GridCal is a top tier power systems planning and simulation software. As such it has all the static analysis studies that you can think of, plus linear and non-linear optimization functions. Some of these functions are well known, while others you may have never heard of as they are a product of cutting-edge research.
GridCal 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 a student diving into practical algorithms, GridCal's got your back. It's a high quality product made for all of us now and for the future generations.
GridCal is a software made in the Python programming language. Therefore, it needs a Python interpreter installed in your operative system.
The GridCal project is divided in three packages:
- GridCalEngine: A package with the database and calculations logic.
- GridCalServer: A package that serves an API-rest to use GridCalEngine remotelly.
- GridCal: A package that contains the Graphical User Interface (GUI) and operates with
GridCalEngineandGridCalServerseamlessly.
To install everything, you only need to install the GridCal package and the others will beinstalled as dependencies.
If you don't know what is this Python thing, we offer a windows installation:
This will install GridCal as a normal windows program and you need not to worry about any of the previous instructions. Still, if you need some guidance, the following video might be of assistance: Setup tutorial (video).
We recommend to install the latest version of Python and then, install GridCal with the following terminal command:
pip install GridCal
You may need to use pip3 if you are under Linux or MacOS, both of which
come with Python pre-installed already.
python3 -m venv gc5venv
source gc5venv/bin/activate
pip install GridCal
gridcalOnce you install GridCal in your local Python distribution, you can run the graphical user interface with the following terminal command:
gridcal
If this doesn't work, try:
python -c "from GridCal.ExecuteGridCal import runGridCal; runGridCal()"
You may save this command in a shortcut for easy future access.
Some of you may only need GridCal as a library for some other purpose like batch calculations, AI training or simple scripting. Whatever it may be, you can get the GridCal engine with the following terminal command:
pip install GridCalEngine
This will install the GridCalEngine package that is a dependency of GridCal.
Again, you may need to use pip3 if you are under Linux or MacOS.
GridCal is packed with feautures:
- Large collection of devices to model electricity grids
- AC/DC multi-grid power flow
- 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)
- 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 (gridcal .xlsx/.gridcal/.json, cgmes, psse .raw/.rawx)
All of these are industry tested algoriths, some of which surpass most comemercially 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.
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 GridCal.
-
GridCal PlayGround repository with some notebooks and examples.
-
The tests may serve as a valuable source of examples.
Since day one, GridCal was 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.
All simulations in GridCal 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.
GridCal 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 infraestructure such as the load, the generation, the rating, etc.
-
The time series record the variations of the magnitudes that can vary. These are aplied along with the infrastructure definition.
In GridCal, the inputs do not get modified by the simulation results. This very important concept, helps maintaining the independence of the inputs and outputs, allowing the replicability of the results. This key feature is not true for other open-source of comercial 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. This steps benefits greatly the efficiency of the numerical calculations since the arrays are aligned in memory. The GridCal data model is object-oriented, while the numerical circuit is array-oriented (despite beign packed into objects)
import GridCalEngine.api as gce
# load a grid
my_grid = gce.open_file("my_file.gridcal")In the case of CIM/CGMES, you may need to pass a list of files or a single zip file:
import GridCalEngine.api as gce
# load a grid from many xml files
my_grid = gce.open_file(["grid_EQ.xml", "grid_TP.xml", "grid_SV.xml", ])
# or from a single zip
my_grid = gce.open_file("my_cgmes_set_of_files.zip")GridCal supports a plethora of file formats:
- CIM 16 (.zip and .xml)
- CGMES 2.4.15 and 3.0 (.zip and .xml)
- PSS/e raw and rawx versions 29 to 35, including USA market excahnge RAW-30 specifics.
- Matpower .m files directly.
