diff --git a/scripts/solve_network.py b/scripts/solve_network.py
index 302f1be2..665274b7 100644
--- a/scripts/solve_network.py
+++ b/scripts/solve_network.py
@@ -230,79 +230,70 @@ def add_solar_potential_constraints(n, config):
     }
     rename = {"Generator-ext": "Generator"}
 
-    gen_index = n.generators[n.generators.p_nom_extendable].index
-    filters = [
-        ("solar", True),
-        ("thermal", False),
-        ("rooftop", False),
-    ]  ## filter all utility solar generation except solar thermal
-    solar = reduce(
-        lambda gen_index, f: gen_index[gen_index.str.contains(f[0]) == f[1]],
-        filters,
-        gen_index,
-    )
+    solar_carriers = ["solar", "solar-hsat"]
+    solar = n.generators[n.generators.carrier.isin(solar_carriers)
+                         & n.generators.p_nom_extendable].index
+    
     solar_today = n.generators[
         (n.generators.carrier == "solar") & (n.generators.p_nom_extendable)
     ].index
     solar_hsat = n.generators[(n.generators.carrier == "solar-hsat")].index
 
-    if not solar.empty:
+    if solar.empty: return
 
-        land_use = pd.DataFrame(1, index=solar, columns=["land_use_factor"])
-        for key in land_use_factors.keys():
-            land_use = land_use.apply(
-                lambda x: (x * land_use_factors[key]) if key in x.name else x, axis=1
-            )
-
-        if "m" in snakemake.wildcards.clusters:
-            location = pd.Series(
-                [" ".join(i.split(" ")[:2]) for i in n.generators.index],
-                index=n.generators.index,
-            )
-            ggrouper = pd.Series(
-                n.generators.loc[solar].index.rename("bus").map(location),
-                index=n.generators.loc[solar].index,
-            ).to_xarray()
-            rhs = (
-                n.generators.loc[solar_today, "p_nom_max"]
-                .groupby(
-                    n.generators.loc[solar_today].index.rename("bus").map(location)
-                )
-                .sum()
-                - n.generators.loc[solar_hsat, "p_nom_opt"]
-                .groupby(n.generators.loc[solar_hsat].index.rename("bus").map(location))
-                .sum()
-                * land_use_factors["solar-hsat"]
-            ).clip(lower=0)
-
-        else:
-            location = (
-                n.buses.location
-                if "location" in n.buses.columns
-                else pd.Series(n.buses.index, index=n.buses.index)
-            )
-            ggrouper = n.generators.loc[solar].bus
-            rhs = (
-                n.generators.loc[solar_today, "p_nom_max"]
-                .groupby(n.generators.loc[solar_today].bus.map(location))
-                .sum()
-                - n.generators.loc[solar_hsat, "p_nom_opt"]
-                .groupby(n.generators.loc[solar_hsat].bus.map(location))
-                .sum()
-                * land_use_factors["solar-hsat"]
-            ).clip(lower=0)
-
-        lhs = (
-            (
-                n.model["Generator-p_nom"].rename(rename).loc[solar]
-                * land_use.squeeze().values
-            )
-            .groupby(ggrouper)
-            .sum()
+    land_use = pd.DataFrame(1, index=solar, columns=["land_use_factor"])
+    for carrier, factor in land_use_factors.items():
+        land_use = land_use.apply(
+            lambda x: (x * factor) if carrier in x.name else x, axis=1
         )
 
-        print("adding solar rooftop constraints...")
-        n.model.add_constraints(lhs <= rhs, name="solar_potential")
+    if "m" in snakemake.wildcards.clusters:
+        location = pd.Series(
+            [" ".join(i.split(" ")[:2]) for i in n.generators.index],
+            index=n.generators.index,
+        )
+        ggrouper = pd.Series(
+            n.generators.loc[solar].index.rename("bus").map(location),
+            index=n.generators.loc[solar].index,
+        ).to_xarray()
+        rhs = (
+            n.generators.loc[solar_today, "p_nom_max"]
+            .groupby(
+                n.generators.loc[solar_today].index.rename("bus").map(location)
+            )
+            .sum()
+            - n.generators.loc[solar_hsat, "p_nom_opt"]
+            .groupby(n.generators.loc[solar_hsat].index.rename("bus").map(location))
+            .sum()
+            * land_use_factors["solar-hsat"]
+        ).clip(lower=0)
+
+    else:
+        location = (
+            pd.Series(n.buses.index, index=n.buses.index)
+        )
+        ggrouper = n.generators.loc[solar].bus
+        rhs = (
+            n.generators.loc[solar_today, "p_nom_max"]
+            .groupby(n.generators.loc[solar_today].bus.map(location))
+            .sum()
+            - n.generators.loc[solar_hsat, "p_nom_opt"]
+            .groupby(n.generators.loc[solar_hsat].bus.map(location))
+            .sum()
+            * land_use_factors["solar-hsat"]
+        ).clip(lower=0)
+
+    lhs = (
+        (
+            n.model["Generator-p_nom"].rename(rename).loc[solar]
+            * land_use.squeeze()
+        )
+        .groupby(ggrouper)
+        .sum()
+    )
+
+    print("adding solar rooftop constraints...")
+    n.model.add_constraints(lhs <= rhs, name="solar_potential")
 
 
 def add_co2_sequestration_limit(n, limit=200):
@@ -1053,13 +1044,13 @@ if __name__ == "__main__":
         from _helpers import mock_snakemake
 
         snakemake = mock_snakemake(
-            "solve_sector_network",
-            configfiles="../config/test/config.perfect.yaml",
+            "solve_network",
+            configfiles="../config/test/config.electricity.yaml",
             simpl="",
             opts="",
-            clusters="37",
-            ll="v1.0",
-            sector_opts="CO2L0-1H-T-H-B-I-A-dist1",
+            clusters="5",
+            ll="v1.5",
+            sector_opts="Co2L-24h",
             planning_horizons="2030",
         )
     configure_logging(snakemake)