NESTA, The NICTA Energy System Test Case Archive

Abstract

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.

This archive is discontinued

Cited by in corpus (26)

• The Power Grid Library for Benchmarking AC Optimal Power Flow Algorithms
• New Formulation and Strong MISOCP Relaxations for AC Optimal Transmission Switching Problem
• AC Power Flow Data in MATPOWER and QCQP Format: iTesla, RTE Snapshots, and PEGASE
• A New Voltage Stability-Constrained Optimal Power Flow Model: Sufficient Condition, SOCP Representation, and Relaxation
• On the Interaction between Autonomous Mobility on Demand Systems and Power Distribution Networks -- An Optimal Power Flow Approach
• Privacy-Preserving Obfuscation for Distributed Power Systems
• High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow
• Robust Optimization for Electricity Generation
• Learning Hard Optimization Problems: A Data Generation Perspective
• An MISOCP-Based Decomposition Approach for the Unit Commitment Problem with AC Power Flows
• A Low-Rank Coordinate-Descent Algorithm for Semidefinite Programming Relaxations of Optimal Power Flow
• LP approximations to mixed-integer polynomial optimization problems
• Strengthening the SDP Relaxation of AC Power Flows with Convex Envelopes, Bound Tightening, and Lifted Nonlinear Cuts
• A Two-level ADMM Algorithm for AC OPF with Global Convergence Guarantees
• SOCP Convex Relaxation-Based Simultaneous State Estimation and Bad Data Identification
• Robust Short-term Operation of AC Power Network with Injection Uncertainties
• Deep Learning for Power System Security Assessment
• The Value of Including Unimodality Information in Distributionally Robust Optimal Power Flow
• Tight Piecewise Convex Relaxations for Global Optimization of Optimal Power Flow
• Realistic Differentially-Private Transmission Power Flow Data Release
• A Spatial Branch-and-Cut Method for Nonconvex QCQP with Bounded Complex Variables
• Towards Understanding the Unreasonable Effectiveness of Learning AC-OPF Solutions
• A Method for Quickly Bounding the Optimal Objective Value of an OPF Problem using a Semidefinite Relaxation and a Local Solution
• Proving Global Optimality of ACOPF Solutions
• An Efficient Approach for obtaining Feasible solutions from SOCP formulation of ACOPF
• Bilevel Optimization for Differentially Private Optimization in Energy Systems