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arXiv 2307.04880eess.SYcs.SY
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惯性约束发电调度：样本选择、嵌入式优化建模与计算增强

Inertia-Constrained Generation Scheduling: Sample Selection, Learning-Embedded Optimization Modeling, and Computational Enhancement

University of Houston（休斯顿大学）；Georgia Institute of Technology（佐治亚理工学院）

Mingjian Tuo, Fan Jiang, Xingpeng Li, Pascal Van Hentenryck

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AI总结：

本文提出基于机器学习的惯性约束发电调度模型，通过样本选择和优化建模提升计算效率，验证了其在可再生能源电网中的有效性。

摘要：

日前发电调度通常通过求解安全约束机组组合（SCUC）问题进行。然而，随着基于逆变器的资源快速增加，电网惯性显著降低，影响动态稳定性系统。传统SCUC（T-SCUC）缺乏惯性要求，可能不再适用于以可再生能源为主的电网。为此，本文提出主动线性化稀疏神经网络嵌入SCUC（ALSNN-SCUC）模型，利用机器学习（ML）整合系统动态性能。通过在战略选择的数据样本上离线训练多输出深度神经网络（DNN）模型，准确预测频率稳定性指标：区域频率变化率（RoCoF）和频率谷值。通过结构稀疏性和主动ReLU线性化，修剪冗余DNN神经元，显著减少其大小，即使在高稀疏率下也能保持预测准确性。将基于ML的频率稳定性预测器嵌入SCUC作为约束，提出的ALSNN-SCUC模型在保证频率稳定性后G-1故障情况下最小化计算复杂性。案例研究表明，提出的ALSNN-SCUC能够强制执行预设的频率要求而不过于保守，优于包括T-SCUC在内的五个基准模型。所提出的稀疏化和主动线性化策略可将DNN-SCUC计算时间分别减少95%以上，证明了所提ALSNN-SCUC模型的有效性和可扩展性。

英文摘要：

Day-ahead generation scheduling is typically conducted by solv-ing security-constrained unit commitment (SCUC) problem. However, with fast-growing of inverter-based resources, grid inertia has been dramatically reduced, compromising the dy-namic stability system. Traditional SCUC (T-SCUC), without any inertia requirements, may no longer be effective for renewa-bles-dominated grids. To address this, we propose the active linearized sparse neural network-embedded SCUC (ALSNN-SCUC) model, utilizing machine learning (ML) to incorporate system dynamic performance. A multi-output deep neural net-work (DNN) model is trained offline on strategically-selected data samples to accurately predict frequency stability metrics: locational RoCoF and frequency nadir. Structured sparsity and active ReLU linearization are implemented to prune redundant DNN neurons, significantly reducing its size while ensuring pre-diction accuracy even at high sparsity levels. By embedding this ML-based frequency stability predictor into SCUC as con-straints, the proposed ALSNN-SCUC model minimizes its com-putational complexity while ensuring frequency stability follow-ing G-1 contingency. Case studies show that the proposed ALSNN-SCUC can enforce pre-specified frequency requirements without being overly conservative, outperforming five bench-mark models including T-SCUC, two physics-based SCUC, and two ML-based SCUC. The proposed sparsification and active linearization strategies can reduce the DNN-SCUC computing time by over 95% for both IEEE 24-bus and 118-bus systems, demonstrating the effectiveness and scalability of the proposed ALSNN-SCUC model.

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