#Residential case study sim1.createScenario(name='Resi_repower', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e) #create the scenario, name and mod file attach for mat in MATERIALS: - materialfile = os.path.join(baselinesfolder, 'baseline_material_mass_'+str(mat)+'.csv') + materialfile_m = os.path.join(baselinesfolder, 'baseline_material_mass_'+str(mat)+'.csv') materialfile_e = os.path.join(baselinesfolder, 'baseline_material_energy_'+str(mat)+'.csv') sim1.scenario['Resi_repower'].addMaterial(mat, massmatfile=materialfile_m, energymatfile=materialfile_e) # add all materials listed in MATERIALS@@ -14965,7 +14965,7 @@
CdTeRamp_full
-<matplotlib.legend.Legend at 0x1bd577ff450>
+<matplotlib.legend.Legend at 0x2507ccbe6d0>
@@ -15039,7 +15039,9 @@ CdTeRamp_fullCdTeRamp_full
-In [19]:
+In [44]:
decommisioned = sim1_yearly.filter(like='Decommisioned')
@@ -15156,7 +15158,7 @@ CdTeRamp_full
- Out[19]:
+ Out[44]:
@@ -15193,7 +15195,7 @@ CdTeRamp_full
-In [15]:
+In [45]:
plt.plot(sim1_energyGen/1e6)
@@ -15217,7 +15219,7 @@ CdTeRamp_full
- Out[15]:
+ Out[45]:
@@ -15254,13 +15256,13 @@ CdTeRamp_full
-In [72]:
+In [46]:
cumu_energy_gen = sim1_energyGen.cumsum().loc[[2060]]
cumu_energy_gen_MWh = cumu_energy_gen/1e6
-cumu_energy_gen_MWh.columns = ['Keep_[MWh]', 'Repower_[MWh]']
-cumu_energy_gen_MWh.index=['Energy_Gen']
+cumu_energy_gen_MWh.columns = ['Keep', 'Repower']
+cumu_energy_gen_MWh.index=['Energy Gen [MWh]']
cumu_energy_gen_MWh
@@ -15279,7 +15281,7 @@ CdTeRamp_full
- Out[72]:
+ Out[46]:
@@ -15302,13 +15304,13 @@ CdTeRamp_full
Keep_[MWh]
- Repower_[MWh]
+ Keep
+ Repower
- Energy_Gen
+ Energy Gen [MWh]
148.307821
365.792917
@@ -15328,7 +15330,7 @@ CdTeRamp_full
-In [73]:
+In [47]:
-In [75]:
+In [48]:
cumu_energy_MWh = cumu_energy_gen_MWh.copy()
-cumu_energy_MWh.loc['Energy_Demand','Keep_[MWh]'] = energydemand_keep
-cumu_energy_MWh.loc['Energy_Demand','Repower_[MWh]'] = energydemand_repower
+cumu_energy_MWh.loc['Energy Demand [MWh]','Keep'] = energydemand_keep
+cumu_energy_MWh.loc['Energy Demand [MWh]','Repower'] = energydemand_repower
cumu_energy_MWh
@@ -15370,7 +15372,7 @@ CdTeRamp_full
- Out[75]:
+ Out[48]:
@@ -15393,20 +15395,20 @@ CdTeRamp_full
Keep_[MWh]
- Repower_[MWh]
+ Keep
+ Repower
- Energy_Gen
+ Energy Gen [MWh]
148.307821
365.792917
- Energy_Demand
+ Energy Demand [MWh]
22.049576
- 35.809028
+ 37.518583
@@ -15424,11 +15426,11 @@ CdTeRamp_full
-In [78]:
+In [52]:
#EROI
-cumu_energy_MWh.loc['EROI'] = cumu_energy_MWh.loc['Energy_Gen']/cumu_energy_MWh.loc['Energy_Demand']
+cumu_energy_MWh.loc['EROI'] = cumu_energy_MWh.loc['Energy Gen [MWh]']/cumu_energy_MWh.loc['Energy Demand [MWh]']
cumu_energy_MWh
@@ -15447,7 +15449,7 @@ CdTeRamp_full
- Out[78]:
+ Out[52]:
@@ -15470,25 +15472,25 @@ CdTeRamp_full
Keep_[MWh]
- Repower_[MWh]
+ Keep
+ Repower
- Energy_Gen
+ Energy Gen [MWh]
148.307821
365.792917
- Energy_Demand
+ Energy Demand [MWh]
22.049576
- 35.809028
+ 37.518583
EROI
6.726108
- 10.215103
+ 9.749646
@@ -15506,7 +15508,7 @@ CdTeRamp_full
-In [50]:
+In [53]:
-In [ ]:
+In [54]:
-
+#wastes
+modwaste_keep = sim1_cumu.iloc[-1]['WasteAll_Module_SIPS_Resi_keep_[Tonnes]']
+modwaste_repower = sim1_cumu.iloc[-1]['WasteAll_Module_SIPS_Resi_repower_[Tonnes]']
+
+
+
+
+
+
+
+
+
+
+
+
+In [55]:
+
+
+plt.plot(sim1_yearly.loc[:,'VirginStock_Module_SIPS_Resi_keep_[Tonnes]'], label = 'matdemand_keep')
+plt.plot(sim1_yearly.loc[:,'VirginStock_Module_SIPS_Resi_repower_[Tonnes]'], ls='--', label = 'matdemand_repower')
+
+plt.plot(sim1_yearly.loc[:,'WasteAll_Module_SIPS_Resi_keep_[Tonnes]'], label = 'waste_keep')
+plt.plot(sim1_yearly.loc[:,'WasteAll_Module_SIPS_Resi_repower_[Tonnes]'], ls='--', label='waste_repower')
+
+plt.ylim(0,0.5)
+plt.ylabel('Material Demand and Wastes [tonnes]')
+plt.legend()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Out[55]:
+
+
+
+
+
+<matplotlib.legend.Legend at 0x25009bf7150>
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+In [56]:
+
+
+cumu_metrics = cumu_energy_MWh.copy()
+cumu_metrics.loc['Virgin Material Demands [tonnes]','Keep'] = modmass_keep
+cumu_metrics.loc['Virgin Material Demands [tonnes]','Repower'] = modmass_repower
+cumu_metrics.loc['Wastes [tonnes]', 'Keep'] = modwaste_keep
+cumu_metrics.loc['Wastes [tonnes]', 'Repower'] = modwaste_repower
+
+round(cumu_metrics,2)
@@ -15535,6 +15627,80 @@ CdTeRamp_full
+
+
+
+
+
+
+
+
+
+
+ Out[56]:
+
+
+
+
+
+
+
+
+
+ Keep
+ Repower
+
+
+
+
+ Energy Gen [MWh]
+ 148.31
+ 365.79
+
+
+ Energy Demand [MWh]
+ 22.05
+ 37.52
+
+
+ EROI
+ 6.73
+ 9.75
+
+
+ Virgin Material Demands [tonnes]
+ 0.41
+ 0.84
+
+
+ Wastes [tonnes]
+ 0.39
+ 0.81
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/docs/dev/SIPS2025-CaseStudies.ipynb b/docs/dev/SIPS2025-CaseStudies.ipynb
index 69e0682..47c5d22 100644
--- a/docs/dev/SIPS2025-CaseStudies.ipynb
+++ b/docs/dev/SIPS2025-CaseStudies.ipynb
@@ -142,7 +142,7 @@
"#Residential case study\n",
"sim1.createScenario(name='Resi_repower', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e) #create the scenario, name and mod file attach\n",
"for mat in MATERIALS:\n",
- " materialfile = os.path.join(baselinesfolder, 'baseline_material_mass_'+str(mat)+'.csv')\n",
+ " materialfile_m = os.path.join(baselinesfolder, 'baseline_material_mass_'+str(mat)+'.csv')\n",
" materialfile_e = os.path.join(baselinesfolder, 'baseline_material_energy_'+str(mat)+'.csv')\n",
" sim1.scenario['Resi_repower'].addMaterial(mat, massmatfile=materialfile_m, energymatfile=materialfile_e) # add all materials listed in MATERIALS"
]
@@ -251,7 +251,7 @@
{
"data": {
"text/plain": [
- ""
+ ""
]
},
"execution_count": 11,
@@ -309,7 +309,9 @@
"********************\n",
"Finished Area+Power Generation Calculations\n",
"==> Working on Material : glass\n",
+ "Recycled surplus End of Sim for Mat glass Scenario Resi_repower = 8.411539478439826e-05 tonnes.\n",
"==> Working on Material : aluminium_frames\n",
+ "Recycled surplus End of Sim for Mat aluminium_frames Scenario Resi_repower = 0.000995863752972422 tonnes.\n",
"==> Working on Material : silver\n",
"==> Working on Material : silicon\n",
"==> Working on Material : copper\n",
@@ -376,7 +378,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 44,
"id": "6db33248-50a3-4274-a1b3-a9ced04a20e7",
"metadata": {},
"outputs": [
@@ -386,7 +388,7 @@
"Text(0, 0.5, 'Decommissioned Capacity [kW_dc]')"
]
},
- "execution_count": 19,
+ "execution_count": 44,
"metadata": {},
"output_type": "execute_result"
},
@@ -411,7 +413,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 45,
"id": "eedd27d0-17da-4752-8479-1191cd246ead",
"metadata": {},
"outputs": [
@@ -421,7 +423,7 @@
"Text(0, 0.5, 'Annual Energy Generation [MWh]')"
]
},
- "execution_count": 15,
+ "execution_count": 45,
"metadata": {},
"output_type": "execute_result"
},
@@ -445,7 +447,7 @@
},
{
"cell_type": "code",
- "execution_count": 72,
+ "execution_count": 46,
"id": "5b55fdcd-bfc4-4742-a365-8816b5cc38a9",
