Usage Guide

This package was designed to facilitate the management of node and branch data in Excel, allowing users to easily convert the data into CSV format for seamless import into Python for calculations. For a detailed guide on this process, I highly recommend referring to CSV files for importing data.

Alternatively, you can also construct your grid directly in Python. Below is the fundamental approach to creating a grid.

Creating a Grid

This is the basic way to create a grid. This grid is the same as running MATACDC case5_stagg and case5_stagg_MTDC [1].

Case 5 Stagg Grid

import pyflow_acdc as pyf

pyf.initialize_pyflowacdc()

S_base = 100

AC_node_1 = pyf.Node_AC(node_type='Slack', Voltage_0=1.06, theta_0=0, kV_base=345)
AC_node_2 = pyf.Node_AC(node_type='PV', Voltage_0=1, theta_0=0.1, kV_base=345,Power_Gained=0.4,Power_load=0.2,Reactive_load=0.1)
AC_node_3 = pyf.Node_AC(node_type='PQ', Voltage_0=1, theta_0=0.1, kV_base=345,Power_load=0.45,Reactive_load=0.15)
AC_node_4 = pyf.Node_AC(node_type='PQ', Voltage_0=1, theta_0=0.1, kV_base=345,Power_load=0.4,Reactive_load=0.05)
AC_node_5 = pyf.Node_AC(node_type='PQ', Voltage_0=1, theta_0=0.1, kV_base=345,Power_load=0.6,Reactive_load=0.1)

AC_line_1 = pyf.Line_AC(AC_node_1, AC_node_2,r=0.02,x=0.06,b=0.06,MVA_rating=150)
AC_line_2 = pyf.Line_AC(AC_node_1, AC_node_3,r=0.08,x=0.24,b=0.05,MVA_rating=100)
AC_line_3 = pyf.Line_AC(AC_node_2, AC_node_3,r=0.06,x=0.18,b=0.04,MVA_rating=100)
AC_line_4 = pyf.Line_AC(AC_node_2, AC_node_4,r=0.06,x=0.18,b=0.04,MVA_rating=100)
AC_line_5 = pyf.Line_AC(AC_node_2, AC_node_5,r=0.04,x=0.12,b=0.03,MVA_rating=100)
AC_line_6 = pyf.Line_AC(AC_node_3, AC_node_4,r=0.01,x=0.03,b=0.02,MVA_rating=100)
AC_line_7 = pyf.Line_AC(AC_node_4, AC_node_5,r=0.08,x=0.24,b=0.05,MVA_rating=100)



DC_node_1 = pyf.Node_DC(node_type='P', Voltage_0=1,kV_base=345)
DC_node_2 = pyf.Node_DC(node_type='Slack', Voltage_0=1,kV_base=345)
DC_node_3 = pyf.Node_DC(node_type='P', Voltage_0=1,kV_base=345)

DC_line_1 = pyf.Line_DC(DC_node_1, DC_node_2,r=0.052,MW_rating=100,polarity='sm')
DC_line_2 = pyf.Line_DC(DC_node_2, DC_node_3,r=0.052,MW_rating=100,polarity='sm')
DC_line_3 = pyf.Line_DC(DC_node_1, DC_node_3,r=0.073,MW_rating=100,polarity='sm')

Converter_1 = pyf.AC_DC_converter('PQ', 'PAC'  , AC_node_2, DC_node_1, P_AC=-0.6, Q_AC=-0.4, P_DC=0, Transformer_resistance=0.0015, Transformer_reactance=0.121, Phase_Reactor_R=0.0001, Phase_Reactor_X=0.16428, Filter=0.0887, Droop=0, kV_base=345, MVA_max=120)
Converter_2 = pyf.AC_DC_converter('PV', 'Slack', AC_node_3, DC_node_2, Transformer_resistance=0.0015, Transformer_reactance=0.121, Phase_Reactor_R=0.0001, Phase_Reactor_X=0.16428, Filter=0.0887, Droop=0, kV_base=345, MVA_max=120)
Converter_3 = pyf.AC_DC_converter('PQ', 'PAC'  , AC_node_5, DC_node_3, P_AC=0.35, Q_AC=0.05, Transformer_resistance=0.0015, Transformer_reactance=0.121, Phase_Reactor_R=0.0001, Phase_Reactor_X=0.16428, Filter=0.0887, Droop=0, kV_base=345, MVA_max=120)

