Note

You can download this example as a Jupyter notebook or start it in interactive mode.

Wind Turbine combined with Heat Pump and Water Tank#

In this example the heat demand is supplied by a wind turbine in combination with a heat pump and a water tank that stores hot water with a standing loss.

[1]:
import pypsa
import pandas as pd
from pyomo.environ import Constraint
ERROR 1: PROJ: proj_create_from_database: Open of /home/docs/checkouts/readthedocs.org/user_builds/pypsa/conda/v0.26.0/share/proj failed
[2]:
network = pypsa.Network()
network.set_snapshots(pd.date_range("2016-01-01 00:00", "2016-01-01 03:00", freq="H"))

network.add("Bus", "0", carrier="AC")
network.add("Bus", "0 heat", carrier="heat")

network.add("Carrier", "wind")
network.add("Carrier", "heat")

network.add(
    "Generator",
    "wind turbine",
    bus="0",
    carrier="wind",
    p_nom_extendable=True,
    p_max_pu=[0.0, 0.2, 0.7, 0.4],
    capital_cost=500,
)

network.add("Load", "heat demand", bus="0 heat", p_set=20.0)

# NB: Heat pump has changing efficiency (properly the Coefficient of Performance, COP)
# due to changing ambient temperature
network.add(
    "Link",
    "heat pump",
    bus0="0",
    bus1="0 heat",
    efficiency=[2.5, 3.0, 3.2, 3.0],
    capital_cost=1000,
    p_nom_extendable=True,
)

network.add(
    "Store",
    "water tank",
    bus="0 heat",
    e_cyclic=True,
    e_nom_extendable=True,
    standing_loss=0.01,
)
[3]:
network.optimize()
INFO:linopy.model: Solve problem using Glpk solver
INFO:linopy.io: Writing time: 0.06s
INFO:linopy.constants: Optimization successful:
Status: ok
Termination condition: optimal
Solution: 19 primals, 39 duals
Objective: 2.35e+04
Solver model: not available
Solver message: optimal

INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, Link-ext-p-lower, Link-ext-p-upper, Store-ext-e-lower, Store-ext-e-upper, Store-energy_balance were not assigned to the network.
GLPSOL--GLPK LP/MIP Solver 5.0
Parameter(s) specified in the command line:
 --lp /tmp/linopy-problem-itbq2tv6.lp --output /tmp/linopy-solve-05y8d7_8.sol
Reading problem data from '/tmp/linopy-problem-itbq2tv6.lp'...
39 rows, 19 columns, 66 non-zeros
206 lines were read
GLPK Simplex Optimizer 5.0
39 rows, 19 columns, 66 non-zeros
Preprocessing...
20 rows, 16 columns, 43 non-zeros
Scaling...
 A: min|aij| =  2.000e-01  max|aij| =  3.200e+00  ratio =  1.600e+01
Problem data seem to be well scaled
Constructing initial basis...
Size of triangular part is 19
      0: obj =   0.000000000e+00 inf =   1.980e+02 (4)
      9: obj =   2.549967375e+04 inf =   0.000e+00 (0)
*    11: obj =   2.350058582e+04 inf =   0.000e+00 (0)
OPTIMAL LP SOLUTION FOUND
Time used:   0.0 secs
Memory used: 0.1 Mb (56692 bytes)
Writing basic solution to '/tmp/linopy-solve-05y8d7_8.sol'...
[3]:
('ok', 'optimal')
[4]:
pd.DataFrame({attr: network.stores_t[attr]["water tank"] for attr in ["p", "e"]})
[4]:
p e
snapshot
2016-01-01 00:00:00 20.00000 4.37671
2016-01-01 01:00:00 4.33294 0.00000
2016-01-01 02:00:00 -13.42310 13.42310
2016-01-01 03:00:00 -11.33410 24.62290
[5]:
pd.DataFrame({attr: network.links_t[attr]["heat pump"] for attr in ["p0", "p1"]})
[5]:
p0 p1
snapshot
2016-01-01 00:00:00 0.00000 -0.00000
2016-01-01 01:00:00 5.22235 -15.66705
2016-01-01 02:00:00 10.44470 -33.42304
2016-01-01 03:00:00 10.44470 -31.33410
[6]:
network.stores.loc[["water tank"]].T
[6]:
Store water tank
attribute
bus 0 heat
type
carrier heat
e_nom 0.0
e_nom_mod 0.0
e_nom_extendable True
e_nom_min 0.0
e_nom_max inf
e_min_pu 0.0
e_max_pu 1.0
e_initial 0.0
e_initial_per_period False
e_cyclic True
e_cyclic_per_period True
p_set 0.0
q_set 0.0
sign 1.0
marginal_cost 0.0
marginal_cost_quadratic 0.0
capital_cost 0.0
standing_loss 0.01
build_year 0
lifetime inf
e_nom_opt 24.6229
n_mod 0
[7]:
network.generators.loc[["wind turbine"]].T
[7]:
Generator wind turbine
attribute
bus 0
control PQ
type
p_nom 0.0
p_nom_mod 0.0
p_nom_extendable True
p_nom_min 0.0
p_nom_max inf
p_min_pu 0.0
p_max_pu 1.0
p_set 0.0
q_set 0.0
sign 1.0
carrier wind
marginal_cost 0.0
marginal_cost_quadratic 0.0
build_year 0
lifetime inf
capital_cost 500.0
efficiency 1.0
committable False
start_up_cost 0.0
shut_down_cost 0.0
stand_by_cost 0.0
min_up_time 0
min_down_time 0
up_time_before 1
down_time_before 0
ramp_limit_up NaN
ramp_limit_down NaN
ramp_limit_start_up 1.0
ramp_limit_shut_down 1.0
weight 1.0
p_nom_opt 26.1118
n_mod 0