This paper proposes and develops 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 non-stationary characteristics of energy generation and electricity markets, and with a minimal computational burden. The performance of our approach is analyzed based on several numerical experiments, showing both better adaptability to non-stationary uncertain parameters and significant economic gains.
翻译:本文在在线学习和优化框架内,提出并发展了电力市场风能交易的新算法。特别是,我们将梯度下限算法的成因适应变式与特效驱动新闻供应商模型的最新进展结合起来。这导致一种在线提供方法,既能利用数据丰富的环境,又能适应能源产生和电力市场的非固定性特点,并承担最小的计算负担。我们方法的绩效根据若干数字实验进行了分析,显示了更好地适应非固定的不确定参数和显著的经济收益。