AC_nodes = [AC_node_1, AC_node_2, AC_node_3, AC_node_4, AC_node_5]
DC_nodes = [DC_node_1, DC_node_2, DC_node_3]
AC_lines = [AC_line_1, AC_line_2, AC_line_3, AC_line_4, AC_line_5, AC_line_6, AC_line_7]
DC_lines = [DC_line_1, DC_line_2, DC_line_3]
Converters = [Converter_1, Converter_2, Converter_3]


grid = pyf.Grid(S_base,AC_nodes, AC_lines,Converters,DC_nodes, DC_lines)
res= pyf.Results(grid,decimals=3)


time,tol,ps_iterations = pyf.ACDC_sequential(grid)

res.All()

Adding Components

Grids can also be built in the opposite order, creating the core grid first, then adding elements.

import pyflow_acdc as pyf

pyf.initialize_pyflowacdc()

grid = pyf.Grid(100)
res = pyf.Results(grid)

ac_node_1 = pyf.add_AC_node(grid,node_type='Slack', Voltage_0=1.06, theta_0=0, kV_base=345)
ac_node_2 = pyf.add_AC_node(grid,node_type='PV', Voltage_0=1, theta_0=0.1, kV_base=345,Power_Gained=0.4,Power_load=0.2,Reactive_load=0.1)
ac_node_3 = pyf.add_AC_node(grid,node_type='PQ', Voltage_0=1, theta_0=0.1, kV_base=345,Power_load=0.45,Reactive_load=0.15)
ac_node_4 = pyf.add_AC_node(grid,node_type='PQ', Voltage_0=1, theta_0=0.1, kV_base=345,Power_load=0.4,Reactive_load=0.05)
ac_node_5 = pyf.add_AC_node(grid,node_type='PQ', Voltage_0=1, theta_0=0.1, kV_base=345,Power_load=0.6,Reactive_load=0.1)

ac_line_1 = pyf.add_line_AC(grid,ac_node_1,ac_node_2,r=0.02,x=0.06,b=0.06,MVA_rating=150)
ac_line_2 = pyf.add_line_AC(grid,ac_node_1,ac_node_3,r=0.08,x=0.24,b=0.05,MVA_rating=100)
ac_line_3 = pyf.add_line_AC(grid,ac_node_2,ac_node_3,r=0.06,x=0.18,b=0.04,MVA_rating=100)
ac_line_4 = pyf.add_line_AC(grid,ac_node_2,ac_node_4,r=0.06,x=0.18,b=0.04,MVA_rating=100)
ac_line_5 = pyf.add_line_AC(grid,ac_node_2,ac_node_5,r=0.04,x=0.12,b=0.03,MVA_rating=100)
ac_line_6 = pyf.add_line_AC(grid,ac_node_3,ac_node_4,r=0.01,x=0.03,b=0.02,MVA_rating=100)
ac_line_7 = pyf.add_line_AC(grid,ac_node_4,ac_node_5,r=0.08,x=0.24,b=0.05,MVA_rating=100)

dc_node_1 = pyf.add_DC_node(grid,node_type='P', Voltage_0=1,kV_base=345)
dc_node_2 = pyf.add_DC_node(grid,node_type='Slack', Voltage_0=1,kV_base=345)
dc_node_3 = pyf.add_DC_node(grid,node_type='P', Voltage_0=1,kV_base=345)

dc_line_1 = pyf.add_line_DC(grid,dc_node_1,dc_node_2,r=0.052,MW_rating=100,polarity='sm')
dc_line_2 = pyf.add_line_DC(grid,dc_node_2,dc_node_3,r=0.052,MW_rating=100,polarity='sm')
dc_line_3 = pyf.add_line_DC(grid,dc_node_1,dc_node_3,r=0.073,MW_rating=100,polarity='sm')


