3 citations · 8 across the 5 of their papers we have counts for
6 papers
Exact and Evolutionary Algorithms for Sequential Multi-Objective Transmission Topology Planning
Job Groeneveld, Miguel Muñoz, Jan Viebahn +1
We study day-ahead transmission topology control for high-voltage grid operation under security constraints. The operational task is to select, over a 24-hour horizon, a sequ…
Learning Topology Actions for Power Grid Control: A Graph-Based Soft-Label Imitation Learning Approach
Mohamed Hassouna, Clara Holzhüter, Malte Lehna +4
The rising proportion of renewable energy in the electricity mix introduces significant operational challenges for power grid operators. Effective power grid management demands ada…
Centrally Coordinated Multi-Agent Reinforcement Learning for Power Grid Topology Control
Barbera de Mol, Davide Barbieri, Jan Viebahn +1
Power grid operation is becoming more complex due to the increase in generation of renewable energy. The recent series of Learning To Run a Power Network (L2RPN) competitions have…
Graph Neural Networks for Transmission Grid Topology Control: Busbar Information Asymmetry and Heterogeneous Representations
Matthijs de Jong, Jan Viebahn, Yuliya Shapovalova
Factors such as the proliferation of renewable energy and electrification contribute to grid congestion as a pressing problem. Topology control is an appealing method for relieving…
Imitation Learning for Intra-Day Power Grid Operation through Topology Actions
Matthijs de Jong, Jan Viebahn, Yuliya Shapovalova
Power grid operation is becoming increasingly complex due to the increase in generation of renewable energy. The recent series of Learning To Run a Power Network (L2RPN) competitio…
Hierarchical Reinforcement Learning for Power Network Topology Control
Blazej Manczak, Jan Viebahn, Herke van Hoof
Learning in high-dimensional action spaces is a key challenge in applying reinforcement learning (RL) to real-world systems. In this paper, we study the possibility of controlling…