Power Grid Model is an open source, high-performance distribution grid calculation library with functionalities such as power flow, state estimation and short circuit calculations.
In this three-hour workshop, you will learn about Power Grid Model and its advantages over other software, in addition to getting hands-on experience by performing power flow calculations for a single timestep, N-1 and time-series.
4. Where would we use it?
• Network Planning
• Contingency Analysis
PGM function
Network
data
• Topology
• Component
attributes
Assumed
load/generat
ion profile
Output
• Bus / Node Voltage
• Magnitude
• Angle
• Power flow at
branches
(Power Flow Calculation)
What-if Analysis
5. PGM function
(State Estimation)
Where would we use it?
• Estimated States of Real Data
• Bad Data Detection
• Input for Control Operations
Network
data
• Topology
• Component
attributes
Measurements
• Power flow
• Voltage
Output
• Bus / Node Voltage
• Magnitude
• Angle
• Power flow at
branches
• Deviation in all
measurement values
6. PGM function
(Short Circuit Calculation Based on Standard IEC-60909)
Where would we use it?
• Relay Co-ordination
• Network Planning
Network
data
• Topology
• Component
attributes
Fault(s)
• Impedance
• Location
• Type
Output
• Bus / Node Voltage
at fault conditions
• Magnitude
• Angle
• Steady state short
circuit current
flowing through all
components
7. Usage from existing solutions
• Vision
- Current strategy
Vision .xlsx Exports → PGM inputs (.xlsx)
- Future plan
Vision .vnf file → PGM inputs (.xlsx)
from power_grid_model import PowerGridModel
from power_grid_model_io.converters.vision_excel_converter import VisionExcelConverter
# Convert Vision file
vision_converter = VisionExcelConverter(source_file="vision_file.xlsx")
input_data, extra_info = vision_converter.load_input_data()
# Perform power flow calculation
grid = PowerGridModel(input_data=input_data)
output_data = grid.calculate_power_flow()
8. Usage from existing solutions
• Vision
- Current strategy
Vision .xlsx Exports → PGM inputs (.xlsx)
- Future plan
Vision .vnf file → PGM inputs (.xlsx)
• Pandapower from power_grid_model import PowerGridModel
from power_grid_model_io.converters import PandaPowerConverter
# Convert pandapower net
pp_converter = PandaPowerConverter()
input_data, extra_info = pp_converter.load_input_data(pp_net)
# Perform power flow calculation
grid = PowerGridModel(input_data=input_data)
output_data = grid.calculate_power_flow()
from power_grid_model import PowerGridModel
from power_grid_model_io.converters.vision_excel_converter import VisionExcelConverter
# Convert Vision file
vision_converter = VisionExcelConverter(source_file="vision_file.xlsx")
input_data, extra_info = vision_converter.load_input_data()
# Perform power flow calculation
grid = PowerGridModel(input_data=input_data)
output_data = grid.calculate_power_flow()
9. Usage from existing solutions
• Vision
- Current strategy
Vision .xlsx Exports → PGM inputs (.xlsx)
- Future plan
Vision .vnf file → PGM inputs (.xlsx)
• Pandapower from power_grid_model import PowerGridModel
from power_grid_model_io.converters import PandaPowerConverter
# Convert pandapower net
pp_converter = PandaPowerConverter()
input_data, extra_info = pp_converter.load_input_data(pp_net)
# Perform power flow calculation
grid = PowerGridModel(input_data=input_data)
output_data = grid.calculate_power_flow()
from power_grid_model import PowerGridModel
from power_grid_model_io.converters.vision_excel_converter import VisionExcelConverter
# Convert Vision file
vision_converter = VisionExcelConverter(source_file="vision_file.xlsx")
input_data, extra_info = vision_converter.load_input_data()
# Perform power flow calculation
grid = PowerGridModel(input_data=input_data)
output_data = grid.calculate_power_flow()
import pandapower as pp
# Run power-grid-model directly from pandapower
pp.runpp_pgm(net)
10. • Iterative methods:
Exact solution within given input tolerance
• Newton Raphson
– Traditional and robust.
• Iterative Current
– Faster than Newton-Raphson in certain cases. Jacobi method. Equivalent to backward-forward sweep
in radial networks.
• Linear methods:
Approximate solution. Use only when voltage of bus (p.u.) ≈ 1
• Linear Impedance
– All loads are modelled as constant impedance.
• Linear Current
– Loads and generations assume supplied voltage as 1 p.u. (i.e. single iteration of iterative current).
(Power Flow Calculation)
Calculation method
