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Converter-dominated grids exhibit complex, interacting stability phenomena that defy traditional analysis, pushing the boundaries of both model-based and data-driven approaches.
GENCO achieves up to 85x speedups in optimal power flow analysis while maintaining higher feasibility and optimality than classical methods.
Effective curling strategies can be learned entirely through self-supervision, matching expert heuristics without human-annotated data.
Bridging the gap between reinforcement learning and control theory could unlock new synergies in optimizing unknown dynamical systems.
Skip the expensive modeling step: this data-driven approach to traffic light control directly optimizes traffic flow using real-world data, slashing travel times and emissions in a massive Zürich simulation.