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e2nIEE / pandapower

Convenient Power System Modelling and Analysis based on PYPOWER and pandas
https://www.pandapower.org
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Issue Tutorial opf_dcline.ipynb. pp.runopp: Optimal Power Flow did not converge! #1100

Open ballesm opened 3 years ago

ballesm commented 3 years ago

Greetings, I downloaded pandapower 2.5.0 with Python 3.8.6. I am running tutorial opf_dcline.ipynb. When I run lines

import pandapower as pp
from numpy import array
net = pp.create_empty_network()

b1 = pp.create_bus(net, 380)
b2 = pp.create_bus(net, 380)
b3 = pp.create_bus(net, 380)
b4 = pp.create_bus(net, 380)
b5 = pp.create_bus(net, 380)

l1 = pp.create_line(net, b1, b2, 30, "490-AL1/64-ST1A 380.0")
l2 = pp.create_line(net, b3, b4, 20, "490-AL1/64-ST1A 380.0")
l3 = pp.create_line(net, b4, b5, 20, "490-AL1/64-ST1A 380.0")

dcl1 = pp.create_dcline(net, name="dc line", from_bus=b2, to_bus=b3, p_mw=200, loss_percent=1.0, 
                  loss_mw=0.5, vm_from_pu=1.01, vm_to_pu=1.012, max_p_mw=1000,
                  in_service=True)

eg1 = pp.create_ext_grid(net, b1, 1.02, min_p_mw=0.)
eg2 = pp.create_ext_grid(net, b5, 1.02, min_p_mw=0.)

l1 = pp.create_load(net, bus=b4, p_mw=800, controllable = False)

costeg0 = pp.create_poly_cost(net, 0, 'ext_grid', cp1_eur_per_mw=10)
costeg1 = pp.create_poly_cost(net, 1, 'ext_grid', cp1_eur_per_mw=8)
net.bus['max_vm_pu'] = 1.5
net.line['max_loading_percent'] = 1000

pp.runopp(net)

the following error arises. 

Traceback (most recent call last):

  File "C:\Ancillary_Analysis\PYTHON\Pandapower\Code\opf_dcline.py", line 29, in <module>
    pp.runopp(net)

  File "C:\WPy64-3860\python-3.8.6.amd64\lib\site-packages\pandapower\run.py", line 357, in runopp
    _optimal_powerflow(net, verbose, suppress_warnings, **kwargs)

  File "C:\WPy64-3860\python-3.8.6.amd64\lib\site-packages\pandapower\optimal_powerflow.py", line 70, in _optimal_powerflow
    raise OPFNotConverged("Optimal Power Flow did not converge!")

OPFNotConverged: Optimal Power Flow did not converge!

I do not change any lines of code from the published tutorial. All other tutorials work, just dc lines do not converge. Do you have any idea of the reason? I did not find any solutions and pp.diagnostic(net) find no issue Thank you

renato-monaro commented 3 years ago

I am facing the same problem. I tried to run opf for a custom system and got a converge error. Then I tried the example opf_dcline to check if the error was on my side, but I got converge error.

friederikemeier commented 3 years ago

This is really strange, as it was running before. I don't have the time to check this myself. But I suggest to run the code with an older version of pandapower, where it was still working. And then extract the net._ppc and compare it to the net._ppc you are getting with the actual version. What are the differences there? I guess we introduced a change in pd2ppc, that we didn't test with dc_line and OPF...

dfurquim commented 3 years ago

I tried the DC Line dispatch with pandapower OPF example with versions 2.5.0 and 2.7.0 and got the same convergence error:

OPFNotConverged: Optimal Power Flow did not converge!
---------------------------------------------------------------------------
OPFNotConverged                           Traceback (most recent call last)
<ipython-input-6-1fde0be86c4f> in <module>
----> 1 pp.runopp(net)

C:\ProgramData\Anaconda3\lib\site-packages\pandapower\run.py in runopp(net, verbose, calculate_voltage_angles, check_connectivity, suppress_warnings, switch_rx_ratio, delta, init, numba, trafo3w_losses, consider_line_temperature, **kwargs)
    355     _check_bus_index_and_print_warning_if_high(net)
    356     _check_gen_index_and_print_warning_if_high(net)
--> 357     _optimal_powerflow(net, verbose, suppress_warnings, **kwargs)
    358 
    359 

C:\ProgramData\Anaconda3\lib\site-packages\pandapower\optimal_powerflow.py in _optimal_powerflow(net, verbose, suppress_warnings, **kwargs)
     68 
     69     if not result["success"]:
---> 70         raise OPFNotConverged("Optimal Power Flow did not converge!")
     71 
     72     # ppci doesn't contain out of service elements, but ppc does -> copy results accordingly

OPFNotConverged: Optimal Power Flow did not converge!

