DistributedMind: Decentralized LLM Agents for Resilient Microgrid Control
Abstract
A novel framework combining large language model agents with distributed control systems for autonomous microgrid management. The system uses LLM-powered agents to handle both high-level planning and low-level control decisions, while leveraging hybrid-triggered communication for efficient coordination across the network.
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Research Gap Analysis
Current microgrid control systems lack intelligent decision-making capabilities, while LLM applications haven't been explored in critical infrastructure control. This research bridges the gap between advanced AI and practical energy system management.
DistributedMind: Decentralized LLM Agents for Resilient Microgrid Control
Motivation
Microgrids face increasing complexity with the integration of renewable energy sources, IoT devices, and dynamic load patterns. While current control systems use traditional optimization methods, they often struggle with uncertain conditions and complex decision-making. Meanwhile, LLMs have shown remarkable capabilities in reasoning and planning but haven't been applied to critical infrastructure control. This research proposes bridging this gap by creating a distributed network of LLM-powered agents that can manage microgrids more intelligently and resiliently.
Proposed Approach
The system architecture consists of three main components:
1. Hierarchical LLM Agent Network
- Local agents: Lightweight LLMs deployed at individual microgrid nodes
- Regional coordinators: More powerful models handling area-wide optimization
- Global orchestrator: High-level planning and system-wide coordination
2. Hybrid Communication Framework
- Event-triggered communication for critical updates
- Self-triggered sampling for routine monitoring
- Dynamic consensus mechanisms for agent coordination
3. Resilient Control Layer
- Integration with traditional control systems for fail-safe operation
- Real-time adaptation using reinforcement learning
- Robust fallback mechanisms for communication failures
Expected Outcomes
- Improved system resilience through intelligent adaptation
- Reduced communication overhead (40-50% estimated reduction)
- Better handling of uncertainty and dynamic conditions
- Seamless integration of renewable sources
- Enhanced fault detection and recovery
Potential Applications
- Smart city energy management
- Industrial microgrids
- Remote community power systems
- Military base energy infrastructure
- Disaster recovery operations
Proposed Methodology
Deploy a hierarchical network of specialized LLM agents using hybrid-triggered communication protocols, combined with traditional control systems for robust operation.
Potential Impact
Could revolutionize how microgrids are managed, making them more resilient, efficient, and capable of handling complex scenarios while reducing operational costs and improving renewable energy integration.