VeraGrid

VeraGrid

VeraGrid is a top tier power systems planning and simulation software. As such, it has all the analysis studies that you can think of, such as linear and non-linear optimization functions, RMS, EMT, short circuit, stochastic, blackout cascading, investments evaluations and many more. Some of these functions are well known, while others you may have never heard of as they are a product of cutting-edge research.

VeraGrid started in 2015 with a clear objective: create a solid programming library and a user-friendly interface. This straightforward approach sparked many innovations — some driven by the necessity for commercial use, and others fueled by curiosity and research.

Whether you’re a pro needing free tools, a researcher wanting a real-world tested platform, a teacher sharing commercial-grade software insights, or student diving into practical algorithms, VeraGrid’s got your back. It’s a high quality product made for all of us now and for the future generations.

Installation

VeraGrid is a software made in the Python programming language. Therefore, it needs a Python interpreter installed in your operative system.

The VeraGrid project is divided in three packages:

• VeraGridEngine: A package with the database and calculations logic.
• VeraGridServer: A package that serves an API-rest to use VeraGridEngine remotely.
• VeraGrid: A package that contains the Graphical User Interface (GUI) and operates with VeraGridEngine and VeraGridServer seamlessly.

To install everything, you only need to install the VeraGrid package and the others will be installed as dependencies.

One-line terminal installer (Linux, macOS, Windows)

Run the command for your operating system in your terminal:

Linux:

curl -sSL https://raw.githubusercontent.com/SanPen/VeraGrid/master/src/install_on_linux.sh | bash

macOS:

curl -sSL https://raw.githubusercontent.com/SanPen/VeraGrid/master/src/install_on_macos.sh | bash

Windows (Command Prompt / PowerShell):

curl -sSL https://raw.githubusercontent.com/SanPen/VeraGrid/master/src/install_on_windows.cmd -o install_veragrid.cmd && install_veragrid.cmd && del install_veragrid.cmd

This automatically:

• Creates a dedicated Python virtual environment in your user directory.
• Installs the latest VeraGrid package and all dependencies via pip.
• Sets up desktop and application menu shortcuts with the VeraGrid icon.
• Associates .veragrid and .gridcal file formats so double-clicking files opens them directly in VeraGrid.

Standalone setup

If you don’t know what is this Python thing, we offer a Windows installation:

Windows setup

This will install VeraGrid as a normal windows program, and you don’t need to worry about any of the previous instructions. Still, if you need some guidance, the following video might be of assistance: Setup tutorial (video).

Package installation

We recommend installing the latest version of Python and then, install VeraGrid with the following terminal command:

pip install VeraGrid

You may need to use pip3 if you are under Linux or macOS, both of which come with Python pre-installed already.

Install into an environment

python3 -m venv gc5venv
source gc5venv/bin/activate
pip install VeraGrid
veragrid

Run the graphical user interface

Once you install VeraGrid in your local Python distribution, you can run the graphical user interface with the following terminal command:

veragrid

If this doesn’t work, try:

python -c "from VeraGrid.ExecuteVeraGrid import runVeraGrid; runVeraGrid()"

You may save this command in a shortcut for easy future access.

Install only the engine

Some of you may only need VeraGrid as a library for some other purpose like batch calculations, AI training or simple scripting. Whatever it may be, you can get the VeraGrid engine with the following terminal command:

pip install VeraGridEngine

This will install the VeraGridEngine package that is a dependency of VeraGrid.

Again, you may need to use pip3 if you are under Linux or MacOS.

Features

VeraGrid is packed with features:

• Large collection of devices to model electricity grids
• AC/DC multi-grid power flow
• 3-phase unbalanced power flow and short circuit
• AC/DC multi-grid linear optimal power flow
• AC linear analysis (PTDF & LODF)
• AC linear net transfer capacity calculation
• AC+HVDC optimal net transfer capacity calculation
• AC/DC Stochastic power flow
• AC Short circuit
• AC Continuation power flow
• Contingency analysis (Power flow and LODF variants)
• Sigma analysis (one-shot stability analysis)
• RMS simulation in positive sequence.
• EMT simulation in unbalanced ABC formulation.
• Investments analysis.
• Bus-branch schematic.
• Substation-line map diagram.
• Time series and snapshot for most simulations.
• Overhead tower designer.
• Inputs analysis.
• Model bug report and repair.
• Import many formats (PSSe .raw/rawx, epc, dgs, matpower, pypsa, json, cim, cgmes).
• Export in many formats (veragrid .xlsx/.veragrid/.json, matpower, cgmes, psse .raw/.rawx).

All of these are industry tested algorithms, some of which surpass most commercially available software. The aim is to be a drop-in replacement for the expensive and less usable commercial software, so that you can work, research and learn with it.

Resources

In an effort to ease the simulation and construction of grids, We have included extra materials to work with. These are included in the standalone setups.

• Load profiles for your projects.
• Grids from IEEE and other open projects.
• Equipment catalogue (Wires, Cables and Transformers) ready to use in VeraGrid.

Project information

• Documentation: https://veragrid.readthedocs.io/
• Source repository: https://github.com/SanPen/VeraGrid
• Bug reports and feature requests: https://github.com/SanPen/VeraGrid/issues
• Support and discussion: SUPPORT.md
• Contributing guide: CONTRIBUTING.md
• Build instructions: BUILD.md
• Test and quality checks: TESTING.md
• Security policy: SECURITY.md
• Release process: RELEASE.md

Tutorials and examples

• Grid modelling and getting started

• Making a grid with profiles

• The tests may serve as a valuable source of examples.

Matpower grids

Matpower’s excellent formulations and consistency has allowed this and other projects to develop, relying on its sound math. That is why VeraGrid reads Matpower cases out of the box, without you having to do anything special. And of course, VeraGrid solves all Matpower 8 provided grids, solving the continental USA case in about 1 second:

[]

Find the results at the benchmarks page for more details.

Results simulated with AMD 9750x and 64 GB of RAM under Ubuntu 24.04. All solved using Newton-Raphson, and only using the provided solution that comes with the files when the flat start fails.

Cool right?

API

Since day one, VeraGrid has been meant to be used as a library as much as it was meant to be used from the user interface. Following, we include some usage examples, but feel free to check the documentation out where you will find a complete description of the theory, the models and the objects.

Understanding the program structure

VeraGrid structure is composed by objects arranged in a “database” and by “structs” at a deeper level. Learn here why.

All simulations in VeraGrid are handled by the simulation drivers. The structure is as follows:

Any driver is fed with the data model (MultiCircuit object), the respective driver options, and often another object relative to specific inputs for that driver. The driver is run, storing the driver results object. Although this may seem overly complicated, it has proven to be maintainable and very convenient.

Snapshot vs. time series

VeraGrid has dual structure to handle legacy cases (snapshot), as well as cases with many variations (time series)

• A snapshot is the grid for a particular moment in time. This includes the infrastructure plus the variable values of that infrastructure such as the load, the generation, the rating, etc.

• The time series records the variations of the magnitudes that can vary. These are applied along with the infrastructure definition.

In VeraGrid, the inputs do not get modified by the simulation results. This essential concept helps to maintain the independence of the inputs and outputs, allowing the replicability of the results. This key feature is not true for other open-source or commercial programs.

A snapshot or any point of the time series, may be compiled to a NumericalCircuit. This object holds the numerical arrays and matrices of a time step, ready for the numerical methods. For those simulations that require many time steps, a collection of NumericalCircuit is compiled and used.

