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
```

```
[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 linear problem using Glpk solver
INFO:linopy.io: Writing time: 0.16s
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
```

```
GLPSOL--GLPK LP/MIP Solver 5.0
Parameter(s) specified in the command line:
--lp /tmp/linopy-problem-5h0l4q3x.lp --output /tmp/linopy-solve-kg7vmoig.sol
Reading problem data from '/tmp/linopy-problem-5h0l4q3x.lp'...
39 rows, 19 columns, 66 non-zeros
264 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-kg7vmoig.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_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 |

capital_cost | 0.0 |

standing_loss | 0.01 |

build_year | 0 |

lifetime | inf |

e_nom_opt | 24.6229 |

```
[7]:
```

```
network.generators.loc[["wind turbine"]].T
```

```
[7]:
```

Generator | wind turbine |
---|---|

attribute | |

bus | 0 |

control | PQ |

type | |

p_nom | 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 |

build_year | 0 |

lifetime | inf |

capital_cost | 500.0 |

efficiency | 1.0 |

committable | False |

start_up_cost | 0.0 |

shut_down_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 |

p_nom_opt | 26.1118 |