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.

In [1]:

import wecgrid

example3 = wecgrid.Engine()

example3.case("./grid_models/IEEE_39_bus.raw")
example3.load(["pypsa"])

import wecgrid example3 = wecgrid.Engine() example3.case("./grid_models/IEEE_39_bus.raw") example3.load(["pypsa"])

---

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.

In [2]:

#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

In [3]:

###### 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¶

In [4]:

# 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

    

In [5]:

# 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                                         
2025-09-06 00:00:00   300  0.000135  0.0 -0.003430
2025-09-06 00:05:00   600  0.000135  0.0 -0.003430
2025-09-06 00:10:00   900  0.000197  0.0  0.002427
2025-09-06 00:15:00  1200  0.000192  0.0  0.001373
2025-09-06 00:20:00  1500  0.000151  0.0  0.000987

Out[5]:

<AxesSubplot:title={'center':'Wave Elevation - RM3'}, xlabel='time(s)', ylabel='meters'>

In [6]:

# 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                                         
2025-09-06 00:00:00   300  0.000135  0.0 -0.003430
2025-09-06 00:05:00   600  0.000135  0.0 -0.003430
2025-09-06 00:10:00   900  0.000197  0.0  0.002427
2025-09-06 00:15:00  1200  0.000192  0.0  0.001373
2025-09-06 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¶

In [7]:

grid = example3.pypsa.grid
grid

grid = example3.pypsa.grid grid

Out[7]:

GridState:
├─ Components:
│   ├─ bus:   42 components
│   ├─ gen:   13 components
│   ├─ line:  37 components
│   └─ load:  31 components
├─ Case: IEEE 39 bus
└─ Modeler: pypsa

In [8]:

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

In [9]:

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¶

In [10]:

example3.simulate(load_curve=True) # takes some time!
example3.pypsa.report

example3.simulate(load_curve=True) # takes some time! example3.pypsa.report

PyPSA Simulating:   0%|          | 0/288 [00:00<?, ?step/s]

PyPSA Simulating: 100%|██████████| 288/288 [02:00<00:00,  2.38step/s]

Out[10]:

SolveReport:
├─ Converged: Successful
├─ Simulation Time: 120.86 s
├─ Num Steps: 288
├─ Case: IEEE 39 bus
└─ Modeler: pypsa

---

Lets review our solve report first¶

In [11]:

report = example3.pypsa.report.dataframe
report

report = example3.pypsa.report.dataframe report

Out[11]:

	iter_time	converged	pf_solve_time	pf_solve_iter	snapshot_time	snapshot	message
0	0.507838	1	0.356890	4	0.143951	2025-09-06 00:00:00	converged
1	0.495973	1	0.350481	4	0.143494	2025-09-06 00:05:00	converged
2	0.492415	1	0.330468	4	0.160948	2025-09-06 00:10:00	converged
3	0.499115	1	0.320513	4	0.175604	2025-09-06 00:15:00	converged
4	0.877876	1	0.681783	4	0.192094	2025-09-06 00:20:00	converged
...	...	...	...	...	...	...	...
283	0.419026	1	0.303061	4	0.114963	2025-09-06 23:35:00	converged
284	0.515340	1	0.405365	4	0.108978	2025-09-06 23:40:00	converged
285	0.383005	1	0.256556	4	0.124436	2025-09-06 23:45:00	converged
286	0.417869	1	0.285910	4	0.129960	2025-09-06 23:50:00	converged
287	0.479465	1	0.348888	4	0.127578	2025-09-06 23:55:00	converged

288 rows × 7 columns

In [12]:

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')

Out[12]:

<AxesSubplot:xlabel='time', ylabel='iterations'>

---

Analyze and Visualize Results¶

Create comprehensive plots to analyze WEC performance and grid impact over time.

In [13]:

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'])

Out[13]:

(<Figure size 1200x600 with 1 Axes>,
 <AxesSubplot:title={'center':'PYPSA: Bus Voltage Magnitude'}, xlabel='Time', ylabel='Voltage (pu)'>)

In [14]:

plot.wec_analysis()

plot.wec_analysis()

In [ ]: