Evolutionary Algorithm Generator: an Optimal Power Flow Calculation Method for Active Distribution Networks Using Large Language Models

The optimal power flow (OPF) is considered essential for the planning and operation of power distribution networks. However, with the increasing complexity of active distribution networks (ADN), conventional optimization methods for OPF encounter significant limitations. Currently, the rapid emergence and maturation of large language models (LLMs), e.g., GPT-4 and DeepSeek, have introduced new opportunities. Therefore, this paper presents the Evolutionary Algorithm Generator (EAG), an intelligent heuristic framework that integrates large language models with Evolutionary Computation (EC) to efficiently solve the ADN-OPF problem. EAG consists of a generation module and an evolutionary module. By leveraging the generalization ability and automatic code generation of LLMs, EAG automates the entire process from algorithm design to optimization, enhancing both efficiency and performance. The effects of different models and prompt strategies on ADN-OPF performance were evaluated through extensive experiments across diverse test cases. The numerical results based on the IEEE 33 bus system demonstrate superior computational performance compared to traditional intelligent algorithms. This research offers a novel perspective on the intelligent design of optimization algorithms for power systems and represents a significant application of LLMs in the field of electrical engineering.