Research on AGC Performance During Wind Power Ramping Based on Deep Reinforcement Learning Article Swipe
Dongying Zhang
,
Huiting Zhang
,
Xu Zhang
,
Xiaoyu Li
,
Kaiqi Ren
,
Yongxu Zhang
,
Yunbo Guo
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/access.2020.3000784
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/access.2020.3000784
With the increase in wind power penetration, wind power ramping events have increasingly influenced tie line power control in the power grid. Large power changes during ramping events make it difficult to accurately track the scheduling plans of tie lines and can even lead to overrun. Determining how to evaluate the control performance of automatic generation control (AGC) for wind power ramping has become an urgent problem. In this context, this paper studies the control performance of AGC for wind power ramping based on deep reinforcement learning. First, a tie line power control model of a power system with an AGC module is established. Then, measured data, which include thermal power, wind power, hydropower output and tie line power data, and a deep reinforcement learning method are combined for AGC parameter estimation based on the deep Q-network (DQN) algorithm. Next, the AGC parameter in different scenarios are fit by using measured phasor measurement unit (PMU) data, and on the basis of the fitted model, AGC performance evaluation is performed for wind power ramping events. Finally, the simulation results verify the feasibility and effectiveness of analysing the relationship between wind power ramping and AGC performance based on the AGC parameter fitting model.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2020.3000784
- https://ieeexplore.ieee.org/ielx7/6287639/8948470/09110876.pdf
- OA Status
- gold
- Cited By
- 25
- References
- 19
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3034586071
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- OpenAlex ID
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https://openalex.org/W3034586071Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2020.3000784Digital Object Identifier
- Title
-
Research on AGC Performance During Wind Power Ramping Based on Deep Reinforcement LearningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-01-01Full publication date if available
- Authors
-
Dongying Zhang, Huiting Zhang, Xu Zhang, Xiaoyu Li, Kaiqi Ren, Yongxu Zhang, Yunbo GuoList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2020.3000784Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/8948470/09110876.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://ieeexplore.ieee.org/ielx7/6287639/8948470/09110876.pdfDirect OA link when available
- Concepts
-
Automatic Generation Control, Wind power, Computer science, Electric power system, Control theory (sociology), Context (archaeology), Power control, Power (physics), Engineering, Control (management), Artificial intelligence, Electrical engineering, Biology, Physics, Paleontology, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
25Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 4, 2024: 5, 2023: 6, 2022: 4, 2021: 5Per-year citation counts (last 5 years)
- References (count)
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19Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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