"metadata": {},
"outputs": [
@@ -470,13 +472,13 @@
" \n",
" \n",
" Keep_[MWh] \n",
- " Repower_[MWh] \n",
+ " Keep \n",
+ " Repower \n",
" \n",
" \n",
" \n",
" \n",
- " Energy_Gen \n",
+ " Energy Gen [MWh] \n",
" 148.307821 \n",
" 365.792917 \n",
" \n",
@@ -485,11 +487,11 @@
""
],
"text/plain": [
- " Keep_[MWh] Repower_[MWh]\n",
- "Energy_Gen 148.307821 365.792917"
+ " Keep Repower\n",
+ "Energy Gen [MWh] 148.307821 365.792917"
]
},
- "execution_count": 72,
+ "execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
@@ -497,14 +499,14 @@
"source": [
"cumu_energy_gen = sim1_energyGen.cumsum().loc[[2060]]\n",
"cumu_energy_gen_MWh = cumu_energy_gen/1e6\n",
- "cumu_energy_gen_MWh.columns = ['Keep_[MWh]', 'Repower_[MWh]']\n",
- "cumu_energy_gen_MWh.index=['Energy_Gen']\n",
+ "cumu_energy_gen_MWh.columns = ['Keep', 'Repower']\n",
+ "cumu_energy_gen_MWh.index=['Energy Gen [MWh]']\n",
"cumu_energy_gen_MWh"
]
},
{
"cell_type": "code",
- "execution_count": 73,
+ "execution_count": 47,
"id": "245df34e-ddc9-416c-9eb3-512ab8d8341c",
"metadata": {},
"outputs": [],
@@ -516,7 +518,7 @@
},
{
"cell_type": "code",
- "execution_count": 75,
+ "execution_count": 48,
"id": "cce5ad74-8191-4b71-b4f1-dbea5c280317",
"metadata": {},
"outputs": [
@@ -541,46 +543,46 @@
" \n",
" \n",
" Keep_[MWh] \n",
- " Repower_[MWh] \n",
+ " Keep \n",
+ " Repower \n",
" \n",
" \n",
" \n",
" \n",
- " Energy_Gen \n",
+ " Energy Gen [MWh] \n",
" 148.307821 \n",
" 365.792917 \n",
" \n",
" \n",
- " Energy_Demand \n",
+ " Energy Demand [MWh] \n",
" 22.049576 \n",
- " 35.809028 \n",
+ " 37.518583 \n",
" \n",
" \n",
"\n",
" "
],
"text/plain": [
- " Keep_[MWh] Repower_[MWh]\n",
- "Energy_Gen 148.307821 365.792917\n",
- "Energy_Demand 22.049576 35.809028"
+ " Keep Repower\n",
+ "Energy Gen [MWh] 148.307821 365.792917\n",
+ "Energy Demand [MWh] 22.049576 37.518583"
]
},
- "execution_count": 75,
+ "execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cumu_energy_MWh = cumu_energy_gen_MWh.copy()\n",
- "cumu_energy_MWh.loc['Energy_Demand','Keep_[MWh]'] = energydemand_keep\n",
- "cumu_energy_MWh.loc['Energy_Demand','Repower_[MWh]'] = energydemand_repower\n",
+ "cumu_energy_MWh.loc['Energy Demand [MWh]','Keep'] = energydemand_keep\n",
+ "cumu_energy_MWh.loc['Energy Demand [MWh]','Repower'] = energydemand_repower\n",
"cumu_energy_MWh"
]
},
{
"cell_type": "code",
- "execution_count": 78,
+ "execution_count": 52,
"id": "6319d6ed-18d2-4869-8d66-4a307731c6d5",
"metadata": {},
"outputs": [
@@ -605,51 +607,51 @@
" \n",
" \n",
" Keep_[MWh] \n",
- " Repower_[MWh] \n",
+ " Keep \n",
+ " Repower \n",
" \n",
" \n",
" \n",
" \n",
- " Energy_Gen \n",
+ " Energy Gen [MWh] \n",
" 148.307821 \n",
" 365.792917 \n",
" \n",
" \n",
- " Energy_Demand \n",
+ " Energy Demand [MWh] \n",
" 22.049576 \n",
- " 35.809028 \n",
+ " 37.518583 \n",
" \n",
" \n",
" EROI \n",
" 6.726108 \n",
- " 10.215103 \n",
+ " 9.749646 \n",
" \n",
" \n",
"\n",
""
],
"text/plain": [
- " Keep_[MWh] Repower_[MWh]\n",
- "Energy_Gen 148.307821 365.792917\n",
- "Energy_Demand 22.049576 35.809028\n",
- "EROI 6.726108 10.215103"
+ " Keep Repower\n",
+ "Energy Gen [MWh] 148.307821 365.792917\n",
+ "Energy Demand [MWh] 22.049576 37.518583\n",
+ "EROI 6.726108 9.749646"
]
},
- "execution_count": 78,
+ "execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#EROI\n",
- "cumu_energy_MWh.loc['EROI'] = cumu_energy_MWh.loc['Energy_Gen']/cumu_energy_MWh.loc['Energy_Demand']\n",
+ "cumu_energy_MWh.loc['EROI'] = cumu_energy_MWh.loc['Energy Gen [MWh]']/cumu_energy_MWh.loc['Energy Demand [MWh]']\n",
"cumu_energy_MWh"
]
},
{
"cell_type": "code",
- "execution_count": 50,
+ "execution_count": 53,
"id": "77030757-12bb-4e0e-834e-6a23fbd59d8c",
"metadata": {},
"outputs": [],
@@ -661,11 +663,139 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "id": "720d84de-79ee-45bc-976f-4d2261852ea4",
+ "execution_count": 54,
+ "id": "7a822a59-7d0d-4c03-90e6-9fe451be307a",
"metadata": {},
"outputs": [],
- "source": []
+ "source": [
+ "#wastes\n",
+ "modwaste_keep = sim1_cumu.iloc[-1]['WasteAll_Module_SIPS_Resi_keep_[Tonnes]']\n",
+ "modwaste_repower = sim1_cumu.iloc[-1]['WasteAll_Module_SIPS_Resi_repower_[Tonnes]']\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "id": "80bbdad5-bff1-4933-9305-0c1f65ff8349",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 55,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(sim1_yearly.loc[:,'VirginStock_Module_SIPS_Resi_keep_[Tonnes]'], label = 'matdemand_keep')\n",
+ "plt.plot(sim1_yearly.loc[:,'VirginStock_Module_SIPS_Resi_repower_[Tonnes]'], ls='--', label = 'matdemand_repower')\n",
+ "\n",
+ "plt.plot(sim1_yearly.loc[:,'WasteAll_Module_SIPS_Resi_keep_[Tonnes]'], label = 'waste_keep')\n",
+ "plt.plot(sim1_yearly.loc[:,'WasteAll_Module_SIPS_Resi_repower_[Tonnes]'], ls='--', label='waste_repower')\n",
+ "\n",
+ "plt.ylim(0,0.5)\n",
+ "plt.ylabel('Material Demand and Wastes [tonnes]')\n",
+ "plt.legend()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "id": "720d84de-79ee-45bc-976f-4d2261852ea4",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ " \n",
+ " Keep \n",
+ " Repower \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " Energy Gen [MWh] \n",
+ " 148.31 \n",
+ " 365.79 \n",
+ " \n",
+ " \n",
+ " Energy Demand [MWh] \n",
+ " 22.05 \n",
+ " 37.52 \n",
+ " \n",
+ " \n",
+ " EROI \n",
+ " 6.73 \n",
+ " 9.75 \n",
+ " \n",
+ " \n",
+ " Virgin Material Demands [tonnes] \n",
+ " 0.41 \n",
+ " 0.84 \n",
+ " \n",
+ " \n",
+ " Wastes [tonnes] \n",
+ " 0.39 \n",
+ " 0.81 \n",
+ " \n",
+ " \n",
+ "
\n",
+ ""
+ ],
+ "text/plain": [
+ " Keep Repower\n",
+ "Energy Gen [MWh] 148.31 365.79\n",
+ "Energy Demand [MWh] 22.05 37.52\n",
+ "EROI 6.73 9.75\n",
+ "Virgin Material Demands [tonnes] 0.41 0.84\n",
+ "Wastes [tonnes] 0.39 0.81"
+ ]
+ },
+ "execution_count": 56,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "cumu_metrics = cumu_energy_MWh.copy()\n",
+ "cumu_metrics.loc['Virgin Material Demands [tonnes]','Keep'] = modmass_keep\n",
+ "cumu_metrics.loc['Virgin Material Demands [tonnes]','Repower'] = modmass_repower\n",
+ "cumu_metrics.loc['Wastes [tonnes]', 'Keep'] = modwaste_keep\n",
+ "cumu_metrics.loc['Wastes [tonnes]', 'Repower'] = modwaste_repower\n",
+ "\n",
+ "round(cumu_metrics,2)"
+ ]
},
{
"cell_type": "markdown",
diff --git a/docs/dev/SIPS2025-CaseStudies.py b/docs/dev/SIPS2025-CaseStudies.py
index a442903..cb03f9a 100644
--- a/docs/dev/SIPS2025-CaseStudies.py
+++ b/docs/dev/SIPS2025-CaseStudies.py
@@ -87,7 +87,7 @@
#Residential case study
sim1.createScenario(name='Resi_repower', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e) #create the scenario, name and mod file attach
for mat in MATERIALS:
- materialfile = os.path.join(baselinesfolder, 'baseline_material_mass_'+str(mat)+'.csv')
+ materialfile_m = os.path.join(baselinesfolder, 'baseline_material_mass_'+str(mat)+'.csv')
materialfile_e = os.path.join(baselinesfolder, 'baseline_material_energy_'+str(mat)+'.csv')