converter_1 = pyf.add_ACDC_converter(grid,ac_node_2, dc_node_1,'PQ', 'PAC' , P_AC_MW=-60, Q_AC_MVA=-40, Transformer_resistance=0.0015, Transformer_reactance=0.121, Phase_Reactor_R=0.0001, Phase_Reactor_X=0.16428, Filter=0.0887, Droop=0, kV_base=345, MVA_max=120)
converter_2 = pyf.add_ACDC_converter(grid,ac_node_3, dc_node_2,'PV', 'Slack', Transformer_resistance=0.0015, Transformer_reactance=0.121, Phase_Reactor_R=0.0001, Phase_Reactor_X=0.16428, Filter=0.0887, Droop=0, kV_base=345, MVA_max=120)
converter_3 = pyf.add_ACDC_converter(grid,ac_node_5, dc_node_3,'PQ', 'PAC'  , P_AC_MW=35, Q_AC_MVA=5, Transformer_resistance=0.0015, Transformer_reactance=0.121, Phase_Reactor_R=0.0001, Phase_Reactor_X=0.16428, Filter=0.0887, Droop=0, kV_base=345, MVA_max=120)

time,tol,ps_iterations = pyf.ACDC_sequential(grid)
res.All()

Running a Power Flow

Examples of running a power flow…

import pyflow_acdc as pyf

[grid,res]=pyf.PEI_grid()

time,tol,ps_iterations = pyf.ACDC_sequential(grid,QLimit=False)

res.All()
print ('------')

Running an Optimal Power Flow

To run this, you need to have the OPF optional installed. This includes the following packages:

• pyomo

• ipopt

Quick Example

import pyflow_acdc as pyf
obj = {'Energy_cost'  : 1}

[grid,res]=pyf.case39_acdc()

model, timing_info, model_res,solver_stats=pyf.Optimal_PF(grid,ObjRule=obj)

res.All()
print ('------')

It is important that for optimal power flow generators are added to the grid before running.

Detailed Example

Taking the Case 5 from the IEEE PES Power Grid Library [2].

import pyflow_acdc as pyf
import pandas as pd
S_base=100

nodes_AC_data = [
    {'type': 'PV', 'Voltage_0': 1.0, 'theta_0': 0.0, 'kV_base': 230.0, 'Power_Gained': 0, 'Reactive_Gained': 0, 'Power_load': 0.0, 'Reactive_load': 0.0, 'Node_id': '1.0'},
    {'type': 'PQ', 'Voltage_0': 1.0, 'theta_0': 0.0, 'kV_base': 230.0, 'Power_Gained': 0, 'Reactive_Gained': 0, 'Power_load': 3.0, 'Reactive_load': 0.9861, 'Node_id': '2.0'},
    {'type': 'PV', 'Voltage_0': 1.0, 'theta_0': 0.0, 'kV_base': 230.0, 'Power_Gained': 0, 'Reactive_Gained': 0, 'Power_load': 3.0, 'Reactive_load': 0.9861, 'Node_id': '3.0'},
    {'type': 'Slack', 'Voltage_0': 1.0, 'theta_0': 0.0, 'kV_base': 230.0, 'Power_Gained': 0, 'Reactive_Gained': 0, 'Power_load': 4.0, 'Reactive_load': 1.3147, 'Node_id': '4.0'},
    {'type': 'PV', 'Voltage_0': 1.0, 'theta_0': 0.0, 'kV_base': 230.0, 'Power_Gained': 0, 'Reactive_Gained': 0, 'Power_load': 0.0, 'Reactive_load': 0.0, 'Node_id': '5.0'}
]
nodes_AC = pd.DataFrame(nodes_AC_data)

