linopy v0.5.8 with objective value is 0 when re-running the code

[jome1]

Version Checks (indicate both or one)

• I have confirmed this bug exists on the lastest release of Linopy.

• I have confirmed this bug exists on the current master branch of Linopy.

Issue Description

I am using linopy v0.5.8 in a Jupyter Notebook to solve a pypsa toy model.
When running the notebook the first time everything is fine, but when changing a parameter in a pypsa component and running the code again, the onjective value is 0. I have to restart the Kernel everytime to make it work.

Using linopy v0.5.5 everything is fine.

Reproducible Example

import pandas as pd
import pypsa

ts = pd.DataFrame(
    {
        "load_mw": [10, 15, 30, 20],
        "wind_pu": [1,1,1,1],
    },
    index=pd.to_datetime([
        "2019-01-01 00:00:00",
        "2019-01-01 01:00:00",
        "2019-01-01 02:00:00",
        "2019-01-01 03:00:00",
    ])
)
ts.index.name = "timestamp"

n = pypsa.Network()
n.set_snapshots(ts.index)  # snapshots 0..x-1

# Set snapshot weightings (hours each snapshot represents)
n.snapshot_weightings[:] = 1  # Each snapshot represents x hour

# Add all carrier definitions upfront
n.add("Carrier", carriers, color=colors)

# Add a single bus
n.add("Bus", "bus", carrier="AC")

# add load
n.add("Load", "load", p_set=ts['load_mw'], bus="bus")
    
n.add(
    "Generator",
    "gas",
    carrier="gas",
    bus="bus",
    p_nom=10,  
    p_nom_min=10,
    marginal_cost=70,
    capital_cost=30000,
    p_nom_extendable=True,
)

n.add(
    "Generator",
    "wind",
    carrier="wind",
    bus="bus",
    p_nom=10,  
    p_nom_min=10,
    marginal_cost=0,
    capital_cost=100000,
    p_max_pu=ts['wind_pu'],
    p_nom_extendable=True,
)

n.optimize(solver_name="highs")

Expected Behavior

when changing the parameters of a component and rerunning the code, a new plausible result should be shown.

Installed Versions

Details

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