As sugested by @friederikemeier, I ran the code with an older version (2.0.0) and it worked. Then, I extracted the net._ppc I got with the older version (2.0.0) and compared with the net._ppc from the actual version (2.7.0). There are some major differences, e.g., 'bus', 'gen', 'branch' and some other fields are repeted in the 2.7.0 case. Moreover, when compared to the net._ppc from version 2.5.0 there are some other differences. Check it out:

net._ppc with pandapower 2.7.0

{'baseMVA': 1,
 'version': 2,
 'bus': array([[ 0.00000000e+00,  3.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          1.00000000e+00,  1.02000000e+00,  0.00000000e+00,
          3.80000000e+02,  1.00000000e+00,  2.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
        [ 1.00000000e+00,  2.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          1.00000000e+00,  1.01000000e+00, -4.85950424e-01,
          3.80000000e+02,  1.00000000e+00,  2.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
        [ 2.00000000e+00,  2.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          1.00000000e+00,  1.01200000e+00, -7.25627293e-01,
          3.80000000e+02,  1.00000000e+00,  2.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
        [ 3.00000000e+00,  1.00000000e+00,  8.00000000e+02,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          1.00000000e+00,  1.01283464e+00, -1.14418392e+00,
          3.80000000e+02,  1.00000000e+00,  2.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
        [ 4.00000000e+00,  3.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          1.00000000e+00,  1.02000000e+00,  0.00000000e+00,
          3.80000000e+02,  1.00000000e+00,  2.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00]]),
 'branch': array([[ 0.00000000e+00+0.j,  1.00000000e+00+0.j,  1.22576177e-05+0.j,
          5.25623269e-05+0.j,  1.49703173e+01+0.j,  2.50000000e+02+0.j,
          2.50000000e+02+0.j,  2.50000000e+02+0.j,  1.00000000e+00+0.j,
          0.00000000e+00+0.j,  1.00000000e+00+0.j, -3.60000000e+02+0.j,
          3.60000000e+02+0.j,  2.00732612e+02+0.j,  1.40161556e+02+0.j,
         -2.00000000e+02+0.j, -1.52443185e+02+0.j,  0.00000000e+00+0.j,
          0.00000000e+00+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,
          0.00000000e+00+0.j,  0.00000000e+00+0.j],
        [ 2.00000000e+00+0.j,  3.00000000e+00+0.j,  8.17174515e-06+0.j,
          3.50415512e-05+0.j,  9.98021154e+00+0.j,  2.50000000e+02+0.j,
          2.50000000e+02+0.j,  2.50000000e+02+0.j,  1.00000000e+00+0.j,
          0.00000000e+00+0.j,  1.00000000e+00+0.j, -3.60000000e+02+0.j,
          3.60000000e+02+0.j,  1.97500000e+02+0.j, -7.44917589e+01+0.j,
         -1.97150356e+02+0.j,  6.57614729e+01+0.j,  0.00000000e+00+0.j,
          0.00000000e+00+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,
          0.00000000e+00+0.j,  0.00000000e+00+0.j],
        [ 3.00000000e+00+0.j,  4.00000000e+00+0.j,  8.17174515e-06+0.j,
          3.50415512e-05+0.j,  9.98021154e+00+0.j,  2.50000000e+02+0.j,
          2.50000000e+02+0.j,  2.50000000e+02+0.j,  1.00000000e+00+0.j,
          0.00000000e+00+0.j,  1.00000000e+00+0.j, -3.60000000e+02+0.j,
          3.60000000e+02+0.j, -6.02849644e+02+0.j, -6.57614729e+01+0.j,
          6.05773987e+02+0.j,  6.79907246e+01+0.j,  0.00000000e+00+0.j,
          0.00000000e+00+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,
          0.00000000e+00+0.j,  0.00000000e+00+0.j]]),
 'gen': array([[ 0.00000000e+00,  2.00732612e+02,  1.40161556e+02,
          0.00000000e+00,  0.00000000e+00,  1.02000000e+00,
          1.00000000e+00,  1.00000000e+00,  1.00000000e+09,
         -1.00000000e+09,  0.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
        [ 4.00000000e+00,  6.05773987e+02,  6.79907246e+01,
          0.00000000e+00,  0.00000000e+00,  1.02000000e+00,
          1.00000000e+00,  1.00000000e+00,  1.00000000e+09,
         -1.00000000e+09,  0.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
        [ 2.00000000e+00,  1.97500000e+02, -7.44917589e+01,
          1.00000000e+09, -1.00000000e+09,  1.01200000e+00,
                     nan,  1.00000000e+00,  1.00000000e+03,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
        [ 1.00000000e+00, -2.00000000e+02, -1.52443185e+02,
          1.00000000e+09, -1.00000000e+09,  1.01000000e+00,
                     nan,  1.00000000e+00,  0.00000000e+00,
         -1.00000000e+03,  0.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00]]),
 'internal': {'Ybus': <5x5 sparse matrix of type '<class 'numpy.complex128'>'
    with 11 stored elements in Compressed Sparse Row format>,
  'Yf': <3x5 sparse matrix of type '<class 'numpy.complex128'>'
    with 6 stored elements in Compressed Sparse Row format>,
  'Yt': <3x5 sparse matrix of type '<class 'numpy.complex128'>'
    with 6 stored elements in Compressed Sparse Row format>,
  'branch_is': array([ True,  True,  True]),
  'gen_is': array([ True,  True,  True,  True]),
  'DLF': array([], dtype=complex128),
  'buses_ord_bfs_nets': array([], dtype=float64),
  'ref_gens': array([0, 1]),
  'Bbus': <5x5 sparse matrix of type '<class 'numpy.complex128'>'
    with 11 stored elements in Compressed Sparse Column format>,
