Benchmarks for the Optimal Power Flow Problem

Overview

Power Grid Lib - Optimal Power Flow

This benchmark library is curated and maintained by the IEEE PES Task Force on Benchmarks for Validation of Emerging Power System Algorithms and is designed to evaluate a well established version of the the AC Optimal Power Flow problem. This introductory video and detailed report present the motivations and goals of this benchmark library. In particular, these cases are designed for benchmarking algorithms that solve the following Non-Convex Nonlinear Program,

  The Mathematical Model of the Optimal Power Flow Problem  

A detailed description of this mathematical model is available here. All of the cases files are curated in the MATPOWER data format. Open-source reference implementations are available in MATPOWER and PowerModels.jl and baseline results are reported in BASELINE.md.

Problem Variants

These cases may also be useful for benchmarking the following variants of the Optimal Power Flow problem,

  • DC Optimal Power Flow
  • AC Optimal Transmission Switching
  • DC Optimal Transmission Switching

That said, these cases are curated with the AC Optimal Power Flow problem in mind. Application to other domains and problem variants should be done with discretion.

Case File Overview

A forthcoming technical report will detail the sources, motivations, and procedures for curating these case files.

In this repository the network data files are organized into the following three broad groups:

  • /*.m - base case benchmarks as originally specified
  • /api/*.m - heavily loaded test cases (i.e. binding thermal limit constraints)
  • /sad/*.m - small phase angle difference cases (i.e. binding phase angle difference constraints)

Contributions

All case files are provided under a Creative Commons Attribution License, which allows anyone to share or adapt these cases as long as they give appropriate credit to the orginal author, provide a link to the license, and indicate if changes were made.

Community-based recommendations and contributions are welcome and encouraged in all PGLib repositories. Please feel free to submit comments and questions in the issue tracker. Corrections and new network contributions are welcome via pull requests. All data contributions are subject to a quality assurance review by the repository curator(s).

Citation Guidelines

This repository is not static. Consequently, it is critically important to indicate the version number when referencing this repository in scholarly work.

Users of this these cases are encouraged to cite the original source documents that are indicated in the file headers and the achrive report.

Comments
  • DC Baselines, Constraints, and Inf

    DC Baselines, Constraints, and Inf

    Hello, I have some related questions about the DC OPF baselines.

    1. It seems that for many of the typical operating conditions, the DC approximation better minimizes the cost than the full AC solution. Is this expected? Does this factor in any constraint violations?

    2. In some of the small angle difference cases, the objective values for the DC approximation are listed as "Inf". Does that indicate a constraint violation?

    opened by ElPiloto 5
  • radial test cases

    radial test cases

    It seems none of the test cases in pglib-opf are radial? That makes it hard to use any of these benchmarks to use/extend them for models that require a consistent definition of upstream/downstream, e.g. as in [1] below.

    I recall that the NESTA archive had a /rad m file collection. Was there any discussion on including that in pglib-opf? What happened to it?

    [1] Dvorkin, V., Fioretto, F., Van Hentenryck, P., Kazempour, J., & Pinson, P. (2020). Differentially Private Optimal Power Flow for Distribution Grids, 1, 1–9. Retrieved from http://arxiv.org/abs/2004.03921

    opened by frederikgeth 5
  • Help In SDP-Relaxation method for solving OPF Problem

    Help In SDP-Relaxation method for solving OPF Problem

    Hello Sir, i came to know about you from your videos of Convex Relaxations in Youtube... Sir i need help from you, i am stuck in my project work....i am trying to find an optimze a system 3m9b for test....

    and i wrote the optimization problem like this.... for i=1:1 cvx_begin cvx_solver sedumi

    variables u(npv,1) variable W(2n,2n) symmetric summ=trace(YYreal(:,:,1)*W); for i=2:n summ=summ+trace(YYreal(:,:,i)*W); end for i=1:npv u(i,1)==trace(YYreal(:,:,i+npq)W);
    (This u contais the PV buses active power generation...(whose optimal value has to be found)) end minimize(w
    (sum(u)+trace(YYreal(:,:,n)*W)))

    subject to for i=1:npq (this are equality constraints "calculated active power=specified active power" for pv&pq buses) trace(YYreal(:,:,i)*W)-(Pg(i,1)-Pl(i,1))==0; trace(YYreal(:,:,i)*W)-(Pg(i,1)-Pl(i,1))==0; end for i=1:npv (this bounds i thought to apply after getting a local optimal solution from Newtons Method) trace(YYreal(:,:,i+npq)*W)+Pl(i+npq,1)>=-0.2 trace(YYreal(:,:,i+npq)*W)+Pl(i+npq,1)<=3 end

    for i=1:npq (this are equality constraints "calculated reactive power=specified reactive power" for only pq buses) trace(YYimag(:,:,i)*W)-(Qg(i,1)-Ql(i,1))==0; trace(YYimag(:,:,i)*W)-(Qg(i,1)-Ql(i,1))==0; end

    W==semidefinite(2*n); W>=0; cvx_end w=w+1 for i=1:npv Pg(i+npq,1)=trace(YYreal(:,:,i+npq)*W)+Pl(i+npq,1); end end

    Sir, in the paper it is "Zero Duality Gap In Optimal Power Flow" that rank of W matrix variable should come=1 when the duality gap is "0". and for that we applied weight method.(w is the weight)..

