A deep reinforcement learning framework for continuous intraday market bidding

Author:

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

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Reference37 articles.

1. Aïd, R., Gruet, P., & Pham, H. (2016). An optimal trading problem in intraday electricity markets. Mathematics and Financial Economics, 10(1), 49–85.

2. Baillo, A., Ventosa, M., Rivier, M., & Ramos, A. (2004). Optimal offering strategies for generation companies operating in electricity spot markets. IEEE Transactions on Power Systems, 19(2), 745–753. https://doi.org/10.1109/TPWRS.2003.821429.

3. Balardy, C. (2017a). German continuous intraday market: Orders book’s behavior over the trading session. In Meeting the energy demands of emerging economies, 40th IAEE international conference, June 18–21, 2017. International Association for Energy Economics.

4. Balardy, C. (2017b). An analysis of the bid-ask spread in the German power continuous market. In Heading towards sustainable energy systems: Evolution or revolution? 15th IAEE European conference, September 3–6, 2017. International Association for Energy Economics.

5. Bertrand, G., & Papavasiliou, A. (2019). Adaptive trading in continuous intraday electricity markets for a storage unit. IEEE Transactions on Power Systems. https://doi.org/10.1109/TPWRS.2019.2957246.

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