With the large-scale integration of distributed devices and emerging loads

distribution systems face challenges such as structural complexity

dynamic variability

and multi-source risks

overwhelming traditional physics-based modeling and machine learning methods. Large language models (LLM)

with their advantages in multi-modal fusion and complex task reasoning

offer a novel approach to revolutionizing operation paradigms. This paper systematically analyzes the technical bottlenecks under the structural evolution and functional transformation of distribution systems

reviews the potential value and current application status of LLMs in power system operation

and highlights their current limitations in grid cognition

functional positioning

and application architecture. In light of this

this paper proposes a technical path for a specialized LLM for distribution system operation

which is founded on building the model's cognitive capabilities for the grid and is characterized by a paradigm of orchestrating various algorithmic tools. Furthermore

it explores the capability system and development directions of this model from three dimensions: situational awareness

task decision-making

and application mode design. Finally

it envisions application prospects for the LLM in risk monitoring

field operation and maintenance

cross-regional generalization

and vehicle network interaction

while emphasizing key technical challenges in data representation

physics-informed mechanism embedding

and reliable deployment

with the aim of providing a reference for the intelligent development of new power distribution systems.