Research Index

Applying large language model memory architectures to enhance power grid management and renewable energy integration. The approach combines HippoRAG's associative memory techniques with non-linear control systems to create an adaptive, predictive grid management system that can handle complex multi-energy scenarios while optimizing for efficiency and stability.

A novel framework combining steerable LLM reasoning (SEAL) with multi-agent microgrid control systems to enhance security, efficiency, and resilience. The system uses LLM-based agents to detect attacks, optimize power distribution, and coordinate responses while maintaining grid stability through calibrated reasoning paths.

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.