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.
AC Power Flow Data in MATPOWER and QCQP Format: iTesla, RTE Snapshots, and PEGASE
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
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.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
The authors introduce a GPU-accelerated hierarchical multi-area state estimation method that maintains full device residency via fixed-sparsity SIMD kernels and local Schur boundary condensation for efficient large-scale power system monitoring.
citing papers explorer
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Navigating the Safety-Fidelity Trade-off: Massive-Variate Time Series Forecasting for Power Systems via Probabilistic Scenarios
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.
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GPU-Native Multi-Area State Estimation via SIMD Abstraction and Boundary Condensation
The authors introduce a GPU-accelerated hierarchical multi-area state estimation method that maintains full device residency via fixed-sparsity SIMD kernels and local Schur boundary condensation for efficient large-scale power system monitoring.