Example 3¶
Multiple WEC-Farm with load curve¶
This notebook demonstrates applying Multiple WEC-Farm configurations:
- Setting up a grid system with 3 WEC farms
- Reviewing Load curve
- Running time-domain simulations
- Analyzing WEC performance and grid impact
This example uses the IEEE 39 case system with wave energy converters and load curve.
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import wecgrid
example3 = wecgrid.Engine()
example3.case("./grid_models/IEEE_39_bus.raw")
example3.load(["pypsa","psse"])
import wecgrid
example3 = wecgrid.Engine()
example3.case("./grid_models/IEEE_39_bus.raw")
example3.load(["pypsa","psse"])
PSS®E software initialized
Apply the WEC-Farms¶
Lets create 3 WEC Farm. Two of the Farms will have 10 RM3 devices each, while the fourth will have 10 LUPA devices. The LUPA simulation data was pulled from the pre-loaded WEC-Grid database found here. Specifics about the LUPA run can be found in the documentation.
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#Information about the wec-sim runs in database can accessed using
example3.database.wecsim_runs()
#Information about the wec-sim runs in database can accessed using
example3.database.wecsim_runs()
Out[2]:
| wec_sim_id | model_type | sim_duration_sec | delta_time | wave_height_m | wave_period_sec | wave_spectrum | wave_class | wave_seed | simulation_hash | created_at | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2 | LUPA | 86400.0 | 0.1 | 2.5 | 8.0 | PM | irregular | 8 | LUPA_2.5m_8.0s_8 | 2025-08-19 19:59:49 |
| 1 | 1 | RM3 | 86400.0 | 0.1 | 2.5 | 8.0 | PM | irregular | 94 | RM3_2.5m_8.0s_94 | 2025-08-19 19:45:01 |
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###### RM3 Farm
example3.apply_wec(
farm_name = "RM3-FARM",
size = 10,
wec_sim_id = 1,
bus_location=40, # create a new bus for farm
connecting_bus = 39,
)
###### RM3 Farm 2
example3.apply_wec(
farm_name = "RM3-FARM2",
size = 10,
wec_sim_id = 1,
bus_location=41, # create a new bus for farm
connecting_bus = 25,
)
###### LUPA Farm
example3.apply_wec(
farm_name = "LUPA-FARM",
size = 10,
wec_sim_id = 2, # LUPA
bus_location=42, # create a new bus for farm
connecting_bus = 29, # Connect to bus 1 or swing bus
)
###### RM3 Farm
example3.apply_wec(
farm_name = "RM3-FARM",
size = 10,
wec_sim_id = 1,
bus_location=40, # create a new bus for farm
connecting_bus = 39,
)
###### RM3 Farm 2
example3.apply_wec(
farm_name = "RM3-FARM2",
size = 10,
wec_sim_id = 1,
bus_location=41, # create a new bus for farm
connecting_bus = 25,
)
###### LUPA Farm
example3.apply_wec(
farm_name = "LUPA-FARM",
size = 10,
wec_sim_id = 2, # LUPA
bus_location=42, # create a new bus for farm
connecting_bus = 29, # Connect to bus 1 or swing bus
)
WEC Farm added: RM3-FARM WEC Farm added: RM3-FARM2 WEC Farm added: LUPA-FARM
Review WEC-Farm / WEC-Device Data¶
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# Farm Data
WECFarms = example3.wec_farms # list
RM3_Farm_1 = WECFarms[0]
LUPA_Farm_1 = WECFarms[1]
print(RM3_Farm_1)
print(LUPA_Farm_1)
# Farm Data
WECFarms = example3.wec_farms # list
RM3_Farm_1 = WECFarms[0]
LUPA_Farm_1 = WECFarms[1]
print(RM3_Farm_1)
print(LUPA_Farm_1)
WECFarm:
├─ name: 'RM3-FARM'
├─ size: 10
├─ model: 'RM3'
├─ bus_location: 40
├─ connecting_bus: 39
└─ sim_id: 1
Base: 100.0 MVA
WECFarm:
├─ name: 'RM3-FARM2'
├─ size: 10
├─ model: 'RM3'
├─ bus_location: 41
