Automated Heuristic Design for Unit Commitment Using Large Language Models
This is an incremental advance for power system operators, offering a new method to enhance economic efficiency in scheduling.
The paper tackled the Unit Commitment problem in power system scheduling by proposing a Function Space Search method based on large language models, which outperformed genetic algorithms in a 10-unit case with improvements in sampling time, evaluation time, and total operating cost.
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.