  'J': <4x4 sparse matrix of type '<class 'numpy.float64'>'
    with 10 stored elements in Compressed Sparse Row format>,
  'Vm_it': None,
  'Va_it': None,
  'bus': array([[ 0.00000000e+00,  3.00000000e+00,  0.00000000e+00,
           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
           1.00000000e+00,  1.02000000e+00,  0.00000000e+00,
           3.80000000e+02,  1.00000000e+00,  2.00000000e+00,
           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
         [ 1.00000000e+00,  2.00000000e+00,  0.00000000e+00,
           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
           1.00000000e+00,  1.01000000e+00, -4.85950424e-01,
           3.80000000e+02,  1.00000000e+00,  2.00000000e+00,
           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
         [ 2.00000000e+00,  2.00000000e+00,  0.00000000e+00,
           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
           1.00000000e+00,  1.01200000e+00, -7.25627293e-01,
           3.80000000e+02,  1.00000000e+00,  2.00000000e+00,
           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
         [ 3.00000000e+00,  1.00000000e+00,  8.00000000e+02,
           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
           1.00000000e+00,  1.01283464e+00, -1.14418392e+00,
           3.80000000e+02,  1.00000000e+00,  2.00000000e+00,
           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
         [ 4.00000000e+00,  3.00000000e+00,  0.00000000e+00,
           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
           1.00000000e+00,  1.02000000e+00,  0.00000000e+00,
           3.80000000e+02,  1.00000000e+00,  2.00000000e+00,
           0.00000000e+00,  0.00000000e+00,  0.00000000e+00]]),
  'gen': array([[ 0.00000000e+00,  2.00732612e+02,  1.40161556e+02,
           0.00000000e+00,  0.00000000e+00,  1.02000000e+00,
           1.00000000e+00,  1.00000000e+00,  1.00000000e+09,
          -1.00000000e+09,  0.00000000e+00,  0.00000000e+00,
           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
         [ 4.00000000e+00,  6.05773987e+02,  6.79907246e+01,
           0.00000000e+00,  0.00000000e+00,  1.02000000e+00,
           1.00000000e+00,  1.00000000e+00,  1.00000000e+09,
          -1.00000000e+09,  0.00000000e+00,  0.00000000e+00,
           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
         [ 2.00000000e+00,  1.97500000e+02, -7.44917589e+01,
           1.00000000e+09, -1.00000000e+09,  1.01200000e+00,
                      nan,  1.00000000e+00,  1.00000000e+03,
           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
         [ 1.00000000e+00, -2.00000000e+02, -1.52443185e+02,
           1.00000000e+09, -1.00000000e+09,  1.01000000e+00,
                      nan,  1.00000000e+00,  0.00000000e+00,
          -1.00000000e+03,  0.00000000e+00,  0.00000000e+00,
           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
           0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
           0.00000000e+00,  0.00000000e+00,  0.00000000e+00]]),
  'branch': array([[ 0.00000000e+00+0.j,  1.00000000e+00+0.j,  1.22576177e-05+0.j,
           5.25623269e-05+0.j,  1.49703173e+01+0.j,  2.50000000e+02+0.j,
           2.50000000e+02+0.j,  2.50000000e+02+0.j,  1.00000000e+00+0.j,
           0.00000000e+00+0.j,  1.00000000e+00+0.j, -3.60000000e+02+0.j,
           3.60000000e+02+0.j,  2.00732612e+02+0.j,  1.40161556e+02+0.j,
          -2.00000000e+02+0.j, -1.52443185e+02+0.j,  0.00000000e+00+0.j,
           0.00000000e+00+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,
           0.00000000e+00+0.j,  0.00000000e+00+0.j],
         [ 2.00000000e+00+0.j,  3.00000000e+00+0.j,  8.17174515e-06+0.j,
           3.50415512e-05+0.j,  9.98021154e+00+0.j,  2.50000000e+02+0.j,
           2.50000000e+02+0.j,  2.50000000e+02+0.j,  1.00000000e+00+0.j,
           0.00000000e+00+0.j,  1.00000000e+00+0.j, -3.60000000e+02+0.j,
           3.60000000e+02+0.j,  1.97500000e+02+0.j, -7.44917589e+01+0.j,
          -1.97150356e+02+0.j,  6.57614729e+01+0.j,  0.00000000e+00+0.j,
           0.00000000e+00+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,
           0.00000000e+00+0.j,  0.00000000e+00+0.j],
         [ 3.00000000e+00+0.j,  4.00000000e+00+0.j,  8.17174515e-06+0.j,
           3.50415512e-05+0.j,  9.98021154e+00+0.j,  2.50000000e+02+0.j,
           2.50000000e+02+0.j,  2.50000000e+02+0.j,  1.00000000e+00+0.j,
           0.00000000e+00+0.j,  1.00000000e+00+0.j, -3.60000000e+02+0.j,
           3.60000000e+02+0.j, -6.02849644e+02+0.j, -6.57614729e+01+0.j,
           6.05773987e+02+0.j,  6.79907246e+01+0.j,  0.00000000e+00+0.j,
           0.00000000e+00+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,
           0.00000000e+00+0.j,  0.00000000e+00+0.j]]),
  'baseMVA': 1,
  'V': array([1.02      +0.j        , 1.00996367-0.00856615j,
         1.01191884-0.01281622j, 1.01263269-0.02022474j,
         1.02      +0.j        ]),
  'pv': array([1, 2], dtype=int64),
  'pq': array([3], dtype=int64),
  'ref': array([0, 4], dtype=int64),
  'Sbus': array([ 200. +0.j, -200. +0.j,  197.5+0.j, -800. +0.j,  602.5+0.j])},
 'success': True,
 'et': 1.6326789855957031,
 'iterations': 3}