sim1.scenario['Resi_repower'].addMaterial(mat, massmatfile=materialfile_m, energymatfile=materialfile_e) # add all materials listed in MATERIALS
@@ -163,7 +163,7 @@
sim1_allenergy, sim1_energyGen, sim1_energyDemands_all = sim1.aggregateEnergyResults()
-# In[19]:
+# In[44]:
decommisioned = sim1_yearly.filter(like='Decommisioned')
@@ -173,7 +173,7 @@
plt.ylabel('Decommissioned Capacity [kW_dc]')
-# In[15]:
+# In[45]:
plt.plot(sim1_energyGen/1e6)
@@ -182,17 +182,17 @@
plt.ylabel('Annual Energy Generation [MWh]')
-# In[72]:
+# In[46]:
cumu_energy_gen = sim1_energyGen.cumsum().loc[[2060]]
cumu_energy_gen_MWh = cumu_energy_gen/1e6
-cumu_energy_gen_MWh.columns = ['Keep_[MWh]', 'Repower_[MWh]']
-cumu_energy_gen_MWh.index=['Energy_Gen']
+cumu_energy_gen_MWh.columns = ['Keep', 'Repower']
+cumu_energy_gen_MWh.index=['Energy Gen [MWh]']
cumu_energy_gen_MWh
-# In[73]:
+# In[47]:
#sum the energy demands
@@ -200,24 +200,24 @@
energydemand_repower = sim1_energyDemands_all.filter(like='repower').sum(axis=1).sum()/1e6 #MWh
-# In[75]:
+# In[48]:
cumu_energy_MWh = cumu_energy_gen_MWh.copy()
-cumu_energy_MWh.loc['Energy_Demand','Keep_[MWh]'] = energydemand_keep
-cumu_energy_MWh.loc['Energy_Demand','Repower_[MWh]'] = energydemand_repower
+cumu_energy_MWh.loc['Energy Demand [MWh]','Keep'] = energydemand_keep
+cumu_energy_MWh.loc['Energy Demand [MWh]','Repower'] = energydemand_repower
cumu_energy_MWh
-# In[78]:
+# In[52]:
#EROI
-cumu_energy_MWh.loc['EROI'] = cumu_energy_MWh.loc['Energy_Gen']/cumu_energy_MWh.loc['Energy_Demand']
+cumu_energy_MWh.loc['EROI'] = cumu_energy_MWh.loc['Energy Gen [MWh]']/cumu_energy_MWh.loc['Energy Demand [MWh]']
cumu_energy_MWh
-# In[50]:
+# In[53]:
#material demands
@@ -225,10 +225,38 @@
modmass_repower = sim1_yearly.loc[:,'VirginStock_Module_SIPS_Resi_repower_[Tonnes]'].sum()
-# In[ ]:
+# In[54]:
+
+
+#wastes
+modwaste_keep = sim1_cumu.iloc[-1]['WasteAll_Module_SIPS_Resi_keep_[Tonnes]']
+modwaste_repower = sim1_cumu.iloc[-1]['WasteAll_Module_SIPS_Resi_repower_[Tonnes]']
+
+
+# In[55]:
+
+
+plt.plot(sim1_yearly.loc[:,'VirginStock_Module_SIPS_Resi_keep_[Tonnes]'], label = 'matdemand_keep')
+plt.plot(sim1_yearly.loc[:,'VirginStock_Module_SIPS_Resi_repower_[Tonnes]'], ls='--', label = 'matdemand_repower')
+
+plt.plot(sim1_yearly.loc[:,'WasteAll_Module_SIPS_Resi_keep_[Tonnes]'], label = 'waste_keep')
+plt.plot(sim1_yearly.loc[:,'WasteAll_Module_SIPS_Resi_repower_[Tonnes]'], ls='--', label='waste_repower')
+
+plt.ylim(0,0.5)
+plt.ylabel('Material Demand and Wastes [tonnes]')
+plt.legend()
+
+
+# In[56]:
+cumu_metrics = cumu_energy_MWh.copy()
+cumu_metrics.loc['Virgin Material Demands [tonnes]','Keep'] = modmass_keep
+cumu_metrics.loc['Virgin Material Demands [tonnes]','Repower'] = modmass_repower
+cumu_metrics.loc['Wastes [tonnes]', 'Keep'] = modwaste_keep
+cumu_metrics.loc['Wastes [tonnes]', 'Repower'] = modwaste_repower
+round(cumu_metrics,2)
# # Commercial Case Study
-
In [19]:
+In [44]:
decommisioned = sim1_yearly.filter(like='Decommisioned')
@@ -15156,7 +15158,7 @@ CdTeRamp_full
- Out[19]:
+ Out[44]:
@@ -15193,7 +15195,7 @@ CdTeRamp_full
-
In [15]:
+In [45]:
plt.plot(sim1_energyGen/1e6)
@@ -15217,7 +15219,7 @@ CdTeRamp_full
- Out[15]:
+ Out[45]:
@@ -15254,13 +15256,13 @@ CdTeRamp_full
-
@@ -15279,7 +15281,7 @@ CdTeRamp_full
-
In [72]:
+In [46]:
cumu_energy_gen = sim1_energyGen.cumsum().loc[[2060]]
cumu_energy_gen_MWh = cumu_energy_gen/1e6
-cumu_energy_gen_MWh.columns = ['Keep_[MWh]', 'Repower_[MWh]']
-cumu_energy_gen_MWh.index=['Energy_Gen']
+cumu_energy_gen_MWh.columns = ['Keep', 'Repower']
+cumu_energy_gen_MWh.index=['Energy Gen [MWh]']
cumu_energy_gen_MWh
CdTeRamp_full
- Out[72]:
+ Out[46]:
@@ -15302,13 +15304,13 @@ CdTeRamp_full
Keep_[MWh]
- Repower_[MWh]
+ Keep
+ Repower
- Energy_Gen
+ Energy Gen [MWh]
148.307821
365.792917
@@ -15328,7 +15330,7 @@ CdTeRamp_full
-In [73]:
+In [47]:
-In [75]:
+In [48]:
cumu_energy_MWh = cumu_energy_gen_MWh.copy()
-cumu_energy_MWh.loc['Energy_Demand','Keep_[MWh]'] = energydemand_keep
-cumu_energy_MWh.loc['Energy_Demand','Repower_[MWh]'] = energydemand_repower
+cumu_energy_MWh.loc['Energy Demand [MWh]','Keep'] = energydemand_keep
+cumu_energy_MWh.loc['Energy Demand [MWh]','Repower'] = energydemand_repower
cumu_energy_MWh
@@ -15370,7 +15372,7 @@ CdTeRamp_full
- Out[75]:
+ Out[48]:
@@ -15393,20 +15395,20 @@ CdTeRamp_full
Keep_[MWh]
- Repower_[MWh]
+ Keep
+ Repower
- Energy_Gen
+ Energy Gen [MWh]
148.307821
365.792917
- Energy_Demand
+ Energy Demand [MWh]
22.049576
- 35.809028
+ 37.518583
@@ -15424,11 +15426,11 @@ CdTeRamp_full
-In [78]:
+In [52]:
#EROI
-cumu_energy_MWh.loc['EROI'] = cumu_energy_MWh.loc['Energy_Gen']/cumu_energy_MWh.loc['Energy_Demand']
+cumu_energy_MWh.loc['EROI'] = cumu_energy_MWh.loc['Energy Gen [MWh]']/cumu_energy_MWh.loc['Energy Demand [MWh]']
cumu_energy_MWh
@@ -15447,7 +15449,7 @@ CdTeRamp_full
- Out[78]:
+ Out[52]:
@@ -15470,25 +15472,25 @@ CdTeRamp_full
Keep_[MWh]
- Repower_[MWh]
+ Keep
+ Repower
- Energy_Gen
+ Energy Gen [MWh]
148.307821
365.792917
- Energy_Demand
+ Energy Demand [MWh]
22.049576
- 35.809028
+ 37.518583
EROI
6.726108
- 10.215103
+ 9.749646
@@ -15506,7 +15508,7 @@ CdTeRamp_full
-In [50]:
+In [53]:
-In [ ]:
+In [54]:
-
+#wastes
+modwaste_keep = sim1_cumu.iloc[-1]['WasteAll_Module_SIPS_Resi_keep_[Tonnes]']
+modwaste_repower = sim1_cumu.iloc[-1]['WasteAll_Module_SIPS_Resi_repower_[Tonnes]']
+
+
+
+
+
+
+
+
+
+
+
+
+In [55]:
+
+
+plt.plot(sim1_yearly.loc[:,'VirginStock_Module_SIPS_Resi_keep_[Tonnes]'], label = 'matdemand_keep')
+plt.plot(sim1_yearly.loc[:,'VirginStock_Module_SIPS_Resi_repower_[Tonnes]'], ls='--', label = 'matdemand_repower')
+
+plt.plot(sim1_yearly.loc[:,'WasteAll_Module_SIPS_Resi_keep_[Tonnes]'], label = 'waste_keep')
+plt.plot(sim1_yearly.loc[:,'WasteAll_Module_SIPS_Resi_repower_[Tonnes]'], ls='--', label='waste_repower')
+
+plt.ylim(0,0.5)
+plt.ylabel('Material Demand and Wastes [tonnes]')
+plt.legend()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Out[55]:
+
+
+
+
+
+<matplotlib.legend.Legend at 0x25009bf7150>
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+In [56]:
+
+
+cumu_metrics = cumu_energy_MWh.copy()
+cumu_metrics.loc['Virgin Material Demands [tonnes]','Keep'] = modmass_keep
+cumu_metrics.loc['Virgin Material Demands [tonnes]','Repower'] = modmass_repower
+cumu_metrics.loc['Wastes [tonnes]', 'Keep'] = modwaste_keep
+cumu_metrics.loc['Wastes [tonnes]', 'Repower'] = modwaste_repower
+
+round(cumu_metrics,2)
@@ -15535,6 +15627,80 @@ CdTeRamp_full
+
+
+
+
+
+
+
+
+
+
+ Out[56]:
+
+
+
+
+
+
+
+
+
+ Keep
+ Repower
+
+
+
+
+ Energy Gen [MWh]
+ 148.31
+ 365.79
+
+
+ Energy Demand [MWh]
+ 22.05
+ 37.52
+
+
+ EROI
+ 6.73
+ 9.75
+
+
+ Virgin Material Demands [tonnes]
+ 0.41
+ 0.84
+
+
+ Wastes [tonnes]
+ 0.39
+ 0.81
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/docs/dev/SIPS2025-CaseStudies.ipynb b/docs/dev/SIPS2025-CaseStudies.ipynb
