Data-driven voltage control of active distribution networks
As traditional fossil fuel reserves diminish and environmental concerns over air pollution and greenhouse gas emissions rise, the global demand for renewable energy will continue to escalate. However, as renewables are integrated more extensively into distribution networks, numerous problems arise a...
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| Format: | Thesis-Master by Coursework |
| Language: | English |
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Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/170604 |
| _version_ | 1826115945876160512 |
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| author | Guo, Chenxi |
| author2 | Xu Yan |
| author_facet | Xu Yan Guo, Chenxi |
| author_sort | Guo, Chenxi |
| collection | NTU |
| description | As traditional fossil fuel reserves diminish and environmental concerns over air pollution and greenhouse gas emissions rise, the global demand for renewable energy will continue to escalate. However, as renewables are integrated more extensively into distribution networks, numerous problems arise accordingly, with voltage violation being one of the significant challenges. Therefore, voltage/var control (VVC) is introduced to address this issue.
Nowadays, photovoltaic (PV) inverters are increasingly being used in VVC due to their capability to provide fast reactive power support. However, the traditional optimization-based methods face challenges in real-time operation and suffer from modeling restrictions. For these reasons, this paper first presents a PV inverter based decentralized voltage/var control (VVC) framework to provide faster and more flexible control actions. Then, a multi-agent deep reinforcement learning based data-driven method, multi-agent twin delayed deep deterministic policy gradient (MATD3), is proposed to solve the decentralized VVC problem. The simulations conducted on the IEEE 33-bus distribution network demonstrate that the proposed method can achieve both faster response times and sound control performance compared to deep deterministic policy gradient (DDPG) method and conventional optimization-based approaches. |
| first_indexed | 2024-10-01T04:03:24Z |
| format | Thesis-Master by Coursework |
| id | ntu-10356/170604 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:03:24Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1706042023-09-22T15:43:24Z Data-driven voltage control of active distribution networks Guo, Chenxi Xu Yan School of Electrical and Electronic Engineering xuyan@ntu.edu.sg Engineering::Electrical and electronic engineering::Electric power As traditional fossil fuel reserves diminish and environmental concerns over air pollution and greenhouse gas emissions rise, the global demand for renewable energy will continue to escalate. However, as renewables are integrated more extensively into distribution networks, numerous problems arise accordingly, with voltage violation being one of the significant challenges. Therefore, voltage/var control (VVC) is introduced to address this issue. Nowadays, photovoltaic (PV) inverters are increasingly being used in VVC due to their capability to provide fast reactive power support. However, the traditional optimization-based methods face challenges in real-time operation and suffer from modeling restrictions. For these reasons, this paper first presents a PV inverter based decentralized voltage/var control (VVC) framework to provide faster and more flexible control actions. Then, a multi-agent deep reinforcement learning based data-driven method, multi-agent twin delayed deep deterministic policy gradient (MATD3), is proposed to solve the decentralized VVC problem. The simulations conducted on the IEEE 33-bus distribution network demonstrate that the proposed method can achieve both faster response times and sound control performance compared to deep deterministic policy gradient (DDPG) method and conventional optimization-based approaches. Master of Science (Power Engineering) 2023-09-20T08:38:57Z 2023-09-20T08:38:57Z 2023 Thesis-Master by Coursework Guo, C. (2023). Data-driven voltage control of active distribution networks. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/170604 https://hdl.handle.net/10356/170604 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering::Electric power Guo, Chenxi Data-driven voltage control of active distribution networks |
| title | Data-driven voltage control of active distribution networks |
| title_full | Data-driven voltage control of active distribution networks |
| title_fullStr | Data-driven voltage control of active distribution networks |
| title_full_unstemmed | Data-driven voltage control of active distribution networks |
| title_short | Data-driven voltage control of active distribution networks |
| title_sort | data driven voltage control of active distribution networks |
| topic | Engineering::Electrical and electronic engineering::Electric power |
| url | https://hdl.handle.net/10356/170604 |
| work_keys_str_mv | AT guochenxi datadrivenvoltagecontrolofactivedistributionnetworks |