lines_AC_data = [
    {'fromNode': '1.0', 'toNode': '2.0', 'r': 0.00281, 'x': 0.0281, 'g': 0, 'b': 0.00712, 'MVA_rating': 400.0, 'kV_base': 230.0, 'Line_id': '1'},
    {'fromNode': '1.0', 'toNode': '4.0', 'r': 0.00304, 'x': 0.0304, 'g': 0, 'b': 0.00658, 'MVA_rating': 426.0, 'kV_base': 230.0, 'Line_id': '2'},
    {'fromNode': '1.0', 'toNode': '5.0', 'r': 0.00064, 'x': 0.0064, 'g': 0, 'b': 0.03126, 'MVA_rating': 426.0, 'kV_base': 230.0, 'Line_id': '3'},
    {'fromNode': '2.0', 'toNode': '3.0', 'r': 0.00108, 'x': 0.0108, 'g': 0, 'b': 0.01852, 'MVA_rating': 426.0, 'kV_base': 230.0, 'Line_id': '4'},
    {'fromNode': '3.0', 'toNode': '4.0', 'r': 0.00297, 'x': 0.0297, 'g': 0, 'b': 0.00674, 'MVA_rating': 426.0, 'kV_base': 230.0, 'Line_id': '5'},
    {'fromNode': '4.0', 'toNode': '5.0', 'r': 0.00297, 'x': 0.0297, 'g': 0, 'b': 0.00674, 'MVA_rating': 240.0, 'kV_base': 230.0, 'Line_id': '6'}
]
lines_AC = pd.DataFrame(lines_AC_data)

# Create the grid
[grid, res] = pyf.Create_grid_from_data(S_base, nodes_AC, lines_AC, data_in = 'pu')


# Add Generators
pyf.add_gen(grid, '1.0', '1', lf=14, qf=0, MWmax=40.0, MWmin=0.0, MVArmax=30.0, MVArmin=-30.0, PsetMW=20.0, QsetMVA=0.0)
pyf.add_gen(grid, '1.0', '2', lf=15, qf=0, MWmax=170.0, MWmin=0.0, MVArmax=127.5, MVArmin=-127.5, PsetMW=85.0, QsetMVA=0.0)
pyf.add_gen(grid, '3.0', '3', lf=30, qf=0, MWmax=520.0, MWmin=0.0, MVArmax=390.0, MVArmin=-390.0, PsetMW=260.0, QsetMVA=0.0)
pyf.add_gen(grid, '4.0', '4', lf=40, qf=0, MWmax=200.0, MWmin=0.0, MVArmax=150.0, MVArmin=-150.0, PsetMW=100.0, QsetMVA=0.0)
pyf.add_gen(grid, '5.0', '5', lf=10, qf=0, MWmax=600.0, MWmin=0.0, MVArmax=450.0, MVArmin=-450.0, PsetMW=300.0, QsetMVA=0.0)

obj = {'Energy_cost'  : 1}

model, timing_info, model_res,solver_stats=pyf.Optimal_PF(grid,ObjRule=obj)

res.All()
print ('------')

Available test cases:

For Power Flow: - pyf.Stagg5MATACDC() - pyf.PEI_grid() - pyf.pglib_opf_case24_ieee_rts()

For Optimal Power Flow:

• pyf.case_ACTIVSg2000()

• pyf.case24_3zones_acdc()

• pyf.case39_acdc()

• pyf.case39()

• pyf.case118()

• pyf.NS_MTDC()

• pyf.NS_SII()

• pyf.pglib_opf_case5_pjm()

• pyf.pglib_opf_case14_ieee()

• pyf.pglib_opf_case300_ieee()

• pyf.pglib_opf_case588_sdet_acdc()

• pyf.Stagg5MATACDC()

References

[1]

10. Beerten and R. Belmans, “MatACDC - an open source software tool for steady-state analysis and operation of HVDC grids,” 11th IET International Conference on AC and DC Power Transmission, Birmingham, 2015, pp. 1-9, doi: 10.1049/cp.2015.0061. keywords: {Steady-state analysis;HVDC grids;AC/DC systems;power flow modelling},

[2]

https://github.com/power-grid-lib/pglib-opf