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
Problem-943.-ACMP - Problem 943. ACMP

Problem-943.-ACMP В "main.py" расположен вариант моего решения задачи 943 с серв

Konstantin Dyomshin 2 Aug 19, 2022
null 5 Jan 5, 2023
Just Go with the Flow: Self-Supervised Scene Flow Estimation

Just Go with the Flow: Self-Supervised Scene Flow Estimation Code release for the paper Just Go with the Flow: Self-Supervised Scene Flow Estimation,

Himangi Mittal 50 Nov 22, 2022
Code and model benchmarks for "SEVIR : A Storm Event Imagery Dataset for Deep Learning Applications in Radar and Satellite Meteorology"

NeurIPS 2020 SEVIR Code for paper: SEVIR : A Storm Event Imagery Dataset for Deep Learning Applications in Radar and Satellite Meteorology Requirement

USAF - MIT Artificial Intelligence Accelerator 46 Dec 15, 2022
"NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search".

NAS-Bench-301 This repository containts code for the paper: "NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search". The

AutoML-Freiburg-Hannover 57 Nov 30, 2022
Benchmarks for semi-supervised domain generalization.

Semi-Supervised Domain Generalization This code is the official implementation of the following paper: Semi-Supervised Domain Generalization with Stoc

Kaiyang 49 Dec 10, 2022
Sequence modeling benchmarks and temporal convolutional networks

Sequence Modeling Benchmarks and Temporal Convolutional Networks (TCN) This repository contains the experiments done in the work An Empirical Evaluati

CMU Locus Lab 3.5k Jan 1, 2023
Source code and notebooks to reproduce experiments and benchmarks on Bias Faces in the Wild (BFW).

Face Recognition: Too Bias, or Not Too Bias? Robinson, Joseph P., Gennady Livitz, Yann Henon, Can Qin, Yun Fu, and Samson Timoner. "Face recognition:

Joseph P. Robinson 41 Dec 12, 2022
NeurIPS 2021 Datasets and Benchmarks Track

AP-10K: A Benchmark for Animal Pose Estimation in the Wild Introduction | Updates | Overview | Download | Training Code | Key Questions | License Intr

AP-10K 82 Dec 11, 2022
Training code and evaluation benchmarks for the "Self-Supervised Policy Adaptation during Deployment" paper.

Self-Supervised Policy Adaptation during Deployment PyTorch implementation of PAD and evaluation benchmarks from Self-Supervised Policy Adaptation dur

Nicklas Hansen 101 Nov 1, 2022
Benchmark spaces - Benchmarks of how well different two dimensional spaces work for clustering algorithms

benchmark_spaces Benchmarks of how well different two dimensional spaces work fo

Bram Cohen 6 May 7, 2022
POT : Python Optimal Transport

POT: Python Optimal Transport This open source Python library provide several solvers for optimization problems related to Optimal Transport for signa

Python Optimal Transport 1.7k Dec 31, 2022
Official implementation of our CVPR2021 paper "OTA: Optimal Transport Assignment for Object Detection" in Pytorch.

OTA: Optimal Transport Assignment for Object Detection This project provides an implementation for our CVPR2021 paper "OTA: Optimal Transport Assignme

null 217 Jan 3, 2023
Exact Pareto Optimal solutions for preference based Multi-Objective Optimization

Exact Pareto Optimal solutions for preference based Multi-Objective Optimization

Debabrata Mahapatra 40 Dec 24, 2022
Code for paper "Vocabulary Learning via Optimal Transport for Neural Machine Translation"

**Codebase and data are uploaded in progress. ** VOLT(-py) is a vocabulary learning codebase that allows researchers and developers to automaticaly ge

null 416 Jan 9, 2023
A Planar RGB-D SLAM which utilizes Manhattan World structure to provide optimal camera pose trajectory while also providing a sparse reconstruction containing points, lines and planes, and a dense surfel-based reconstruction.

ManhattanSLAM Authors: Raza Yunus, Yanyan Li and Federico Tombari ManhattanSLAM is a real-time SLAM library for RGB-D cameras that computes the camera

null 117 Dec 28, 2022
A Python library for differentiable optimal control on accelerators.

A Python library for differentiable optimal control on accelerators.

Google 80 Dec 21, 2022
Optimal Adaptive Allocation using Deep Reinforcement Learning in a Dose-Response Study

Optimal Adaptive Allocation using Deep Reinforcement Learning in a Dose-Response Study Supplementary Materials for Kentaro Matsuura, Junya Honda, Imad

Kentaro Matsuura 4 Nov 1, 2022
Developed an optimized algorithm which finds the most optimal path between 2 points in a 3D Maze using various AI search techniques like BFS, DFS, UCS, Greedy BFS and A*

Developed an optimized algorithm which finds the most optimal path between 2 points in a 3D Maze using various AI search techniques like BFS, DFS, UCS, Greedy BFS and A*. The algorithm was extremely optimal running in ~15s to ~30s for search spaces as big as 10000000 nodes where a set of 18 actions could be performed at each node in the 3D Maze.

null 1 Mar 28, 2022