Example 4¶
PyPSA v.s PSS/E¶
This notebook demonstrates how to compare the PyPSA and PSS/E results.
pre compiled PSS/E data can be found in the database with the specifics found here.
if you can and have the license, feel free to run the PSSE simulation yourself
This example uses the RTS GMLC case system with wave energy converter farm. (74 buses)
In [1]:
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import wecgrid
example4 = wecgrid.Engine()
example4.case("./grid_models/IEEE_96_bus.raw")
example4.load(["pypsa"])
#example4.load(["pypsa", "psse"]) # Uncomment to load PSSE data
import wecgrid
example4 = wecgrid.Engine()
example4.case("./grid_models/IEEE_96_bus.raw")
example4.load(["pypsa"])
#example4.load(["pypsa", "psse"]) # Uncomment to load PSSE data
C:\Users\PDC-user\alexb\ACEP\WEC-Grid\src\wecgrid\modelers\power_system\base.py:388: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()` tdf[col] = pd.NA
In [2]:
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example4.plot.sld()
example4.plot.sld()
SLD Data Summary: Buses: 73 Lines: 110 Generators: 33 Loads: 51
Apply the WEC-Farms¶
Lets create 3 WEC Farm. These Farms will have 10 RM3 devices each.
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###### RM3 Farm Connected to bus 123
example4.apply_wec(
farm_name = "RM3-FARM",
size = 10,
wec_sim_id = 1,
bus_location=326, # create a new bus for farm
connecting_bus = 123,
)
###### RM3 Farm Connected to bus 123
example4.apply_wec(
farm_name = "RM3-FARM",
size = 10,
wec_sim_id = 1,
bus_location=326, # create a new bus for farm
connecting_bus = 123,
)
WEC Farm added: RM3-FARM
Run Time Series Simulation with load curve¶
In [4]:
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example4.simulate()
example4.pypsa.report
example4.simulate()
example4.pypsa.report
PyPSA Simulating: 0%| | 0/288 [00:00<?, ?step/s]
PyPSA Simulating: 100%|██████████| 288/288 [04:14<00:00, 1.13step/s]
Out[4]:
SolveReport: ├─ Converged: Successful ├─ Simulation Time: 254.69 s ├─ Num Steps: 288 ├─ Case: IEEE 96 bus └─ Modeler: pypsa
In [5]:
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report = example4.pypsa.report.dataframe
report.plot('snapshot', ['iter_time', 'pf_solve_time', 'snapshot_time'], xlabel='time', ylabel='seconds')
report = example4.pypsa.report.dataframe
report.plot('snapshot', ['iter_time', 'pf_solve_time', 'snapshot_time'], xlabel='time', ylabel='seconds')
Out[5]:
<AxesSubplot:xlabel='time', ylabel='seconds'>
Pre-compiled PSSE simulation¶
we can pull the pre-compiled PSSE simulation to be able to compare with PyPSA
In [5]:
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example4.database.grid_sims()
example4.database.grid_sims()
Out[5]:
| grid_sim_id | sim_name | case_name | psse | pypsa | sbase_mva | sim_start_time | sim_end_time | delta_time | notes | created_at | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | PSSE-RTS-GMLC: RM3 Farm | RTS GMLC Hooman | 1 | 0 | 100.0 | 2025-08-24T00:00:00 | 2025-08-24T23:55:00 | 300 | RTS-GMLC grid simulation using PSS/E. The simu... | 2025-08-24 20:19:19 |
| 1 | 2 | PSSE-RTS-GMLC | RTS GMLC Hooman | 1 | 0 | 100.0 | 2025-08-24T00:00:00 | 2025-08-24T23:55:00 | 300 | RTS-GMLC grid simulation using PSS/E. The simu... | 2025-08-24 19:10:55 |
In [6]:
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psse_grid = example4.database.pull_sim(grid_sim_id=1, software='psse')
psse_grid = example4.database.pull_sim(grid_sim_id=1, software='psse')
GridState reconstructed: RTS GMLC Hooman (PSSE) - 74 buses, 99 generators
Review Grid data¶
In [7]:
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psse_grid.bus_t
psse_grid.bus_t
Out[7]:
{'type': Bus_101 Bus_102 Bus_103 Bus_104 Bus_105 Bus_106 Bus_107 \
2025-08-24 00:00:00 PV PV PV PV PQ PQ PV
2025-08-24 00:05:00 PV PV PV PV PQ PQ PV
2025-08-24 00:10:00 PV PV PV PV PQ PQ PV
2025-08-24 00:15:00 PV PV PV PV PQ PQ PV
2025-08-24 00:20:00 PV PV PV PV PQ PQ PV
... ... ... ... ... ... ... ...
