A Generalized Deep Reinforcement Learning Model for Distribution Network Reconfiguration with Power Flow-Based Action-Space Sampling
Distribution network reconfiguration (DNR) is used by utilities to enhance power system performance in various ways, such as reducing line losses. Conventional DNR algorithms rely on accurate values of network parameters and lack scalability and optimality. To tackle these issues, a new data-driven...
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MDPI AG
2024-10-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/20/5187 |
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| author | Nastaran Gholizadeh Petr Musilek |
| author_facet | Nastaran Gholizadeh Petr Musilek |
| author_sort | Nastaran Gholizadeh |
| collection | DOAJ |
| description | Distribution network reconfiguration (DNR) is used by utilities to enhance power system performance in various ways, such as reducing line losses. Conventional DNR algorithms rely on accurate values of network parameters and lack scalability and optimality. To tackle these issues, a new data-driven algorithm based on reinforcement learning is developed for DNR in this paper. The proposed algorithm comprises two main parts. The first part, named action-space sampling, aims at analyzing the network structure, finding all feasible reconfiguration actions, and reducing the size of the action space to only the most optimal actions. In the second part, deep Q-learning (DQN) and dueling DQN methods are used to train an agent to take the best switching actions according to the switch states and loads of the system. The results show that both DQN and dueling DQN are effective in reducing system losses through grid reconfiguration. The proposed methods have faster execution time compared to the conventional methods and are more scalable. |
| format | Article |
| id | doaj-art-70370e57d6cd44afbf7704e0af2e346f |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-70370e57d6cd44afbf7704e0af2e346f2025-08-20T02:11:03ZengMDPI AGEnergies1996-10732024-10-011720518710.3390/en17205187A Generalized Deep Reinforcement Learning Model for Distribution Network Reconfiguration with Power Flow-Based Action-Space SamplingNastaran Gholizadeh0Petr Musilek1Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, CanadaElectrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, CanadaDistribution network reconfiguration (DNR) is used by utilities to enhance power system performance in various ways, such as reducing line losses. Conventional DNR algorithms rely on accurate values of network parameters and lack scalability and optimality. To tackle these issues, a new data-driven algorithm based on reinforcement learning is developed for DNR in this paper. The proposed algorithm comprises two main parts. The first part, named action-space sampling, aims at analyzing the network structure, finding all feasible reconfiguration actions, and reducing the size of the action space to only the most optimal actions. In the second part, deep Q-learning (DQN) and dueling DQN methods are used to train an agent to take the best switching actions according to the switch states and loads of the system. The results show that both DQN and dueling DQN are effective in reducing system losses through grid reconfiguration. The proposed methods have faster execution time compared to the conventional methods and are more scalable.https://www.mdpi.com/1996-1073/17/20/5187data-driven controldeep reinforcement learningdistribution network reconfigurationdeep Q-learningdueling deep Q-learning |
| spellingShingle | Nastaran Gholizadeh Petr Musilek A Generalized Deep Reinforcement Learning Model for Distribution Network Reconfiguration with Power Flow-Based Action-Space Sampling Energies data-driven control deep reinforcement learning distribution network reconfiguration deep Q-learning dueling deep Q-learning |
| title | A Generalized Deep Reinforcement Learning Model for Distribution Network Reconfiguration with Power Flow-Based Action-Space Sampling |
| title_full | A Generalized Deep Reinforcement Learning Model for Distribution Network Reconfiguration with Power Flow-Based Action-Space Sampling |
| title_fullStr | A Generalized Deep Reinforcement Learning Model for Distribution Network Reconfiguration with Power Flow-Based Action-Space Sampling |
| title_full_unstemmed | A Generalized Deep Reinforcement Learning Model for Distribution Network Reconfiguration with Power Flow-Based Action-Space Sampling |
| title_short | A Generalized Deep Reinforcement Learning Model for Distribution Network Reconfiguration with Power Flow-Based Action-Space Sampling |
| title_sort | generalized deep reinforcement learning model for distribution network reconfiguration with power flow based action space sampling |
| topic | data-driven control deep reinforcement learning distribution network reconfiguration deep Q-learning dueling deep Q-learning |
| url | https://www.mdpi.com/1996-1073/17/20/5187 |
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