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.

翻译：基础模型（如大型语言模型）无需针对特定任务收集数据或训练模型即可响应各种无格式查询，这为大规模电力系统的建模与运行创造了众多研究与应用机遇。本文概述了GPT-4这类大型基础模型的开发过程，并探讨了如何将其应用于具有挑战性的电力与能源系统任务中。我们首先通过验证基础模型在电力系统领域四项代表性任务（包括最优潮流、电动汽车调度、电力工程技术报告知识检索及态势感知）上的性能，探究了现有基础模型的潜力。研究结果表明，这类基础模型在提升电力系统运行流程的效率与可靠性方面展现出强大能力。最后，我们就基础模型在电力系统应用中的未来部署提出了建议与展望。