It may seem that this extra step is redundant. However, the compilation step is composed by mere copy operations, which are fast. These steps greatly benefit the efficiency of the numerical calculations since the arrays are aligned in memory. The VeraGrid data model is object-oriented, while the numerical circuit is array-oriented (despite being packed into objects)

Loading a grid

import VeraGridEngine as vg

# load a grid (.veragrid, .m (Matpower), .raw (PSS/e) .rawx (PSS/e), .epc (PSLF), .dgs (PowerFactory)
my_grid = vg.open_file("my_file.veragrid")

In the case of CIM/CGMES, you may need to pass a list of files or a single zip file:

import VeraGridEngine as vg

# load a grid from many xml files
my_grid = vg.open_file(["grid_EQ.xml", "grid_TP.xml", "grid_SV.xml", ])

# or from a single zip assumed to contain CGMES files
my_grid = vg.open_file("my_cgmes_set_of_files.zip")

# or load a grid from a combination of xml and zip files assumed to be CGMES
my_grid = vg.open_file(["grid_EQ.xml", "grid_TP.xml", "grid_SV.xml", "boundary.zip"])

If you need to explore the CGMEs assets before conversion, you’ll need to dive deeper in the API:

import VeraGridEngine as vg

fname = "tests/data/grids/CGMES_2_4_15/IEEE 118 Bus v2.zip"

logger = vg.Logger()
data_parser = vg.CgmesDataParser()
data_parser.load_files(files=[fname])
cgmes_circuit = vg.CgmesCircuit(cgmes_version=data_parser.cgmes_version,
                                cgmes_map_areas_like_raw=False, logger=logger)
cgmes_circuit.parse_files(data_parser=data_parser)

# print all the ac line segment names
for ac_line_segment in cgmes_circuit.cgmes_assets.ACLineSegment_list:
  print(ac_line_segment.name)

# print the logs
logger.print()

VeraGrid supports many file formats:

• CIM 16 (.zip and .xml)
• UCTE.
• CGMES 2.4.15 and 3.0 (.zip and .xml)
• PSS/e raw and rawx versions 29 to 35, including USA market exchange RAW-30 specifics.
• Matpower .m files directly.
• DigSilent .DGS (not fully compatible)
• PSLF .EPC (not fully compatible, supports substation coordinates)

Similarly to CGMES you may be able to use the conversion objects to explore the original formats.

Save a grid

import VeraGridEngine as vg

# load a grid
my_grid = vg.open_file("my_file.veragrid")

# save
vg.save_file(my_grid, "my_file_2.veragrid")

In the case of saving a model in CGMES mode, we need to specify some extra parameters. To simplify we can use the API function save_cgmes_file:

import VeraGridEngine as vg

# load a grid
my_grid = vg.open_file("my_file.veragrid")

# run power flow (this is optional and it is used to generate the SV profile)
pf_results = vg.power_flow(my_grid)

# save the grid in CGMES mode
vg.save_cgmes_file(grid=my_grid,
                   filename="My_cgmes_model.zip",
                   cgmes_boundary_set_path="path_to_the_boundary_set.zip",
                   cgmes_version=vg.CGMESVersions.v2_4_15,
                   pf_results=pf_results)

Creating a Grid using the API objects

We are going to create a very simple 5-node grid from the excellent book Power System Load Flow Analysis by Lynn Powell.

Lynn 5 buses

import VeraGridEngine as vg

# declare a circuit object
grid = vg.MultiCircuit()

# Add the buses and the generators and loads attached
bus1 = vg.Bus('Bus 1', Vnom=20)
# bus1.is_slack = True  # we may mark the bus a slack
grid.add_bus(bus1)

# add a generator to the bus 1
gen1 = vg.Generator('Slack Generator', vset=1.0)
grid.add_generator(bus1, gen1)

# add bus 2 with a load attached
bus2 = vg.Bus('Bus 2', Vnom=20)
grid.add_bus(bus2)
grid.add_load(bus2, vg.Load('load 2', P=40, Q=20))

# add bus 3 with a load attached
bus3 = vg.Bus('Bus 3', Vnom=20)
grid.add_bus(bus3)
grid.add_load(bus3, vg.Load('load 3', P=25, Q=15))

# add bus 4 with a load attached
bus4 = vg.Bus('Bus 4', Vnom=20)
grid.add_bus(bus4)
grid.add_load(bus4, vg.Load('load 4', P=40, Q=20))

# add bus 5 with a load attached
bus5 = vg.Bus('Bus 5', Vnom=20)
grid.add_bus(bus5)
grid.add_load(bus5, vg.Load('load 5', P=50, Q=20))

# add Lines connecting the buses
grid.add_line(vg.Line(bus1, bus2, name='line 1-2', r=0.05, x=0.11, b=0.02))
grid.add_line(vg.Line(bus1, bus3, name='line 1-3', r=0.05, x=0.11, b=0.02))
grid.add_line(vg.Line(bus1, bus5, name='line 1-5', r=0.03, x=0.08, b=0.02))
grid.add_line(vg.Line(bus2, bus3, name='line 2-3', r=0.04, x=0.09, b=0.02))
grid.add_line(vg.Line(bus2, bus5, name='line 2-5', r=0.04, x=0.09, b=0.02))
grid.add_line(vg.Line(bus3, bus4, name='line 3-4', r=0.06, x=0.13, b=0.03))
grid.add_line(vg.Line(bus4, bus5, name='line 4-5', r=0.04, x=0.09, b=0.02))

IEEE9

A more complex example comes from the issue 354. In this example we create the IEEE9 bus grid from line and transformer definition information instead of per-unit values:

import numpy as np
import VeraGridEngine.api as vg

# ieee9 grid
grid9 = vg.MultiCircuit('IEEE-9', Sbase=100, fbase=50)

# Add the buses
bus1 = grid9.add_bus(vg.Bus(name='Bus 1', Vnom=17.16, is_slack=True))
bus2 = grid9.add_bus(vg.Bus(name='Bus 2', Vnom=18.45))
bus3 = grid9.add_bus(vg.Bus(name='Bus 3', Vnom=14.145))
bus4 = grid9.add_bus(vg.Bus(name='Bus 4', Vnom=230))
bus5 = grid9.add_bus(vg.Bus(name='Bus 5', Vnom=230))
bus6 = grid9.add_bus(vg.Bus(name='Bus 6', Vnom=230))
bus7 = grid9.add_bus(vg.Bus(name='Bus 7', Vnom=230))
bus8 = grid9.add_bus(vg.Bus(name='Bus 8', Vnom=230))
bus9 = grid9.add_bus(vg.Bus(name='Bus 9', Vnom=230))

# add generators
grid9.add_generator(bus1, vg.Generator(name='Slack Generator', P=0.0, vset=1.0))
grid9.add_generator(bus2, vg.Generator(name='Gen2', P=163, Sbase=100, vset=1.0))
grid9.add_generator(bus3, vg.Generator(name='Gen3', P=85, vset=1.0))

# add loads
grid9.add_load(bus5, vg.Load(name='Load 1', P=125, Q=50))
grid9.add_load(bus6, vg.Load(name='Load 2', P=90, Q=30))
grid9.add_load(bus8, vg.Load(name='Load 3', P=100, Q=35))

