摘要
This paper introduces the application of a Hybrid Quantum Deep Q-Network (HQDQN) to solve dynamic network reconfiguration problem in distribution networks. Integrating quantum computing with deep reinforcement learning, the HQDQN is tested on the classical IEEE 33-node test feeder. It significantly outperforms traditional Deep Q-Network (DQN) models in energy efficiency. The results demonstrate the potential of quantum-enhanced machine learning algorithms to improve the operational efficiency of power grids.
| 源语言 | 英语 |
|---|---|
| 主期刊名 | 2024 4th International Conference on Smart City and Green Energy, ICSCGE 2024 |
| 出版商 | Institute of Electrical and Electronics Engineers Inc. |
| 页 | 247-251 |
| 页数 | 5 |
| ISBN（电子版） | 9798331506353 |
| DOI | |
| 出版状态 | 已出版 - 2024 |
| 活动 | 4th International Conference on Smart City and Green Energy, ICSCGE 2024 - Sydney, 澳大利亚 期限: 10 12月 2024 → 13 12月 2024 |
出版系列
| 姓名 | 2024 4th International Conference on Smart City and Green Energy, ICSCGE 2024 |
|---|
会议
| 会议 | 4th International Conference on Smart City and Green Energy, ICSCGE 2024 |
|---|---|
| 国家/地区 | 澳大利亚 |
| 市 | Sydney |
| 时期 | 10/12/24 → 13/12/24 |
联合国可持续发展目标
此成果有助于实现下列可持续发展目标：
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可持续发展目标 7 经济适用的清洁能源
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