| Autor: |
Zhiping Gao, Wenwen Kang, Xinghua Chen, Siru Gong, Zongxiong Liu, Degang He, Shen Shi, Xing-Chen Shangguan |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Frontiers in Energy Research, Vol 12 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2296-598X |
| DOI: |
10.3389/fenrg.2024.1357406 |
| Popis: |
The intermittent renewable energy in a virtual power plant (VPP) brings generation uncertainties, which prevents the VPP from providing a reliable and user-friendly power supply. To address this issue, this paper proposes a gated recurrent unit proximal policy optimization (GRUPPO)-based optimal VPP economic dispatch method. First, electrical generation, storage, and consumption are established to form a VPP framework by considering the accessibility of VPP state information. The optimal VPP economic dispatch can then be expressed as a partially observable Markov decision process (POMDP) problem. A novel deep reinforcement learning method called GRUPPO is further developed based on VPP time series characteristics. Finally, case studies are conducted over a 24-h period based on the actual historical data. The test results illustrate that the proposed economic dispatch can achieve a maximum operation cost reduction of 6.5% and effectively smooth the supply–demand uncertainties. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