net._ppc with pandapower 2.0.0

{'baseMVA': 1,
 'version': 2,
 'bus': array([[ 0.00000000e+00,  3.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          1.00000000e+00,  1.02000000e+00,  0.00000000e+00,
          3.80000000e+02,  1.00000000e+00,  2.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
        [ 1.00000000e+00,  2.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          1.00000000e+00,  1.01000000e+00, -4.85950424e-01,
          3.80000000e+02,  1.00000000e+00,  2.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
        [ 2.00000000e+00,  2.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          1.00000000e+00,  1.01200000e+00, -7.25627293e-01,
          3.80000000e+02,  1.00000000e+00,  2.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
        [ 3.00000000e+00,  1.00000000e+00,  8.00000000e+02,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          1.00000000e+00,  1.01283464e+00, -1.14418392e+00,
          3.80000000e+02,  1.00000000e+00,  2.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
        [ 4.00000000e+00,  3.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          1.00000000e+00,  1.02000000e+00,  0.00000000e+00,
          3.80000000e+02,  1.00000000e+00,  2.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00]]),
 'branch': array([[ 0.00000000e+00+0.j,  1.00000000e+00+0.j,  1.22576177e-05+0.j,
          5.25623269e-05+0.j,  1.49703173e+01+0.j,  2.50000000e+02+0.j,
          2.50000000e+02+0.j,  2.50000000e+02+0.j,  1.00000000e+00+0.j,
          0.00000000e+00+0.j,  1.00000000e+00+0.j, -3.60000000e+02+0.j,
          3.60000000e+02+0.j,  2.00732612e+02+0.j,  1.40161556e+02+0.j,
         -2.00000000e+02+0.j, -1.52443185e+02+0.j,  0.00000000e+00+0.j,
          0.00000000e+00+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,
          0.00000000e+00+0.j,  0.00000000e+00+0.j],
        [ 2.00000000e+00+0.j,  3.00000000e+00+0.j,  8.17174515e-06+0.j,
          3.50415512e-05+0.j,  9.98021154e+00+0.j,  2.50000000e+02+0.j,
          2.50000000e+02+0.j,  2.50000000e+02+0.j,  1.00000000e+00+0.j,
          0.00000000e+00+0.j,  1.00000000e+00+0.j, -3.60000000e+02+0.j,
          3.60000000e+02+0.j,  1.97500000e+02+0.j, -7.44917589e+01+0.j,
         -1.97150356e+02+0.j,  6.57614729e+01+0.j,  0.00000000e+00+0.j,
          0.00000000e+00+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,
          0.00000000e+00+0.j,  0.00000000e+00+0.j],
        [ 3.00000000e+00+0.j,  4.00000000e+00+0.j,  8.17174515e-06+0.j,
          3.50415512e-05+0.j,  9.98021154e+00+0.j,  2.50000000e+02+0.j,
          2.50000000e+02+0.j,  2.50000000e+02+0.j,  1.00000000e+00+0.j,
          0.00000000e+00+0.j,  1.00000000e+00+0.j, -3.60000000e+02+0.j,
          3.60000000e+02+0.j, -6.02849644e+02+0.j, -6.57614729e+01+0.j,
          6.05773987e+02+0.j,  6.79907246e+01+0.j,  0.00000000e+00+0.j,
          0.00000000e+00+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,
          0.00000000e+00+0.j,  0.00000000e+00+0.j]]),
 'gen': array([[ 0.00000000e+00,  2.00732612e+02,  1.40161556e+02,
          0.00000000e+00,  0.00000000e+00,  1.02000000e+00,
          1.00000000e+00,  1.00000000e+00,  1.00000000e+09,
         -1.00000000e+09,  0.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
        [ 4.00000000e+00,  6.05773987e+02,  6.79907246e+01,
          0.00000000e+00,  0.00000000e+00,  1.02000000e+00,
          1.00000000e+00,  1.00000000e+00,  1.00000000e+09,
         -1.00000000e+09,  0.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
        [ 2.00000000e+00,  1.97500000e+02, -7.44917589e+01,
          1.00000000e+09, -1.00000000e+09,  1.01200000e+00,
                     nan,  1.00000000e+00,  1.00000000e+03,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
        [ 1.00000000e+00, -2.00000000e+02, -1.52443185e+02,
          1.00000000e+09, -1.00000000e+09,  1.01000000e+00,
                     nan,  1.00000000e+00,  0.00000000e+00,
         -1.00000000e+03,  0.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00]]),
 'internal': {'Ybus': <5x5 sparse matrix of type '<class 'numpy.complex128'>'
    with 11 stored elements in Compressed Sparse Row format>,
  'Yf': <3x5 sparse matrix of type '<class 'numpy.complex128'>'
    with 6 stored elements in Compressed Sparse Row format>,
  'Yt': <3x5 sparse matrix of type '<class 'numpy.complex128'>'
    with 6 stored elements in Compressed Sparse Row format>,
  'branch_is': array([ True,  True,  True]),
  'gen_is': array([ True,  True,  True,  True]),
  'DLF': array([], dtype=complex128),
  'buses_ord_bfs_nets': array([], dtype=float64),
  'ref_gens': array([0, 1]),
  'J': <4x4 sparse matrix of type '<class 'numpy.float64'>'
    with 10 stored elements in Compressed Sparse Row format>,
  'Vm_it': None,
  'Va_it': None},
 'success': True,
 'et': 1.6336281299591064,
 'iterations': 3}

