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

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.

Real-time equality-constrained hybrid state estimation in complex variables

“…1 was set to 10 −6 per-unit. The testing was carried out on the IEEE 118 test network (118), the French very high-voltage 1888 node network (1888), and part of the of the European high voltage transmission network with 9241 nodes [24]. The network information is summarized in Table II, and it includes for each network four instances of measurement placement (denoted by A, B, C, and D) with increasing number of PMU devices; columns 4 to 7 show the number of SCADA measurements, voltage PMU measurements, current PMU measurements, and zero injection measurements (when employed); the complete data sets are available for download from [25].…”

“…For each network in these tables, the performance indices are computed after the state vector is estimated via the CEC, the CNE [19], and the REC [15] methods. Two performance improvement factor (PIF) ratios are used to quantify how the measurement (23) and voltage (24) performance indices of the CNE and REC estimators compare against CEC; these ratios are:…”

Cyberattack Detection in Large-Scale Smart Grids using Chebyshev Graph Convolutional Networks

“…Due to the privacy reasons, there is no publicly available dataset for cyberattack detection, therefore, we generate a synthetic dataset. As a first step, for each t in 1 ≤ t ≤ 36000, we scale load and generation values of each bus in the 2848bus test system [24] by a uniform random value between 0.8 and 1.2; run AC power flow algorithms [25]; and save power measurements after adding 1% noise to them to mimic the timely behavior of the grid. Then, to simulate the cyberattacks, we implement data scale attacks (A s ) [26] and distribution-based attacks (A d ) [27] as of two frequently used cyberattack generation algorithms.…”

Inexact Convex Relaxations for AC Optimal Power Flow: Towards AC Feasibility

“…Note that out of 96 test cases considered, the AC power flow does not converge for four test cases under any of the three loading conditions due to numerical ill-conditioning. The characteristics of these four test cases, which model parts of the French transmission network, are detailed in [40]. These AC power flows also do not converge using PowerModels.jl with IPOPT.…”