activity
20232026
most citedMulti-Agent Reinforcement Learning for Power Grid Topology Optimization

5 citations · 5 across the 6 of their papers we have counts for

collaborators

7 papers

cs.SI2026

Multiplicative Path Weights in Networks: From Katz Connectivity to Mean First-Passage Times

Robbert van der Burg, Bernd Heidergott, Thao Le +1

Signal dispersion, stress propagation in interbank systems, and transmission probabilities in epidemic and reliability models are all captured by studying paths on directed graphs…

math.OC2026

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…

math.OC2026

Random Walks with Traversal Costs: Variance-Aware Performance Analysis and Network Optimization

Thao Le, Robbert van der Burg, Bernd Heidergott +2

We introduce weighted Markovian graphs, a random walk model that decouples the transition dynamics of a Markov chain from (random) edge weights representing the cost of traversing…

eess.SY2025

Optimizing Power Grid Topologies with Reinforcement Learning: A Survey of Methods and Challenges

Erica van der Sar, Alessandro Zocca, Sandjai Bhulai

Power grid operation is becoming increasingly complex due to the rising integration of renewable energy sources and the need for more adaptive control strategies. Reinforcement Lea…

math.OC2024

A First-Order Gradient Approach for the Connectivity Optimization of Markov Chains

Christian P. C. Franssen, Alessandro Zocca, Bernd F. Heidergott

Graphs are commonly used to model various complex systems, including social networks, power grids, transportation networks, and biological systems. In many applications, the connec…

eess.SY2023

Generating synthetic power grids using exponential random graphs models

Francesco Giacomarra, Gianmarco Bet, Alessandro Zocca

Synthetic power grids enable secure, real-world energy system simulations and are crucial for algorithm testing, resilience assessment, and policy formulation. We propose a novel m…

cs.LG2023★ 5 cited

Multi-Agent Reinforcement Learning for Power Grid Topology Optimization

Erica van der Sar, Alessandro Zocca, Sandjai Bhulai

Recent challenges in operating power networks arise from increasing energy demands and unpredictable renewable sources like wind and solar. While reinforcement learning (RL) shows…