Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | 2024 4th International Conference on Smart City and Green Energy, ICSCGE 2024 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 247-251 |
| Number of pages | 5 |
| ISBN (Electronic) | 9798331506353 |
| DOIs | |
| State | Published - 2024 |
| Event | 4th International Conference on Smart City and Green Energy, ICSCGE 2024 - Sydney, Australia Duration: 10 Dec 2024 → 13 Dec 2024 |
Publication series
| Name | 2024 4th International Conference on Smart City and Green Energy, ICSCGE 2024 |
|---|
Conference
| Conference | 4th International Conference on Smart City and Green Energy, ICSCGE 2024 |
|---|---|
| Country/Territory | Australia |
| City | Sydney |
| Period | 10/12/24 → 13/12/24 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- Distribution Network
- Dynamic Network Reconfiguration
- Hybrid Quantum Deep Q-Network
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