From ea0e2f66ce41107addcc018bca05700c9ead542c Mon Sep 17 00:00:00 2001
From: Parisra <102019906+Parisra@users.noreply.github.com>
Date: Mon, 29 Apr 2024 15:58:42 +0200
Subject: [PATCH] Fix empty array error

In case there are no extendable solar generators in the network, the constraint is not implemented. Without this condition, an 'empty array' error will arise.
---
 scripts/solve_network.py | 101 +++++++++++++++------------------------
 1 file changed, 38 insertions(+), 63 deletions(-)

diff --git a/scripts/solve_network.py b/scripts/solve_network.py
index 08521273..de8c4935 100644
--- a/scripts/solve_network.py
+++ b/scripts/solve_network.py
@@ -228,76 +228,51 @@ def add_solar_potential_constraints(n, config):
         "solar-hsat": config["renewable"]["solar"]["capacity_per_sqkm"]
         / config["renewable"]["solar-hsat"]["capacity_per_sqkm"],
     }
-    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_today = n.generators[
-        (n.generators.carrier == "solar") & (n.generators.p_nom_extendable)
-    ].index
-    solar_hsat = n.generators[(n.generators.carrier == "solar-hsat")].index
-    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
-        )
-
     rename = {"Generator-ext": "Generator"}
-    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 = (
+    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_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 :  
+
+        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)
+            )
+            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()
-    )
+        lhs = (
+            (n.model["Generator-p_nom"].rename(rename).loc[solar]
+            *land_use.squeeze().values)
+            .groupby(ggrouper) 
+            .sum()
+        )    
 
-    print("adding solar rooftop constraints...")
-    n.model.add_constraints(lhs <= rhs, name="solar_potential")
+        print('adding solar rooftop constraints...')
+        n.model.add_constraints(lhs <= rhs, name="solar_potential")   
 
 
 def add_co2_sequestration_limit(n, limit=200):