- DigSilent .DGS (not fully compatible)
- PowerWorld .EPC (not fully compatible, supports substation coordinates)
import GridCalEngine.api as gce
# load a grid
my_grid = gce.open_file("my_file.gridcal")
# save
gce.save_file(my_grid, "my_file_2.gridcal")In the case of saving a model in CGMES mode, we need to specify some things:
import GridCalEngine.api as gce
# load a grid
my_grid = gce.open_file("my_file.gridcal")
# run power flow (this is optional and it is used to generate the SV profile)
pf_results = gce.power_flow(my_grid)
# save the grid in CGMES mode
gce.save_cgmes_file(grid=my_grid,
filename="My_cgmes_model.zip",
cgmes_boundary_set_path="path_to_the_boundary_set.zip",
cgmes_version=CGMESVersions.v2_4_15,
pf_results=pf_results)We are going to create a very simple 5-node grid from the excellent book Power System Load Flow Analysis by Lynn Powell.
import GridCalEngine.api as gce
# declare a circuit object
grid = gce.MultiCircuit()
# Add the buses and the generators and loads attached
bus1 = gce.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 = gce.Generator('Slack Generator', vset=1.0)
grid.add_generator(bus1, gen1)
# add bus 2 with a load attached
bus2 = gce.Bus('Bus 2', Vnom=20)
grid.add_bus(bus2)
grid.add_load(bus2, gce.Load('load 2', P=40, Q=20))
# add bus 3 with a load attached
bus3 = gce.Bus('Bus 3', Vnom=20)
grid.add_bus(bus3)
grid.add_load(bus3, gce.Load('load 3', P=25, Q=15))
# add bus 4 with a load attached
bus4 = gce.Bus('Bus 4', Vnom=20)
grid.add_bus(bus4)
grid.add_load(bus4, gce.Load('load 4', P=40, Q=20))
# add bus 5 with a load attached
bus5 = gce.Bus('Bus 5', Vnom=20)
grid.add_bus(bus5)
grid.add_load(bus5, gce.Load('load 5', P=50, Q=20))
# add Lines connecting the buses
grid.add_line(gce.Line(bus1, bus2, name='line 1-2', r=0.05, x=0.11, b=0.02))
grid.add_line(gce.Line(bus1, bus3, name='line 1-3', r=0.05, x=0.11, b=0.02))
grid.add_line(gce.Line(bus1, bus5, name='line 1-5', r=0.03, x=0.08, b=0.02))
grid.add_line(gce.Line(bus2, bus3, name='line 2-3', r=0.04, x=0.09, b=0.02))
grid.add_line(gce.Line(bus2, bus5, name='line 2-5', r=0.04, x=0.09, b=0.02))
grid.add_line(gce.Line(bus3, bus4, name='line 3-4', r=0.06, x=0.13, b=0.03))
grid.add_line(gce.Line(bus4, bus5, name='line 4-5', r=0.04, x=0.09, b=0.02))Using the simplified API:
import os
import GridCalEngine.api as gce
folder = os.path.join('..', 'Grids_and_profiles', 'grids')
fname = os.path.join(folder, 'IEEE39_1W.gridcal')
main_circuit = gce.open_file(fname)
results = gce.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 GridCalEngine.api as gce
folder = os.path.join('..', 'Grids_and_profiles', 'grids')
fname = os.path.join(folder, 'IEEE14_from_raw.gridcal')
main_circuit = gce.open_file(fname)
options = gce.PowerFlowOptions(gce.SolverType.NR, verbose=False)
power_flow = gce.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
1_2_1 156.88 -20.40 -152.59 27.68 -2,040,429,074,673.33
1_5_1 75.51 3.85 -72.75 2.23 385,498,944,321.99
2_3_1 73.24 3.56 -70.91 1.60 356,020,306,394.25
2_4_1 56.13 -1.55 -54.45 3.02 -155,035,233,483.95
2_5_1 41.52 1.17 -40.61 -2.10 117,099,586,051.68
3_4_1 -23.29 4.47 23.66 -4.84 447,311,351,720.93
4_5_1 -61.16 15.82 61.67 -14.20 1,582,364,180,487.11
6_11_1 7.35 3.56 -7.30 -3.44 356,047,085,671.01
6_12_1 7.79 2.50 -7.71 -2.35 250,341,387,213.42
