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Found 8 papers, 0 papers with code
Generating synthetic power grids using exponential random graphs models
Synthetic power grids enable secure, real-world energy system simulations and are crucial for algorithm testing, resilience assessment, and policy formulation.
Multi-Agent Reinforcement Learning for Power Grid Topology Optimization
Recent challenges in operating power networks arise from increasing energy demands and unpredictable renewable sources like wind and solar.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Uncovering Load-Altering Attacks Against N-1 Secure Power Grids: A Rare-Event Sampling Approach
In this work, we aim to uncover spatiotemporal characteristics of LAAs that can lead to serious impact.
RangL: A Reinforcement Learning Competition Platform
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 decision problems.
Interface Networks for Failure Localization in Power Systems
Transmission power systems usually consist of interconnected sub-grids that are operated relatively independently.
Adaptive Network Response to Line Failures in Power Systems
Transmission line failures in power systems propagate and cascade non-locally.
Line Failure Localization of Power Networks Part II: Cut Set Outages
Transmission line failure in power systems prop-agate non-locally, making the control of the resulting outages extremely difficult.
Line Failure Localization of Power Networks Part I: Non-cut Outages
Transmission line failures in power systems propagate non-locally, making the control of the resulting outages extremely difficult.
