In this repository, we provide code for implementing our proposed Evolutionary LLM-Driven Heuristics Framework for Rolling Unit Commitment under Renewable-Induced Forecast Uncertainty.
Our framework uses a dual-role LLM as both heuristic designer and population optimizer. Through parallel crossover and mutation, it automatically generates and optimizes heuristics for Rolling Unit Commitment, improving adaptability, search efficiency, and robustness under renewable-induced forecast uncertainty compared to manually designed methods.
joblib==1.4.2
matplotlib==3.10.0
numpy==2.0.1
openai==1.101.0
pebble==5.1.3
gurobipy==12.0
We use the Unit Commitment dataset, available in (http://groups.di.unipi.it/optimize/Data/UC.html)
Some of the best-generated heuristics ('*.json' files) are in the folder directory '/Docs/Experimental results/Best/'
You can run Run.py in the folder directory '/Test/', and you also need to configure your LLM API key and endpoint.
Contact e-mail:mail_shijianhuang@163.com