index 69e0682..47c5d22 100644
--- a/docs/dev/SIPS2025-CaseStudies.ipynb
+++ b/docs/dev/SIPS2025-CaseStudies.ipynb
@@ -142,7 +142,7 @@
"#Residential case study\n",
"sim1.createScenario(name='Resi_repower', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e) #create the scenario, name and mod file attach\n",
"for mat in MATERIALS:\n",
- " materialfile = os.path.join(baselinesfolder, 'baseline_material_mass_'+str(mat)+'.csv')\n",
+ " materialfile_m = os.path.join(baselinesfolder, 'baseline_material_mass_'+str(mat)+'.csv')\n",
" materialfile_e = os.path.join(baselinesfolder, 'baseline_material_energy_'+str(mat)+'.csv')\n",
" sim1.scenario['Resi_repower'].addMaterial(mat, massmatfile=materialfile_m, energymatfile=materialfile_e) # add all materials listed in MATERIALS"
]
@@ -251,7 +251,7 @@
{
"data": {
"text/plain": [
- ""
+ ""
]
},
"execution_count": 11,
@@ -309,7 +309,9 @@
"********************\n",
"Finished Area+Power Generation Calculations\n",
"==> Working on Material : glass\n",
+ "Recycled surplus End of Sim for Mat glass Scenario Resi_repower = 8.411539478439826e-05 tonnes.\n",
"==> Working on Material : aluminium_frames\n",
+ "Recycled surplus End of Sim for Mat aluminium_frames Scenario Resi_repower = 0.000995863752972422 tonnes.\n",
"==> Working on Material : silver\n",
"==> Working on Material : silicon\n",
"==> Working on Material : copper\n",
@@ -376,7 +378,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 44,
"id": "6db33248-50a3-4274-a1b3-a9ced04a20e7",
"metadata": {},
"outputs": [
@@ -386,7 +388,7 @@
"Text(0, 0.5, 'Decommissioned Capacity [kW_dc]')"
]
},
- "execution_count": 19,
+ "execution_count": 44,
"metadata": {},
"output_type": "execute_result"
},
@@ -411,7 +413,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 45,
"id": "eedd27d0-17da-4752-8479-1191cd246ead",
"metadata": {},
"outputs": [
@@ -421,7 +423,7 @@
"Text(0, 0.5, 'Annual Energy Generation [MWh]')"
]
},
- "execution_count": 15,
+ "execution_count": 45,
"metadata": {},
"output_type": "execute_result"
},
@@ -445,7 +447,7 @@
},
{
"cell_type": "code",
- "execution_count": 72,
+ "execution_count": 46,
"id": "5b55fdcd-bfc4-4742-a365-8816b5cc38a9",
"metadata": {},
"outputs": [
@@ -470,13 +472,13 @@
" \n",
" \n",
" Keep_[MWh] \n",
- " Repower_[MWh] \n",
+ " Keep \n",
+ " Repower \n",
" \n",
" \n",
" \n",
" \n",
- " Energy_Gen \n",
+ " Energy Gen [MWh] \n",
" 148.307821 \n",
" 365.792917 \n",
" \n",
@@ -485,11 +487,11 @@
""
],
"text/plain": [
- " Keep_[MWh] Repower_[MWh]\n",
- "Energy_Gen 148.307821 365.792917"
+ " Keep Repower\n",
+ "Energy Gen [MWh] 148.307821 365.792917"
]
},
- "execution_count": 72,
+ "execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
@@ -497,14 +499,14 @@
"source": [
"cumu_energy_gen = sim1_energyGen.cumsum().loc[[2060]]\n",
"cumu_energy_gen_MWh = cumu_energy_gen/1e6\n",
- "cumu_energy_gen_MWh.columns = ['Keep_[MWh]', 'Repower_[MWh]']\n",
- "cumu_energy_gen_MWh.index=['Energy_Gen']\n",
+ "cumu_energy_gen_MWh.columns = ['Keep', 'Repower']\n",
+ "cumu_energy_gen_MWh.index=['Energy Gen [MWh]']\n",
"cumu_energy_gen_MWh"
]
},
{
"cell_type": "code",
- "execution_count": 73,
+ "execution_count": 47,
"id": "245df34e-ddc9-416c-9eb3-512ab8d8341c",
"metadata": {},
"outputs": [],
@@ -516,7 +518,7 @@
},
{
"cell_type": "code",
- "execution_count": 75,
+ "execution_count": 48,
"id": "cce5ad74-8191-4b71-b4f1-dbea5c280317",
"metadata": {},
"outputs": [
@@ -541,46 +543,46 @@
" \n",
" \n",
" Keep_[MWh] \n",
- " Repower_[MWh] \n",
+ " Keep \n",
+ " Repower \n",
" \n",
" \n",
" \n",
" \n",
- " Energy_Gen \n",
+ " Energy Gen [MWh] \n",
" 148.307821 \n",
" 365.792917 \n",
" \n",
" \n",
- " Energy_Demand \n",
+ " Energy Demand [MWh] \n",
" 22.049576 \n",
- " 35.809028 \n",
+ " 37.518583 \n",
" \n",
" \n",
"\n",
" "
],
"text/plain": [
- " Keep_[MWh] Repower_[MWh]\n",
- "Energy_Gen 148.307821 365.792917\n",
- "Energy_Demand 22.049576 35.809028"
+ " Keep Repower\n",
+ "Energy Gen [MWh] 148.307821 365.792917\n",
+ "Energy Demand [MWh] 22.049576 37.518583"
]
},
- "execution_count": 75,
+ "execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cumu_energy_MWh = cumu_energy_gen_MWh.copy()\n",
- "cumu_energy_MWh.loc['Energy_Demand','Keep_[MWh]'] = energydemand_keep\n",
- "cumu_energy_MWh.loc['Energy_Demand','Repower_[MWh]'] = energydemand_repower\n",
+ "cumu_energy_MWh.loc['Energy Demand [MWh]','Keep'] = energydemand_keep\n",
+ "cumu_energy_MWh.loc['Energy Demand [MWh]','Repower'] = energydemand_repower\n",
"cumu_energy_MWh"
]
},
{
"cell_type": "code",
- "execution_count": 78,
+ "execution_count": 52,
"id": "6319d6ed-18d2-4869-8d66-4a307731c6d5",
"metadata": {},
"outputs": [
@@ -605,51 +607,51 @@
" \n",
" \n",
" Keep_[MWh] \n",
- " Repower_[MWh] \n",
+ " Keep \n",
+ " Repower \n",
" \n",
" \n",
" \n",
" \n",
- " Energy_Gen \n",
+ " Energy Gen [MWh] \n",
" 148.307821 \n",
" 365.792917 \n",
" \n",
" \n",
- " Energy_Demand \n",
+ " Energy Demand [MWh] \n",
" 22.049576 \n",
- " 35.809028 \n",
+ " 37.518583 \n",
" \n",
" \n",
" EROI \n",
" 6.726108 \n",
- " 10.215103 \n",
+ " 9.749646 \n",
" \n",
" \n",
"\n",
""
],
"text/plain": [
- " Keep_[MWh] Repower_[MWh]\n",
- "Energy_Gen 148.307821 365.792917\n",
- "Energy_Demand 22.049576 35.809028\n",
- "EROI 6.726108 10.215103"
+ " Keep Repower\n",
+ "Energy Gen [MWh] 148.307821 365.792917\n",
+ "Energy Demand [MWh] 22.049576 37.518583\n",
+ "EROI 6.726108 9.749646"
]
},
- "execution_count": 78,
+ "execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#EROI\n",
- "cumu_energy_MWh.loc['EROI'] = cumu_energy_MWh.loc['Energy_Gen']/cumu_energy_MWh.loc['Energy_Demand']\n",
+ "cumu_energy_MWh.loc['EROI'] = cumu_energy_MWh.loc['Energy Gen [MWh]']/cumu_energy_MWh.loc['Energy Demand [MWh]']\n",
"cumu_energy_MWh"
]
},
{
"cell_type": "code",
- "execution_count": 50,
+ "execution_count": 53,
"id": "77030757-12bb-4e0e-834e-6a23fbd59d8c",
"metadata": {},
"outputs": [],
@@ -661,11 +663,139 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "id": "720d84de-79ee-45bc-976f-4d2261852ea4",
+ "execution_count": 54,
+ "id": "7a822a59-7d0d-4c03-90e6-9fe451be307a",
"metadata": {},
"outputs": [],
- "source": []
+ "source": [
+ "#wastes\n",
+ "modwaste_keep = sim1_cumu.iloc[-1]['WasteAll_Module_SIPS_Resi_keep_[Tonnes]']\n",
+ "modwaste_repower = sim1_cumu.iloc[-1]['WasteAll_Module_SIPS_Resi_repower_[Tonnes]']\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "id": "80bbdad5-bff1-4933-9305-0c1f65ff8349",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 55,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(sim1_yearly.loc[:,'VirginStock_Module_SIPS_Resi_keep_[Tonnes]'], label = 'matdemand_keep')\n",
+ "plt.plot(sim1_yearly.loc[:,'VirginStock_Module_SIPS_Resi_repower_[Tonnes]'], ls='--', label = 'matdemand_repower')\n",
+ "\n",
+ "plt.plot(sim1_yearly.loc[:,'WasteAll_Module_SIPS_Resi_keep_[Tonnes]'], label = 'waste_keep')\n",
