Abstract-Scenario generation is an important step in the operation and planning of power systems with high renewable penetrations. In this work, we proposed a data-driven approach for scenario generation using generative adversarial networks, which is based on two interconnected deep neural networks. Compared with existing methods based on probabilistic models that are often hard to scale or sample from, our method is datadriven, and captures renewable energy production patterns in both temporal and spatial dimensions for a large number of correlated resources. For validation, we use wind and solar timesseries data from NREL integration data sets. We demonstrate that the proposed method is able to generate realistic wind and photovoltaic power profiles with full diversity of behaviors. We also illustrate how to generate scenarios based on different conditions of interest by using labeled data during training. For example, scenarios can be conditioned on weather events (e.g. high wind day, intense ramp events or large forecasts errors) or time of the year (e,g. solar generation for a day in July). Because of the feedforward nature of the neural networks, scenarios can be generated extremely efficiently without sophisticated sampling techniques.

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A number of scenario reduction techniques have been proposed to make possible the practical implementation of stochastic unit commitment formulations. These scenarioreduction techniques aggregate similar scenarios based on their metrics, such as their probability, hourly magnitudes, or the cost resulting from each scenario. This paper compares these different scenario reduction techniques in terms of the resulting operating cost and the amount of time required to complete computation of the stochastic UC. This comparison is based on Monte Carlo simulations of the resulting generation schedules for a modified version of the 24-bus IEEE-RTS.