2025-08-24 23:35:00 PV PV PV PV PQ PQ PV
2025-08-24 23:40:00 PV PV PV PV PQ PQ PV
2025-08-24 23:45:00 PV PV PV PV PQ PQ PV
2025-08-24 23:50:00 PV PV PV PV PQ PQ PV
2025-08-24 23:55:00 PV PV PV PV PQ PQ PV
Bus_108 Bus_109 Bus_110 ... Bus_317 Bus_318 Bus_319 \
2025-08-24 00:00:00 PQ PQ PQ ... PV PV PV
2025-08-24 00:05:00 PQ PQ PQ ... PV PV PV
2025-08-24 00:10:00 PQ PQ PQ ... PV PV PV
2025-08-24 00:15:00 PQ PQ PQ ... PV PV PV
2025-08-24 00:20:00 PQ PQ PQ ... PV PV PV
... ... ... ... ... ... ... ...
2025-08-24 23:35:00 PQ PQ PQ ... PV PV PV
2025-08-24 23:40:00 PQ PQ PQ ... PV PV PV
2025-08-24 23:45:00 PQ PQ PQ ... PV PV PV
2025-08-24 23:50:00 PQ PQ PQ ... PV PV PV
2025-08-24 23:55:00 PQ PQ PQ ... PV PV PV
Bus_320 Bus_321 Bus_322 Bus_323 Bus_324 Bus_325 Bus_326
2025-08-24 00:00:00 PV PV PV PV PV PQ PV
2025-08-24 00:05:00 PV PV PV PV PV PQ PV
2025-08-24 00:10:00 PV PV PV PV PV PQ PV
2025-08-24 00:15:00 PV PV PV PV PV PQ PV
2025-08-24 00:20:00 PV PV PV PV PV PQ PV
... ... ... ... ... ... ... ...
2025-08-24 23:35:00 PV PV PV PV PV PQ PV
2025-08-24 23:40:00 PV PV PV PV PV PQ PV
2025-08-24 23:45:00 PV PV PV PV PV PQ PV
2025-08-24 23:50:00 PV PV PV PV PV PQ PV
2025-08-24 23:55:00 PV PV PV PV PV PQ PV
[288 rows x 74 columns],
'p': Bus_101 Bus_102 Bus_103 Bus_104 Bus_105 Bus_106 \
2025-08-24 00:00:00 0.6 0.71 -1.8 -0.74 -0.71 -1.36
2025-08-24 00:05:00 0.6 0.71 -1.8 -0.74 -0.71 -1.36
2025-08-24 00:10:00 0.6 0.71 -1.8 -0.74 -0.71 -1.36
2025-08-24 00:15:00 0.6 0.71 -1.8 -0.74 -0.71 -1.36
2025-08-24 00:20:00 0.6 0.71 -1.8 -0.74 -0.71 -1.36
... ... ... ... ... ... ...
2025-08-24 23:35:00 0.6 0.71 -1.8 -0.74 -0.71 -1.36
2025-08-24 23:40:00 0.6 0.71 -1.8 -0.74 -0.71 -1.36
2025-08-24 23:45:00 0.6 0.71 -1.8 -0.74 -0.71 -1.36
2025-08-24 23:50:00 0.6 0.71 -1.8 -0.74 -0.71 -1.36
2025-08-24 23:55:00 0.6 0.71 -1.8 -0.74 -0.71 -1.36
Bus_107 Bus_108 Bus_109 Bus_110 ... Bus_317 \
2025-08-24 00:00:00 2.3 -1.71 -1.75 -1.95 ... 0.0
2025-08-24 00:05:00 2.3 -1.71 -1.75 -1.95 ... 0.0
2025-08-24 00:10:00 2.3 -1.71 -1.75 -1.95 ... 0.0
2025-08-24 00:15:00 2.3 -1.71 -1.75 -1.95 ... 0.0
2025-08-24 00:20:00 2.3 -1.71 -1.75 -1.95 ... 0.0
... ... ... ... ... ... ...