# add transformers
tr1 = vg.Transformer2W(bus_from=bus4, bus_to=bus1, name='T1', HV=230, LV=16.5, nominal_power=247.5, rate=247.5,
                       tap_phase=150 * np.pi / 180)
tr1.fill_design_properties(Pcu=0.0, Pfe=0.0, I0=0.0, Vsc=14.3, Sbase=grid9.Sbase)

tr2 = vg.Transformer2W(bus_from=bus7, bus_to=bus2, name='T2', HV=230, LV=18, nominal_power=192, rate=192,
                       tap_phase=150 * np.pi / 180)
tr2.fill_design_properties(Pcu=0.0, Pfe=0.0, I0=0.0, Vsc=12.0, Sbase=grid9.Sbase)

tr3 = vg.Transformer2W(bus_from=bus9, bus_to=bus3, name='T3', HV=230, LV=13.8, nominal_power=128, rate=128,
                       tap_phase=150 * np.pi / 180)
tr3.fill_design_properties(Pcu=0.0, Pfe=0.0, I0=0.0, Vsc=7.5, Sbase=grid9.Sbase)

grid9.add_transformer2w(tr1)  # 0.5236 => 30°#2.618
grid9.add_transformer2w(tr2)  # 2.618#2.618#0.5236
grid9.add_transformer2w(tr3)  # 0.5236

# add lines
l1 = grid9.add_line(vg.Line(bus_from=bus4, bus_to=bus5, name='line 4-5'))
l1.fill_design_properties(r_ohm=5.3, x_ohm=45.0, c_nf=1060, length=1.0, Imax=1.0, freq=grid9.fBase, Sbase=grid9.Sbase)

l2 = grid9.add_line(vg.Line(bus_from=bus4, bus_to=bus6, name='line 4-6'))
l2.fill_design_properties(r_ohm=9.0, x_ohm=48.7, c_nf=950, length=1.0, Imax=1.0, freq=grid9.fBase, Sbase=grid9.Sbase)

l3 = grid9.add_line(vg.Line(bus_from=bus5, bus_to=bus7, name='line 5-7'))
l3.fill_design_properties(r_ohm=16.9, x_ohm=85.2, c_nf=1840, length=1.0, Imax=1.0, freq=grid9.fBase, Sbase=grid9.Sbase)

l4 = grid9.add_line(vg.Line(bus_from=bus6, bus_to=bus9, name='line 6-9'))
l4.fill_design_properties(r_ohm=20.6, x_ohm=89.9, c_nf=2150, length=1.0, Imax=1.0, freq=grid9.fBase, Sbase=grid9.Sbase)

l5 = grid9.add_line(vg.Line(bus_from=bus7, bus_to=bus8, name='line 7-8'))
l5.fill_design_properties(r_ohm=4.5, x_ohm=38.1, c_nf=870, length=1.0, Imax=1.0, freq=grid9.fBase, Sbase=grid9.Sbase)

l6 = grid9.add_line(vg.Line(bus_from=bus8, bus_to=bus9, name='line 8-9'))
l6.fill_design_properties(r_ohm=6.3, x_ohm=53.3, c_nf=1260, length=1.0, Imax=1.0, freq=grid9.fBase, Sbase=grid9.Sbase)

# save (optional)
vg.save_file(grid9, "IEEE9.veragrid")

# Power flow
options = vg.PowerFlowOptions(vg.SolverType.NR,
                              retry_with_other_methods=False,
                              tolerance=1e-6,
                              control_q=False,
                              control_taps_phase=False,
                              control_taps_modules=False,
                              apply_temperature_correction=False,
                              use_stored_guess=False,
                              initialize_angles=True,
                              verbose=False)

power_flow = vg.PowerFlowDriver(grid9, options)
power_flow.run()

# print the results
resultsDF = power_flow.results.get_bus_df()
resultsDF['V (kV)'] = resultsDF['Vm'] * np.array([bus.Vnom for bus in grid9.buses])
print(resultsDF)

Which yields:

|       | Vm (p.u.) | V(kV)      | Va (deg)   | P (MW)      | Q (MVAr)   |
|-------|-----------|------------|------------|-------------|------------|
| Bus 1 | 1         | 17.16      | 0          | 71.63834    | 27.114476  |
| Bus 2 | 1         | 18.45      | 9.277313   | 163.000003  | 6.973044   |
| Bus 3 | 1         | 14.145     | 4.657059   | 85.000001   | -10.669872 |
| Bus 4 | 1.025709  | 235.91303  | 147.776277 | -0.000009   | 0.000006   |
| Bus 5 | 0.995498  | 228.96449  | 146.002536 | -125.000009 | -49.999972 |
| Bus 6 | 1.01254   | 232.884245 | 146.305199 | -90.000008  | -29.999988 |
| Bus 7 | 1.025576  | 235.882375 | 153.715955 | 0.000034    | 0.000033   |
| Bus 8 | 1.015629  | 233.594652 | 150.721498 | -100.000018 | -34.999992 |
| Bus 9 | 1.032244  | 237.416038 | 151.959026 | 0.000016    | 0.000008   |

Exactly the same results as the example from the book of the issue.

Power Flow

VeraGrid has the most power flow features in any open-source software. The following table shows the features present in each solver:

	Newton Raphson	Powell Dog-leg	Levenberg-Marquardt	Iwamoto	Fast-decoupled	Gauss-seidel	Holomorphic embedding	Linear without voltage modules	Linear with voltage modules
Local voltage control using a Generator.	✅	✅	✅	✅	✅	✅	✅		✅
Remote voltage control using a Generator.	✅	✅	✅	✅	✅
Generator reactive power limits.	✅	✅	✅	✅	✅	✅
Local and remote voltage control using a transformer’s tap changer.	✅	✅	✅
Local active power control using a transformer’s tap changer.	✅	✅	✅
Local reactive power control using a transformer’s tap changer.	✅	✅	✅
Local and remote AC and DC voltage control using a converter.	✅	✅	✅
Local AC and DC active power control using converter.	✅	✅	✅
Local AC reactive power control using a converter.	✅	✅	✅
3-phase unbalanced.	✅	✅	✅

Using the simplified API:

import os
import VeraGridEngine as vg

folder = os.path.join('..', 'Grids_and_profiles', 'grids')
fname = os.path.join(folder, 'IEEE39_1W.veragrid')
main_circuit = vg.open_file(fname)

results = vg.power_flow(main_circuit)

print(main_circuit.name)
print('Converged:', results.converged, 'error:', results.error)
print(results.get_bus_df())
print(results.get_branch_df())

Using the more complex library objects:

import os
import VeraGridEngine as vg

folder = os.path.join('..', 'Grids_and_profiles', 'grids')
fname = os.path.join(folder, 'IEEE14_from_raw.veragrid')
main_circuit = vg.open_file(fname)

options = vg.PowerFlowOptions(vg.SolverType.NR, verbose=False)
power_flow = vg.PowerFlowDriver(main_circuit, options)
power_flow.run()

print(main_circuit.name)
print('Converged:', power_flow.results.converged, 'error:', power_flow.results.error)
print(power_flow.results.get_bus_df())
print(power_flow.results.get_branch_df())

Output:

IEEE14_from_raw

Converged: True error: 5.98e-08

Bus resuts:
           Vm     Va      P      Q
BUS 1    1.06   0.00 232.39 -16.55
BUS 2    1.04  -4.98  18.30  30.86
BUS 3    1.01 -12.73 -94.20   6.08
BUS 4    1.02 -10.31 -47.80   3.90
BUS 5    1.02  -8.77  -7.60  -1.60
BUS 6    1.07 -14.22 -11.20   5.23
BUS 7    1.06 -13.36   0.00   0.00
BUS 8    1.09 -13.36   0.00  17.62
BUS 9    1.06 -14.94 -29.50 -16.60
BUS 10   1.05 -15.10  -9.00  -5.80
BUS 11   1.06 -14.79  -3.50  -1.80
BUS 12   1.06 -15.08  -6.10  -1.60
BUS 13   1.05 -15.16 -13.50  -5.80
BUS 14   1.04 -16.03 -14.90  -5.00

