Volume 9: Sustainable Energy Solutions for Changing the World: Part I

Data-Driven Scenarios Generation for Wind Power Profiles Using Implicit Maximum Likelihood Estimation Wenlong Liao, Birgitte Bak-Jensen, Jayakrishnan Radhakrishna Pillai, Ruijin Zhu, Like Song

Abstract

The scenario generation of wind power profiles is of great significance for the economic operation and stability analysis of the distribution network. In this paper, a novel generative network is proposed to model wind power profiles based on implicit maximum likelihood estimation (IMLE). Firstly, the fake sample closest to each real sample is found to calculate the loss function used for updating weights. After training the model, the new wind power profiles are generated by feeding some Gaussian noises to the generator of the IMLE model. Compared with explicit density models, the IMLE model does not need to artificially assume the probability distribution of wind power profiles. The simulation results show that the proposed approach not only fits the probability distribution of wind power profiles well, but also accurately captures the shape, temporal correlation, and fluctuation of wind power profiles.

Keywords wind power,scenario generation,deep learning,generative network