How Satisfactory Can Deep Reinforcement Learning Methods Simulate Electricity Market Dynamics? Bechmarking via Bi-level Optimization

Research output: Contribution to conference › Paper › Research › peer-review

Original language	English
Publication date	2024
Number of pages	14
Publication status	Accepted/In press - 2024
Event	The 13^th DACH+ Energy Informatics Conference - Lugano, Switzerland Duration: 9 Oct 2024 → 11 Oct 2024

Conference	The 13^th DACH+ Energy Informatics Conference
Country/Territory	Switzerland
City	Lugano
Period	09/10/2024 → 11/10/2024

Cite this

@conference{3784422f56a74f6197bce4fa4c047991,

title = "How Satisfactory Can Deep Reinforcement Learning Methods Simulate Electricity Market Dynamics? Bechmarking via Bi-level Optimization",

author = "Nick Harder and Lesia Mitridati and Farzaneh Pourahmadi and Anke Weidlich and Jalal Kazempour",

year = "2024",

language = "English",

note = "The 13<sup>th</sup> DACH+ Energy Informatics Conference ; Conference date: 09-10-2024 Through 11-10-2024",

}

TY - CONF

T1 - How Satisfactory Can Deep Reinforcement Learning Methods Simulate Electricity Market Dynamics? Bechmarking via Bi-level Optimization

AU - Harder, Nick

AU - Mitridati, Lesia

AU - Pourahmadi, Farzaneh

AU - Weidlich , Anke

AU - Kazempour, Jalal

PY - 2024

Y1 - 2024

M3 - Paper

T2 - The 13<sup>th</sup> DACH+ Energy Informatics Conference

Y2 - 9 October 2024 through 11 October 2024

ER -