Branch results:
                Pf         Qf          Pt         Qt               loading    Ploss     Qloss
1_2_1   156.882887 -20.404291 -152.585286  27.676248 15688288652036.908203 4.297600  7.271957
1_5_1    75.510380   3.854989  -72.747507   2.229360  7551037982438.064453 2.762872  6.084349
2_3_1    73.237578   3.560203  -70.914309   1.602232  7323757808601.912109 2.323269  5.162436
2_4_1    56.131495  -1.550352  -54.454837   3.020689  5613149456668.273438 1.676658  1.470337
2_5_1    41.516214   1.170996  -40.612460  -2.099032  4151621353697.657715 0.903753 -0.928036
3_4_1   -23.285691   4.473114   23.659136  -4.835650 -2328569062725.765625 0.373445 -0.362537
4_5_1   -61.158231  15.823642   61.672651 -14.201004 -6115823108351.800781 0.514420  1.622637
6_11_1    7.353277   3.560471   -7.297904  -3.444512   735327693069.753418 0.055373  0.115959
6_12_1    7.786067   2.503414   -7.714258  -2.353959   778606687855.751465 0.071809  0.149455
6_13_1   17.747977   7.216574  -17.535891  -6.798912  1774797671583.112793 0.212085  0.417662
7_8_1    -0.000000 -17.162967    0.000000  17.623448             -0.001718 0.000000  0.460481
7_9_1    28.074179   5.778690  -28.074179  -4.976621  2807417855964.891602 0.000000  0.802069
9_10_1    5.227551   4.219139   -5.214676  -4.184938   522755058212.680359 0.012875  0.034201
9_14_1    9.426380   3.610007   -9.310226  -3.362932   942638030136.208130 0.116154  0.247075
10_11_1  -3.785324  -1.615061    3.797906   1.644513  -378532426869.186707 0.012581  0.029451
12_13_1   1.614258   0.753959   -1.607959  -0.748260   161425771970.211853 0.006298  0.005698
13_14_1   5.643852   1.747172   -5.589774  -1.637068   564385175482.526855 0.054078  0.110105
4_7_1    28.074176  -9.681066  -28.074176  11.384281  2807417645485.176270 0.000000  1.703214
4_9_1    16.079758  -0.427611  -16.079758   1.732322  1607975830176.256104 0.000000  1.304711
5_6_1    44.087319  12.470682  -44.087319  -8.049520  4408731875605.579102 0.000000  4.421161

Inputs analysis

VeraGrid can perform a summary of the inputs with the InputsAnalysisDriver:

import os
import VeraGridEngine as vg

folder = os.path.join('..', 'Grids_and_profiles', 'grids')
fname = os.path.join(folder, 'IEEE 118 Bus - ntc_areas.veragrid')

main_circuit = vg.open_file(fname)

drv = vg.InputsAnalysisDriver(grid=main_circuit)
mdl = drv.results.mdl(vg.ResultTypes.AreaAnalysis)
df = mdl.to_df()

print(df)

The results per area:

               P    Pgen   Pload  Pbatt  Pstagen      Pmin      Pmax      Q    Qmin    Qmax
IEEE118-3  -57.0   906.0   963.0    0.0      0.0 -150000.0  150000.0 -345.0 -2595.0  3071.0
IEEE118-2 -117.0  1369.0  1486.0    0.0      0.0 -140000.0  140000.0 -477.0 -1431.0  2196.0
IEEE118-1  174.0  1967.0  1793.0    0.0      0.0 -250000.0  250000.0 -616.0 -3319.0  6510.0

Linear analysis

We can run an PTDF equivalent of the power flow with the linear analysis drivers:

import os
import VeraGridEngine as vg

folder = os.path.join('..', 'Grids_and_profiles', 'grids')
fname = os.path.join(folder, 'IEEE 5 Bus.xlsx')

main_circuit = vg.open_file(fname)

# snapshot
results = vg.linear_power_flow(grid=main_circuit)

print("Bus results:\n", results.get_bus_df())
print("Branch results:\n", results.get_branch_df())
print("PTDF:\n", results.mdl(vg.ResultTypes.PTDF).to_df())
print("LODF:\n", results.mdl(vg.ResultTypes.LODF).to_df())

Simulating with a more detailed control of the objects:

import os
import VeraGridEngine as vg

folder = os.path.join('..', 'Grids_and_profiles', 'grids')
fname = os.path.join(folder, 'IEEE 5 Bus.xlsx')

main_circuit = vg.open_file(fname)

options_ = vg.LinearAnalysisOptions(distribute_slack=False, correct_values=True)

# snapshot
sn_driver = vg.LinearAnalysisDriver(grid=main_circuit, options=options_)
sn_driver.run()

print("Bus results:\n", sn_driver.results.get_bus_df())
print("Branch results:\n", sn_driver.results.get_branch_df())
print("PTDF:\n", sn_driver.results.mdl(vg.ResultTypes.PTDF).to_df())
print("LODF:\n", sn_driver.results.mdl(vg.ResultTypes.LODF).to_df())

Output:

Bus results:
         Vm   Va       P    Q
Bus 0  1.0  0.0  2.1000  0.0
Bus 1  1.0  0.0 -3.0000  0.0
Bus 2  1.0  0.0  0.2349  0.0
Bus 3  1.0  0.0 -0.9999  0.0
Bus 4  1.0  0.0  4.6651  0.0

Branch results:
                   Pf   loading
Branch 0-1  2.497192  0.624298
Branch 0-3  1.867892  0.832394
Branch 0-4 -2.265084 -0.828791
Branch 1-2 -0.502808 -0.391900
Branch 2-3 -0.267908 -0.774300
Branch 3-4 -2.400016 -1.000006

PTDF:
                Bus 0     Bus 1     Bus 2  Bus 3     Bus 4
Branch 0-1  0.193917 -0.475895 -0.348989    0.0  0.159538
Branch 0-3  0.437588  0.258343  0.189451    0.0  0.360010
Branch 0-4  0.368495  0.217552  0.159538    0.0 -0.519548
Branch 1-2  0.193917  0.524105 -0.348989    0.0  0.159538
Branch 2-3  0.193917  0.524105  0.651011    0.0  0.159538
Branch 3-4 -0.368495 -0.217552 -0.159538    0.0 -0.480452

LODF:
             Branch 0-1  Branch 0-3  Branch 0-4  Branch 1-2  Branch 2-3  Branch 3-4
Branch 0-1   -1.000000    0.344795    0.307071   -1.000000   -1.000000   -0.307071
Branch 0-3    0.542857   -1.000000    0.692929    0.542857    0.542857   -0.692929
Branch 0-4    0.457143    0.655205   -1.000000    0.457143    0.457143    1.000000
Branch 1-2   -1.000000    0.344795    0.307071   -1.000000   -1.000000   -0.307071
Branch 2-3   -1.000000    0.344795    0.307071   -1.000000   -1.000000   -0.307071
Branch 3-4   -0.457143   -0.655205    1.000000   -0.457143   -0.457143   -1.000000

