Simulation of Extreme Scenarios of Wind Power Intraday Output based on Expanded Sampling Intervals and Improved Sampling Matrices
To simulate the extreme intra-day wind power output scenarios characterized by large fluctuations and strong randomness,we propose a method based on expanding the sampling range and improving the sampling matrix.By specifying quantiles,the Latin hypercube sampling range is expanded,and the sampling probability matrix is improved by quantifying the degree of wind power fluctuation and controlling the maximum fluctuation amplitude of the sampling matrix.Subsequently,a set of scenarios reflecting the temporal characteristics of wind power is obtained through inverse sampling.Extreme intra-day wind power output scenarios are selected using a combination of indicators consisting of single-period extreme values,daily average extreme values,and continuous multi-period extreme values.A case study of a wind farm in the northwest region of China shows that the wind power scenarios simulated using our method outperform those simulated by the Monte Carlo method in terms of both simulation accuracy and scenario richness.Moreover,our method exhibits good performance in simulating extreme wind power output scenarios,providing a technical reference for extreme scenario simulation.
wind powerscenario generationextreme scenariosMonte Carlo samplingLatin hypercubes sampling
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