Heat Pumps and Thermal Storage¶

In this example, a 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.

In [ ]:

import pandas as pd

import pypsa

n = pypsa.Network()
n.set_snapshots(pd.date_range("2025-01-01 00:00", "2025-01-01 03:00", freq="h"))

n.add("Bus", "power", carrier="AC")
n.add("Bus", "heat", carrier="heat")

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

n.add("Load", "heat demand", bus="heat", p_set=20);

import pandas as pd import pypsa n = pypsa.Network() n.set_snapshots(pd.date_range("2025-01-01 00:00", "2025-01-01 03:00", freq="h")) n.add("Bus", "power", carrier="AC") n.add("Bus", "heat", carrier="heat") n.add( "Generator", "wind turbine", bus="power", carrier="wind", p_nom_extendable=True, p_max_pu=[0.0, 0.2, 0.7, 0.4], capital_cost=500, ) n.add("Load", "heat demand", bus="heat", p_set=20);

The heat pump has time-varying efficiency (i.e. its coefficient of performance, COP) due to changing ambient temperatures.

In [ ]:

n.add(
    "Link",
    "heat pump",
    bus0="power",
    bus1="heat",
    efficiency=[2.5, 3.0, 3.2, 3.0],
    capital_cost=1000,
    p_nom_extendable=True,
);

n.add( "Link", "heat pump", bus0="power", bus1="heat", efficiency=[2.5, 3.0, 3.2, 3.0], capital_cost=1000, p_nom_extendable=True, );

The hot water tank has a standing loss of 1% of its state of charge per hour.

In [ ]:

n.add(
    "Store",
    "water tank",
    bus="heat",
    e_cyclic=True,
    e_nom=100,
    standing_loss=0.01,
);

n.add( "Store", "water tank", bus="heat", e_cyclic=True, e_nom=100, standing_loss=0.01, );

The wind turbine and the heat pump can be sized by the optimisation, while the water tank has a fixed size of 100 MWh.

In [ ]:

n.optimize();

n.optimize();

In [ ]:

pd.DataFrame({attr: n.stores_t[attr]["water tank"] for attr in ["p", "e"]}).round(3)

pd.DataFrame({attr: n.stores_t[attr]["water tank"] for attr in ["p", "e"]}).round(3)

In [ ]:

pd.DataFrame({attr: n.links_t[attr]["heat pump"] for attr in ["p0", "p1"]}).round(3)

pd.DataFrame({attr: n.links_t[attr]["heat pump"] for attr in ["p0", "p1"]}).round(3)