Selectively Linearized Neural Network based RoCoF-Constrained Unit Commitment in Low-Inertia Power Systems
Abstract
Conventional synchronous generators are gradually being replaced by inverter-based resources, such transition introduces more complicated operation conditions. And the reduction in system inertia imposes challenges for system operators on maintaining system rate-of-change-of-frequency (RoCoF) security. This paper presents a selectively linearized neural network (SNLNN) based RoCoF-constrained unit commitment (SLNN-RCUC) model. A RoCoF predictor is first trained to predict the system wide highest locational RoCoF based on a high-fidelity simulation dataset. Instead of incorporating the complete neural network into unit commitment, a ReLU linearization method is implemented on active selected neurons to improve the algorithm computational efficiency. The effectiveness of proposed SLNN-RCUC model is demonstrated on the IEEE 24-bus system by conducting time domain simulation on PSS/E
Keywords
Cite
@article{arxiv.2211.08502,
title = {Selectively Linearized Neural Network based RoCoF-Constrained Unit Commitment in Low-Inertia Power Systems},
author = {Mingjian Tuo and Xingpeng Li},
journal= {arXiv preprint arXiv:2211.08502},
year = {2023}
}
Comments
arXiv admin note: substantial text overlap with arXiv:2208.08028