├─ connecting_bus: 25
└─ sim_id: 1
Base: 100.0 MVA
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# WEC Device Data (RM3)
RM3_WEC = RM3_Farm_1.wec_devices[0]
print(RM3_WEC)
print(RM3_WEC.dataframe.head())
RM3_WEC.dataframe.plot("time", "p", title= "Active Power - RM3",ylabel='pu', xlabel='time(s)')
RM3_WEC.dataframe.plot("time", "eta", title= "Wave Elevation - RM3", ylabel='meters',xlabel='time(s)')
# WEC Device Data (RM3)
RM3_WEC = RM3_Farm_1.wec_devices[0]
print(RM3_WEC)
print(RM3_WEC.dataframe.head())
RM3_WEC.dataframe.plot("time", "p", title= "Active Power - RM3",ylabel='pu', xlabel='time(s)')
RM3_WEC.dataframe.plot("time", "eta", title= "Wave Elevation - RM3", ylabel='meters',xlabel='time(s)')
WECDevice:
├─ name: 'RM3_1_0'
├─ model: 'RM3'
├─ bus_location: 40
├─ sim_id: 1
└─ rows: 288
time p q eta
snapshots
2025-08-31 00:00:00 300 0.000135 0.0 -0.003430
2025-08-31 00:05:00 600 0.000135 0.0 -0.003430
2025-08-31 00:10:00 900 0.000197 0.0 0.002427
2025-08-31 00:15:00 1200 0.000192 0.0 0.001373
2025-08-31 00:20:00 1500 0.000151 0.0 0.000987
Out[5]:
<AxesSubplot:title={'center':'Wave Elevation - RM3'}, xlabel='time(s)', ylabel='meters'>
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# WEC Device Data (RM3)
LUPA_WEC = LUPA_Farm_1.wec_devices[0]
print(LUPA_WEC)
print(LUPA_WEC.dataframe.head())
LUPA_WEC.dataframe.plot("time", "p", title= "Active Power - LUPA",ylabel='pu', xlabel='time(s)')
LUPA_WEC.dataframe.plot("time", "eta", title= "Wave Elevation", ylabel='meters',xlabel='time(s)')
# WEC Device Data (RM3)
LUPA_WEC = LUPA_Farm_1.wec_devices[0]
print(LUPA_WEC)
print(LUPA_WEC.dataframe.head())
LUPA_WEC.dataframe.plot("time", "p", title= "Active Power - LUPA",ylabel='pu', xlabel='time(s)')
LUPA_WEC.dataframe.plot("time", "eta", title= "Wave Elevation", ylabel='meters',xlabel='time(s)')
WECDevice:
├─ name: 'RM3_1_0'
├─ model: 'RM3'
├─ bus_location: 41
├─ sim_id: 1
└─ rows: 288
time p q eta
snapshots
2025-08-31 00:00:00 300 0.000135 0.0 -0.003430
2025-08-31 00:05:00 600 0.000135 0.0 -0.003430
2025-08-31 00:10:00 900 0.000197 0.0 0.002427
2025-08-31 00:15:00 1200 0.000192 0.0 0.001373
2025-08-31 00:20:00 1500 0.000151 0.0 0.000987
Out[6]:
<AxesSubplot:title={'center':'Wave Elevation'}, xlabel='time(s)', ylabel='meters'>
Review Grid data¶
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grid = example3.pypsa.grid
grid
grid = example3.pypsa.grid
grid
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GridState: ├─ Components: │ ├─ bus: 42 components │ ├─ gen: 13 components │ ├─ line: 37 components │ └─ load: 31 components ├─ Case: IEEE 39 bus └─ Modeler: pypsa
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example3.plot.sld() # not working well for this case
example3.plot.sld() # not working well for this case
SLD Data Summary: Buses: 42 Lines: 37 Generators: 13 Loads: 31
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load_curves = example3.generate_load_curves()
load_curves.plot(ylabel='pu', title='Load Curves for All Buses', legend=False)
load_curves = example3.generate_load_curves()
load_curves.plot(ylabel='pu', title='Load Curves for All Buses', legend=False)
Out[9]:
<AxesSubplot:title={'center':'Load Curves for All Buses'}, xlabel='time', ylabel='pu'>
Run Time Series Simulation¶
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example3.simulate(load_curve=True) # takes some time!