2025-08-24 23:35:00 2.3 -1.71 -1.75 -1.95 ... 0.0
2025-08-24 23:40:00 2.3 -1.71 -1.75 -1.95 ... 0.0
2025-08-24 23:45:00 2.3 -1.71 -1.75 -1.95 ... 0.0
2025-08-24 23:50:00 2.3 -1.71 -1.75 -1.95 ... 0.0
2025-08-24 23:55:00 2.3 -1.71 -1.75 -1.95 ... 0.0
Bus_318 Bus_319 Bus_320 Bus_321 Bus_322 Bus_323 \
2025-08-24 00:00:00 0.22 -1.81 -1.28 3.55 3.1 7.1
2025-08-24 00:05:00 0.22 -1.81 -1.28 3.55 3.1 7.1
2025-08-24 00:10:00 0.22 -1.81 -1.28 3.55 3.1 7.1
2025-08-24 00:15:00 0.22 -1.81 -1.28 3.55 3.1 7.1
2025-08-24 00:20:00 0.22 -1.81 -1.28 3.55 3.1 7.1
... ... ... ... ... ... ...
2025-08-24 23:35:00 0.22 -1.81 -1.28 3.55 3.1 7.1
2025-08-24 23:40:00 0.22 -1.81 -1.28 3.55 3.1 7.1
2025-08-24 23:45:00 0.22 -1.81 -1.28 3.55 3.1 7.1
2025-08-24 23:50:00 0.22 -1.81 -1.28 3.55 3.1 7.1
2025-08-24 23:55:00 0.22 -1.81 -1.28 3.55 3.1 7.1
Bus_324 Bus_325 Bus_326
2025-08-24 00:00:00 0.0 0.0 0.001353
2025-08-24 00:05:00 0.0 0.0 0.001353
2025-08-24 00:10:00 0.0 0.0 0.001971
2025-08-24 00:15:00 0.0 0.0 0.001916
2025-08-24 00:20:00 0.0 0.0 0.001507
... ... ... ...
2025-08-24 23:35:00 0.0 0.0 0.001602
2025-08-24 23:40:00 0.0 0.0 0.001751
2025-08-24 23:45:00 0.0 0.0 0.001520
2025-08-24 23:50:00 0.0 0.0 0.002188
2025-08-24 23:55:00 0.0 0.0 0.001349
[288 rows x 74 columns],
'q': Bus_101 Bus_102 Bus_103 Bus_104 Bus_105 Bus_106 \
2025-08-24 00:00:00 -0.127828 -0.161203 -0.37 -0.15 -0.14 -0.28
2025-08-24 00:05:00 -0.127828 -0.161203 -0.37 -0.15 -0.14 -0.28
2025-08-24 00:10:00 -0.127826 -0.161201 -0.37 -0.15 -0.14 -0.28
2025-08-24 00:15:00 -0.127826 -0.161202 -0.37 -0.15 -0.14 -0.28
2025-08-24 00:20:00 -0.127828 -0.161203 -0.37 -0.15 -0.14 -0.28
... ... ... ... ... ... ...
2025-08-24 23:35:00 -0.127827 -0.161203 -0.37 -0.15 -0.14 -0.28
2025-08-24 23:40:00 -0.127827 -0.161202 -0.37 -0.15 -0.14 -0.28
2025-08-24 23:45:00 -0.127828 -0.161203 -0.37 -0.15 -0.14 -0.28
2025-08-24 23:50:00 -0.127825 -0.161201 -0.37 -0.15 -0.14 -0.28
2025-08-24 23:55:00 -0.127828 -0.161203 -0.37 -0.15 -0.14 -0.28
Bus_107 Bus_108 Bus_109 Bus_110 ... Bus_317 \
2025-08-24 00:00:00 0.233837 -0.35 -0.36 -0.4 ... 0.0
2025-08-24 00:05:00 0.233837 -0.35 -0.36 -0.4 ... 0.0
2025-08-24 00:10:00 0.233838 -0.35 -0.36 -0.4 ... 0.0
2025-08-24 00:15:00 0.233838 -0.35 -0.36 -0.4 ... 0.0
2025-08-24 00:20:00 0.233837 -0.35 -0.36 -0.4 ... 0.0
... ... ... ... ... ... ...
