5 citations · 5 across the 6 of their papers we have counts for
6 papers
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…
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…
Uncovering Load-Altering Attacks Against N-1 Secure Power Grids: A Rare-Event Sampling Approach
Maldon Patrice Goodridge, Subhash Lakshminarayana, Alessandro Zocca
Load-altering attacks targetting a large number of IoT-based high-wattage devices (e.g., smart electric vehicle charging stations) can lead to serious disruptions of power grid ope…
Analysis of Cascading Failures Due to Dynamic Load-Altering Attacks
Maldon Patrice Goodridge, Alessandro Zocca, Subhash Lakshminarayana
Large-scale load-altering attacks (LAAs) are known to severely disrupt power grid operations by manipulating several internet-of-things (IoT)-enabled load devices. In this work, we…
RangL: A Reinforcement Learning Competition Platform
Viktor Zobernig, Richard A. Saldanha, Jinke He +12
The RangL project hosted by The Alan Turing Institute aims to encourage the wider uptake of reinforcement learning by supporting competitions relating to real-world dynamic decisio…
Slow transitions, slow mixing and starvation in dense random-access networks
Alessandro Zocca, Sem C. Borst, Johan S. H. van Leeuwaarden
We consider dense wireless random-access networks, modeled as systems of particles with hard-core interaction. The particles represent the network users that try to become active a…