Fix stand_by_cost implementation in optimization

doc/release_notes.rst

@@ -28,6 +28,8 @@ Upcoming Release
 
 * Enhancement: Allow optimising dispatch of only a subset of investment periods.
 
+* Bugfix: Fix stand-by-costs optimization, which now works with linopy and native.
+
 PyPSA 0.25.2 (30th September 2023)
 ==================================
 

pypsa/optimization/optimize.py

@@ -158,7 +158,7 @@ def define_objective(n, sns):
         )
         stand_by_cost.columns.name = f"{c}-com"
         status = n.model.variables[f"{c}-status"].loc[:, stand_by_cost.columns]
-        m.objective = m.objective + (status * stand_by_cost).sum()
+        objective.append((status * stand_by_cost).sum())
 
     # investment
     for c, attr in nominal_attrs.items():

test/test_stand_by_cost.py

@@ -2,13 +2,15 @@
 import numpy as np
 import pandas as pd
 import pytest
-from conftest import SUPPORTED_APIS, optimize
+from conftest import optimize
 from numpy.testing import assert_array_almost_equal as equal
 
 import pypsa
 
+SUPPORTED_APIS_STAND_BY_COST = ["linopy", "native"]
 
-@pytest.mark.parametrize("api", SUPPORTED_APIS)
+
+@pytest.mark.parametrize("api", SUPPORTED_APIS_STAND_BY_COST)
 def test_stand_by_cost(api):
     """
     This test is based on https://pypsa.readthedocs.io/en/latest/examples/unit-
@@ -58,3 +60,5 @@ def test_stand_by_cost(api):
     expected_cost = np.array([80000, 120000, 100000, 56010], dtype=float).T
 
     equal(cost.sum(1), expected_cost)
+
+    equal(sum(expected_cost), n.objective)