# Online Decision Making for Trading Wind Energy

arXiv: https://arxiv.org/abs/2209.02009 · HTML: https://mlsanity.com/paper/2209.02009/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 50 references that are in the map.
- First posted on arXiv 5 Sep 2022.

## Builds on (most cited first, 11 of 11)

- [Introduction to Online Convex Optimization](https://mlsanity.com/paper/1909.05207/) (2019)
- [Online Learning: A Modern Introduction Using Convex Optimization](https://mlsanity.com/paper/1912.13213/) (2019)
- [Non-stationary Stochastic Optimization](https://mlsanity.com/paper/1307.5449/) (2013)
- [From Predictive to Prescriptive Analytics](https://mlsanity.com/paper/1402.5481/) (2014)
- [From Data to Decisions: Distributionally Robust Optimization is Optimal](https://mlsanity.com/paper/1704.04118/) (2017)
- [Online Projected Gradient Descent for Stochastic Optimization with Decision-Dependent Distributions](https://mlsanity.com/paper/2107.09721/) (2021)
- [Lazy OCO: Online Convex Optimization on a Switching Budget](https://mlsanity.com/paper/2102.03803/) (2021)
- [Feature-driven Improvement of Renewable Energy Forecasting and Trading](https://mlsanity.com/paper/1907.07580/) (2019)
- [Online Optimization as a Feedback Controller: Stability and Tracking](https://mlsanity.com/paper/1805.09877/) (2018)
- [Online Gradient Descent for Linear Dynamical Systems](https://mlsanity.com/paper/1912.09311/) (2019)
- [Towards Online Optimization for Power Grids](https://mlsanity.com/paper/2109.12510/) (2021)

## Built on by (most cited first, 3 of 3)

- [Value-oriented forecast reconciliation for renewables in electricity markets](https://mlsanity.com/paper/2501.16086/) (2025)
- [Learn to Bid as a Price-Maker Wind Power Producer](https://mlsanity.com/paper/2503.16107/) (2025)
- [Feature-driven reinforcement learning for photovoltaic in continuous intraday trading](https://mlsanity.com/paper/2510.16021/) (2025)

## Abstract (arXiv)

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.