Fine-Tuning LLMs to Generate Economical and Reliable Actions for the Power Grid
A fine-tuning framework for language models that generate corrective power-grid actions while balancing cost and reliability.
04 / Research direction
Power-system testbeds for studying learning, optimization, reliability, market participation, and control.
These projects use power systems as a concrete domain where uncertainty, constraints, and model reliability matter immediately.
Papers
5 papers connected to this topic.
A fine-tuning framework for language models that generate corrective power-grid actions while balancing cost and reliability.
A data-driven market-participation study for building energy systems that weighs energy cost against carbon emissions.
A neural digital-twin approach for approximating distribution-grid physics while keeping optimization and control tractable.
A microgrid sizing study using ordinal optimization to search design alternatives efficiently.
A case study comparing model-free and model-based control choices for battery control problems.