Reinforcement Learning Optimizes Power Dispatch in Decentralized Power Grid
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911969538211840 |
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| author | Lee, Yongsun Choi, Hoyun Pagnier, Laurent Kim, Cook Hyun Lee, Jongshin Jhun, Bukyoung Kim, Heetae Kurths, Juergen Kahng, B. |
| author_facet | Lee, Yongsun Choi, Hoyun Pagnier, Laurent Kim, Cook Hyun Lee, Jongshin Jhun, Bukyoung Kim, Heetae Kurths, Juergen Kahng, B. |
| contents | Effective frequency control in power grids has become increasingly important with the increasing demand for renewable energy sources. Here, we propose a novel strategy for resolving this challenge using graph convolutional proximal policy optimization (GC-PPO). The GC-PPO method can optimally determine how much power individual buses dispatch to reduce frequency fluctuations across a power grid. We demonstrate its efficacy in controlling disturbances by applying the GC-PPO to the power grid of the UK. The performance of GC-PPO is outstanding compared to the classical methods. This result highlights the promising role of GC-PPO in enhancing the stability and reliability of power systems by switching lines or decentralizing grid topology. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_15165 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Reinforcement Learning Optimizes Power Dispatch in Decentralized Power Grid Lee, Yongsun Choi, Hoyun Pagnier, Laurent Kim, Cook Hyun Lee, Jongshin Jhun, Bukyoung Kim, Heetae Kurths, Juergen Kahng, B. Physics and Society Systems and Control Adaptation and Self-Organizing Systems Effective frequency control in power grids has become increasingly important with the increasing demand for renewable energy sources. Here, we propose a novel strategy for resolving this challenge using graph convolutional proximal policy optimization (GC-PPO). The GC-PPO method can optimally determine how much power individual buses dispatch to reduce frequency fluctuations across a power grid. We demonstrate its efficacy in controlling disturbances by applying the GC-PPO to the power grid of the UK. The performance of GC-PPO is outstanding compared to the classical methods. This result highlights the promising role of GC-PPO in enhancing the stability and reliability of power systems by switching lines or decentralizing grid topology. |
| title | Reinforcement Learning Optimizes Power Dispatch in Decentralized Power Grid |
| topic | Physics and Society Systems and Control Adaptation and Self-Organizing Systems |
| url | https://arxiv.org/abs/2407.15165 |