+ "plt.plot(sim1_yearly.loc[:,'WasteAll_Module_SIPS_Resi_repower_[Tonnes]'], ls='--', label='waste_repower')\n",
+ "\n",
+ "plt.ylim(0,0.5)\n",
+ "plt.ylabel('Material Demand and Wastes [tonnes]')\n",
+ "plt.legend()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "id": "720d84de-79ee-45bc-976f-4d2261852ea4",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ " \n",
+ " Keep \n",
+ " Repower \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " Energy Gen [MWh] \n",
+ " 148.31 \n",
+ " 365.79 \n",
+ " \n",
+ " \n",
+ " Energy Demand [MWh] \n",
+ " 22.05 \n",
+ " 37.52 \n",
+ " \n",
+ " \n",
+ " EROI \n",
+ " 6.73 \n",
+ " 9.75 \n",
+ " \n",
+ " \n",
+ " Virgin Material Demands [tonnes] \n",
+ " 0.41 \n",
+ " 0.84 \n",
+ " \n",
+ " \n",
+ " Wastes [tonnes] \n",
+ " 0.39 \n",
+ " 0.81 \n",
+ " \n",
+ " \n",
+ "
\n",
+ ""
+ ],
+ "text/plain": [
+ " Keep Repower\n",
+ "Energy Gen [MWh] 148.31 365.79\n",
+ "Energy Demand [MWh] 22.05 37.52\n",
+ "EROI 6.73 9.75\n",
+ "Virgin Material Demands [tonnes] 0.41 0.84\n",
+ "Wastes [tonnes] 0.39 0.81"
+ ]
+ },
+ "execution_count": 56,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "cumu_metrics = cumu_energy_MWh.copy()\n",
+ "cumu_metrics.loc['Virgin Material Demands [tonnes]','Keep'] = modmass_keep\n",
+ "cumu_metrics.loc['Virgin Material Demands [tonnes]','Repower'] = modmass_repower\n",
+ "cumu_metrics.loc['Wastes [tonnes]', 'Keep'] = modwaste_keep\n",
+ "cumu_metrics.loc['Wastes [tonnes]', 'Repower'] = modwaste_repower\n",
+ "\n",
+ "round(cumu_metrics,2)"
+ ]
},
{
"cell_type": "markdown",
diff --git a/docs/dev/SIPS2025-CaseStudies.py b/docs/dev/SIPS2025-CaseStudies.py
index a442903..cb03f9a 100644
--- a/docs/dev/SIPS2025-CaseStudies.py
+++ b/docs/dev/SIPS2025-CaseStudies.py
@@ -87,7 +87,7 @@
#Residential case study
sim1.createScenario(name='Resi_repower', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e) #create the scenario, name and mod file attach
for mat in MATERIALS:
- materialfile = os.path.join(baselinesfolder, 'baseline_material_mass_'+str(mat)+'.csv')
+ materialfile_m = os.path.join(baselinesfolder, 'baseline_material_mass_'+str(mat)+'.csv')
materialfile_e = os.path.join(baselinesfolder, 'baseline_material_energy_'+str(mat)+'.csv')
sim1.scenario['Resi_repower'].addMaterial(mat, massmatfile=materialfile_m, energymatfile=materialfile_e) # add all materials listed in MATERIALS
@@ -163,7 +163,7 @@
sim1_allenergy, sim1_energyGen, sim1_energyDemands_all = sim1.aggregateEnergyResults()
-# In[19]:
+# In[44]:
decommisioned = sim1_yearly.filter(like='Decommisioned')
@@ -173,7 +173,7 @@
plt.ylabel('Decommissioned Capacity [kW_dc]')
-# In[15]:
+# In[45]:
plt.plot(sim1_energyGen/1e6)
@@ -182,17 +182,17 @@
plt.ylabel('Annual Energy Generation [MWh]')
-# In[72]:
+# In[46]:
cumu_energy_gen = sim1_energyGen.cumsum().loc[[2060]]
cumu_energy_gen_MWh = cumu_energy_gen/1e6
-cumu_energy_gen_MWh.columns = ['Keep_[MWh]', 'Repower_[MWh]']
-cumu_energy_gen_MWh.index=['Energy_Gen']
+cumu_energy_gen_MWh.columns = ['Keep', 'Repower']
+cumu_energy_gen_MWh.index=['Energy Gen [MWh]']
cumu_energy_gen_MWh
-# In[73]:
+# In[47]:
#sum the energy demands
@@ -200,24 +200,24 @@
energydemand_repower = sim1_energyDemands_all.filter(like='repower').sum(axis=1).sum()/1e6 #MWh
-# In[75]:
+# In[48]:
cumu_energy_MWh = cumu_energy_gen_MWh.copy()
-cumu_energy_MWh.loc['Energy_Demand','Keep_[MWh]'] = energydemand_keep
-cumu_energy_MWh.loc['Energy_Demand','Repower_[MWh]'] = energydemand_repower
+cumu_energy_MWh.loc['Energy Demand [MWh]','Keep'] = energydemand_keep
+cumu_energy_MWh.loc['Energy Demand [MWh]','Repower'] = energydemand_repower
cumu_energy_MWh
-# In[78]:
+# In[52]:
#EROI
-cumu_energy_MWh.loc['EROI'] = cumu_energy_MWh.loc['Energy_Gen']/cumu_energy_MWh.loc['Energy_Demand']
+cumu_energy_MWh.loc['EROI'] = cumu_energy_MWh.loc['Energy Gen [MWh]']/cumu_energy_MWh.loc['Energy Demand [MWh]']
cumu_energy_MWh
-# In[50]:
+# In[53]:
#material demands
@@ -225,10 +225,38 @@
modmass_repower = sim1_yearly.loc[:,'VirginStock_Module_SIPS_Resi_repower_[Tonnes]'].sum()
-# In[ ]:
+# In[54]:
+
+
+#wastes
+modwaste_keep = sim1_cumu.iloc[-1]['WasteAll_Module_SIPS_Resi_keep_[Tonnes]']
+modwaste_repower = sim1_cumu.iloc[-1]['WasteAll_Module_SIPS_Resi_repower_[Tonnes]']
+
+
+# In[55]:
+
+
+plt.plot(sim1_yearly.loc[:,'VirginStock_Module_SIPS_Resi_keep_[Tonnes]'], label = 'matdemand_keep')
+plt.plot(sim1_yearly.loc[:,'VirginStock_Module_SIPS_Resi_repower_[Tonnes]'], ls='--', label = 'matdemand_repower')
+
+plt.plot(sim1_yearly.loc[:,'WasteAll_Module_SIPS_Resi_keep_[Tonnes]'], label = 'waste_keep')
+plt.plot(sim1_yearly.loc[:,'WasteAll_Module_SIPS_Resi_repower_[Tonnes]'], ls='--', label='waste_repower')
+
+plt.ylim(0,0.5)
+plt.ylabel('Material Demand and Wastes [tonnes]')
+plt.legend()
+
+
+# In[56]:
+cumu_metrics = cumu_energy_MWh.copy()
+cumu_metrics.loc['Virgin Material Demands [tonnes]','Keep'] = modmass_keep
+cumu_metrics.loc['Virgin Material Demands [tonnes]','Repower'] = modmass_repower
+cumu_metrics.loc['Wastes [tonnes]', 'Keep'] = modwaste_keep
+cumu_metrics.loc['Wastes [tonnes]', 'Repower'] = modwaste_repower
+round(cumu_metrics,2)
# # Commercial Case Study
CdTeRamp_full
-In [73]:
+In [47]:
-In [75]:
+In [48]:
cumu_energy_MWh = cumu_energy_gen_MWh.copy()
-cumu_energy_MWh.loc['Energy_Demand','Keep_[MWh]'] = energydemand_keep
-cumu_energy_MWh.loc['Energy_Demand','Repower_[MWh]'] = energydemand_repower
+cumu_energy_MWh.loc['Energy Demand [MWh]','Keep'] = energydemand_keep
+cumu_energy_MWh.loc['Energy Demand [MWh]','Repower'] = energydemand_repower
cumu_energy_MWh
@@ -15370,7 +15372,7 @@ CdTeRamp_full
- Out[75]:
+ Out[48]:
@@ -15393,20 +15395,20 @@ CdTeRamp_full
Keep_[MWh]
- Repower_[MWh]
+ Keep
+ Repower
- Energy_Gen
+ Energy Gen [MWh]
148.307821
365.792917
- Energy_Demand
+ Energy Demand [MWh]
22.049576
- 35.809028
+ 37.518583
@@ -15424,11 +15426,11 @@ CdTeRamp_full
-In [78]:
+In [52]:
#EROI
-cumu_energy_MWh.loc['EROI'] = cumu_energy_MWh.loc['Energy_Gen']/cumu_energy_MWh.loc['Energy_Demand']
+cumu_energy_MWh.loc['EROI'] = cumu_energy_MWh.loc['Energy Gen [MWh]']/cumu_energy_MWh.loc['Energy Demand [MWh]']
cumu_energy_MWh
@@ -15447,7 +15449,7 @@ CdTeRamp_full
- Out[78]:
+ Out[52]:
@@ -15470,25 +15472,25 @@ CdTeRamp_full
Keep_[MWh]
- Repower_[MWh]
+ Keep
+ Repower
- Energy_Gen
+ Energy Gen [MWh]
148.307821
365.792917
- Energy_Demand
+ Energy Demand [MWh]
22.049576
- 35.809028
+ 37.518583
EROI
6.726108
- 10.215103
+ 9.749646
@@ -15506,7 +15508,7 @@ CdTeRamp_full
-In [50]:
+In [53]:
-In [ ]:
+In [54]:
-
+#wastes
+modwaste_keep = sim1_cumu.iloc[-1]['WasteAll_Module_SIPS_Resi_keep_[Tonnes]']
+modwaste_repower = sim1_cumu.iloc[-1]['WasteAll_Module_SIPS_Resi_repower_[Tonnes]']
+
+
+
+
+
+
+
+
+
+
+
+
+In [55]:
+
+
+plt.plot(sim1_yearly.loc[:,'VirginStock_Module_SIPS_Resi_keep_[Tonnes]'], label = 'matdemand_keep')
+plt.plot(sim1_yearly.loc[:,'VirginStock_Module_SIPS_Resi_repower_[Tonnes]'], ls='--', label = 'matdemand_repower')
