HUGO -- Highlighting Unseen Grid Options: Combining Deep Reinforcement Learning with a Heuristic Target Topology Approach

no code implementations • 1 May 2024 • Malte Lehna, Clara Holzhüter, Sven Tomforde, Christoph Scholz

With the growth of Renewable Energy (RE) generation, the operation of power grids has become increasingly complex.

Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agents

1 code implementation • 3 Apr 2023 • Malte Lehna, Jan Viebahn, Christoph Scholz, Antoine Marot, Sven Tomforde

In this article, we analyse the submitted agent from Binbinchen and provide novel strategies to improve the agent, both for the RL and the rule-based approach.

Management Reinforcement Learning (RL)

Targeted Adversarial Attacks on Wind Power Forecasts

1 code implementation • 29 Mar 2023 • René Heinrich, Christoph Scholz, Stephan Vogt, Malte Lehna

In recent years, researchers proposed a variety of deep learning models for wind power forecasting.

Adversarial Robustness

A Reinforcement Learning Approach for the Continuous Electricity Market of Germany: Trading from the Perspective of a Wind Park Operator

no code implementations • 26 Nov 2021 • Malte Lehna, Björn Hoppmann, René Heinrich, Christoph Scholz

Through their short trading horizon and continuous nature, the intraday markets offer the ability to adjust trading decisions from the day-ahead market or reduce trading risk in a short-term notice.