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The extensive penetration of wind farms (WFs) presents challenges to theoperation of distribution networks (DNs). Building a probability distributionof the aggregated wind power forecast error is of great value for decisionmaking. However, as a result of recent government incentives, many WFs arebeing newly built with little historical data for training distribution models.Moreover, WFs with different stakeholders may refuse to submit the raw data toa data center for model training. To address these problems, a Gaussian mixturemodel (GMM) is applied to build the distribution of the aggregated wind powerforecast error; then, the maximum a posteriori (MAP) estimation method isadopted to overcome the limited training data problem in GMM parameterestimation. Next, a distributed MAP estimation method is developed based on theaverage consensus filter algorithm to address the data privacy issue. Thedistribution control center is introduced into the distributed estimationprocess to acquire more precise estimation results and better adapt to the DNcontrol architecture. The effectiveness of the proposed algorithm isempirically verified using historical data.

A Distributed Probabilistic Modeling Algorithm for the Aggregated Power Forecast Error of Multiple Newly Built Wind Farms