net._ppc with pandapower 2.5.0

{'baseMVA': 1,
 'version': 2,
 'bus': array([[ 0.00000000e+00,  3.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          1.00000000e+00,  1.31388242e+00,  0.00000000e+00,
          3.80000000e+02,  1.00000000e+00,  1.02000000e+00,
          1.02000000e+00, -5.02224630e+03,  1.06682515e-01,
          9.85474186e+10,  9.79347804e+10],
        [ 1.00000000e+00,  2.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          1.00000000e+00,  1.08703858e+00,  5.53828691e+00,
          3.80000000e+02,  1.00000000e+00,  1.50000000e+00,
          0.00000000e+00,  2.66312920e+07, -1.06737160e-01,
          8.19400890e+10,  7.80971425e+10],
        [ 2.00000000e+00,  2.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          1.00000000e+00, -1.70105830e-01,  2.45229110e+02,
          3.80000000e+02,  1.00000000e+00,  1.50000000e+00,
          0.00000000e+00,  4.23112766e+07, -1.00801904e-01,
          5.14623575e+10,  1.27085367e+13],
        [ 3.00000000e+00,  1.00000000e+00,  8.00000000e+02,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          1.00000000e+00, -3.31932204e-02, -6.64099767e+01,
          3.80000000e+02,  1.00000000e+00,  1.50000000e+00,
          0.00000000e+00, -8.25272677e+15, -1.30159136e+16,
          6.05328201e+10,  3.85739092e+11],
        [ 4.00000000e+00,  3.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          1.00000000e+00,  3.13754278e-01,  0.00000000e+00,
          3.80000000e+02,  1.00000000e+00,  1.02000000e+00,
          1.02000000e+00, -5.02304078e+03,  1.91202698e-01,
          7.82997692e+10,  4.83869882e+20]]),
 'branch': array([[ 0.00000000e+00+0.j,  1.00000000e+00+0.j,  1.22576177e-05+0.j,
          5.25623269e-05+0.j,  1.49703173e+01+0.j,  6.31852135e+03+0.j,
          2.50000000e+02+0.j,  2.50000000e+02+0.j,  1.00000000e+00+0.j,
          0.00000000e+00+0.j,  1.00000000e+00+0.j, -3.60000000e+02+0.j,
          3.60000000e+02+0.j, -1.20498354e+03+0.j,  6.06526247e+03+0.j,
          1.47761911e+03+0.j, -4.91793058e+03+0.j,  1.03766523e+08+0.j,
          1.04701391e+08+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,
          0.00000000e+00+0.j,  0.00000000e+00+0.j],
        [ 2.00000000e+00+0.j,  3.00000000e+00+0.j,  8.17174515e-06+0.j,
          3.50415512e-05+0.j,  9.98021154e+00+0.j,  6.31852135e+03+0.j,
          2.50000000e+02+0.j,  2.50000000e+02+0.j,  1.00000000e+00+0.j,
          0.00000000e+00+0.j,  1.00000000e+00+0.j, -3.60000000e+02+0.j,
          3.60000000e+02+0.j,  4.47462806e+01+0.j,  7.08120253e+02+0.j,
          9.74857895e+01+0.j, -9.83597424e+01+0.j,  5.12300663e+10+0.j,
          2.16842202e+14+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,
          0.00000000e+00+0.j,  0.00000000e+00+0.j],
        [ 3.00000000e+00+0.j,  4.00000000e+00+0.j,  8.17174515e-06+0.j,
          3.50415512e-05+0.j,  9.98021154e+00+0.j,  6.31852135e+03+0.j,
          2.50000000e+02+0.j,  2.50000000e+02+0.j,  1.00000000e+00+0.j,
          0.00000000e+00+0.j,  1.00000000e+00+0.j, -3.60000000e+02+0.j,
          3.60000000e+02+0.j,  2.91580054e+02+0.j,  8.23781584e+01+0.j,
