论文标题
低惯性动力系统中有选择性线性化的神经网络基于ROCOF受约束的单位承诺
Selectively Linearized Neural Network based RoCoF-Constrained Unit Commitment in Low-Inertia Power Systems
论文作者
论文摘要
传统的同步发电机逐渐被基于逆变器的资源所取代，这种过渡引入了更复杂的操作条件。系统惯性的减少对系统运营商维持系统变化率（ROCOF）安全性构成了挑战。本文介绍了基于Rocof-strimentiment的单位投入（SLNN-RCUC）模型的选择性线性化神经网络（SNLNN）。首先对ROCOF预测变量进行训练，以根据高保真模拟数据集预测系统宽的位置ROCOF。与其将完整的神经网络纳入单位承诺中，不如在主动选择的神经元上实现Relu线性化方法，以提高算法计算效率。通过对PSS/E进行时域模拟，在IEEE 24总线系统上证明了所提出的SLNN-RCUC模型的有效性
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
