HPH) system could be an ideal approach to combining hydrogen with an installed renewable energy system to improve the flexibility of energy management and reduce power curtailment. However, the intra‐day scheduling of HPH system brings challenges due to the time‐related nonlinear hydropower generation process, the complex energy conversion process and the uncertain natural resource supply. Faced with these challenges, an improved deep deterministic policy gradient (DDPG)‐based data‐driven scheduling algorithm is proposed. In contrast to the prevalent DDPG, two sets of actor‐critic networks are properly designed based on prior knowledge‐based deep neural networks for the considered complex uncertain system to search for near‐optimal policies and approximate actor‐value functions. In addition, customized reward functions are proposed with the consideration of interactions among different energy supplies, which helps to improve convergence speed and stability. Finally, the case study results demonstrate that the proposed system model and the optimal energy management strategy based on the improved DDPG algorithm can guide the electricity‐hydrogen system to achieve rapid response and more reasonable energy management.https://doi.org/10.1049/rpg2.12693data‐driven algorithmdeep reinforcement learninghydrogen deviceintegrated renewable energy systemreal‐time scheduling |
| spellingShingle | Yaping Zhao Jingsi Huang Endong Xu Jianxiao Wang Xiaoyun Xu A data‐driven scheduling approach for integrated electricity‐hydrogen system based on improved DDPG IET Renewable Power Generation data‐driven algorithm deep reinforcement learning hydrogen device integrated renewable energy system real‐time scheduling |
| title | A data‐driven scheduling approach for integrated electricity‐hydrogen system based on improved DDPG |
| title_full | A data‐driven scheduling approach for integrated electricity‐hydrogen system based on improved DDPG |
| title_fullStr | A data‐driven scheduling approach for integrated electricity‐hydrogen system based on improved DDPG |
| title_full_unstemmed | A data‐driven scheduling approach for integrated electricity‐hydrogen system based on improved DDPG |
| title_short | A data‐driven scheduling approach for integrated electricity‐hydrogen system based on improved DDPG |
| title_sort | data driven scheduling approach for integrated electricity hydrogen system based on improved ddpg |
| topic | data‐driven algorithm deep reinforcement learning hydrogen device integrated renewable energy system real‐time scheduling |
| url | https://doi.org/10.1049/rpg2.12693 |
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