论文标题
基于深度学习的安全受限的单位承诺考虑了低惯性电源系统中的位置频率稳定性
Deep Learning based Security-Constrained Unit Commitment Considering Locational Frequency Stability in Low-Inertia Power Systems
论文作者
论文摘要
以电力系统脱碳的目的，常规同步发电机逐渐被转换器互换的可再生代替代。由于系统惯性大大降低，这种过渡引起了对系统频率和频率变化率（ROCOF）安全性的关注。现有的努力主要来自统一的系统频率响应模型，该模型可能无法捕获系统的所有特征。为了确保位置频率安全性，本文介绍了基于ROCOF受约束的单位承诺（DNN-RCUC）模型的深神经网络（DNN）。对Rocof预测变量进行了训练，以根据高保真模拟数据集预测最高位置ROCOF。训练样本是在各种情况下从模型中生成的，可以避免模拟差异和系统不稳定性。然后将受过训练的网络重新构成一组混合组合线性约束，代表单位承诺中的位置限制性约束。在IEEE 24总线系统上研究了提出的DNN-RCUC模型。 PSS/E的时域模拟结果证明了所提出的算法的有效性。
With the goal of electricity system decarbonization, conventional synchronous generators are gradually replaced by converter-interfaced renewable generations. Such transition is causing concerns over system frequency and rate-of-change-of-frequency (RoCoF) security due to significant reduction in system inertia. Existing efforts are mostly derived from uniform system frequency response model which may fail to capture all characteristics of the systems. To ensure the locational frequency security, this paper presents a deep neural network (DNN) based RoCoF-constrained unit commitment (DNN-RCUC) model. RoCoF predictor is trained to predict the highest locational RoCoF based on a high-fidelity simulation dataset. Training samples are generated from models over various scenarios, which can avoid simulation divergence and system instability. The trained network is then reformulated into a set of mixed-integer linear constraints representing the locational RoCoF-limiting constraints in unit commitment. The proposed DNN-RCUC model is studied on the IEEE 24-bus system. Time domain simulation results on PSS/E demonstrate the effectiveness of the proposed algorithm.
