A large language model for advanced power dispatch

Abstract

Power dispatch is essential for providing society with stable, cost-effective, and eco-friendly electricity. However, traditional methods falter as power systems grow in scale and complexity, struggling with multitasking, swift problem-solving, and human-machine collaboration. This paper introduces Grid Artificial Intelligent Assistant (GAIA), a pioneering Large Language Model (LLM) designed to assist with a variety of power system operational tasks, including operation adjustment, operation monitoring, and black start scenarios. We have developed a novel dataset construction technique that harnesses various data sources to fine-tune GAIA for optimal performance in this domain. This approach streamlines LLM training, allowing for the seamless integration of multidimensional data in power system management. Additionally, we have crafted specialized prompt strategies to boost GAIA's input-output efficiency in dispatch scenarios. When evaluated on the ElecBench benchmark, GAIA surpasses the baseline model Large Language Model Meta AI-2 (LLaMA2) on multiple metrics. In practical applications, GAIA has demonstrated its ability to enhance decision-making processes, improve operational efficiency, and facilitate better human-machine interactions in power dispatch operations. This paper expands the application of LLMs to power dispatch and validates their practical utility, paving the way for future innovations in this field.