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

TL;DR

This paper proposes a distributed Gaussian mixture model-based algorithm using MAP estimation and consensus filtering to accurately model the aggregated wind power forecast error, especially with limited data and privacy constraints.

Contribution

It introduces a novel distributed MAP estimation approach with a distribution control center for modeling wind forecast errors under data scarcity and privacy restrictions.

Findings

Effective in modeling forecast error distribution with limited data.

Improves estimation accuracy through a distributed consensus approach.

Validated using real historical wind farm data.

Abstract

The extensive penetration of wind farms (WFs) presents challenges to the operation of distribution networks (DNs). Building a probability distribution of the aggregated wind power forecast error is of great value for decision making. However, as a result of recent government incentives, many WFs are being newly built with little historical data for training distribution models. Moreover, WFs with different stakeholders may refuse to submit the raw data to a data center for model training. To address these problems, a Gaussian mixture model (GMM) is applied to build the distribution of the aggregated wind power forecast error; then, the maximum a posteriori (MAP) estimation method is adopted to overcome the limited training data problem in GMM parameter estimation. Next, a distributed MAP estimation method is developed based on the average consensus filter algorithm to address the data privacy issue. The distribution control center is introduced into the distributed estimation process to acquire more precise estimation results and better adapt to the DN control architecture. The effectiveness of the proposed algorithm is empirically verified using historical data.