Xueqian Wang^*, Hongsheng Su

Energy Engineering, Vol.122, No.2, pp. 667-680, 2025, DOI:10.32604/ee.2024.058102 - 31 January 2025

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 More >

Lu Li¹, Yuzhen Fan², Xinglang Su^1,*, Gefei Qiu¹

Energy Engineering, Vol.118, No.3, pp. 565-580, 2021, DOI:10.32604/EE.2021.014627 - 22 March 2021

Abstract Because of the randomness and uncertainty, integration of large-scale wind farms in a power system will exert significant influences on the distribution of power flow. This paper uses polynomial normal transformation method to deal with non-normal random variable correlation, and solves probabilistic load flow based on Kriging method. This method is a kind of smallest unbiased variance estimation method which estimates unknown information via employing a point within the confidence scope of weighted linear combination. Compared with traditional approaches which need a greater number of calculation times, long simulation time, and large memory space, Kriging More >