用大模型自动设计电力调度启发式算法，更省时更省钱。
Automated Heuristic Design for Unit Commitment Using Large Language Models
- 用大语言模型在函数空间搜索优化调度方案
- 10台机组测试中，总成本更低、计算更快
- 适合电力系统优化与智能算法研究者
机组组合（Unit Commitment, UC）问题是电力系统最优调度的经典难题。多年研究与实践表明，制定合理的机组组合方案能显著提升电力系统运行的经济性。近年来，随着机器学习和拉格朗日松弛法等技术的引入，求解方法日益多样化，但仍面临精度与鲁棒性挑战。本文提出一种基于大语言模型的函数空间搜索（Function Space Search, FunSearch）方法，结合预训练大语言模型与评估器，通过程序搜索与演化过程创造性生成合理解。仿真实验以10台机组为例，结果表明，相比遗传算法，FunSearch在采样时间、评估时间及系统总运行成本方面均表现更优，展现出解决UC问题的巨大潜力。
原文摘要 · Abstract (English)
The Unit Commitment (UC) problem is a classic challenge in the optimal scheduling of power systems. Years of research and practice have shown that formulating reasonable unit commitment plans can significantly improve the economic efficiency of power systems' operations. In recent years, with the introduction of technologies such as machine learning and the Lagrangian relaxation method, the solution methods for the UC problem have become increasingly diversified, but still face challenges in terms of accuracy and robustness. This paper proposes a Function Space Search (FunSearch) method based on large language models. This method combines pre-trained large language models and evaluators to creatively generate solutions through the program search and evolution process while ensuring their rationality. In simulation experiments, a case of unit commitment with \(10\) units is used mainly. Compared to the genetic algorithm, the results show that FunSearch performs better in terms of sampling time, evaluation time, and total operating cost of the system, demonstrating its great potential as an effective tool for solving the UC problem.
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