Operating smart grids by customizing large model agents

Area of Science:

• Electrical Engineering
• Artificial Intelligence
• Computer Science

Background:

• Smart grid operations face increasing complexity due to renewable energy integration, electrification, and digital advancements.
• Power systems are becoming more data-rich and dynamic, challenging human operators.
• Existing operational frameworks require adaptation to manage these evolving complexities.

Purpose of the Study:

• To explore the potential of large model agents in supporting human operators within smart grid control rooms.
• To outline a practical methodology for deploying large model agents in grid operations.
• To ensure the safe and effective integration of AI into critical infrastructure management.

Main Methods:

• Review of current smart grid operational challenges and AI capabilities.
• Conceptualization of large model agents tailored for control room tasks.
• Development of a phased implementation strategy including data preparation, knowledge grounding, safety protocols, and human oversight.

Main Results:

• Customized large model agents can enhance operator capabilities in monitoring, cross-functional coordination, and decision-making.
• A structured pathway for integrating these agents into existing grid operations is proposed.
• The proposed method emphasizes data preparation, domain knowledge integration, safety validation, and gradual deployment.

Conclusions:

• Large model agents offer a viable solution to support human operators in complex smart grid environments.
• A careful, staged deployment with continuous human supervision is crucial for successful integration.
• This approach facilitates the beneficial introduction of AI into critical grid operations.