  The integration of experiment technologies with large language models (LLMs)
is transforming scientific research, offering AI capabilities beyond
specialized problem-solving to becoming research assistants for human
scientists. In power systems, simulations are essential for research. However,
LLMs face significant challenges in power system simulations due to limited
pre-existing knowledge and the complexity of power grids. To address this
issue, this work proposes a modular framework that integrates expertise from
both the power system and LLM domains. This framework enhances LLMs' ability to
perform power system simulations on previously unseen tools. Validated using 34
simulation tasks in Daline, a (optimal) power flow simulation and linearization
toolbox not yet exposed to LLMs, the proposed framework improved GPT-4o's
simulation coding accuracy from 0% to 96.07%, also outperforming the ChatGPT-4o
web interface's 33.8% accuracy (with the entire knowledge base uploaded). These
results highlight the potential of LLMs as research assistants in power
systems.