+
+plt.plot(sim1_yearly.loc[:,'WasteAll_Module_SIPS_Resi_keep_[Tonnes]'], label = 'waste_keep')
+plt.plot(sim1_yearly.loc[:,'WasteAll_Module_SIPS_Resi_repower_[Tonnes]'], ls='--', label='waste_repower')
+
+plt.ylim(0,0.5)
+plt.ylabel('Material Demand and Wastes [tonnes]')
+plt.legend()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Out[55]:
+
+
+
+
+
+<matplotlib.legend.Legend at 0x25009bf7150>
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+In [56]:
+
+
+cumu_metrics = cumu_energy_MWh.copy()
+cumu_metrics.loc['Virgin Material Demands [tonnes]','Keep'] = modmass_keep
+cumu_metrics.loc['Virgin Material Demands [tonnes]','Repower'] = modmass_repower
+cumu_metrics.loc['Wastes [tonnes]', 'Keep'] = modwaste_keep
+cumu_metrics.loc['Wastes [tonnes]', 'Repower'] = modwaste_repower
+
+round(cumu_metrics,2)
@@ -15535,6 +15627,80 @@ CdTeRamp_full
+
+
+
+
+
+
+
+
+
+
+ Out[56]:
+
+
+
+
+
+
+
+
+
+ Keep
+ Repower
+
+
+
+
+ Energy Gen [MWh]
+ 148.31
+ 365.79
+
+
+ Energy Demand [MWh]
+ 22.05
+ 37.52
+
+
+ EROI
+ 6.73
+ 9.75
+
+
+ Virgin Material Demands [tonnes]
+ 0.41
+ 0.84
+
+
+ Wastes [tonnes]
+ 0.39
+ 0.81
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/docs/dev/SIPS2025-CaseStudies.ipynb b/docs/dev/SIPS2025-CaseStudies.ipynb
index 69e0682..47c5d22 100644
--- a/docs/dev/SIPS2025-CaseStudies.ipynb
+++ b/docs/dev/SIPS2025-CaseStudies.ipynb
@@ -142,7 +142,7 @@
"#Residential case study\n",
"sim1.createScenario(name='Resi_repower', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e) #create the scenario, name and mod file attach\n",
"for mat in MATERIALS:\n",
- " materialfile = os.path.join(baselinesfolder, 'baseline_material_mass_'+str(mat)+'.csv')\n",
+ " materialfile_m = os.path.join(baselinesfolder, 'baseline_material_mass_'+str(mat)+'.csv')\n",
" materialfile_e = os.path.join(baselinesfolder, 'baseline_material_energy_'+str(mat)+'.csv')\n",
" sim1.scenario['Resi_repower'].addMaterial(mat, massmatfile=materialfile_m, energymatfile=materialfile_e) # add all materials listed in MATERIALS"
]
@@ -251,7 +251,7 @@
{
"data": {
"text/plain": [
- ""
+ ""
]
},
"execution_count": 11,
@@ -309,7 +309,9 @@
"********************\n",
"Finished Area+Power Generation Calculations\n",
"==> Working on Material : glass\n",
+ "Recycled surplus End of Sim for Mat glass Scenario Resi_repower = 8.411539478439826e-05 tonnes.\n",
"==> Working on Material : aluminium_frames\n",
+ "Recycled surplus End of Sim for Mat aluminium_frames Scenario Resi_repower = 0.000995863752972422 tonnes.\n",
"==> Working on Material : silver\n",
"==> Working on Material : silicon\n",
"==> Working on Material : copper\n",
@@ -376,7 +378,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 44,
"id": "6db33248-50a3-4274-a1b3-a9ced04a20e7",
"metadata": {},
"outputs": [
@@ -386,7 +388,7 @@
"Text(0, 0.5, 'Decommissioned Capacity [kW_dc]')"
]
},
- "execution_count": 19,
+ "execution_count": 44,
"metadata": {},
"output_type": "execute_result"
},
@@ -411,7 +413,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 45,
"id": "eedd27d0-17da-4752-8479-1191cd246ead",
"metadata": {},
"outputs": [
@@ -421,7 +423,7 @@
"Text(0, 0.5, 'Annual Energy Generation [MWh]')"
]
},
- "execution_count": 15,
+ "execution_count": 45,
"metadata": {},
"output_type": "execute_result"
},
@@ -445,7 +447,7 @@
},
{
"cell_type": "code",
- "execution_count": 72,
+ "execution_count": 46,
"id": "5b55fdcd-bfc4-4742-a365-8816b5cc38a9",
"metadata": {},
"outputs": [
@@ -470,13 +472,13 @@
" \n",
" \n",
" Keep_[MWh] \n",
- " Repower_[MWh] \n",
+ " Keep \n",
+ " Repower \n",
" \n",
" \n",
" \n",
" \n",
- " Energy_Gen \n",
+ " Energy Gen [MWh] \n",
" 148.307821 \n",
" 365.792917 \n",
" \n",
@@ -485,11 +487,11 @@
""
],
"text/plain": [
- " Keep_[MWh] Repower_[MWh]\n",
- "Energy_Gen 148.307821 365.792917"
+ " Keep Repower\n",
+ "Energy Gen [MWh] 148.307821 365.792917"
]
},
- "execution_count": 72,
+ "execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
@@ -497,14 +499,14 @@
"source": [
"cumu_energy_gen = sim1_energyGen.cumsum().loc[[2060]]\n",
"cumu_energy_gen_MWh = cumu_energy_gen/1e6\n",
- "cumu_energy_gen_MWh.columns = ['Keep_[MWh]', 'Repower_[MWh]']\n",
- "cumu_energy_gen_MWh.index=['Energy_Gen']\n",
+ "cumu_energy_gen_MWh.columns = ['Keep', 'Repower']\n",
+ "cumu_energy_gen_MWh.index=['Energy Gen [MWh]']\n",
"cumu_energy_gen_MWh"
]
},
{
"cell_type": "code",
- "execution_count": 73,
+ "execution_count": 47,
"id": "245df34e-ddc9-416c-9eb3-512ab8d8341c",
"metadata": {},
"outputs": [],
@@ -516,7 +518,7 @@
},
{
"cell_type": "code",
- "execution_count": 75,
+ "execution_count": 48,
"id": "cce5ad74-8191-4b71-b4f1-dbea5c280317",
"metadata": {},
"outputs": [
@@ -541,46 +543,46 @@
" \n",
" \n",
" Keep_[MWh] \n",
- " Repower_[MWh] \n",
+ " Keep \n",
+ " Repower \n",
" \n",
" \n",
" \n",
" \n",
- " Energy_Gen \n",
+ " Energy Gen [MWh] \n",
" 148.307821 \n",
" 365.792917 \n",
" \n",
" \n",
- " Energy_Demand \n",
+ " Energy Demand [MWh] \n",
" 22.049576 \n",
- " 35.809028 \n",
+ " 37.518583 \n",
" \n",
" \n",
"\n",
" "
],
"text/plain": [
- " Keep_[MWh] Repower_[MWh]\n",
- "Energy_Gen 148.307821 365.792917\n",
- "Energy_Demand 22.049576 35.809028"
+ " Keep Repower\n",
+ "Energy Gen [MWh] 148.307821 365.792917\n",
+ "Energy Demand [MWh] 22.049576 37.518583"
]
},
- "execution_count": 75,
+ "execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cumu_energy_MWh = cumu_energy_gen_MWh.copy()\n",
- "cumu_energy_MWh.loc['Energy_Demand','Keep_[MWh]'] = energydemand_keep\n",
- "cumu_energy_MWh.loc['Energy_Demand','Repower_[MWh]'] = energydemand_repower\n",
+ "cumu_energy_MWh.loc['Energy Demand [MWh]','Keep'] = energydemand_keep\n",
+ "cumu_energy_MWh.loc['Energy Demand [MWh]','Repower'] = energydemand_repower\n",
"cumu_energy_MWh"
]
},
{
"cell_type": "code",
- "execution_count": 78,
+ "execution_count": 52,
"id": "6319d6ed-18d2-4869-8d66-4a307731c6d5",
"metadata": {},
"outputs": [
@@ -605,51 +607,51 @@
" \n",
" \n",
" Keep_[MWh] \n",
- " Repower_[MWh] \n",
+ " Keep \n",
+ " Repower \n",
" \n",
" \n",
" \n",
" \n",
- " Energy_Gen \n",
+ " Energy Gen [MWh] \n",
" 148.307821 \n",
" 365.792917 \n",
" \n",
" \n",
- " Energy_Demand \n",
+ " Energy Demand [MWh] \n",
" 22.049576 \n",
- " 35.809028 \n",
+ " 37.518583 \n",
" \n",
" \n",
" EROI \n",
" 6.726108 \n",
- " 10.215103 \n",
+ " 9.749646 \n",
" \n",
" \n",
"\n",
""
],
"text/plain": [
- " Keep_[MWh] Repower_[MWh]\n",
- "Energy_Gen 148.307821 365.792917\n",
- "Energy_Demand 22.049576 35.809028\n",
- "EROI 6.726108 10.215103"
+ " Keep Repower\n",
+ "Energy Gen [MWh] 148.307821 365.792917\n",
+ "Energy Demand [MWh] 22.049576 37.518583\n",
+ "EROI 6.726108 9.749646"
]
},
- "execution_count": 78,
+ "execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#EROI\n",
- "cumu_energy_MWh.loc['EROI'] = cumu_energy_MWh.loc['Energy_Gen']/cumu_energy_MWh.loc['Energy_Demand']\n",
