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Multi-Scenario Probabilistic Load Flow Calculation Considering Wind Speed Correlation

by Xueqian Wang*, Hongsheng Su

School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China

* Corresponding Author: Xueqian Wang. Email: email

Energy Engineering 2025, 122(2), 667-680. https://doi.org/10.32604/ee.2024.058102

Abstract

As the proportion of new energy increases, the traditional cumulant method (CM) produces significant errors when performing probabilistic load flow (PLF) calculations with large-scale wind power integrated. Considering the wind speed correlation, a multi-scenario PLF calculation method that combines random sampling and segmented discrete wind farm power was proposed. Firstly, based on constructing discrete scenes of wind farms, the Nataf transform is used to handle the correlation between wind speeds. Then, the random sampling method determines the output probability of discrete wind power scenarios when wind speed exhibits correlation. Finally, the PLF calculation results of each scenario are weighted and superimposed following the total probability formula to obtain the final power flow calculation result. Verified in the IEEE standard node system, the absolute percent error (APE) for the mean and standard deviation (SD) of the node voltages and branch active power are all within 1%, and the average root mean square (AMSR) values of the probability curves are all less than 1%.

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Cite This Article

APA Style
Wang, X., Su, H. (2025). Multi-scenario probabilistic load flow calculation considering wind speed correlation. Energy Engineering, 122(2), 667–680. https://doi.org/10.32604/ee.2024.058102
Vancouver Style
Wang X, Su H. Multi-scenario probabilistic load flow calculation considering wind speed correlation. Energ Eng. 2025;122(2):667–680. https://doi.org/10.32604/ee.2024.058102
IEEE Style
X. Wang and H. Su, “Multi-Scenario Probabilistic Load Flow Calculation Considering Wind Speed Correlation,” Energ. Eng., vol. 122, no. 2, pp. 667–680, 2025. https://doi.org/10.32604/ee.2024.058102



cc Copyright © 2025 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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