Online decision making for trading wind energy

Abstract We propose and develop a new algorithm for trading wind energy in electricity markets, within an online learning optimization framework. In particular, we combine component-wise adaptive variant of the gradient descent with recent advances feature-driven newsvendor model. This results offering approach capable leveraging data-rich environments, while adapting to nonstationary characteristics generation also minimal computational burden. The performance our is analyzed based on several numerical experiments, showing both better adaptability uncertain parameters significant economic gains.