2025-08-24 23:35:00 0.233837 -0.35 -0.36 -0.4 ... 0.0
2025-08-24 23:40:00 0.233838 -0.35 -0.36 -0.4 ... 0.0
2025-08-24 23:45:00 0.233837 -0.35 -0.36 -0.4 ... 0.0
2025-08-24 23:50:00 0.233839 -0.35 -0.36 -0.4 ... 0.0
2025-08-24 23:55:00 0.233837 -0.35 -0.36 -0.4 ... 0.0
Bus_318 Bus_319 Bus_320 Bus_321 Bus_322 Bus_323 \
2025-08-24 00:00:00 -0.047644 -0.37 -0.26 -0.035264 -0.399664 0.751288
2025-08-24 00:05:00 -0.047644 -0.37 -0.26 -0.035264 -0.399664 0.751288
2025-08-24 00:10:00 -0.047645 -0.37 -0.26 -0.035265 -0.399664 0.751290
2025-08-24 00:15:00 -0.047645 -0.37 -0.26 -0.035265 -0.399664 0.751290
2025-08-24 00:20:00 -0.047644 -0.37 -0.26 -0.035265 -0.399664 0.751288
... ... ... ... ... ... ...
2025-08-24 23:35:00 -0.047644 -0.37 -0.26 -0.035265 -0.399664 0.751289
2025-08-24 23:40:00 -0.047645 -0.37 -0.26 -0.035265 -0.399664 0.751289
2025-08-24 23:45:00 -0.047644 -0.37 -0.26 -0.035265 -0.399664 0.751288
2025-08-24 23:50:00 -0.047645 -0.37 -0.26 -0.035265 -0.399664 0.751291
2025-08-24 23:55:00 -0.047644 -0.37 -0.26 -0.035264 -0.399664 0.751288
Bus_324 Bus_325 Bus_326
2025-08-24 00:00:00 0.0 0.0 -0.302477
2025-08-24 00:05:00 0.0 0.0 -0.302477
2025-08-24 00:10:00 0.0 0.0 -0.302637
2025-08-24 00:15:00 0.0 0.0 -0.302623
2025-08-24 00:20:00 0.0 0.0 -0.302517
... ... ... ...
2025-08-24 23:35:00 0.0 0.0 -0.302541
2025-08-24 23:40:00 0.0 0.0 -0.302580
2025-08-24 23:45:00 0.0 0.0 -0.302520
2025-08-24 23:50:00 0.0 0.0 -0.302693
2025-08-24 23:55:00 0.0 0.0 -0.302476
[288 rows x 74 columns],
'v_mag': Bus_101 Bus_102 Bus_103 Bus_104 Bus_105 Bus_106 \
2025-08-24 00:00:00 1.0468 1.0467 1.012285 1.017577 1.0362 1.033708
2025-08-24 00:05:00 1.0468 1.0467 1.012285 1.017577 1.0362 1.033708
2025-08-24 00:10:00 1.0468 1.0467 1.012284 1.017577 1.0362 1.033708
2025-08-24 00:15:00 1.0468 1.0467 1.012284 1.017577 1.0362 1.033708
2025-08-24 00:20:00 1.0468 1.0467 1.012284 1.017577 1.0362 1.033708
... ... ... ... ... ... ...
2025-08-24 23:35:00 1.0468 1.0467 1.012284 1.017577 1.0362 1.033708
2025-08-24 23:40:00 1.0468 1.0467 1.012284 1.017577 1.0362 1.033708
2025-08-24 23:45:00 1.0468 1.0467 1.012284 1.017577 1.0362 1.033708
2025-08-24 23:50:00 1.0468 1.0467 1.012284 1.017577 1.0362 1.033708
2025-08-24 23:55:00 1.0468 1.0467 1.012285 1.017577 1.0362 1.033708
Bus_107 Bus_108 Bus_109 Bus_110 ... Bus_317 \
2025-08-24 00:00:00 1.05 1.01711 1.026819 1.051622 ... 1.047476
2025-08-24 00:05:00 1.05 1.01711 1.026819 1.051622 ... 1.047476
2025-08-24 00:10:00 1.05 1.01711 1.026818 1.051622 ... 1.047476
2025-08-24 00:15:00 1.05 1.01711 1.026818 1.051622 ... 1.047476
2025-08-24 00:20:00 1.05 1.01711 1.026818 1.051622 ... 1.047476
... ... ... ... ... ... ...