Linear vs non-linear analysis comparison

Now let’s make a comparison between the linear flows and the non-linear flows from Newton-Raphson:

import os
from matplotlib import pyplot as plt
import VeraGridEngine as vg

plt.style.use('fivethirtyeight')

folder = os.path.join('..', 'Grids_and_profiles', 'grids')
fname = os.path.join(folder, 'IEEE39_1W.veragrid')
main_circuit = vg.open_file(fname)

ptdf_driver = vg.LinearAnalysisTimeSeriesDriver(grid=main_circuit)
ptdf_driver.run()

pf_options_ = vg.PowerFlowOptions(solver_type=vg.SolverType.NR)
ts_driver = vg.PowerFlowTimeSeriesDriver(grid=main_circuit, options=pf_options_)
ts_driver.run()

fig = plt.figure(figsize=(30, 6))
ax1 = fig.add_subplot(131)
ax1.set_title('Newton-Raphson based flow')
ax1.plot(ts_driver.results.Sf.real)
ax1.set_ylabel('MW')
ax1.set_xlabel('Time')

ax2 = fig.add_subplot(132)
ax2.set_title('PTDF based flow')
ax2.plot(ptdf_driver.results.Sf.real)
ax2.set_ylabel('MW')
ax2.set_xlabel('Time')

ax3 = fig.add_subplot(133)
ax3.set_title('Difference')
diff = ts_driver.results.Sf.real - ptdf_driver.results.Sf.real
ax3.plot(diff)
ax3.set_ylabel('MW')
ax3.set_xlabel('Time')

fig.set_tight_layout(tight=True)

plt.show()

Linear optimization

import os
import numpy as np
import VeraGridEngine as vg

folder = os.path.join('..', 'Grids_and_profiles', 'grids')
fname = os.path.join(folder, 'IEEE39_1W.veragrid')

main_circuit = vg.open_file(fname)

# declare the snapshot opf
opf_options = vg.OptimalPowerFlowOptions(mip_solver=vg.MIPSolvers.HIGHS)
opf_driver = vg.OptimalPowerFlowDriver(grid=main_circuit, options=opf_options)

print('Solving...')
opf_driver.run()

print("Status:", opf_driver.results.converged)
print('Angles\n', np.angle(opf_driver.results.voltage))
print('Branch loading\n', opf_driver.results.loading)
print('Gen power\n', opf_driver.results.generator_power)
print('Nodal prices \n', opf_driver.results.bus_shadow_prices)

# declare the time series opf
opf_ts_driver = vg.OptimalPowerFlowTimeSeriesDriver(grid=main_circuit)

print('Solving...')
opf_ts_driver.run()

print("Status:", opf_ts_driver.results.converged)
print('Angles\n', np.angle(opf_ts_driver.results.voltage))
print('Branch loading\n', opf_ts_driver.results.loading)
print('Gen power\n', opf_ts_driver.results.generator_power)
print('Nodal prices \n', opf_ts_driver.results.bus_shadow_prices)

Run a linear optimization and verify with power flow

Often ties, you want to dispatch the generation using a linear optimization, to then verify the results using the power exact power flow. With VeraGrid, to do so is as easy as passing the results of the OPF into the PowerFlowDriver:

import os
import VeraGridEngine as vg

folder = os.path.join('..', 'Grids_and_profiles', 'grids')
fname = os.path.join(folder, 'IEEE39_1W.veragrid')

main_circuit = vg.open_file(fname)

# declare the snapshot opf
opf_driver = vg.OptimalPowerFlowDriver(grid=main_circuit)
opf_driver.run()

# create the power flow driver, with the OPF results
pf_options = vg.PowerFlowOptions(solver_type=vg.SolverType.NR)
pf_driver = vg.PowerFlowDriver(grid=main_circuit,
                               options=pf_options,
                               opf_results=opf_driver.results)
pf_driver.run()

# Print results
print('Converged:', pf_driver.results.converged, '\nError:', pf_driver.results.error)
print(pf_driver.results.get_bus_df())
print(pf_driver.results.get_branch_df())

Output:

Converged: True 
Error: 5.553046023010211e-09
             Vm         Va           P           Q
bus 0  1.027155 -21.975050  -97.600000  -44.200000
bus 1  1.018508 -17.390151   -0.000000    0.000000
bus 2  0.979093 -21.508225 -322.000000   -2.400000
bus 3  0.934378 -19.864840 -500.000000 -184.000000
bus 4  0.931325 -16.488297   -0.000000    0.000000
bus 5  0.932254 -15.184820   -0.000000    0.000000
bus 6  0.925633 -18.287327 -233.800000  -84.000000
bus 7  0.927339 -19.147130 -522.000000 -176.600000
bus 8  1.008660 -21.901696   -6.500000   66.600000
bus 9  0.933232 -12.563826    0.000000    0.000000
bus 10 0.931530 -13.489535    0.000000   -0.000000
bus 11 0.911143 -13.919901   -8.530000  -88.000000
bus 12 0.932956 -14.194410   -0.000000    0.000000
bus 13 0.939456 -18.071831   95.000000   80.000000
bus 14 0.947946 -24.501201 -320.000000 -153.000000
bus 15 0.969547 -25.398839 -329.000000  -32.300000
bus 16 0.975073 -24.329289   -0.000000    0.000000
bus 17 0.974923 -23.729596 -158.000000  -30.000000
bus 18 0.978267 -32.658992    0.000000   -0.000000
bus 19 0.976962 -38.320718 -680.000000 -103.000000
bus 20 0.975875 -21.466364 -274.000000 -115.000000
bus 21 1.005675 -15.328363    0.000000    0.000000
bus 22 1.005660 -16.083736 -247.500000  -84.600000
bus 23 0.977732 -24.971264 -308.600000   92.200000
bus 24 1.008485 -18.545657 -224.000000  -47.200000
bus 25 1.000534 -20.462156 -139.000000  -17.000000
bus 26 0.981806 -23.507873 -281.000000  -75.500000
bus 27 1.008509 -15.740313 -206.000000  -27.600000
bus 28 1.012968 -12.490634 -283.500000  -26.900000
bus 29 1.049900  -8.627698  900.000000  251.046579
bus 30 0.982000   0.000000  959.172868  323.252930
bus 31 0.945335  -0.791018  900.000000  150.000000
bus 32 0.997200 -32.044975   80.000000  129.407620
bus 33 1.006817 -38.408267    0.000000  167.000000
bus 34 1.039299  -8.255317  900.000000  300.000000
bus 35 1.060037  -8.077926  550.259634  240.000000
bus 36 1.027500 -16.918435  128.970365   82.680976
bus 37 1.026500  -4.776516  900.000000  103.207961
bus 38 1.030000 -23.362551 -204.000000    6.956520
                   Pf          Qf          Pt          Qt     loading     Ploss       Qloss
branch 0  -199.490166    9.886924  200.882852  -66.631030  -33.248361  1.392685  -56.744105
branch 1   101.890166  -54.086924 -101.789768  -22.751166   10.189017  0.100398  -76.838090
branch 2   494.939507  226.957177 -491.146020 -208.562681   98.987901  3.793487   18.394496
branch 3   204.177641  -52.633324 -201.227524   41.260633   40.835528  2.950117  -11.372692
branch 4  -900.000000 -107.692823  900.000000  251.046579 -100.000000  0.000000  143.353757
branch 5  -110.112636  203.416014  110.898270 -210.820460  -22.022527  0.785633   -7.404446
branch 6   279.258656    2.746666 -278.361852  -12.311738   55.851731  0.896804   -9.565072
branch 7  -396.736291   53.024640  398.210339  -41.118140  -66.122715  1.474048   11.906501
branch 8  -214.161979  -26.204180  214.585979   20.909705  -42.832396  0.424000   -5.294474
branch 9  -757.052621   31.704768  758.376760  -18.259085  -63.087718  1.324139   13.445683
branch 10  358.842282    9.413372 -357.652308   -5.501385   39.871365  1.189974    3.911986
branch 11  510.748653   42.617618 -508.932128  -24.515536   56.749850  1.816525   18.102081
branch 12 -309.952545   33.292523  310.738787  -36.144659  -64.573447  0.786242   -2.852137
branch 13 -959.172868  -57.651055  959.172868  323.252930  -53.287382 -0.000000  265.601874
branch 14  275.132128  -59.484464 -274.764014   57.022428   30.570236  0.368114   -2.462036
branch 15  110.416322 -228.121043 -108.890865  216.489512   12.268480  1.525457  -11.631531
branch 16  102.390865 -149.889512 -102.210232   29.707685   11.376763  0.180633 -120.181827
branch 17  327.473237    5.932303 -326.980326   -6.970962   54.578873  0.492911   -1.038659
branch 18  572.526763  -42.245014 -571.014280   52.157064   95.421127  1.512483    9.912050
branch 19 -900.000000   36.312711  900.000000  150.000000 -100.000000  0.000000  186.312711
branch 20  -16.202399  -42.051495   16.241539   43.115621   -3.240480  0.039140    1.064126
branch 21    7.672399  -45.948505   -7.630574   47.085615    1.534480  0.041825    1.137109
branch 22  578.644854  -99.242678 -575.095683  123.970310   96.440809  3.549172   24.727632
branch 23  455.509704  -64.880015 -451.229578   83.883725   75.918284  4.280126   19.003709
branch 24  131.229578 -236.883725 -130.530951  228.460261   21.871596  0.698627   -8.423463
branch 25 -201.616968  -48.791645  201.933110   40.123973  -33.602828  0.316142   -8.667672
branch 26  610.218277  -68.716891 -603.829849  117.741029  101.703046  6.388428   49.024138
branch 27 -480.680339  -12.436131  482.646709   21.510035  -80.113390  1.966370    9.073904
branch 28 -126.390019 -130.815594  126.492978  126.394116  -21.065003  0.102959   -4.421477
branch 29 -120.254199    6.410659  120.361852  -17.688262  -20.042367  0.107653  -11.277602
branch 30  -81.678911  -46.534633   81.783480   17.137675  -13

