Page 501 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

be integrated to predict and mitigate potential failures, further enhancing grid stability and
resilience.

5� Interoperability and Industry Adoption 4.5: Manufacturing

We will work on designing standardized APIs and protocols to ensure seamless compatibility
with existing power grid dispatch systems. Pilot programs will be initiated in collaboration with
regional power utilities to validate system performance under real-world conditions. Feedback
from these implementations will guide iterative system improvements.

By focusing on these areas, the Intelligent Section Flow Agent system will evolve into a robust,
intelligent, and scalable solution, driving innovation in power grid management and supporting
the sustainable development of energy systems.

3 Use Case Requirements

Hardware Requirements:

For development/training: Minimum 16-core CPU + 64GB RAM (Recommended: 32-core CPU
+ 128GB RAM + GPU acceleration). One core acts as the central node, while the remaining
cores serve as edge computing nodes. Distributed training requires multiple Linux nodes with
≥10GbE high-speed network. Deployment needs edge devices supporting Python inference
(Docker containerization recommended), with industrial PCs for real-time control scenarios.

Environment Requirements:
Python ≥3.7 (3.8/3.9 recommended) with virtual environment dependency management. Core
software stack:
• ray[rllib] (distributed RL framework)
• torch/tensorflow (deep learning backend)
• gymnasium (environment interface)
• pandas/numpy (data processing)

Power system simulation requires power flow simulators, with tensorboard recommended for
real-time monitoring.

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