| Autor: |
ZHOU Yi, ZHOU Liangcai, DING Jiali, GAO Jianing |
| Jazyk: |
čínština |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
Shanghai Jiaotong Daxue xuebao, Vol 55, Iss S2, Pp 7-14 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
1006-2467 |
| DOI: |
10.16183/j.cnki.jsjtu.2021.S2.002 |
| Popis: |
In the pursuit of carbon neutrality, huge changes on the power supply side and the load side have brought forward new requirements and challenges for grid operation and dispatchers. A low-cost and effective measure is real-time power grid network topology optimization and control (NTOC). However, except for the simplest action of line switching, the combinatorial and non-linear nature of the NTOC problem has made existing approaches infeasible for grids of reasonable scales. This paper proposes a novel artificial intelligence (AI) based approach for maximizing available transfer capabilities (ATCs) via network topology control considering various practical constraints and uncertainties. First, imitation learning is utilized to provide a good initial policy for the AI agent. Then, the agent is trained through deep reinforcement learning with a novel guided exploration technique, which significantly improves the training efficiency. Finally, an early warning mechanism is designed to help the agent identify a proper action time, which effectively improves the fault tolerance and robustness of the method. The effectiveness of the proposed approach is tested by using open-sourced data of the IEEE 14-note system. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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