    Sir.. for some values of w i get solution as 'NAN'. and for some i get an optimal solution...but the 'W matrix' never comes of rank 1...

    i dont know where i am going wrong...but please help me with this....

    opened by 12146 1
  • Line limits units (`rateA`)

    Line limits units (`rateA`)

    Hi, first, thanks for your work aggregating and building this library!

    I'm trying to use the 1354pegase case and am implementing my own simplified opf model where I want to impose line current constraints for line l = (i,j) according to

    (|y_ij| |V_i - V_j|)^2 <= rhs
    

    for V_i, V_j the complex voltages at buses i and j and |y_ij| is the magnitude of the (i,j) element of the admittance matrix. (Btw, I'm ignoring tap adjustments now...)

    However, I'm not sure what the units of the rhs should be from the pglib case. According to Table V of the report (https://arxiv.org/abs/1908.02788), it seems that rateA is a thermal limit that was determined by the TL-UB method from Section V.B.2. Does this mean that the rateA is already normalized by baseMVA and given in p.u. form? Or should I divide rateA by 100 to get the p.u. (and then square it to set the value of the rhs).

    Thanks!

    opened by jacob-roth 1
  • Tranformer Parameter Checks

    Tranformer Parameter Checks

    In some cases all tap settings are 1.0, I check should be made so that this only occurs when the value is not 1.0 or the branch is connecting two voltage levels.

    opened by ccoffrin 1
  • Inverted Generator Bounds

    Inverted Generator Bounds

    Some inactive generators have infeasible active power bounds (i.e. pmax < pmin). Resolve this by ensuring,

    pmin = min(pmin,pmax)
    pmax = min(pmin,pmax)
    

    in all generators.

    opened by ccoffrin 0
  • Generator LB higher than UB in 1888_rte__api

    Generator LB higher than UB in 1888_rte__api

    In pglib_opf_case1888_rte__api.m, the real power lower bound for the generator at bus 1689 (line 2,044 of the `.m' file) is 280.0, but the upper bound has been modified to be 64 (from 930 in the original case). Is this intentional? If so, what does it mean if the generator is turned on?

    Thank you for your help!

    opened by emma58 2
  • Error when solving case89_pegase__api and case240_pserc__api

    Error when solving case89_pegase__api and case240_pserc__api

    Hello,

    I got the following error when solving OPF for 2 test cases: case89_pegase__api and case240_pserc__api with the MATPOWER function runopf. It seems that there is a problem when generator bound Pmax is 0.

    Error using makeAvl (line 52) makeAvl: either Qmin or Qmax must be equal to zero for each dispatchable load.

    Error in opf_setup (line 171) [Avl, lvl, uvl] = makeAvl(baseMVA, gen);

    Error in opf (line 198) om = opf_setup(mpc, mpopt);

    Error in runopf (line 75) [r, success] = opf(casedata, mpopt);

    Best regards,

    Christian

    opened by cbingane 7
  • Add 68-Bus System

    Add 68-Bus System

    Explore the possibility of including a network derived from the 68-Bus, 16-Machine, 5-Area Dynamic Test System.

    Related Links:

    • http://sites.ieee.org/pes-psdp/benchmark-systems-2/
    • https://electricgrids.engr.tamu.edu/electric-grid-test-cases/
    • http://icseg.iti.illinois.edu/new-england-68-bus-test-system/
    • http://sites.ieee.org/pes-resource-center/files/2015/08/PES_TR18_Benchmark-Systems-for-Small-Signal-Stability-Analysis-and-Control.pdf
    • http://www.sel.eesc.usp.br/ieee/NETS68/New_England_New_York_68_Bus_System_study_report.pdf
    opened by ccoffrin 0
Releases(v21.07)
Owner
A Library of IEEE PES Power Grid Benchmarks
A Library of IEEE PES Power Grid Benchmarks
Github project for Attention-guided Temporal Coherent Video Object Matting.

