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Inertia-Constrained Generation Scheduling: Sample Selection, Learning-Embedded Optimization Modeling, and Computational Enhancement
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DOI：10.1109/TPWRS.2025.3639407.png)
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Abstract
En 中文
Day-ahead generation scheduling is conducted by solving security-constrained unit commitment (SCUC) problem. Fast-growing inverter-based resources dramatically reduces grid inertia, compromising system dynamic stability. Traditional SCUC (T-SCUC), without any inertia requirements, may no longer be effective for renewables-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 network (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 prediction accuracy even at high sparsity levels. By embedding this ML-based frequency stability predictor into SCUC as constraints, the proposed ALSNN-SCUC model minimizes its computational complexity while ensuring frequency stability following G-1 contingency. Case studies show that the proposed ALSNN-SCUC can enforce pre-specified frequency requirements without being overly conservative, outperforming five benchmark 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.
Keywords:
Deep learning
frequency deviation
frequency stability
linearization
low-inertia power systems
sparse neural network
rate of change of frequency
unit commitment
Deep learning
frequency deviation
frequency stability
linearization
low-inertia power systems
sparse neural network
rate of change of frequency
unit commitment
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