NESTA, The NICTA Energy System Test Case Archive
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Abstract (arXiv)
In recent years the power systems research community has seen an explosion of work applying operations research techniques to challenging power network optimization problems. Regardless of the application under consideration, all of these works rely on power system test cases for evaluation and validation. However, many of the well established power system test cases were developed as far back as the 1960s with the aim of testing AC power flow algorithms. It is unclear if these power flow test cases are suitable for power system optimization studies. This report surveys all of the publicly available AC transmission system test cases, to the best of our knowledge, and assess their suitability for optimization tasks. It finds that many of the traditional test cases are missing key network operation constraints, such as line thermal limits and generator capability curves. To incorporate these missing constraints, data driven models are developed from a variety of publicly available data sources. The resulting extended test cases form a compressive archive, NESTA, for the evaluation and validation of power system optimization algorithms.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Nov 2014.
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Citing papers per year: 2021: 5; 2022: 0; 2023: 2; 2024: 1; 2025: 0; 2026: 1.
9 papers.
- 2021Learning Hard Optimization Problems: A Data Generation Perspectivecited by 21
- 2021Data-Driven Time Series Reconstruction for Modern Power Systems Researchcited by 7
- 2023Predict-Then-Optimize by Proxy: Learning Joint Models of Prediction and Optimizationcited by 4
- 2024Learning Joint Models of Prediction and Optimizationcited by 3
- 2023Analyzing and Enhancing the Backward-Pass Convergence of Unrolled Optimizationcited by 1
- 2026Towards Systematic Generalization for Power Grid Optimization Problemscited by 1
- 2021Load Encoding for Learning AC-OPFcited by 0
- 2021Realistic Differentially-Private Transmission Power Flow Data Releasecited by 0
- 2021Towards Understanding the Unreasonable Effectiveness of Learning AC-OPF Solutionscited by 0
- 2026Towards Systematic Generalization for Power Grid Optimization Problems3 May 2026
- 2024Learning Joint Models of Prediction and Optimization7 Sep 2024
- 2023Analyzing and Enhancing the Backward-Pass Convergence of Unrolled Optimization28 Dec 2023
- 2023Predict-Then-Optimize by Proxy: Learning Joint Models of Prediction and Optimization22 Nov 2023
- 2021Towards Understanding the Unreasonable Effectiveness of Learning AC-OPF Solutions22 Nov 2021
- 2021Data-Driven Time Series Reconstruction for Modern Power Systems Research26 Oct 2021
- 2021Learning Hard Optimization Problems: A Data Generation Perspective4 Jun 2021
- 2021Realistic Differentially-Private Transmission Power Flow Data Release25 Mar 2021
- 2021Load Encoding for Learning AC-OPF11 Jan 2021