MetaCog-Grid: Uncertainty-Aware LLMs for Resilient Microgrid Control
Abstract
A novel framework combining LLM metacognition capabilities with distributed microgrid control systems to enhance power grid resilience. The system uses uncertainty quantification from LLMs to make robust decisions about power distribution and load balancing, while incorporating real-time sensor data and weather forecasts.
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Research Gap Analysis
Current systems lack integration between LLM capabilities and physical control systems, while existing LLM applications don't adequately address their metacognitive limitations in critical infrastructure contexts
MetaCog-Grid: Uncertainty-Aware LLMs for Resilient Microgrid Control
Motivation
Current microgrid control systems struggle with uncertainty from renewable energy sources and demand fluctuations. While Large Language Models (LLMs) show promise in complex decision-making, their metacognitive limitations pose risks in critical infrastructure. This research proposes integrating uncertainty-aware LLMs with distributed microgrid control systems to create more resilient and adaptive power networks.
Proposed Approach
1. Metacognitive Enhancement Layer
- Develop a specialized training framework for LLMs focused on power systems domain knowledge
- Implement uncertainty quantification methods based on model confidence scores
- Create a validation system that cross-references predictions with physical constraints
2. Distributed Control Architecture
- Design a hybrid-triggered control system that combines event-triggered and self-triggered mechanisms
- Integrate LLM predictions with traditional control algorithms using a weighted decision framework
- Implement a hierarchical validation system for LLM outputs
3. Adaptive Learning System
- Develop continuous learning mechanisms that update the model based on operational outcomes
- Implement a distributed memory system for storing and retrieving relevant historical scenarios
- Create feedback loops between physical measurements and model predictions
Expected Outcomes
- Improved renewable energy integration with 20-30% better uncertainty management
- Reduced frequency regulation errors by up to 40%
- Enhanced system resilience during communication failures
- More efficient load balancing and power distribution
Potential Applications
- Smart city power management
- Renewable energy integration
- Emergency response systems
- Industrial microgrids
- Rural electrification projects
Proposed Methodology
Develop a hybrid system that combines uncertainty-aware LLMs with distributed control mechanisms, incorporating both physical constraints and metacognitive validation
Potential Impact
Could revolutionize microgrid management by enabling more reliable renewable energy integration, reducing outages, and improving overall grid resilience while addressing the critical challenge of AI system reliability in infrastructure applications