Online decision making for trading wind energy

Miguel Angel Muñoz (), Pierre Pinson () and Jalal Kazempour ()
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Miguel Angel Muñoz: University of Malaga
Pierre Pinson: Imperial College London
Jalal Kazempour: Technical University of Denmark

Computational Management Science, 2023, vol. 20, issue 1, No 33, 31 pages

Abstract: Abstract 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.

Keywords: Decision making under uncertainty; Online learning; Electricity market; Newsvendor model (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10287-023-00462-2

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