          3.89325814e+02+0.j,  2.83694180e+03+0.j,  7.35352911e+09+0.j,
          2.35302643e+15+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,
          0.00000000e+00+0.j,  0.00000000e+00+0.j]]),
 'gen': array([[ 0.00000000e+00,  3.53122506e+08,  2.34719406e+03,
          1.00000000e+09, -1.00000000e+09,  1.31388242e+00,
          1.00000000e+00,  1.00000000e+00,  1.00000000e+09,
         -1.00000000e-10,  0.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          5.25424948e+03,  1.02797953e+04,  6.79172630e+03,
          6.79161962e+03],
        [ 4.00000000e+00,  3.53123867e+08, -3.87342276e+03,
          1.00000000e+09, -1.00000000e+09,  3.13754278e-01,
          1.00000000e+00,  1.00000000e+00,  1.00000000e+09,
         -1.00000000e-10,  0.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          5.25420475e+03,  1.02798852e+04,  6.79176856e+03,
          6.79157736e+03],
        [ 2.00000000e+00,  3.39074858e+00,  5.73116479e+03,
          1.00000000e+09, -1.00000000e+09, -1.70105830e-01,
                     nan,  1.00000000e+00,  1.00000000e+03,
         -1.00000000e-10,  0.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          2.12931643e+06,  9.83899833e+05,  6.79162256e+03,
          6.79172336e+03],
        [ 1.00000000e+00, -4.03045990e-02, -2.35609481e+03,
          1.00000000e+09, -1.00000000e+09,  1.08703858e+00,
                     nan,  1.00000000e+00,  1.00000000e-10,
         -1.00000000e+03,  0.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
          1.14205827e+04,  1.41384059e+07,  6.79161959e+03,
          6.79172633e+03]]),
 'internal': {'Ybus': array([], dtype=complex128),
  'Yf': array([], dtype=complex128),
  'Yt': array([], dtype=complex128),
  'branch_is': array([ True,  True,  True]),
  'gen_is': array([ True,  True,  True,  True]),
  'DLF': array([], dtype=complex128),
  'buses_ord_bfs_nets': array([], dtype=float64),
  'ref_gens': array([0, 1])},
 'gencost': array([[ 2.,  0.,  0.,  2., 10.,  0.],
        [ 2.,  0.,  0.,  2.,  8.,  0.],
        [ 2.,  0.,  0.,  0.,  0.,  0.],
        [ 2.,  0.,  0.,  0.,  0.,  0.]]),
 'dcline': array([[0.5, 1. ]]),
 'userfcn': {'formulation': [{'fcn': <function pandapower.optimal_powerflow._add_dcline_constraints(om, net)>,
    'args': This pandapower network includes the following parameter tables:
       - bus (5 elements)
       - load (1 element)
       - gen (2 elements)
       - ext_grid (2 elements)
       - line (3 elements)
       - dcline (1 element)
       - poly_cost (2 elements)
     and the following results tables:
       - res_bus (5 elements)
       - res_line (3 elements)
       - res_ext_grid (2 elements)
       - res_load (1 element)
       - res_gen (2 elements)
       - res_dcline (1 element)}]},
 'om': 
 VARIABLES               name       i1       iN        N
 =========              ------    -----    -----   ------
               0:          Va        0        5        5
               1:          Vm        5       10        5
               2:          Pg       10       14        4
               3:          Qg       14       18        4
   var['NS'] = 4                  var['N'] = 18