提示：代码过长已自动截断

Hydro linear OPF

The following example loads and runs the linear optimization for a system that integrates fluid elements into a regular electrical grid.

import os
import VeraGridEngine as vg

folder = os.path.join('..', 'Grids_and_profiles', 'grids')
fname = os.path.join(folder, 'hydro_simple.veragrid')
grid = vg.open_file(fname)

# Run the simulation
opf_driver = vg.OptimalPowerFlowTimeSeriesDriver(grid=grid)

print('Solving...')
opf_driver.run()

print('Gen power\n', opf_driver.results.generator_power)
print('Branch loading\n', opf_driver.results.loading)
print('Reservoir level\n', opf_driver.results.fluid_node_current_level)

Output:

OPF results:

time                | p2x_1_gen | pump_1_gen | turbine_1_gen | slack_gen
------------------- | --------- | ---------- | ------------- | ---------
2023-01-01 00:00:00 | 0.0       | -6.8237821 | 6.0           | 11.823782
2023-01-01 01:00:00 | 0.0       | -6.8237821 | 6.0           | 11.823782
2023-01-01 02:00:00 | 0.0       | -6.8237821 | 6.0           | 11.823782
2023-01-01 03:00:00 | 0.0       | -6.8237821 | 6.0           | 11.823782
2023-01-01 04:00:00 | 0.0       | -6.8237821 | 6.0           | 11.823782
2023-01-01 05:00:00 | 0.0       | -6.8237821 | 6.0           | 11.823782
2023-01-01 06:00:00 | 0.0       | -6.8237821 | 6.0           | 11.823782
2023-01-01 07:00:00 | 0.0       | -6.8237821 | 6.0           | 11.823782
2023-01-01 08:00:00 | 0.0       | -6.8237821 | 6.0           | 11.823782
2023-01-01 09:00:00 | 0.0       | -6.8237821 | 6.0           | 11.823782


time                | line1  | line2 | line3     | line4
------------------- | ------ | ----- | --------- | -----
2023-01-01 00:00:00 | 100.0  | 0.0   | 68.237821 | 40.0
2023-01-01 01:00:00 | 100.0  | 0.0   | 68.237821 | 40.0
2023-01-01 02:00:00 | 100.0  | 0.0   | 68.237821 | 40.0
2023-01-01 03:00:00 | 100.0  | 0.0   | 68.237821 | 40.0
2023-01-01 04:00:00 | 100.0  | 0.0   | 68.237821 | 40.0
2023-01-01 05:00:00 | 100.0  | 0.0   | 68.237821 | 40.0
2023-01-01 06:00:00 | 100.0  | 0.0   | 68.237821 | 40.0
2023-01-01 07:00:00 | 100.0  | 0.0   | 68.237821 | 40.0
2023-01-01 08:00:00 | 100.0  | 0.0   | 68.237821 | 40.0
2023-01-01 09:00:00 | 100.0  | 0.0   | 68.237821 | 40.0


time                | f1         | f2  | f3  | f4        
------------------- | ---------- | --- | --- | ----------
2023-01-01 00:00:00 | 49.998977  | 0.0 | 0.0 | 50.001022
2023-01-01 01:00:00 | 49.997954  | 0.0 | 0.0 | 50.002046
2023-01-01 02:00:00 | 49.996931  | 0.0 | 0.0 | 50.003068
2023-01-01 03:00:00 | 49.995906  | 0.0 | 0.0 | 50.004093
2023-01-01 04:00:00 | 49.994884  | 0.0 | 0.0 | 50.005116
2023-01-01 05:00:00 | 49.993860  | 0.0 | 0.0 | 50.006139
2023-01-01 06:00:00 | 49.992838  | 0.0 | 0.0 | 50.007162
2023-01-01 07:00:00 | 49.991814  | 0.0 | 0.0 | 50.008185
2023-01-01 08:00:00 | 49.990792  | 0.0 | 0.0 | 50.009208
2023-01-01 09:00:00 | 49.989768  | 0.0 | 0.0 | 50.010231

Short circuit

VeraGrid has unbalanced short circuit calculations. Now let’s run a line-ground short circuit in the third bus of the South island of New Zealand grid example from reference book Computer Analysis of Power Systems by J. Arrillaga and C.P. Arnold

import os
import VeraGridEngine as vg

folder = os.path.join('..', 'Grids_and_profiles', 'grids')
fname = os.path.join(folder, 'South Island of New Zealand.veragrid')

grid = vg.open_file(filename=fname)

# Define fault index explicitly
fault_index = 2

# Run a Line-Ground short circuit on the bus at index 2
# Since we do not provide any power flow results, it will run one for us
results = vg.short_circuit(grid, fault_index, fault_type=vg.FaultType.LG)

print("Short circuit power: ", results.SCpower[fault_index])

A more elaborated way to run the simulation, controlling all the steps:

import os
import VeraGridEngine as vg

folder = os.path.join('..', 'Grids_and_profiles', 'grids')
fname = os.path.join(folder, 'South Island of New Zealand.veragrid')

grid = vg.open_file(filename=fname)

pf_options = vg.PowerFlowOptions()
pf = vg.PowerFlowDriver(grid, pf_options)
pf.run()

fault_index = 2
sc_options = vg.ShortCircuitOptions()

grid.add_short_circuit_event(
  vg.ShortCircuitEvent(
    device=grid.buses[fault_index],
    fault_type=vg.FaultType.LG,
    method=vg.MethodShortCircuit.sequences,
    phases=vg.PhasesShortCircuit.a
  )
)

sc = vg.ShortCircuitDriver(grid, options=sc_options,
                           pf_options=pf_options,
                           pf_results=pf.results,
                           pf_results3ph=None)
sc.run()

print("Short circuit power: ", sc.results.SCpower[fault_index])

Output:

Short circuit power:  -217.00 MW - 680.35j MVAr

Sequence voltage, currents and powers are also available.

