A deep reinforcement learning framework for continuous intraday market bidding

DOI10.1007/s10994-021-06020-8OpenAlexW3179456407MaRDI QIDQ2071376

Adrien Bolland, Alexandre Huynen, Christelle Wynants, Thibaut Théate, Ioannis Boukas, Bertrand Cornélusse, Damien Ernst, Martin Buchwald

Publication date: 28 January 2022

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2004.05940

zbMATH Keywords

Markov decision processdeep reinforcement learningasynchronous fitted Q iterationenergy storage controlEuropean continuous intraday markets

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items

Optimal bidding functions for renewable energies in sequential electricity markets

Uses Software

• Approxrl

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• An optimal trading problem in intraday electricity markets
• Bidding in sequential electricity markets: the Nordic case
• Stochastic programming for optimizing bidding strategies of a Nordic hydropower producer
• Heuristic decision rules for short-term trading of renewable energy with co-located energy storage
• \({\mathcal Q}\)-learning
• Sell or store? An ADP approach to marketing renewable energy
• Gas storage valuation in incomplete markets