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Power Grid Model Workshop
18 January 2024
Alliander N.V. | powergridmodel@lists.lfenergy.org
Agenda (UTC+1)
• 14:00 Welcome + Environment setup
• 14:10 PGM introduction
- Model Function​:Power-flow calculation / State estimation / Short circuit calculation / …...
- Usage from existing solutions : Vision / Pandapower
- Calculation methods
• 14:20 Power flow example + Power flow assignment 1(10), 2 + 3(5), 4(2)
• 15:00 Coffee break
• 15:10 Power flow example + Power flow assignment 5(10), 6(20)
• 16:00 Coffee break
• 16:10 Error handling, State estimation, Short circuit, Serialization
• 17:00 End
Environment Setup
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
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
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
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()
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()
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)
• 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
• Iterative methods:
Linear weighted least squares
(State Estimation)
Calculation method
• Iterative methods:
Linear weighted least squar
• via link:
- https://github.com/PowerGridModel/power-grid-model-workshop​
• Extra info:
- https://power-grid-model.readthedocs.io/en/stable/
- https://power-grid-model-io.readthedocs.io/en/stable/
Workshop
Serialization
• Load and dump PGM datasets from/to:
- JSON
- Msgpack
• Example:
- https://power-grid-model.readthedocs.io/en/stable/examples/Serialization%20Example.html
Assignment time
● Assignment 1
● Assignment 2
● Assignment 3
● Assignment 4
● Assignment 5
● Assignment 6
Thanks!

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Power Grid Model Workshop - 18 January 2024

  • 1. Power Grid Model Workshop 18 January 2024 Alliander N.V. | powergridmodel@lists.lfenergy.org
  • 2. Agenda (UTC+1) • 14:00 Welcome + Environment setup • 14:10 PGM introduction - Model Function​:Power-flow calculation / State estimation / Short circuit calculation / …... - Usage from existing solutions : Vision / Pandapower - Calculation methods • 14:20 Power flow example + Power flow assignment 1(10), 2 + 3(5), 4(2) • 15:00 Coffee break • 15:10 Power flow example + Power flow assignment 5(10), 6(20) • 16:00 Coffee break • 16:10 Error handling, State estimation, Short circuit, Serialization • 17:00 End
  • 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
  • 11. • Iterative methods: Linear weighted least squares (State Estimation) Calculation method
  • 12. • Iterative methods: Linear weighted least squar • via link: - https://github.com/PowerGridModel/power-grid-model-workshop​ • Extra info: - https://power-grid-model.readthedocs.io/en/stable/ - https://power-grid-model-io.readthedocs.io/en/stable/ Workshop
  • 13. Serialization • Load and dump PGM datasets from/to: - JSON - Msgpack • Example: - https://power-grid-model.readthedocs.io/en/stable/examples/Serialization%20Example.html
  • 14. Assignment time ● Assignment 1 ● Assignment 2 ● Assignment 3 ● Assignment 4 ● Assignment 5 ● Assignment 6