+ "cumu_energy_MWh.loc['EROI'] = cumu_energy_MWh.loc['Energy Gen [MWh]']/cumu_energy_MWh.loc['Energy Demand [MWh]']\n",
"cumu_energy_MWh"
]
},
{
"cell_type": "code",
- "execution_count": 50,
+ "execution_count": 53,
"id": "77030757-12bb-4e0e-834e-6a23fbd59d8c",
"metadata": {},
"outputs": [],
@@ -661,11 +663,139 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "id": "720d84de-79ee-45bc-976f-4d2261852ea4",
+ "execution_count": 54,
+ "id": "7a822a59-7d0d-4c03-90e6-9fe451be307a",
"metadata": {},
"outputs": [],
- "source": []
+ "source": [
+ "#wastes\n",
+ "modwaste_keep = sim1_cumu.iloc[-1]['WasteAll_Module_SIPS_Resi_keep_[Tonnes]']\n",
+ "modwaste_repower = sim1_cumu.iloc[-1]['WasteAll_Module_SIPS_Resi_repower_[Tonnes]']\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "id": "80bbdad5-bff1-4933-9305-0c1f65ff8349",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 55,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(sim1_yearly.loc[:,'VirginStock_Module_SIPS_Resi_keep_[Tonnes]'], label = 'matdemand_keep')\n",
+ "plt.plot(sim1_yearly.loc[:,'VirginStock_Module_SIPS_Resi_repower_[Tonnes]'], ls='--', label = 'matdemand_repower')\n",
+ "\n",
+ "plt.plot(sim1_yearly.loc[:,'WasteAll_Module_SIPS_Resi_keep_[Tonnes]'], label = 'waste_keep')\n",
+ "plt.plot(sim1_yearly.loc[:,'WasteAll_Module_SIPS_Resi_repower_[Tonnes]'], ls='--', label='waste_repower')\n",
+ "\n",
+ "plt.ylim(0,0.5)\n",
+ "plt.ylabel('Material Demand and Wastes [tonnes]')\n",
+ "plt.legend()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "id": "720d84de-79ee-45bc-976f-4d2261852ea4",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ " \n",
+ " Keep \n",
+ " Repower \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " Energy Gen [MWh] \n",
+ " 148.31 \n",
+ " 365.79 \n",
+ " \n",
+ " \n",
+ " Energy Demand [MWh] \n",
+ " 22.05 \n",
+ " 37.52 \n",
+ " \n",
+ " \n",
+ " EROI \n",
+ " 6.73 \n",
+ " 9.75 \n",
+ " \n",
+ " \n",
+ " Virgin Material Demands [tonnes] \n",
+ " 0.41 \n",
+ " 0.84 \n",
+ " \n",
+ " \n",
+ " Wastes [tonnes] \n",
+ " 0.39 \n",
+ " 0.81 \n",
+ " \n",
+ " \n",
+ "
\n",
+ ""
+ ],
+ "text/plain": [
+ " Keep Repower\n",
+ "Energy Gen [MWh] 148.31 365.79\n",
+ "Energy Demand [MWh] 22.05 37.52\n",
+ "EROI 6.73 9.75\n",
+ "Virgin Material Demands [tonnes] 0.41 0.84\n",
+ "Wastes [tonnes] 0.39 0.81"
+ ]
+ },
+ "execution_count": 56,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "cumu_metrics = cumu_energy_MWh.copy()\n",
+ "cumu_metrics.loc['Virgin Material Demands [tonnes]','Keep'] = modmass_keep\n",
+ "cumu_metrics.loc['Virgin Material Demands [tonnes]','Repower'] = modmass_repower\n",
+ "cumu_metrics.loc['Wastes [tonnes]', 'Keep'] = modwaste_keep\n",
+ "cumu_metrics.loc['Wastes [tonnes]', 'Repower'] = modwaste_repower\n",
+ "\n",
+ "round(cumu_metrics,2)"
+ ]
},
{
"cell_type": "markdown",
diff --git a/docs/dev/SIPS2025-CaseStudies.py b/docs/dev/SIPS2025-CaseStudies.py
index a442903..cb03f9a 100644
--- a/docs/dev/SIPS2025-CaseStudies.py
+++ b/docs/dev/SIPS2025-CaseStudies.py
@@ -87,7 +87,7 @@
#Residential case study
sim1.createScenario(name='Resi_repower', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e) #create the scenario, name and mod file attach
for mat in MATERIALS:
- materialfile = os.path.join(baselinesfolder, 'baseline_material_mass_'+str(mat)+'.csv')
+ materialfile_m = os.path.join(baselinesfolder, 'baseline_material_mass_'+str(mat)+'.csv')
materialfile_e = os.path.join(baselinesfolder, 'baseline_material_energy_'+str(mat)+'.csv')
sim1.scenario['Resi_repower'].addMaterial(mat, massmatfile=materialfile_m, energymatfile=materialfile_e) # add all materials listed in MATERIALS
@@ -163,7 +163,7 @@
sim1_allenergy, sim1_energyGen, sim1_energyDemands_all = sim1.aggregateEnergyResults()
-# In[19]:
+# In[44]:
decommisioned = sim1_yearly.filter(like='Decommisioned')
@@ -173,7 +173,7 @@
plt.ylabel('Decommissioned Capacity [kW_dc]')
-# In[15]:
+# In[45]:
plt.plot(sim1_energyGen/1e6)
@@ -182,17 +182,17 @@
plt.ylabel('Annual Energy Generation [MWh]')
-# In[72]:
+# In[46]:
cumu_energy_gen = sim1_energyGen.cumsum().loc[[2060]]
cumu_energy_gen_MWh = cumu_energy_gen/1e6
-cumu_energy_gen_MWh.columns = ['Keep_[MWh]', 'Repower_[MWh]']
-cumu_energy_gen_MWh.index=['Energy_Gen']
+cumu_energy_gen_MWh.columns = ['Keep', 'Repower']
+cumu_energy_gen_MWh.index=['Energy Gen [MWh]']
cumu_energy_gen_MWh
-# In[73]:
+# In[47]:
#sum the energy demands
@@ -200,24 +200,24 @@
energydemand_repower = sim1_energyDemands_all.filter(like='repower').sum(axis=1).sum()/1e6 #MWh
-# In[75]:
+# In[48]:
cumu_energy_MWh = cumu_energy_gen_MWh.copy()
-cumu_energy_MWh.loc['Energy_Demand','Keep_[MWh]'] = energydemand_keep
-cumu_energy_MWh.loc['Energy_Demand','Repower_[MWh]'] = energydemand_repower
+cumu_energy_MWh.loc['Energy Demand [MWh]','Keep'] = energydemand_keep
+cumu_energy_MWh.loc['Energy Demand [MWh]','Repower'] = energydemand_repower
cumu_energy_MWh
-# In[78]:
+# In[52]:
#EROI
-cumu_energy_MWh.loc['EROI'] = cumu_energy_MWh.loc['Energy_Gen']/cumu_energy_MWh.loc['Energy_Demand']
+cumu_energy_MWh.loc['EROI'] = cumu_energy_MWh.loc['Energy Gen [MWh]']/cumu_energy_MWh.loc['Energy Demand [MWh]']
cumu_energy_MWh
-# In[50]:
+# In[53]:
#material demands
@@ -225,10 +225,38 @@
modmass_repower = sim1_yearly.loc[:,'VirginStock_Module_SIPS_Resi_repower_[Tonnes]'].sum()
-# In[ ]:
+# In[54]:
+
+
+#wastes
+modwaste_keep = sim1_cumu.iloc[-1]['WasteAll_Module_SIPS_Resi_keep_[Tonnes]']
+modwaste_repower = sim1_cumu.iloc[-1]['WasteAll_Module_SIPS_Resi_repower_[Tonnes]']
+
+
+# In[55]:
+
+
+plt.plot(sim1_yearly.loc[:,'VirginStock_Module_SIPS_Resi_keep_[Tonnes]'], label = 'matdemand_keep')
+plt.plot(sim1_yearly.loc[:,'VirginStock_Module_SIPS_Resi_repower_[Tonnes]'], ls='--', label = 'matdemand_repower')
+
+plt.plot(sim1_yearly.loc[:,'WasteAll_Module_SIPS_Resi_keep_[Tonnes]'], label = 'waste_keep')
+plt.plot(sim1_yearly.loc[:,'WasteAll_Module_SIPS_Resi_repower_[Tonnes]'], ls='--', label='waste_repower')
+
+plt.ylim(0,0.5)
+plt.ylabel('Material Demand and Wastes [tonnes]')
+plt.legend()
+
+
+# In[56]:
+cumu_metrics = cumu_energy_MWh.copy()
+cumu_metrics.loc['Virgin Material Demands [tonnes]','Keep'] = modmass_keep
+cumu_metrics.loc['Virgin Material Demands [tonnes]','Repower'] = modmass_repower
+cumu_metrics.loc['Wastes [tonnes]', 'Keep'] = modwaste_keep
+cumu_metrics.loc['Wastes [tonnes]', 'Repower'] = modwaste_repower
+round(cumu_metrics,2)
# # Commercial Case Study
In [73]:
+In [47]:
-
@@ -15370,7 +15372,7 @@ CdTeRamp_full
-
In [75]:
+In [48]:
cumu_energy_MWh = cumu_energy_gen_MWh.copy()
-cumu_energy_MWh.loc['Energy_Demand','Keep_[MWh]'] = energydemand_keep
-cumu_energy_MWh.loc['Energy_Demand','Repower_[MWh]'] = energydemand_repower
+cumu_energy_MWh.loc['Energy Demand [MWh]','Keep'] = energydemand_keep
+cumu_energy_MWh.loc['Energy Demand [MWh]','Repower'] = energydemand_repower
cumu_energy_MWh
CdTeRamp_full