 NON-LINEAR CONSTRAINTS  name       i1       iN        N
 ====================== ------    -----    -----   ------
               0:        Pmis        0        5        5
               1:        Qmis        5       10        5
               2:          Sf       10       13        3
               3:          St       13       16        3
      nln.NS = 4                     nln.N = 16

 LINEAR CONSTRAINTS      name       i1       iN        N
 ==================     ------    -----    -----   ------
               0:         PQh        0        0        0
               1:         PQl        0        0        0
               2:          vl        0        0        0
               3:         ang        0        0        0
               4:      dcline        0        1        1
      lin.NS = 5                      lin.N = 1

 COSTS  :  <none>
   userdata = 
 {'Apqdata': {'h': array([], dtype=float64), 'l': array([], dtype=float64), 'ipql': array([], dtype=int64), 'ipqh': array([], dtype=int64)}, 'iang': array([], dtype=int64)},
 'x': array([ 0.00000000e+00,  9.66613415e-02,  4.28005538e+00, -1.15907275e+00,
         0.00000000e+00,  1.31388242e+00,  1.08703858e+00, -1.70105830e-01,
        -3.31932204e-02,  3.13754278e-01,  3.53122506e+08,  3.53123867e+08,
         3.39074858e+00, -4.03045990e-02,  2.34719406e+03, -3.87342276e+03,
         5.73116479e+03, -2.35609481e+03]),
 'mu': {'var': {'l': array([2.08469606e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
          3.47758050e+18, 9.79347804e+10, 7.80971425e+10, 1.27085367e+13,
          3.85739092e+11, 4.83869882e+20, 1.02797953e+04, 1.02798852e+04,
          9.83899833e+05, 1.41384059e+07, 6.79161962e+03, 6.79157736e+03,
          6.79172336e+03, 6.79172633e+03]),
   'u': array([0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
          0.00000000e+00, 9.85474186e+10, 8.19400890e+10, 5.14623575e+10,
          6.05328201e+10, 7.82997692e+10, 5.25424948e+03, 5.25420475e+03,
          2.12931643e+06, 1.14205827e+04, 6.79172630e+03, 6.79176856e+03,
          6.79162256e+03, 6.79161959e+03])},
  'nln': {'l': array([5.02224630e+03, 0.00000000e+00, 0.00000000e+00, 8.25272677e+15,
          5.02304078e+03, 0.00000000e+00, 1.06737160e-01, 1.00801904e-01,
          1.30159136e+16, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
          0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]),
   'u': array([0.00000000e+00, 2.66312920e+07, 4.23112766e+07, 0.00000000e+00,
          0.00000000e+00, 1.06682515e-01, 0.00000000e+00, 0.00000000e+00,
          0.00000000e+00, 1.91202698e-01, 1.03766523e+08, 5.12300663e+10,
          7.35352911e+09, 1.04701391e+08, 2.16842202e+14, 2.35302643e+15])},
  'lin': {'l': array([0.]), 'u': array([40758277.25647715])}},
 'f': 6356216001.098068,
 'var': {'val': {'Va': array([ 0.        ,  0.09666134,  4.28005538, -1.15907275,  0.        ]),
   'Vm': array([ 1.31388242,  1.08703858, -0.17010583, -0.03319322,  0.31375428]),
   'Pg': array([ 3.53122506e+08,  3.53123867e+08,  3.39074858e+00, -4.03045990e-02]),
   'Qg': array([ 2347.19405661, -3873.42275984,  5731.16478521, -2356.09481101])},
  'mu': {'l': {'Va': array([2.08469606e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
           3.47758050e+18]),
    'Vm': array([9.79347804e+10, 7.80971425e+10, 1.27085367e+13, 3.85739092e+11,
           4.83869882e+20]),
    'Pg': array([1.02797953e+04, 1.02798852e+04, 9.83899833e+05, 1.41384059e+07]),
