In [46]:
import pandas as pd
import numpy as np
from GridCal.Engine import *
%matplotlib inline
Let's create a new grid object:
In [47]:
grid = MultiCircuit(name='lynn 5 bus')
Define this first bus with all the parameters for demonstration purposes:
In [48]:
bus1 = Bus(name='Bus1',
vnom=10, # Nominal voltage in kV
vmin=0.9, # Bus minimum voltage in per unit
vmax=1.1, # Bus maximum voltage in per unit
xpos=0, # Bus x position in pixels
ypos=0, # Bus y position in pixels
height=0, # Bus height in pixels
width=0, # Bus width in pixels
active=True, # Is the bus active?
is_slack=False, # Is this bus a slack bus?
area='Default', # Area (for grouping purposes only)
zone='Default', # Zone (for grouping purposes only)
substation='Default' # Substation (for grouping purposes only)
)
The rest of the buses are defined with the default parameters:
In [49]:
bus2 = Bus(name='Bus2')
bus3 = Bus(name='Bus3')
bus4 = Bus(name='Bus4')
bus5 = Bus(name='Bus5')
Add the bus objects to the circuit:
In [50]:
grid.add_bus(bus1)
grid.add_bus(bus2)
grid.add_bus(bus3)
grid.add_bus(bus4)
grid.add_bus(bus5)
In GridCal, the loads, generators etc. are stored within each bus object.
We'll define the first load completely:
In [51]:
l2 = Load(name='Load',
G=0, # Impedance of the ZIP model in MVA at the nominal voltage
B=0,
Ir=0,
Ii=0, # Current of the ZIP model in MVA at the nominal voltage
P=40,
Q=20, # Power of the ZIP model in MVA
P_prof=None, # Impedance profile
Q_prof=None, # Current profile
Ir_prof=None, # Power profile
Ii_prof=None,
G_prof=None,
B_prof=None,
active=True, # Is active?
mttf=0.0, # Mean time to failure
mttr=0.0 # Mean time to recovery
)
grid.add_load(bus2, l2)
Out[51]:
Define the others with the default parameters:
In [52]:
grid.add_load(bus3, Load(P=25, Q=15))
grid.add_load(bus4, Load(P=40, Q=20))
grid.add_load(bus5, Load(P=50, Q=20))
Out[52]:
In [53]:
g1 = Generator(
name='gen',
active_power=0.0, # Active power in MW, since this generator is used to set the slack , is 0
voltage_module=1.0, # Voltage set point to control
Qmin=-9999, # minimum reactive power in MVAr
Qmax=9999, # Maximum reactive power in MVAr
Snom=9999, # Nominal power in MVA
power_prof=None, # power profile
vset_prof=None, # voltage set point profile
active=True # Is active?
)
grid.add_generator(bus1, g1)
Out[53]:
In [54]:
br1 = Line(bus_from=bus1,
bus_to=bus2,
name='Line 1-2',
r=0.05, # resistance of the pi model in per unit
x=0.11, # reactance of the pi model in per unit
b=0.02, # susceptance of the pi model in per unit
rate=50, # Rate in MVA
active=True, # is the branch active?
mttf=0, # Mean time to failure
mttr=0, # Mean time to recovery
length=1, # Length in km (to be used with templates)
template=None # Branch template (The default one is void)
)
grid.add_branch(br1)
All other branches with necessary parameters only:
In [55]:
grid.add_branch(Line(bus1, bus3, name='Line 1-3', r=0.05, x=0.11, b=0.02, rate=50))
grid.add_branch(Line(bus1, bus5, name='Line 1-5', r=0.03, x=0.08, b=0.02, rate=80))
grid.add_branch(Line(bus2, bus3, name='Line 2-3', r=0.04, x=0.09, b=0.02, rate=3))
grid.add_branch(Line(bus2, bus5, name='Line 2-5', r=0.04, x=0.09, b=0.02, rate=10))
grid.add_branch(Line(bus3, bus4, name='Line 3-4', r=0.06, x=0.13, b=0.03, rate=30))
grid.add_branch(Line(bus4, bus5, name='Line 4-5', r=0.04, x=0.09, b=0.02, rate=30))
Define power flow options:
In [56]:
power_flow_options = PowerFlowOptions(
solver_type=SolverType.NR, # Base method to use
verbose=False, # Verbose option where available
tolerance=1e-6, # power error in p.u.
max_iter=25, # maximum iteration number
control_q=True # if to control the reactive power
)
Declare and execute the power flow simulation
In [57]:
pf = PowerFlowDriver(grid, power_flow_options)
pf.run()
Now, let's compose a data frame with the voltage results:
In [58]:
headers = ['Vm (p.u.)', 'Va (Deg)', 'Vre', 'Vim']
Vm = np.abs(pf.results.voltage)
Va = np.angle(pf.results.voltage, deg=True)
Vre = pf.results.voltage.real
Vim = pf.results.voltage.imag
data = np.c_[Vm, Va, Vre, Vim]
v_df = pd.DataFrame(data=data, columns=headers, index=grid.bus_names)
print('\n', v_df)
Let's do the same for the branch results:
In [59]:
headers = ['Loading (%)', 'Current(p.u.)', 'Power (MVA)']
loading = np.abs(pf.results.loading) * 100
current = np.abs(pf.results.Ibranch)
power = np.abs(pf.results.Sbranch)
data = np.c_[loading, current, power]
br_df = pd.DataFrame(data=data, columns=headers, index=grid.branch_names)
print('\n', br_df)
Finally, print the execution metrics:
In [60]:
print('\nError:', pf.results.error)
print('Elapsed time (s):', pf.results.get_report_dataframe(0), '\n')
print(v_df)
print()
print(br_df)