基于深度学习和感兴趣区域主动采样的预防性机组组合频率约束编码
Encoding Frequency Constraints in Preventive Unit Commitment Using Deep Learning with Region-of-Interest Active Sampling
摘要：
随着可再生能源的日益普及，频率响应及其安全性是电力系统可靠运行的重要问题。频率约束机组组合（FCUC）的建议，以解决这一挑战。尽管现有的努力，在机组组合（UC）的频率特性建模，目前的策略只能处理过于简化的低阶频率响应模型，不考虑广泛的操作条件。本文提出了一种适用于高可再生能源渗透率下FCUC的通用数据驱动框架。深度神经网络（DNN）被训练为使用真实数据或高保真仿真数据来预测频率响应。接下来，DNN被重新制定为一组混合整数线性约束，以纳入普通UC制定。在数据生成阶段，考虑了所有可能的功率注入，并提出了一个区域的利益主动采样，包括功率注入样本的频率最低点接近UFLC阈值，这显着提高了频率约束的准确性FCUC。在IEEE 39节点系统上验证了所提出的FCUC。利用PSS/E全阶动态模型仿真验证了FCUC在频率安全投入中的有效性。
英文摘要：
With the increasing penetration of renewable energy, frequency response and its security are of significant concerns for reliable power system operations. Frequency-constrained unit commitment (FCUC) is proposed to address this challenge. Despite existing efforts in modeling frequency characteristics in unit commitment (UC), current strategies can only handle oversimplified low-order frequency response models and do not consider wide-range operating conditions. This paper presents a generic data-driven framework for FCUC under high renewable penetration. Deep neural networks (DNNs) are trained to predict the frequency response using real data or high-fidelity simulation data. Next, the DNN is reformulated as a set of mixed-integer linear constraints to be incorporated into the ordinary UC formulation. In the data generation phase, all possible power injections are considered, and a region-of-interests active sampling is proposed to include power injection samples with frequency nadirs closer to the UFLC threshold, which significantly enhances the accuracy of frequency constraints in FCUC. The proposed FCUC is verified on the the IEEE 39-bus system. Then, a full-order dynamic model simulation using PSS/E verifies the effectiveness of FCUC in frequency-secure generator commitments.
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