6_13_1 17.75 7.22 -17.54 -6.80 721,657,405,311.13
7_8_1 -0.00 -17.16 0.00 17.62 -1,716,296,745,837.05
7_9_1 28.07 5.78 -28.07 -4.98 577,869,015,291.12
9_10_1 5.23 4.22 -5.21 -4.18 421,913,877,670.92
9_14_1 9.43 3.61 -9.31 -3.36 361,000,694,981.35
10_11_1 -3.79 -1.62 3.80 1.64 -161,506,127,162.22
12_13_1 1.61 0.75 -1.61 -0.75 75,395,885,855.71
13_14_1 5.64 1.75 -5.59 -1.64 174,717,248,747.17
4_7_1 28.07 -9.68 -28.07 11.38 -968,106,634,094.39
4_9_1 16.08 -0.43 -16.08 1.73 -42,761,145,748.20
5_6_1 44.09 12.47 -44.09 -8.05 1,247,068,151,943.25
GridCal can perform a summary of the inputs with the InputsAnalysisDriver:
import os
import GridCalEngine.api as gce
folder = os.path.join('..', 'Grids_and_profiles', 'grids')
fname = os.path.join(folder, 'IEEE 118 Bus - ntc_areas.gridcal')
main_circuit = gce.open_file(fname)
drv = gce.InputsAnalysisDriver(grid=main_circuit)
mdl = drv.results.mdl(gce.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
We can run an PTDF equivalent of the power flow with the linear analysys drivers:
import os
import GridCalEngine.api as gce
folder = os.path.join('..', 'Grids_and_profiles', 'grids')
fname = os.path.join(folder, 'IEEE 5 Bus.xlsx')
main_circuit = gce.open_file(fname)
options_ = gce.LinearAnalysisOptions(distribute_slack=False, correct_values=True)
# snapshot
sn_driver = gce.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(gce.ResultTypes.PTDF).to_df())
print("LODF:\n", sn_driver.results.mdl(gce.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
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 GridCalEngine.api as gce
plt.style.use('fivethirtyeight')
folder = os.path.join('..', 'Grids_and_profiles', 'grids')
fname = os.path.join(folder, 'IEEE39_1W.gridcal')
main_circuit = gce.open_file(fname)
ptdf_driver = gce.LinearAnalysisTimeSeriesDriver(grid=main_circuit)
ptdf_driver.run()
pf_options_ = gce.PowerFlowOptions(solver_type=gce.SolverType.NR)
ts_driver = gce.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()
import os
import numpy as np
import GridCalEngine.api as gce
folder = os.path.join('..', 'Grids_and_profiles', 'grids')
fname = os.path.join(folder, 'IEEE39_1W.gridcal')
main_circuit = gce.open_file(fname)
# declare the snapshot opf
opf_driver = gce.OptimalPowerFlowDriver(grid=main_circuit)
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 = gce.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)Often ties, you want to dispatch the generation using a linear optimization, to then verify the results using the power exact power flow. With GridCal, to do so is as easy as passing the results of the OPF into the PowerFlowDriver:
import os
import GridCalEngine.api as gce
folder = os.path.join('..', 'Grids_and_profiles', 'grids')
fname = os.path.join(folder, 'IEEE39_1W.gridcal')
main_circuit = gce.open_file(fname)
# declare the snapshot opf
opf_driver = gce.OptimalPowerFlowDriver(grid=main_circuit)
opf_driver.run()
# create the power flow driver, with the OPF results
pf_options = gce.PowerFlowOptions(solver_type=gce.SolverType.NR)
pf_driver = gce.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:
OPF results:
Va P Shadow price
Bus 1 0.00 0.0 0.0
Bus 2 -2.22 0.0 0.0
Bus 3 -1.98 0.0 0.0
Bus 4 -2.12 0.0 0.0
Bus 5 -2.21 0.0 0.0
Pf Pt Tap angle Loading
Branch 1 -31.46 31.46 0.0 -44.94
