Automated Heuristic Design for Unit Commitment Using Large Language Models

TL;DR

This paper introduces FunSearch, a novel method leveraging large language models to generate and evaluate unit commitment solutions, improving efficiency and cost-effectiveness over traditional algorithms.

Contribution

It presents a new approach combining large language models with search and evaluation techniques for unit commitment optimization.

Findings

FunSearch outperforms genetic algorithms in sampling and evaluation time.

It reduces total system operating costs.

Demonstrates potential for large-scale power system applications.

Abstract

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.