Attention-guided Temporal Coherent Video Object Matting This is the Github project for our paper Attention-guided Temporal Coherent Video Object Matti

71 Dec 19, 2022
Code for the paper "How Attentive are Graph Attention Networks?"

How Attentive are Graph Attention Networks? This repository is the official implementation of How Attentive are Graph Attention Networks?. The PyTorch

175 Dec 29, 2022
Azion the best solution of Edge Computing in the world.

Azion Edge Function docker action Create or update an Edge Functions on Azion Edge Nodes. The domain name is the key for decision to a create or updat

8 Jul 16, 2022
[ICCV'21] NEAT: Neural Attention Fields for End-to-End Autonomous Driving

NEAT: Neural Attention Fields for End-to-End Autonomous Driving Paper | Supplementary | Video | Poster | Blog This repository is for the ICCV 2021 pap

254 Jan 02, 2023
Vector Quantization, in Pytorch

Vector Quantization - Pytorch A vector quantization library originally transcribed from Deepmind's tensorflow implementation, made conveniently into a

Phil Wang 665 Jan 08, 2023
Using NumPy to solve the equations of fluid mechanics together with Finite Differences, explicit time stepping and Chorin's Projection methods

Computational Fluid Dynamics in Python Using NumPy to solve the equations of fluid mechanics 🌊 🌊 🌊 together with Finite Differences, explicit time

Felix Köhler 4 Nov 12, 2022
A multilingual version of MS MARCO passage ranking dataset

mMARCO A multilingual version of MS MARCO passage ranking dataset This repository presents a neural machine translation-based method for translating t

75 Dec 27, 2022
Expressive Body Capture: 3D Hands, Face, and Body from a Single Image

Expressive Body Capture: 3D Hands, Face, and Body from a Single Image [Project Page] [Paper] [Supp. Mat.] Table of Contents License Description Fittin

Vassilis Choutas 1.3k Jan 07, 2023
Compute descriptors for 3D point cloud registration using a multi scale sparse voxel architecture

MS-SVConv : 3D Point Cloud Registration with Multi-Scale Architecture and Self-supervised Fine-tuning Compute features for 3D point cloud registration

42 Jul 25, 2022
The official MegEngine implementation of the ICCV 2021 paper: GyroFlow: Gyroscope-Guided Unsupervised Optical Flow Learning

[ICCV 2021] GyroFlow: Gyroscope-Guided Unsupervised Optical Flow Learning This is the official implementation of our ICCV2021 paper GyroFlow. Our pres

MEGVII Research 36 Sep 07, 2022
Official repository for the paper, MidiBERT-Piano: Large-scale Pre-training for Symbolic Music Understanding.

MidiBERT-Piano Authors: Yi-Hui (Sophia) Chou, I-Chun (Bronwin) Chen Introduction This is the official repository for the paper, MidiBERT-Piano: Large-

137 Dec 15, 2022
Official code for "Distributed Deep Learning in Open Collaborations" (NeurIPS 2021)

Distributed Deep Learning in Open Collaborations This repository contains the code for the NeurIPS 2021 paper "Distributed Deep Learning in Open Colla

Yandex Research 96 Sep 15, 2022
Real-time 3D multi-person detection made easy with OpenPose and the ZED

OpenPose ZED This sample show how to simply use the ZED with OpenPose, the deep learning framework that detects the skeleton from a single 2D image. T

blanktec 5 Nov 06, 2020
Official Implementation of CoSMo: Content-Style Modulation for Image Retrieval with Text Feedback

CoSMo.pytorch Official Implementation of CoSMo: Content-Style Modulation for Image Retrieval with Text Feedback, Seungmin Lee*, Dongwan Kim*, Bohyung

Seung Min Lee 54 Dec 08, 2022
1st place solution in CCF BDCI 2021 ULSEG challenge

1st place solution in CCF BDCI 2021 ULSEG challenge This is the source code of the 1st place solution for ultrasound image angioma segmentation task (

Chenxu Peng 30 Nov 22, 2022
Simulations for Turring patterns on an apically expanding domain. T

Turing patterns on expanding domain Simulations for Turring patterns on an apically expanding domain. The details about the models and numerical imple

Yue Liu 0 Aug 03, 2021
AOT-GAN for High-Resolution Image Inpainting (codebase for image inpainting)

AOT-GAN for High-Resolution Image Inpainting Arxiv Paper | AOT-GAN: Aggregated Contextual Transformations for High-Resolution Image Inpainting Yanhong

Multimedia Research 214 Jan 03, 2023
Repository for training material for the 2022 SDSC HPC/CI User Training Course

hpc-training-2022 Repository for training material for the 2022 SDSC HPC/CI Training Series HPC/CI Training Series home https://www.sdsc.edu/event_ite

sdsc-hpc-training-org 21 Jul 27, 2022
A simple approach to emable dense segmentation with ViT.

Vision Transformer Segmentation Network This implementation of ViT in pytorch uses a super simple and straight-forward way of generating an output of

HReynaud 5 Jan 03, 2023
Edge-aware Guidance Fusion Network for RGB-Thermal Scene Parsing

EGFNet Edge-aware Guidance Fusion Network for RGB-Thermal Scene Parsing Dataset and Results Test maps: 百度网盘 提取码:zust Citation @ARTICLE{ author={Zhou,

ShaohuaDong 10 Dec 08, 2022