Branch 1 -1.84 1.84 0.0 -10.20
Branch 1 -1.84 1.84 0.0 -9.18
Branch 1 0.14 -0.14 0.0 1.37
Branch 1 -48.30 48.30 0.0 -53.67
Branch 1 -35.24 35.24 0.0 -58.73
Branch 1 -4.62 4.62 0.0 -23.11
Power flow results:
Converged: True
Error: 3.13e-11
Vm Va P Q
Bus 1 1.00 0.00 1.17e+02 12.90
Bus 2 0.97 -2.09 -4.00e+01 -20.00
Bus 3 0.98 -1.96 -2.50e+01 -15.00
Bus 4 1.00 -2.61 2.12e-09 32.83
Bus 5 0.98 -2.22 -5.00e+01 -20.00
Pf Qf Pt Qt Loading
Branch 1 -31.37 -2.77 31.88 1.93 -44.81
Branch 2 -1.61 13.59 1.74 -16.24 -8.92
Branch 3 -1.44 -20.83 1.61 19.24 -7.21
Branch 4 0.46 5.59 -0.44 -7.46 4.62
Branch 5 -49.02 -4.76 49.77 4.80 -54.47
Branch 6 -34.95 -6.66 35.61 6.16 -58.25
Branch 7 -4.60 -5.88 4.62 4.01 -23.02
The following example loads and runs the linear optimization for a system that integrates fluid elements into a regular electrical grid.
import os
import GridCalEngine.api as gce
folder = os.path.join('..', 'Grids_and_profiles', 'grids')
fname = os.path.join(folder, 'hydro_simple.gridcal')
grid = gce.open_file(fname)
# Run the simulation
opf_driver = gce.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
GridCal 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 GridCalEngine.api as gce
folder = os.path.join('..', 'Grids_and_profiles', 'grids')
fname = os.path.join(folder, 'South Island of New Zealand.gridcal')
grid = gce.open_file(filename=fname)
pf_options = gce.PowerFlowOptions()
pf = gce.PowerFlowDriver(grid, pf_options)
pf.run()
fault_index = 2
sc_options = gce.ShortCircuitOptions(bus_index=fault_index,
fault_type=gce.FaultType.LG)
sc = gce.ShortCircuitDriver(grid, options=sc_options,
pf_options=pf_options,
pf_results=pf.results)
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.
import os
from matplotlib import pyplot as plt
import GridCalEngine.api as gce
plt.style.use('fivethirtyeight')
folder = os.path.join('..', 'Grids_and_profiles', 'grids')
fname = os.path.join(folder, 'South Island of New Zealand.gridcal')
# open the grid file
main_circuit = gce.FileOpen(fname).open()
# we need to initialize with a power flow solution
pf_options = gce.PowerFlowOptions()
power_flow = gce.PowerFlowDriver(grid=main_circuit, options=pf_options)
power_flow.run()
# declare the CPF options
vc_options = gce.ContinuationPowerFlowOptions(step=0.001,
approximation_order=gce.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=gce.CpfStopAt.Full,
verbose=False)
# We compose the target direction
base_power = power_flow.results.Sbus / main_circuit.Sbase
vc_inputs = gce.ContinuationPowerFlowInput(Sbase=base_power,
Vbase=power_flow.results.voltage,
Starget=base_power * 2)
# declare the CPF driver and run
vc = gce.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(gce.ResultTypes.BusActivePower)
res.plot(ax=ax1)
ax2 = fig.add_subplot(122)
res = vc.results.mdl(gce.ResultTypes.BusVoltage)
res.plot(ax=ax2)
plt.tight_layout()
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
from GridCalEngine.api import *
from GridCalEngine.enumerations import ContingencyMethod
folder = os.path.join('..', 'Grids_and_profiles', 'grids')
fname = os.path.join(folder, 'IEEE 5 Bus.xlsx')
main_circuit = FileOpen(fname).open()
branches = main_circuit.get_branches()
# manually generate the contingencies
for i, br in enumerate(branches):
