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

翻译：我们提出并开发了一种新的算法，用于在电力市场中交易风电，该算法基于在线学习与优化框架。具体而言，我们将梯度下降算法的分量自适应变体与特征驱动报童模型的最新进展相结合。由此产生了一种在线报价方法，该方法能够充分利用数据丰富的环境，同时适应能源发电和电力市场的非平稳特性，且计算负担极小。基于多个数值实验，我们分析了该方法的性能，结果表明其不仅对非平稳不确定参数具有更好的适应性，还能带来显著的经济收益。