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000849933 1001_ $$0P:(DE-Juel1)169298$$aWeber, Juliane$$b0$$ufzj
000849933 245__ $$aModeling long correlation times using additive binary Markov chains: Applications to wind generation time series
000849933 260__ $$aWoodbury, NY$$bInst.$$c2018
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000849933 520__ $$aWind power generation exhibits a strong temporal variability, which is crucial for system integration in highly renewable power systems. Different methods exist to simulate wind power generation but they often cannot represent the crucial temporal fluctuations properly. We apply the concept of additive binary Markov chains to model a wind generation time series consisting of two states: periods of high and low wind generation. The only input parameter for this model is the empirical autocorrelation function. The two-state model is readily extended to stochastically reproduce the actual generation per period. To evaluate the additive binary Markov chain method, we introduce a coarse model of the electric power system to derive backup and storage needs. We find that the temporal correlations of wind power generation, the backup need as a function of the storage capacity, and the resting time distribution of high and low wind events for different shares of wind generation can be reconstructed.
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000849933 7001_ $$0P:(DE-HGF)0$$aZachow, Christopher$$b1
000849933 7001_ $$0P:(DE-Juel1)162277$$aWitthaut, Dirk$$b2$$eCorresponding author$$ufzj
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