Large Foundation Models for Power Systems

TL;DR

This paper explores the application of large foundation models like GPT-4 in power systems, demonstrating their potential to improve efficiency and reliability across various tasks without task-specific training.

Contribution

It evaluates the performance of existing foundation models on key power system tasks and discusses their potential deployment in power system operations.

Findings

Foundation models perform well on power system tasks

They enhance efficiency and reliability in power system operations

Potential for future deployment in large-scale power systems

Abstract

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.