Large Foundation Models for Power Systems
Chenghao Huang Siyang Li Ruohong Liu Hao Wang Yize Chen
Foundation models, such as Large Language Models (LLMs), can respond to a
wide range of format-free queries without any task-specific data collection or
model training, creating various research and application opportunities for the
modeling and operation of large-scale power systems. In this paper, we outline
how such large foundation model such as GPT-4 are developed, and discuss how
they can be leveraged in challenging power and energy system tasks. We first
investigate the potential of existing foundation models by validating their
performance on four representative tasks across power system domains, including
the optimal power flow (OPF), electric vehicle (EV) scheduling, knowledge
retrieval for power engineering technical reports, and situation awareness. Our
results indicate strong capabilities of such foundation models on boosting the
efficiency and reliability of power system operational pipelines. We also
provide suggestions and projections on future deployment of foundation models
in power system applications.