Note
You can download this example as a Jupyter notebook or start it in interactive mode.
Flow Plot Example#
Here, we are going to import a network and plot the electricity flow
[1]:
import warnings
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import pandas as pd
from shapely.errors import ShapelyDeprecationWarning
import pypsa
warnings.filterwarnings("ignore", category=ShapelyDeprecationWarning)
plt.rc("figure", figsize=(10, 8))
Import and optimize a network#
[2]:
n = pypsa.examples.ac_dc_meshed(from_master=True)
n.optimize()
WARNING:pypsa.io:Importing network from PyPSA version v0.17.1 while current version is v0.32.0. Read the release notes at https://pypsa.readthedocs.io/en/latest/release_notes.html to prepare your network for import.
INFO:pypsa.io:Imported network ac-dc-meshed.nc has buses, carriers, generators, global_constraints, lines, links, loads
WARNING:pypsa.consistency:The following buses have carriers which are not defined:
Index(['London', 'Norwich', 'Norwich DC', 'Manchester', 'Bremen', 'Bremen DC',
'Frankfurt', 'Norway', 'Norway DC'],
dtype='object', name='Bus')
WARNING:pypsa.consistency:The following lines have carriers which are not defined:
Index(['0', '1', '2', '3', '4', '5', '6'], dtype='object', name='Line')
WARNING:pypsa.consistency:The following lines have zero x, which could break the linear load flow:
Index(['2', '3', '4'], dtype='object', name='Line')
WARNING:pypsa.consistency:The following lines have zero r, which could break the linear load flow:
Index(['0', '1', '5', '6'], dtype='object', name='Line')
WARNING:pypsa.consistency:The following links have carriers which are not defined:
Index(['Norwich Converter', 'Norway Converter', 'Bremen Converter', 'DC link'], dtype='object', name='Link')
INFO:linopy.model: Solve problem using Highs solver
INFO:linopy.io: Writing time: 0.05s
INFO:linopy.constants: Optimization successful:
Status: ok
Termination condition: optimal
Solution: 188 primals, 468 duals
Objective: -3.47e+06
Solver model: available
Solver message: optimal
INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, Line-ext-s-lower, Line-ext-s-upper, Link-ext-p-lower, Link-ext-p-upper, Kirchhoff-Voltage-Law were not assigned to the network.
Running HiGHS 1.9.0 (git hash: fa40bdf): Copyright (c) 2024 HiGHS under MIT licence terms
Coefficient ranges:
Matrix [1e-02, 1e+00]
Cost [9e-03, 3e+03]
Bound [2e+07, 2e+07]
RHS [9e-01, 1e+03]
Presolving model
391 rows, 187 cols, 930 nonzeros 0s
305 rows, 101 cols, 1042 nonzeros 0s
303 rows, 99 cols, 1058 nonzeros 0s
Presolve : Reductions: rows 303(-165); columns 99(-89); elements 1058(+51)
Solving the presolved LP
Using EKK dual simplex solver - serial
Iteration Objective Infeasibilities num(sum)
0 -2.1204510016e+07 Pr: 102(98953); Du: 0(4.73182e-11) 0s
126 -3.4742560406e+06 Pr: 0(0) 0s
Solving the original LP from the solution after postsolve
Model name : linopy-problem-lx2fjbrq
Model status : Optimal
Simplex iterations: 126
Objective value : -3.4742560406e+06
Relative P-D gap : 1.0722555433e-15
HiGHS run time : 0.00
Writing the solution to /tmp/linopy-solve-beouz462.sol
[2]:
('ok', 'optimal')
Get mean generator power by bus and carrier:
[3]:
gen = n.generators.assign(g=n.generators_t.p.mean()).groupby(["bus", "carrier"]).g.sum()
Plot the electricity flows:
[4]:
# links are not displayed for prettier output ('link_widths=0')
n.plot(
bus_sizes=gen / 5e3,
bus_colors={"gas": "indianred", "wind": "midnightblue"},
margin=0.5,
flow="mean",
line_widths=0.1,
link_widths=0,
)
plt.show()
/home/docs/checkouts/readthedocs.org/user_builds/pypsa/envs/latest/lib/python3.13/site-packages/cartopy/mpl/feature_artist.py:144: UserWarning: facecolor will have no effect as it has been defined as "never".
warnings.warn('facecolor will have no effect as it has been '
Plot the electricity flows with a different projection and a colored map:
[5]:
# links are not displayed for prettier output ('link_widths=0')
n.plot(
bus_sizes=gen / 5e3,
bus_colors={"gas": "indianred", "wind": "midnightblue"},
margin=0.5,
flow="mean",
line_widths=0.1,
link_widths=0,
projection=ccrs.EqualEarth(),
color_geomap=True,
)
plt.show()
/home/docs/checkouts/readthedocs.org/user_builds/pypsa/envs/latest/lib/python3.13/site-packages/cartopy/mpl/feature_artist.py:144: UserWarning: facecolor will have no effect as it has been defined as "never".
warnings.warn('facecolor will have no effect as it has been '
Set arbitrary values as flow argument using the MultiIndex of n.branches():
[6]:
flow = pd.Series(10, index=n.branches().index)
[7]:
flow
[7]:
component name
Line 0 10
1 10
2 10
3 10
4 10
5 10
6 10
Link Norwich Converter 10
Norway Converter 10
Bremen Converter 10
DC link 10
dtype: int64
[8]:
# links are not displayed for prettier output ('link_widths=0')
n.plot(
bus_sizes=gen / 5e3,
bus_colors={"gas": "indianred", "wind": "midnightblue"},
margin=0.5,
flow=flow,
line_widths=2.7,
link_widths=0,
projection=ccrs.EqualEarth(),
color_geomap=True,
)
plt.show()
/home/docs/checkouts/readthedocs.org/user_builds/pypsa/envs/latest/lib/python3.13/site-packages/cartopy/mpl/feature_artist.py:144: UserWarning: facecolor will have no effect as it has been defined as "never".
warnings.warn('facecolor will have no effect as it has been '
Adjust link colors according to their mean load:
[9]:
# Pandas series with MultiIndex
# links are not displayed for prettier output ('link_widths=0')
collection = n.plot(
bus_sizes=gen / 5e3,
bus_colors={"gas": "indianred", "wind": "midnightblue"},
margin=0.5,
flow=flow,
line_widths=2.7,
link_widths=0,
projection=ccrs.EqualEarth(),
color_geomap=True,
line_colors=n.lines_t.p0.mean().abs(),
)
plt.colorbar(collection[2], fraction=0.04, pad=0.004, label="Flow in MW")
plt.show()
/home/docs/checkouts/readthedocs.org/user_builds/pypsa/envs/latest/lib/python3.13/site-packages/cartopy/mpl/feature_artist.py:144: UserWarning: facecolor will have no effect as it has been defined as "never".
warnings.warn('facecolor will have no effect as it has been '
