arXiv:2606.00561v1 Announce Type: new Abstract: Deep reinforcement learning (RL) offers a promising route to real-time power grid operation, yet large neural policies are costly to evaluate, hard to deploy on constrained hardware, and opaque to operators. We ask whether a Proximal Policy Optimization (PPO) agent for grid topology control can be compressed into compact tree-based surrogates without losing operational performance. A PPO teacher is trained on Grid2Op's standard 14-bus environment with a stability-oriented reward, using stress-focused data collection on critical, high-loading stat
The increasing complexity of power grids and the urgent need for real-time, resilient operation are driving the adoption of AI, but practical deployment faces challenges with interpretability and hardware constraints, which this research aims to address.
Improving the deployability and interpretability of AI for critical infrastructure like power grids is crucial for ensuring grid stability, enabling renewable energy integration, and building operator trust in autonomous systems.
The ability to distil complex AI policies into simpler, interpretable models could accelerate the real-world deployment of advanced AI for grid management, moving from theoretical promise to practical application.
- · Grid operators
- · AI hardware developers
- · Renewable energy integration
- · Energy utilities
- · Legacy grid management systems
- · Systems requiring high human oversight without AI integration
More robust and efficient power grid operations due to deployable AI.
Accelerated integration of distributed energy resources and higher grid resilience against disruptions.
Enhanced energy security and potential for AI-driven energy markets, requiring new regulatory frameworks.
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