example3.pypsa.report
example3.simulate(load_curve=True) # takes some time!
example3.pypsa.report
PSS®E Simulating: 0%| | 1/288 [00:00<01:02, 4.57step/s]
PSS®E Simulating: 100%|██████████| 288/288 [00:55<00:00, 5.15step/s] PyPSA Simulating: 100%|██████████| 288/288 [03:09<00:00, 1.52step/s]
Out[10]:
SolveReport: ├─ Converged: Successful ├─ Simulation Time: 189.40 s ├─ Num Steps: 288 ├─ Case: IEEE 39 bus └─ Modeler: pypsa
Lets review our solve report first¶
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report = example3.pypsa.report.dataframe
report
report = example3.pypsa.report.dataframe
report
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| iter_time | converged | pf_solve_time | pf_solve_iter | snapshot_time | snapshot | message | |
|---|---|---|---|---|---|---|---|
| 0 | 0.640627 | 1 | 0.437494 | 4 | 0.203133 | 2025-08-31 00:00:00 | converged |
| 1 | 0.625019 | 1 | 0.421890 | 4 | 0.203129 | 2025-08-31 00:05:00 | converged |
| 2 | 0.640651 | 1 | 0.421881 | 4 | 0.203135 | 2025-08-31 00:10:00 | converged |
| 3 | 0.640644 | 1 | 0.437521 | 4 | 0.203123 | 2025-08-31 00:15:00 | converged |
| 4 | 0.765651 | 1 | 0.562525 | 4 | 0.203126 | 2025-08-31 00:20:00 | converged |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 283 | 0.625022 | 1 | 0.421889 | 4 | 0.203133 | 2025-08-31 23:35:00 | converged |
| 284 | 0.640640 | 1 | 0.421882 | 4 | 0.218758 | 2025-08-31 23:40:00 | converged |
| 285 | 0.625014 | 1 | 0.421882 | 4 | 0.203132 | 2025-08-31 23:45:00 | converged |
| 286 | 0.640640 | 1 | 0.406255 | 4 | 0.218755 | 2025-08-31 23:50:00 | converged |
| 287 | 0.625016 | 1 | 0.421888 | 4 | 0.203128 | 2025-08-31 23:55:00 | converged |
288 rows × 7 columns
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report.plot('snapshot', ['iter_time', 'pf_solve_time', 'snapshot_time'], xlabel='time', ylabel='seconds')
report.plot('snapshot', 'pf_solve_iter', xlabel='time', ylabel='iterations')
report.plot('snapshot', ['iter_time', 'pf_solve_time', 'snapshot_time'], xlabel='time', ylabel='seconds')
report.plot('snapshot', 'pf_solve_iter', xlabel='time', ylabel='iterations')
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<AxesSubplot:xlabel='time', ylabel='iterations'>
Analyze and Visualize Results¶
Create comprehensive plots to analyze WEC performance and grid impact over time.
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plot = example3.plot
plot.bus()
plot.bus(bus=['Bus_31']) # Swing Bus
plot.bus(bus=['Bus_40', 'Bus_41','Bus_42']) # WEC-Farm Buses
plot.bus(parameter='v_mag',bus=['Bus_40', 'Bus_41','Bus_42'])
plot = example3.plot
plot.bus()
plot.bus(bus=['Bus_31']) # Swing Bus
plot.bus(bus=['Bus_40', 'Bus_41','Bus_42']) # WEC-Farm Buses
plot.bus(parameter='v_mag',bus=['Bus_40', 'Bus_41','Bus_42'])
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(<Figure size 1200x600 with 1 Axes>,
<AxesSubplot:title={'center':'PYPSA: Bus Voltage Magnitude'}, xlabel='Time', ylabel='Voltage (pu)'>)
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plot.wec_analysis()
plot.wec_analysis()
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