Large Language Model-based power dispatch agent: Framework, application and challenges

With the growing integration of renewable energy and electronic devices, power dispatch tasks face increasing complexity due to internal and external factors, such as weather uncertainty, the long-term impact of energy storage operation, and the fluctuations in the electricity market. Recently, the large language model (LLM) Agent has been proposed, incorporating the LLM with human-like capabilities. This promotes building the LLM-based power dispatch agent to address the abovementioned problems. However, the framework of LLM-based power dispatch agents, key capabilities, and associated challenges remain unclear. Therefore, this paper proposes a comprehensive LLM-based Power Dispatch Agent framework, encompassing perception, planning, memory, reflection, and action modules, to tackle real-world tasks. Then, the capabilities and potential applications of LLM-based Power Dispatch Agent among different power dispatch-related tasks are explored. Finally, the challenges of the LLM-based power dispatch agent are discussed in relation to the requirements of power dispatch and current technologies. • Presents a novel Large Language Model-based agent framework for power dispatch operations. • Enables complex power grid control and decision-making through natural language interaction. • Automates multi-step planning and tool invocation for solving critical dispatch tasks. • Validates high performance and robustness across diverse scenarios on standard IEEE test systems.