2025-08-24 23:35:00 1.05 1.01711 1.026818 1.051622 ... 1.047475
2025-08-24 23:40:00 1.05 1.01711 1.026818 1.051622 ... 1.047476
2025-08-24 23:45:00 1.05 1.01711 1.026818 1.051622 ... 1.047475
2025-08-24 23:50:00 1.05 1.01711 1.026818 1.051622 ... 1.047476
2025-08-24 23:55:00 1.05 1.01711 1.026819 1.051622 ... 1.047476
Bus_318 Bus_319 Bus_320 Bus_321 Bus_322 Bus_323 \
2025-08-24 00:00:00 1.05 1.039002 1.043571 1.05 1.05 1.05
2025-08-24 00:05:00 1.05 1.039002 1.043571 1.05 1.05 1.05
2025-08-24 00:10:00 1.05 1.039002 1.043571 1.05 1.05 1.05
2025-08-24 00:15:00 1.05 1.039002 1.043571 1.05 1.05 1.05
2025-08-24 00:20:00 1.05 1.039002 1.043571 1.05 1.05 1.05
... ... ... ... ... ... ...
2025-08-24 23:35:00 1.05 1.039002 1.043571 1.05 1.05 1.05
2025-08-24 23:40:00 1.05 1.039002 1.043571 1.05 1.05 1.05
2025-08-24 23:45:00 1.05 1.039002 1.043571 1.05 1.05 1.05
2025-08-24 23:50:00 1.05 1.039002 1.043571 1.05 1.05 1.05
2025-08-24 23:55:00 1.05 1.039002 1.043571 1.05 1.05 1.05
Bus_324 Bus_325 Bus_326
2025-08-24 00:00:00 0.998677 1.049229 1.0
2025-08-24 00:05:00 0.998677 1.049229 1.0
2025-08-24 00:10:00 0.998677 1.049229 1.0
2025-08-24 00:15:00 0.998677 1.049229 1.0
2025-08-24 00:20:00 0.998677 1.049229 1.0
... ... ... ...
2025-08-24 23:35:00 0.998677 1.049229 1.0
2025-08-24 23:40:00 0.998677 1.049229 1.0
2025-08-24 23:45:00 0.998677 1.049229 1.0
2025-08-24 23:50:00 0.998677 1.049229 1.0
2025-08-24 23:55:00 0.998677 1.049229 1.0
[288 rows x 74 columns],
'angle_deg': Bus_101 Bus_102 Bus_103 Bus_104 Bus_105 \
2025-08-24 00:00:00 -8.567648 -8.626429 -7.993178 -10.741543 -10.928630
2025-08-24 00:05:00 -8.567648 -8.626429 -7.993178 -10.741543 -10.928630
2025-08-24 00:10:00 -8.567255 -8.626040 -7.992678 -10.741161 -10.928258
2025-08-24 00:15:00 -8.567290 -8.626074 -7.992723 -10.741195 -10.928290
2025-08-24 00:20:00 -8.567551 -8.626332 -7.993054 -10.741447 -10.928537
... ... ... ... ... ...
2025-08-24 23:35:00 -8.567490 -8.626273 -7.992978 -10.741389 -10.928480
2025-08-24 23:40:00 -8.567396 -8.626180 -7.992857 -10.741298 -10.928390
2025-08-24 23:45:00 -8.567542 -8.626324 -7.993043 -10.741439 -10.928529
2025-08-24 23:50:00 -8.567118 -8.625903 -7.992502 -10.741027 -10.928126
2025-08-24 23:55:00 -8.567651 -8.626432 -7.993182 -10.741545 -10.928633
Bus_106 Bus_107 Bus_108 Bus_109 Bus_110 ... \
2025-08-24 00:00:00 -13.157954 -3.988651 -9.344119 -8.460734 -10.214824 ...
2025-08-24 00:05:00 -13.157954 -3.988651 -9.344119 -8.460734 -10.214824 ...
2025-08-24 00:10:00 -13.157595 -3.988230 -9.343723 -8.460360 -10.214471 ...
2025-08-24 00:15:00 -13.157626 -3.988267 -9.343759 -8.460392 -10.214502 ...
2025-08-24 00:20:00 -13.157866 -3.988546 -9.344021 -8.460642 -10.214735 ...
... ... ... ... ... ... ...
2025-08-24 23:35:00 -13.157810 -3.988482 -9.343961 -8.460585 -10.214682 ...
2025-08-24 23:40:00 -13.157722 -3.988380 -9.343864 -8.460492 -10.214597 ...
2025-08-24 23:45:00 -13.157857 -3.988536 -9.344013 -8.460634 -10.214727 ...