# add a contingency group
group = 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 = Contingency(device_idtag=br.idtag, name=br.name, group=group)
main_circuit.add_contingency(con)
# add a special contingency
group = ContingencyGroup(name="Special contingency")
main_circuit.add_contingency_group(group)
main_circuit.add_contingency(Contingency(device_idtag=branches[3].idtag,
name=branches[3].name, group=group))
main_circuit.add_contingency(Contingency(device_idtag=branches[5].idtag,
name=branches[5].name, group=group))
pf_options = PowerFlowOptions(solver_type=SolverType.NR)
# declare the contingency options
options_ = ContingencyAnalysisOptions(use_provided_flows=False,
Pf=None,
contingency_method=ContingencyMethod.PowerFlow,
# if no power flow options are provided
# a linear power flow is used
pf_options=pf_options)
linear_multiple_contingencies = LinearMultiContingencies(grid=main_circuit)
simulation = ContingencyAnalysisDriver(grid=main_circuit,
options=options_,
linear_multiple_contingencies=linear_multiple_contingencies)
simulation.run()
# print results
df = simulation.results.mdl(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.
Now lets program the example from the state estimation reference book State Estimation in Electric Power Systems by A. Monticelli.
from GridCalEngine.api import *
m_circuit = MultiCircuit()
b1 = Bus('B1', is_slack=True)
b2 = Bus('B2')
b3 = Bus('B3')
br1 = Line(b1, b2, name='Br1', r=0.01, x=0.03, rate=100.0)
br2 = Line(b1, b3, name='Br2', r=0.02, x=0.05, rate=100.0)
br3 = Line(b2, b3, name='Br3', r=0.03, x=0.08, rate=100.0)
# add measurements
m_circuit.add_pf_measurement(PfMeasurement(0.888, 0.008, br1))
m_circuit.add_pf_measurement(PfMeasurement(1.173, 0.008, br2))
m_circuit.add_qf_measurement(QfMeasurement(0.568, 0.008, br1))
m_circuit.add_qf_measurement(QfMeasurement(0.663, 0.008, br2))
m_circuit.add_pi_measurement(PiMeasurement(-0.501, 0.01, b2))
m_circuit.add_qi_measurement(QiMeasurement(-0.286, 0.01, b2))
m_circuit.add_vm_measurement(VmMeasurement(1.006, 0.004, b1))
m_circuit.add_vm_measurement(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 = StateEstimation(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
Br1 89.299199 55.882169 0.0 0.0 55.882169
Br2 117.102446 66.761871 0.0 0.0 66.761871
Br3 38.591163 22.775597 0.0 0.0 22.775597
- Join the Discord GridCal channel for a friendly chat, or quick question.
- Submit questions or comments to our form.
- Submit bugs or requests in the Issues section.
- Simply email santiago@gridcal.org
GridCal is licensed under the MIT License v3.0 (MIT)
In practical terms this means that:
- You can use GridCal for commercial work.
- You can sell commercial services based on GridCal.
- If you distrubute GridCal, you must distribute GridCal's source code as well. That is always achieved in practice with python code.
- GridCal license does not propagate to works that are not a derivative of GridCal. An example of a derivative work is if you write a module of the program, the the license of the modue must be MIT too. An example of a non-derivative work is if you use GridCal's API for something else without modifying the API itself, for instance, using it as a library for another program.
Nonetheless, read the license carefully.
All trademarks mentioned in the documentation or the source code belong to their respective owners.