LAPSE:2024.0886
Published Article
LAPSE:2024.0886
A Fast Reliability Evaluation Strategy for Power Systems under High Proportional Renewable Energy—A Hybrid Data-Driven Method
June 7, 2024
Abstract
With the increasing scale of the power system, the increasing penetration of renewable energy, and the increasing uncertainty factors, traditional reliability evaluation methods based on Monte Carlo simulation have greatly reduced computational efficiency in complex power systems and cannot meet the requirements of real-time and rapid evaluation. This article proposes a hybrid data-driven strategy to achieve a rapid assessment of power grid reliability on two levels: offline training and online evaluation. Firstly, this article derives explicit analytical expressions for reliability indicators and component parameters, avoiding the computational burden of repetitive Monte Carlo simulation. Next, a large number of samples are quickly generated by parsing expressions to train convolutional neural networks (CNNs), and the system reliability index is quickly calculated under changing operating conditions through CNNs. Finally, the effectiveness and feasibility of the proposed method are verified through an improved RTS-79 testing system. The calculation results show that the method proposed in this article can achieve an online solution of second-level reliability indicators while ensuring calculation accuracy.
With the increasing scale of the power system, the increasing penetration of renewable energy, and the increasing uncertainty factors, traditional reliability evaluation methods based on Monte Carlo simulation have greatly reduced computational efficiency in complex power systems and cannot meet the requirements of real-time and rapid evaluation. This article proposes a hybrid data-driven strategy to achieve a rapid assessment of power grid reliability on two levels: offline training and online evaluation. Firstly, this article derives explicit analytical expressions for reliability indicators and component parameters, avoiding the computational burden of repetitive Monte Carlo simulation. Next, a large number of samples are quickly generated by parsing expressions to train convolutional neural networks (CNNs), and the system reliability index is quickly calculated under changing operating conditions through CNNs. Finally, the effectiveness and feasibility of the proposed method are verified through an improved RTS-79 testing system. The calculation results show that the method proposed in this article can achieve an online solution of second-level reliability indicators while ensuring calculation accuracy.
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Keywords
convolutional neural network, explicit analytical expressions, hybrid data-driven strategy, power system, reliability index
Suggested Citation
Zhang J, Wang B, Ma H, Li Y, Yang M, Wang H, Ma F. A Fast Reliability Evaluation Strategy for Power Systems under High Proportional Renewable Energy—A Hybrid Data-Driven Method. (2024). LAPSE:2024.0886
Author Affiliations
Zhang J: Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network, Wuhan University, Wuhan 430072, China; School of Electrical and Automation, Wuhan University, Wuhan 430072, China
Wang B: Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network, Wuhan University, Wuhan 430072, China; School of Electrical and Automation, Wuhan University, Wuhan 430072, China
Ma H: Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network, Wuhan University, Wuhan 430072, China; School of Electrical and Automation, Wuhan University, Wuhan 430072, China
Li Y: China Electric Power Research Institute, Haidian District, Beijing 100192, China
Yang M: State Grid Huaining Power Supply Company, Huaining 246121, China
Wang H: Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network, Wuhan University, Wuhan 430072, China; School of Electrical and Automation, Wuhan University, Wuhan 430072, China; Department of Electrical & Computer Engineering
Ma F: Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network, Wuhan University, Wuhan 430072, China; School of Electrical and Automation, Wuhan University, Wuhan 430072, China
Wang B: Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network, Wuhan University, Wuhan 430072, China; School of Electrical and Automation, Wuhan University, Wuhan 430072, China
Ma H: Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network, Wuhan University, Wuhan 430072, China; School of Electrical and Automation, Wuhan University, Wuhan 430072, China
Li Y: China Electric Power Research Institute, Haidian District, Beijing 100192, China
Yang M: State Grid Huaining Power Supply Company, Huaining 246121, China
Wang H: Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network, Wuhan University, Wuhan 430072, China; School of Electrical and Automation, Wuhan University, Wuhan 430072, China; Department of Electrical & Computer Engineering
Ma F: Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network, Wuhan University, Wuhan 430072, China; School of Electrical and Automation, Wuhan University, Wuhan 430072, China
Journal Name
Processes
Volume
12
Issue
3
First Page
608
Year
2024
Publication Date
2024-03-19
ISSN
2227-9717
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Original Submission
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PII: pr12030608, Publication Type: Journal Article
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LAPSE:2024.0886
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https://doi.org/10.3390/pr12030608
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[v1] (Original Submission)
Jun 7, 2024
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Jun 7, 2024
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