Abstract
There exists strong correlation among wind speed, solar irradiation and load. The time series model can characterize this correlation but subsequent power flow calculation based on such model is time-consuming. In this paper, we applied the Bayesian Networks (BN) and Pair-Copula methods to jointly establishing a wind-solar-load probability model, which can extract the time axis of wind speed, light intensity and load data. Firstly, the modeling principles, characteristics, similarities and differences between BN and Pair-Copula modeling are analyzed. Then, the modeling accuracy, sample quality and modeling efficiency of the two probabilistic models were compared. The results show that the BN probability model can be more comprehensive and accurate in describing the complex structure of wind-solar-load correlation. The effectiveness of BN model is also verified by performing the probabilistic power flow calculation on a standard IEEE 118-bus test case.
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