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OPFData: Large-scale datasets for AC optimal power flow with topological perturbations
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Solving the AC optimal power flow problem (AC-OPF) is critical to the efficient and safe planning and operation of power grids. Small efficiency improvements in this domain have the potential to lead to billions of dollars of cost savings, and significant reductions in emissions from fossil fuel generators. Recent work on data-driven solution methods for AC-OPF shows the potential for large speed improvements compared to traditional solvers; however, no large-scale open datasets for this problem exist. We present the largest readily-available collection of solved AC-OPF problems to date. This collection is orders of magnitude larger than existing readily-available datasets, allowing training of high-capacity data-driven models. Uniquely, it includes topological perturbations - a critical requirement for usage in realistic power grid operations. We hope this resource will spur the community to scale research to larger grid sizes with variable topology.
Forward citations
Cited by 4 Pith papers
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Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets
A physics-guided diffusion model produces synthetic power flow samples that are more AC-feasible and slightly closer to ground truth than unconstrained diffusion on IEEE 5, 24, and 118 bus systems.
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PGLearn -- An Open-Source Learning Toolkit for Optimal Power Flow
PGLearn provides a large open-source dataset collection and toolkit with AC, DC, and SOC-OPF primal and dual solutions, time-series data for large grids, and benchmarking tools for ML-based OPF methods.
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A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method
Sampling the total active power load instead of individual loads produces more diverse AC-OPF datasets, and a slack-variable formulation lets the generator scale to 4,661-bus grids.
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Enhancing Power Flow Estimation with Topology-Aware Gated Graph Neural Networks
A gated graph neural network predicts AC power flow voltages and angles on IEEE 30 to 1354 bus systems with reported gains over GNN baselines, though internal inconsistencies weaken the evidence.
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