Scenario Generation for Wind Power Using Generative Adversarial Networks. Issue 1 (1st August 2022)

• Record Type: Journal Article
• Authors: Tiancheng, Shi; Gejirifu, De; Hao, Cong; Wenzhang, Guo; Yalin, Zhong; Long, Qian
• Is Part Of: Journal of physics. Volume 2320:Issue 1(2022)
• Publication Date: 2022-08-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2320/1/012019
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Scenarios generation is a critical part in planning and operation in high renewable energy penetratied power systems. However, the statistical assumptions of traditional parametric methods may not hold for all types of wind farms. In this paper, a data-driven artificial intelligence approach is presented to generate wind power output scenarios based on generative adversarial networks (GANs). Unlike traditional probabilistic model-based techniques which are typically difficult to scale or sample, the proposed method is data-driven and captures patterns of wind power generation. First, the GAN network structure is constructed, and the Wasserstein distance is employed as the discriminator's loss function. The GAN training then enables the generator to learn random noise and actual history data. Finally, the scenario generation approach based on Monte Carlo simulation and GANs are compared. It shows that the scenarios generated by proposed method can accurately describe the uncertainty of wind power output.