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AC Power Flow Data in MATPOWER and QCQP Format: iTesla, RTE Snapshots, and PEGASE

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arxiv 1603.01533 v3 pith:KDDKLNZQ submitted 2016-03-04 math.OC

Cédric Josz , Stéphane Fliscounakis , Jean Maeght , Patrick Panciatici This is my paper · ORCID
classification math.OC
keywords datatestcasesformatfourfrenchhigh-voltagematpower
verification ladder T0 review T1 audit T2 compute T3 formal
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In this paper, we publish nine new test cases in MATPOWER format. Four test cases are French very high-voltage grid generated by the offline plateform of iTesla: part of the data was sampled. Four test cases are RTE snapshots of the full French very high-voltage and high-voltage grid that come from French SCADAs via the Convergence software. The ninth and largest test case is a pan-European ficticious data set that stems from the PEGASE project. It complements the four PEGASE test cases that we previously published in MATPOWER version 5.1 in March 2015. We also provide a MATLAB code to transform the data into standard mathematical optimization format. Computational results confirming the validity of the data are presented in this paper.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Navigating the Safety-Fidelity Trade-off: Massive-Variate Time Series Forecasting for Power Systems via Probabilistic Scenarios

    cs.LG 2026-06 unverdicted novelty 7.0 of 10

    Introduces PowerPhase benchmark for massive-variate power-system forecasting and PowerForge model that achieves best average rank on safety-fidelity metrics across all tested grids.

  2. PGLearn -- An Open-Source Learning Toolkit for Optimal Power Flow

    cs.LG 2025-05 conditional novelty 6.0 of 10

    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.

  3. Enhancing Power Flow Estimation with Topology-Aware Gated Graph Neural Networks

    eess.SY 2025-07 reject novelty 3.0 of 10

    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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