    'Qg': array([6791.61961533, 6791.57735624, 6791.72335827, 6791.72632516])},
   'u': {'Va': array([0., 0., 0., 0., 0.]),
    'Vm': array([9.85474186e+10, 8.19400890e+10, 5.14623575e+10, 6.05328201e+10,
           7.82997692e+10]),
    'Pg': array([   5254.24947742,    5254.20475499, 2129316.42587851,
             11420.58270769]),
    'Qg': array([6791.72629785, 6791.76855893, 6791.62255636, 6791.619588  ])}}},
 'lin': {'mu': {'l': {'dcline': array([0.])},
   'u': {'dcline': array([40758277.25647715])}}},
 'nln': {'mu': {'l': {'Pmis': array([5.02224630e+03, 0.00000000e+00, 0.00000000e+00, 8.25272677e+15,
           5.02304078e+03]),
    'Qmis': array([0.00000000e+00, 1.06737160e-01, 1.00801904e-01, 1.30159136e+16,
           0.00000000e+00]),
    'Sf': array([0., 0., 0.]),
    'St': array([0., 0., 0.])},
   'u': {'Pmis': array([       0.        , 26631291.98680441, 42311276.62225478,
                  0.        ,        0.        ]),
    'Qmis': array([0.10668251, 0.        , 0.        , 0.        , 0.1912027 ]),
    'Sf': array([1.03766523e+08, 5.12300663e+10, 7.35352911e+09]),
    'St': array([1.04701391e+08, 2.16842202e+14, 2.35302643e+15])}}},
 'et': 0.43579602241516113,
 'success': False,
 'raw': {'xr': array([ 0.00000000e+00,  9.66613415e-02,  4.28005538e+00, -1.15907275e+00,
          0.00000000e+00,  1.31388242e+00,  1.08703858e+00, -1.70105830e-01,
         -3.31932204e-02,  3.13754278e-01,  3.53122506e+08,  3.53123867e+08,
          3.39074858e+00, -4.03045990e-02,  2.34719406e+03, -3.87342276e+03,
          5.73116479e+03, -2.35609481e+03]),
  'pimul': array([ 5.02224630e+03, -2.66312920e+07, -4.23112766e+07,  8.25272677e+15,
          5.02304078e+03, -1.06682515e-01,  1.06737160e-01,  1.00801904e-01,
          1.30159136e+16, -1.91202698e-01, -1.03766523e+08, -5.12300663e+10,
         -7.35352911e+09, -1.04701391e+08, -2.16842202e+14, -2.35302643e+15,
         -4.07582773e+07,  2.08469606e+00,  0.00000000e+00,  0.00000000e+00,
          0.00000000e+00,  3.47758050e+18, -6.12638178e+08, -3.84294654e+09,
          1.26570743e+13,  3.25206271e+11,  4.83869882e+20,  5.02554586e+03,
          5.02568044e+03, -1.14541659e+06,  1.41269853e+07, -1.06682515e-01,
         -1.91202698e-01,  1.00801904e-01,  1.06737160e-01]),
  'info': False,
  'output': {'iterations': 14,
   'hist': [{'feascond': 0.49999884066272154,
     'gradcond': 417016083.7431096,
     'compcond': 20.21933146970322,
     'costcond': 0.0,
     'gamma': 1,
     'stepsize': 0,
     'obj': 9000000000.0,
     'alphap': 0,
     'alphad': 0},
    {'feascond': 0.4981502365268503,
     'gradcond': 44456492.365925916,
     'compcond': 20.219773380976193,
     'costcond': 0.003697196295855314,
     'gamma': 31476588.858320918,
     'stepsize': 707105764.7299194,
     'obj': 8966725196.36534,
     'alphap': 0.0036972057076065648,
     'alphad': 0.003672738719070787},
    {'feascond': 0.49808785961003094,
     'gradcond': 37704006.62833323,
     'compcond': 20.222844195114146,
     'costcond': 0.00012201163106607171,
     'gamma': 31477563.046548188,
     'stepsize': 707581473.4642133,
     'obj': 8965631150.378693,
     'alphap': 0.00012189530942332611,
     'alphad': 1.2728721568511263e-07},
    {'feascond': 0.4980860637843843,
     'gradcond': 51682.95198931866,
     'compcond': 20.24590094710802,
     'costcond': 3.3123920870571332e-06,
     'gamma': 31513341.06281615,
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