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

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 govern -ment 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. His research interests include power system probabilistic analysis and renewable energy generation.Chen Shen (M'98-SM'07) received his B.E. and Ph.D. degrees in Electrical

Supply Inadequacy Risk Evaluation of Stand-Alone Renewable Powered Heat-Electricity Energy Systems: A Data-Driven Robust Approach

“…Here, in order to increase the estimation accuracy, we adopt the Gaussian mixture model (GMM) to estimate the PDF. GMM is a mixture of several Gaussian distributions and could characterize the uncertainties obeying arbitrary distributions, which has been widely used to fit probability distributions of renewable power recently [41], [42]. Then we generate 100,000 samples based on the estimated PDF and calculate the two risk measures (P-LSE and E-LSC) using Monte Carlo simulation.…”

Privacy-preserving Spatiotemporal Scenario Generation of Renewable Energies: A Federated Deep Generative Learning Approach

“…Some distributed structures considering data security and privacy protection have been studied in [13], [15], [16]. Reference [13] formulates a privacy-preserving framework that combines data transformation techniques with the alternating direction method of multipliers for renewable energy forecasting.…”

“…Reference [13] formulates a privacy-preserving framework that combines data transformation techniques with the alternating direction method of multipliers for renewable energy forecasting. In [15], a distributed maximum a posteriori probability estimation method is developed to build a Gaussian mixture model of aggregated wind power forecast error for addressing the data privacy problem. Regarding distributed DGM, reference [16] proposes a brainstorming GAN (BGAN) for generating data when multiple agents are unwilling to share data due to privacy issues.…”

Privacy-preserving distributed parameter estimation for probability distribution of wind power forecast error

“…To take the wind power correlation into account, one should first establish the joint probability distribution of the wind power and the corresponding forecast data from correlated WFs. Then, one can derive the conditional probability distribution of the forecast error under a given forecast value from the joint one [12], [13]. Many statistical distribution models as well as the corresponding estimation algorithms have been investigated by researchers to characterize wind power uncertainty [14], [15].…”

“…This is because that GMM can characterize multivariate random variables subject to an arbitrary distribution with remarkable performance [13].…”