Scenario generation is an important step in the operation and planning of power systems. In this talk, we present a data-driven approach for scenario generation using the popular generative adversarial networks, where to deep neural networks are used in tandem. Compared with existing methods that are often hard to scale or sample from, our method is easy to train, robust, and captures both spatial and temporal patterns in renewable generation. In addition, we show that different conditional information can be embedded in the framework. Because of the feedforward nature of the neural networks, scenarios can be generated extremely efficiently.
Baosen Zhang is the Keith & Nancy Rattie Endowed Career Development Professor in the department of Electrical Engineering at the University of Washington. He received his undergraduate degree in engineering science from the University of Toronto, Toronto, ON, Canada, in 2008 and the Ph.D. degree Department of Electrical Engineering and Computer Science, University of California at Berkeley in 2013. Before joining UW, he was postdoctoral scholar at Stanford University.