Continuation power flow

VeraGrid can run continuation power flows (voltage collapse studies)

import os
from matplotlib import pyplot as plt
import VeraGridEngine as vg

plt.style.use('fivethirtyeight')

folder = os.path.join('..', 'Grids_and_profiles', 'grids')
fname = os.path.join(folder, 'South Island of New Zealand.veragrid')

# open the grid file
main_circuit = vg.open_file(fname)

# Run the continuation power flow with the default options
# Since we do not provide any power flow results, it will run one for us
results = vg.continuation_power_flow(grid=main_circuit,
                                     factor=2.0,
                                     stop_at=vg.CpfStopAt.Full)

# plot the results
fig = plt.figure(figsize=(18, 6))

ax1 = fig.add_subplot(121)
res = results.mdl(vg.ResultTypes.BusActivePower)
res.plot(ax=ax1)

ax2 = fig.add_subplot(122)
res = results.mdl(vg.ResultTypes.BusVoltageModule)
res.plot(ax=ax2)

plt.tight_layout()

plt.show()

A more elaborated way to run the simulation, controlling all the steps:

import os
from matplotlib import pyplot as plt
import VeraGridEngine as vg

plt.style.use('fivethirtyeight')

folder = os.path.join('..', 'Grids_and_profiles', 'grids')
fname = os.path.join(folder, 'South Island of New Zealand.veragrid')

# open the grid file
main_circuit = vg.FileOpen(fname).open()

# we need to initialize with a power flow solution
pf_options = vg.PowerFlowOptions()
power_flow = vg.PowerFlowDriver(grid=main_circuit, options=pf_options)
power_flow.run()

# declare the CPF options
vc_options = vg.ContinuationPowerFlowOptions(step=0.001,
                                             approximation_order=vg.CpfParametrization.ArcLength,
                                             adapt_step=True,
                                             step_min=0.00001,
                                             step_max=0.2,
                                             error_tol=1e-3,
                                             tol=1e-6,
                                             max_it=20,
                                             stop_at=vg.CpfStopAt.Full,
                                             verbose=False)

# We compose the target direction
factor = 2.0
base_power = power_flow.results.Sbus / main_circuit.Sbase
vc_inputs = vg.ContinuationPowerFlowInput(Sbase=base_power,
                                          Vbase=power_flow.results.voltage,
                                          Starget=base_power * factor)

# declare the CPF driver and run
vc = vg.ContinuationPowerFlowDriver(grid=main_circuit,
                                    options=vc_options,
                                    inputs=vc_inputs,
                                    pf_options=pf_options)
vc.run()

# plot the results
fig = plt.figure(figsize=(18, 6))

ax1 = fig.add_subplot(121)
res = vc.results.mdl(vg.ResultTypes.BusActivePower)
res.plot(ax=ax1)

ax2 = fig.add_subplot(122)
res = vc.results.mdl(vg.ResultTypes.BusVoltageModule)
res.plot(ax=ax2)

plt.tight_layout()

plt.show()

Contingency analysis

GriCal has contingency simulations, and it features a quite flexible way of defining contingencies. Firs you define a contingency group, and then define individual events that are assigned to that contingency group. The simulation then tries all the contingency groups and apply the events registered in each group:

import os
import VeraGridEngine as vg

folder = os.path.join('Grids_and_profiles', 'grids')
fname = os.path.join(folder, 'IEEE 5 Bus.xlsx')

main_circuit = vg.open_file(fname)

branches = main_circuit.get_branches()

# manually generate the contingencies
for i, br in enumerate(branches):
  # add a contingency group
  group = vg.ContingencyGroup(name="contingency {}".format(i + 1))
  main_circuit.add_contingency_group(group)

  # add the branch contingency to the groups, only groups are failed at once
  con = vg.Contingency(device=br, name=br.name, group=group)
  main_circuit.add_contingency(con)

# add a special contingency
group = vg.ContingencyGroup(name="Special contingency")
main_circuit.add_contingency_group(group)
main_circuit.add_contingency(vg.Contingency(device=branches[3], name=branches[3].name, group=group))
main_circuit.add_contingency(vg.Contingency(device=branches[5], name=branches[5].name, group=group))

pf_options = vg.PowerFlowOptions(solver_type=vg.SolverType.NR)

# declare the contingency options
options_ = vg.ContingencyAnalysisOptions(use_provided_flows=False,
                                         Pf=None,
                                         contingency_method=vg.ContingencyMethod.PowerFlow,
                                         # if no power flow options are provided
                                         # a linear power flow is used
                                         pf_options=pf_options)

# Get all the defined contingency groups from the circuit
contingency_groups = main_circuit.get_contingency_groups()

# Pass the list of contingency groups as required
linear_multiple_contingencies = vg.LinearMultiContingencies(grid=main_circuit,
                                                            contingency_groups_used=contingency_groups)

simulation = vg.ContingencyAnalysisDriver(grid=main_circuit,
                                          options=options_,
                                          linear_multiple_contingencies=linear_multiple_contingencies)

simulation.run()

# print results
df = simulation.results.mdl(vg.ResultTypes.BranchActivePowerFrom).to_df()
print("Contingency flows:\n", df)

Output:

Contingency flows:
                       Branch 0-1  Branch 0-3  Branch 0-4  Branch 1-2  Branch 2-3  Branch 3-4
# contingency 1          0.000000  322.256814 -112.256814 -300.000000 -277.616985 -350.438026
# contingency 2        314.174885    0.000000 -104.174887   11.387545   34.758624 -358.359122
# contingency 3        180.382705   29.617295    0.000000 -120.547317  -97.293581 -460.040537
# contingency 4        303.046401  157.540574 -250.586975    0.000000   23.490000 -214.130663
# contingency 5        278.818887  170.710914 -239.529801  -23.378976    0.000000 -225.076976
# contingency 6        323.104522  352.002620 -465.107139   20.157096   43.521763    0.000000
# Special contingency  303.046401  372.060738 -465.107139    0.000000   23.490000    0.000000

This simulation can also be done for time series.

Contingency analysis time series

To perform the contingency analysis of a time series, it’s easier to directly usi the API:

import os
import VeraGridEngine as vg

folder = os.path.join('Grids_and_profiles', 'grids')
fname = os.path.join(folder, 'IEEE39_1W.veragrid')
main_circuit = vg.open_file(fname)

results = vg.contingencies_ts(circuit=main_circuit,
                              detailed_massive_report=False,
                              contingency_deadband=0.0,
                              contingency_method=vg.ContingencyMethod.PowerFlow)

Note that the grid must have the declared contingencies saved already. Also note that the results are statistics, and you will not get a cube because for large grids that demand terabytes of RAM memory.