2025-08-24 23:50:00 -13.157468 -3.988083 -9.343584 -8.460227 -10.214347 ...
2025-08-24 23:55:00 -13.157957 -3.988653 -9.344122 -8.460737 -10.214827 ...
Bus_317 Bus_318 Bus_319 Bus_320 Bus_321 \
2025-08-24 00:00:00 5.119365 6.217644 0.849944 2.455866 6.905159
2025-08-24 00:05:00 5.119365 6.217644 0.849944 2.455866 6.905159
2025-08-24 00:10:00 5.119983 6.218256 0.850579 2.456509 6.905776
2025-08-24 00:15:00 5.119928 6.218202 0.850523 2.456452 6.905721
2025-08-24 00:20:00 5.119518 6.217796 0.850102 2.456026 6.905313
... ... ... ... ... ...
2025-08-24 23:35:00 5.119614 6.217890 0.850199 2.456125 6.905406
2025-08-24 23:40:00 5.119762 6.218038 0.850353 2.456280 6.905555
2025-08-24 23:45:00 5.119533 6.217810 0.850116 2.456040 6.905327
2025-08-24 23:50:00 5.120199 6.218472 0.850802 2.456735 6.905991
2025-08-24 23:55:00 5.119361 6.217639 0.849939 2.455861 6.905156
Bus_322 Bus_323 Bus_324 Bus_325 Bus_326
2025-08-24 00:00:00 12.932658 4.056374 -5.497188 4.597470 9.132870
2025-08-24 00:05:00 12.932658 4.056374 -5.497188 4.597470 9.132870
2025-08-24 00:10:00 12.933276 4.057022 -5.496562 4.598123 9.139927
2025-08-24 00:15:00 12.933221 4.056964 -5.496617 4.598064 9.139299
2025-08-24 00:20:00 12.932813 4.056535 -5.497032 4.597632 9.134625
... ... ... ... ... ...
2025-08-24 23:35:00 12.932906 4.056634 -5.496936 4.597732 9.135706
2025-08-24 23:40:00 12.933056 4.056790 -5.496785 4.597890 9.137410
2025-08-24 23:45:00 12.932825 4.056550 -5.497018 4.597646 9.134779
2025-08-24 23:50:00 12.933492 4.057249 -5.496343 4.598352 9.142400
2025-08-24 23:55:00 12.932654 4.056369 -5.497192 4.597465 9.132820
[288 rows x 74 columns]}
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example4.pypsa.grid
example4.pypsa.grid
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GridState: ├─ Components: │ ├─ bus: 74 components │ ├─ gen: 99 components │ ├─ line: 106 components │ └─ load: 51 components ├─ Case: RTS GMLC Hooman └─ Modeler: pypsa
Analyze, visualize and compare results¶
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# add our 'external' grid to WEC-Grid
example4.plot.add_grid(software='psse', grid_state=psse_grid)
# add our 'external' grid to WEC-Grid
example4.plot.add_grid(software='psse', grid_state=psse_grid)
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example4.plot.bus("pypsa",)
example4.plot.bus("psse")
example4.plot.bus(software="pypsa", bus=['Bus_326'])
example4.plot.bus(software="psse", bus=['Bus_326'])
example4.plot.bus(software="pypsa", bus=['Bus_123'])
example4.plot.bus(software="psse", bus=['Bus_123'])
example4.plot.bus("pypsa",)
example4.plot.bus("psse")
example4.plot.bus(software="pypsa", bus=['Bus_326'])
example4.plot.bus(software="psse", bus=['Bus_326'])
example4.plot.bus(software="pypsa", bus=['Bus_123'])
example4.plot.bus(software="psse", bus=['Bus_123'])
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(<Figure size 1200x600 with 1 Axes>,
<AxesSubplot:title={'center':'PSSE: Bus Active Power (net)'}, xlabel='Time', ylabel='Active Power [pu]'>)
compare¶
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example4.plot.compare_modelers(grid_component='bus', name=['Bus_326'], parameter='p')
example4.plot.compare_modelers(grid_component='bus', name=['Bus_123'], parameter='p')
example4.plot.compare_modelers(grid_component='bus', name=['Bus_113'], parameter='p')
example4.plot.compare_modelers(grid_component='bus', name=['Bus_326'], parameter='p')
example4.plot.compare_modelers(grid_component='bus', name=['Bus_123'], parameter='p')
example4.plot.compare_modelers(grid_component='bus', name=['Bus_113'], parameter='p')
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