Equivalent modelling for active distribution networks (ADNs) is essential for improving the efficiency of analysing transmission networks. Current equivalent modelling methods for ADNs neglect the probabilistic characteristics of renewable energy sources (RESs) and loads. To address this issue, this study proposes a probabilistic equivalent modelling method (PEMM) for ADNs considering the uncertainty of RESs and loads. The uncertainty of the RESs and loads is transferred to the equivalent boundary bus injection using the properties of cumulant and power transfer matrices. The PEMM is extended to incorporate the correlations of RESs through an orthogonal transformation. A sampling method using the Gaussian copula function is employed to generate the correlated samples and the joint cumulants, providing the input data for the PEMM. The comparative results of the case studies on two different test systems demonstrate the effectiveness of the PEMM. The equivalent model developed in this study is a practical solution for analysing the transmission network efficiently and taking the uncertainty of RESs and loads in the ADNs into account simultaneously.
Large scale wind power integration is the main way of wind power development in China. In simulation research, a large scale wind farm is necessary equivalent to be one aggregated model. The existing equivalence methods only consider the external characteristics of wind farm, and ignored the coupling relationship between units and power system. Through a massive simulation at different wind power mode scheduling and unit's operating point, this paper shows a wind farm operation condition, which its impacts on power system is severe. A practical equivalence method based on wind farm operation condition is proposed, and the effect of the method is validated by comparing the aggregated and detailed models. Index Terms--equivalence method; large scale wind farm; transient stability; wind power mode scheduling; wind turbine generator operating point; wind farm operating condition
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