State estimation

Now let’s program the example from the state estimation reference book State Estimation in Electric Power Systems by A. Monticelli.

import VeraGridEngine as vg

m_circuit = vg.MultiCircuit()

b1 = vg.Bus('B1', is_slack=True)
b2 = vg.Bus('B2')
b3 = vg.Bus('B3')

br1 = vg.Line(b1, b2, name='Br1', r=0.01, x=0.03, rate=100.0)
br2 = vg.Line(b1, b3, name='Br2', r=0.02, x=0.05, rate=100.0)
br3 = vg.Line(b2, b3, name='Br3', r=0.03, x=0.08, rate=100.0)

# add measurements
m_circuit.add_pf_measurement(vg.PfMeasurement(0.888, 0.008, br1))
m_circuit.add_pf_measurement(vg.PfMeasurement(1.173, 0.008, br2))

m_circuit.add_qf_measurement(vg.QfMeasurement(0.568, 0.008, br1))
m_circuit.add_qf_measurement(vg.QfMeasurement(0.663, 0.008, br2))

m_circuit.add_pi_measurement(vg.PiMeasurement(-0.501, 0.01, b2))
m_circuit.add_qi_measurement(vg.QiMeasurement(-0.286, 0.01, b2))

m_circuit.add_vm_measurement(vg.VmMeasurement(1.006, 0.004, b1))
m_circuit.add_vm_measurement(vg.VmMeasurement(0.968, 0.004, b2))

m_circuit.add_bus(b1)
m_circuit.add_bus(b2)
m_circuit.add_bus(b3)

m_circuit.add_line(br1)
m_circuit.add_line(br2)
m_circuit.add_line(br3)

# Declare the simulation driver and run
se = vg.StateEstimationDriver(circuit=m_circuit)
se.run()

print(se.results.get_bus_df())
print(se.results.get_branch_df())

Output:

          Vm        Va         P        Q
B1  0.999629  0.000000  2.064016  1.22644
B2  0.974156 -1.247547  0.000000  0.00000
B3  0.943890 -2.745717  0.000000  0.00000

            Pf        Qf          Pt         Qt    loading    Ploss    Qloss
Br1  89.299199 55.882169  -88.188659 -52.550550  89.299199 1.110540 3.331619
Br2 117.102446 66.761871 -113.465724 -57.670065 117.102446 3.636722 9.091805
Br3  38.591163 22.775597  -37.956374 -21.082828  38.591163 0.634789 1.692770

Export the results

A simple function is available to export the results of a driver.

import os
import VeraGridEngine as vg

fname = os.path.join("data", "grids", "IEEE39_1W.veragrid")
grid = vg.open_file(fname)

# create the driver
pf_driver = vg.PowerFlowTimeSeriesDriver(grid=grid,
                                         options=vg.PowerFlowOptions(),
                                         time_indices=grid.get_all_time_indices())
# run
pf_driver.run()

# Save the driver results in a zip file with CSV files inside
vg.export_drivers(drivers_list=[pf_driver], file_name="IEEE39_1W_results.zip")

You could save many drivers in the same zip file passing then into the list drivers_list.

Also there is a function to save from the results objects themselves:

import os
import VeraGridEngine as vg

fname = os.path.join("data", "grids", "IEEE39_1W.veragrid")
grid = vg.open_file(fname)

# run with the API shortcut functions
pf_results = vg.power_flow(grid)
pf_ts_results = vg.power_flow_ts(grid)

# Save the driver results in a zip file with CSV files inside
vg.export_results(results_list=[pf_results, pf_ts_results], file_name="IEEE39_1W_results.zip")

Client - Server operation

To use the veragrid server, you need to install the VeraGridServer python package. Once this is done, the veragridservercommand will be available on the system. To launch the server, simply type veragridserver. This will launch a VeraGrid server on the machine, on port 8000. This is https://localhost:8000

An example on how to send a grid from a script to the server:

import os
import asyncio
import VeraGridEngine as vg

# path to your file
fname = os.path.join('..', '..', '..', 'Grids_and_profiles', 'grids', "IEEE57.veragrid")

# read veragrid file
grid_ = vg.open_file(fname)

# define instruction for the server
instruction = vg.RemoteInstruction(operation=vg.SimulationTypes.NoSim)

# generate json to send
model_data = vg.gather_model_as_jsons_for_communication(circuit=grid_, instruction=instruction)

# get the sever certificate
vg.get_certificate(base_url="https://localhost:8000",
                   certificate_path=vg.get_certificate_path(),
                   pwd="")

# send json
reply_from_server = asyncio.get_event_loop().run_until_complete(
  vg.send_json_data(json_data=model_data,
                    endpoint_url="https://localhost:8000/upload",
                    certificate=vg.get_certificate_path())
)

print(reply_from_server)

Tests

VeraGrid uses pytest for automatic software testing.

If you make changes to VeraGrid that you plan to submit, first make sure that all tests are still passing. You can do this locally with pytest.

If you have added new functionality, you should also add a new function that tests this functionality. Pytest automatically detects all functions in the src/tests folder that start with test_ and are located in a file that also starts with test_ as relevant test cases. Unit tests (for pytest) are included in src/tests. As defined in pytest.ini, all files matching test_*.py are executed by running pytest.

Files matching *_test.py are not executed; they were not formatted specifically for pytest but were mostly done for manual testing and documentation purposes.

Additional tests should be developed for each new and existing feature. pytest should be run before each commit to prevent easily detectable bugs.

Bug reporting

You have found a bug in VeraGrid or have a suggestion for a new functionality? Then get in touch with us by opening up an issue on the issue board to discuss possible new developments with the community and the maintainers.

Contributing

All contributions to the VeraGrid repository are made through pull requests to the devel branch. You can either submit a pull request from the devel branch of your fork or create a special feature branch that you keep the changes on. A feature branch is the way to go if you have multiple issues that you are working on in parallel and want to submit with separate pull requests. If you only have small, one-time changes to submit, you can also use the devel branch to submit your pull request.

However, it is best to discuss your contribution before the pull request is ready to be officially submitted. We only accept high quality contributions that align with the project design. Those are heavily reviewed, and you may expect joint work with us if your proposal is deemed good enough.

An easier alternative to contribute is to use the VeraGrid objects and functions to produce your contribution in a script-like fashion. Again, if that meets the functional and quality standards that we impose, we’ll take care of the integration.

Contributions like a typo fixes must be addressed via issues since we don’t allow those as pull requests.

All contributions must come with testing.

Contact

• Join the VeraGrid chat channel for a friendly chat, or quick question.
• Submit questions or comments to our form.
• Submit bugs or requests in the Issues section.

License

VeraGrid is licensed under the Mozilla Public License 2.0 (MPLv2)

In practical terms, this means that:

• You can use VeraGrid for commercial work.
• You can sell commercial services based on VeraGrid.
• If you distribute VeraGrid, you don’t need to distribute VeraGrid’s source code. However, with python in practice you do.
• VeraGrid license does not propagate, even if you use VeraGrid or pieces of it in your code. However, you must retain the individual files licensing.

Nonetheless, read the license carefully.

Disclaimer

All trademarks mentioned in the documentation or the source code belong to their respective owners.