- Out[75]:
+ Out[48]:
@@ -15393,20 +15395,20 @@ CdTeRamp_full
Keep_[MWh]
- Repower_[MWh]
+ Keep
+ Repower
- Energy_Gen
+ Energy Gen [MWh]
148.307821
365.792917
- Energy_Demand
+ Energy Demand [MWh]
22.049576
- 35.809028
+ 37.518583
@@ -15424,11 +15426,11 @@ CdTeRamp_full
CdTeRamp_full
-
@@ -15447,7 +15449,7 @@ CdTeRamp_full
-
In [78]:
+In [52]:
#EROI
-cumu_energy_MWh.loc['EROI'] = cumu_energy_MWh.loc['Energy_Gen']/cumu_energy_MWh.loc['Energy_Demand']
+cumu_energy_MWh.loc['EROI'] = cumu_energy_MWh.loc['Energy Gen [MWh]']/cumu_energy_MWh.loc['Energy Demand [MWh]']
cumu_energy_MWh
CdTeRamp_full
- Out[78]:
+ Out[52]:
@@ -15470,25 +15472,25 @@ CdTeRamp_full
Keep_[MWh]
- Repower_[MWh]
+ Keep
+ Repower
- Energy_Gen
+ Energy Gen [MWh]
148.307821
365.792917
- Energy_Demand
+ Energy Demand [MWh]
22.049576
- 35.809028
+ 37.518583
EROI
6.726108
- 10.215103
+ 9.749646
@@ -15506,7 +15508,7 @@ CdTeRamp_full
CdTeRamp_full
-
In [50]:
+In [53]:
-
+
+
+In [ ]:
+In [54]:
-
+
+
+#wastes
+modwaste_keep = sim1_cumu.iloc[-1]['WasteAll_Module_SIPS_Resi_keep_[Tonnes]']
+modwaste_repower = sim1_cumu.iloc[-1]['WasteAll_Module_SIPS_Resi_repower_[Tonnes]']
+
+
+
+
+
+
+
+
+
+
+In [55]:
+
+
+
+
+
+
+plt.plot(sim1_yearly.loc[:,'VirginStock_Module_SIPS_Resi_keep_[Tonnes]'], label = 'matdemand_keep')
+plt.plot(sim1_yearly.loc[:,'VirginStock_Module_SIPS_Resi_repower_[Tonnes]'], ls='--', label = 'matdemand_repower')
+
+plt.plot(sim1_yearly.loc[:,'WasteAll_Module_SIPS_Resi_keep_[Tonnes]'], label = 'waste_keep')
+plt.plot(sim1_yearly.loc[:,'WasteAll_Module_SIPS_Resi_repower_[Tonnes]'], ls='--', label='waste_repower')
+
+plt.ylim(0,0.5)
+plt.ylabel('Material Demand and Wastes [tonnes]')
+plt.legend()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Out[55]:
+
+
+
+
+
+
+
+<matplotlib.legend.Legend at 0x25009bf7150>+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
\n",
" Keep_[MWh] \n",
- " Repower_[MWh] \n",
+ " Keep \n",
+ " Repower \n",
" \n",
" \n",
" \n",
" \n",
- " Energy_Gen \n",
+ " Energy Gen [MWh] \n",
" 148.307821 \n",
" 365.792917 \n",
" \n",
" \n",
- " Energy_Demand \n",
+ " Energy Demand [MWh] \n",
" 22.049576 \n",
- " 35.809028 \n",
+ " 37.518583 \n",
" \n",
" \n",
" EROI \n",
" 6.726108 \n",
- " 10.215103 \n",
+ " 9.749646 \n",
" \n",
" \n",
"\n",
""
],
"text/plain": [
- " Keep_[MWh] Repower_[MWh]\n",
- "Energy_Gen 148.307821 365.792917\n",
- "Energy_Demand 22.049576 35.809028\n",
- "EROI 6.726108 10.215103"
+ " Keep Repower\n",
+ "Energy Gen [MWh] 148.307821 365.792917\n",
+ "Energy Demand [MWh] 22.049576 37.518583\n",
+ "EROI 6.726108 9.749646"
]
},
- "execution_count": 78,
+ "execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#EROI\n",
- "cumu_energy_MWh.loc['EROI'] = cumu_energy_MWh.loc['Energy_Gen']/cumu_energy_MWh.loc['Energy_Demand']\n",
+ "cumu_energy_MWh.loc['EROI'] = cumu_energy_MWh.loc['Energy Gen [MWh]']/cumu_energy_MWh.loc['Energy Demand [MWh]']\n",
"cumu_energy_MWh"
]
},
{
"cell_type": "code",
- "execution_count": 50,
+ "execution_count": 53,
"id": "77030757-12bb-4e0e-834e-6a23fbd59d8c",
"metadata": {},
"outputs": [],
@@ -661,11 +663,139 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "id": "720d84de-79ee-45bc-976f-4d2261852ea4",
+ "execution_count": 54,
+ "id": "7a822a59-7d0d-4c03-90e6-9fe451be307a",
"metadata": {},
"outputs": [],
- "source": []
+ "source": [
+ "#wastes\n",
+ "modwaste_keep = sim1_cumu.iloc[-1]['WasteAll_Module_SIPS_Resi_keep_[Tonnes]']\n",
+ "modwaste_repower = sim1_cumu.iloc[-1]['WasteAll_Module_SIPS_Resi_repower_[Tonnes]']\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "id": "80bbdad5-bff1-4933-9305-0c1f65ff8349",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 55,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
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+ "
+
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+
In [56]:
+
+
+
+
@@ -15535,6 +15627,80 @@ cumu_metrics = cumu_energy_MWh.copy()
+cumu_metrics.loc['Virgin Material Demands [tonnes]','Keep'] = modmass_keep
+cumu_metrics.loc['Virgin Material Demands [tonnes]','Repower'] = modmass_repower
+cumu_metrics.loc['Wastes [tonnes]', 'Keep'] = modwaste_keep
+cumu_metrics.loc['Wastes [tonnes]', 'Repower'] = modwaste_repower
+
+round(cumu_metrics,2)
CdTeRamp_full
+
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+
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+
+
+
+
+
+
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+Out[56]:
+
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+
+
+
+
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+
+
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+
+| + | Keep | +Repower | +
|---|---|---|
| Energy Gen [MWh] | +148.31 | +365.79 | +
| Energy Demand [MWh] | +22.05 | +37.52 | +
| EROI | +6.73 | +9.75 | +
| Virgin Material Demands [tonnes] | +0.41 | +0.84 | +
| Wastes [tonnes] | +0.39 | +0.81 | +
diff --git a/docs/dev/SIPS2025-CaseStudies.ipynb b/docs/dev/SIPS2025-CaseStudies.ipynb
index 69e0682..47c5d22 100644
--- a/docs/dev/SIPS2025-CaseStudies.ipynb
+++ b/docs/dev/SIPS2025-CaseStudies.ipynb
@@ -142,7 +142,7 @@
"#Residential case study\n",
"sim1.createScenario(name='Resi_repower', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e) #create the scenario, name and mod file attach\n",
"for mat in MATERIALS:\n",
- " materialfile = os.path.join(baselinesfolder, 'baseline_material_mass_'+str(mat)+'.csv')\n",
+ " materialfile_m = os.path.join(baselinesfolder, 'baseline_material_mass_'+str(mat)+'.csv')\n",
" materialfile_e = os.path.join(baselinesfolder, 'baseline_material_energy_'+str(mat)+'.csv')\n",
" sim1.scenario['Resi_repower'].addMaterial(mat, massmatfile=materialfile_m, energymatfile=materialfile_e) # add all materials listed in MATERIALS"
]
@@ -251,7 +251,7 @@
{
"data": {
"text/plain": [
- ""
+ ""
]
},
"execution_count": 11,
@@ -309,7 +309,9 @@
"********************\n",
"Finished Area+Power Generation Calculations\n",
"==> Working on Material : glass\n",
+ "Recycled surplus End of Sim for Mat glass Scenario Resi_repower = 8.411539478439826e-05 tonnes.\n",
"==> Working on Material : aluminium_frames\n",
+ "Recycled surplus End of Sim for Mat aluminium_frames Scenario Resi_repower = 0.000995863752972422 tonnes.\n",
"==> Working on Material : silver\n",
"==> Working on Material : silicon\n",
"==> Working on Material : copper\n",
@@ -376,7 +378,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 44,
"id": "6db33248-50a3-4274-a1b3-a9ced04a20e7",
"metadata": {},
"outputs": [
@@ -386,7 +388,7 @@
"Text(0, 0.5, 'Decommissioned Capacity [kW_dc]')"
]
},
- "execution_count": 19,
+ "execution_count": 44,
"metadata": {},
"output_type": "execute_result"
},
@@ -411,7 +413,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 45,
"id": "eedd27d0-17da-4752-8479-1191cd246ead",
"metadata": {},
"outputs": [
@@ -421,7 +423,7 @@
"Text(0, 0.5, 'Annual Energy Generation [MWh]')"
]
},
- "execution_count": 15,
+ "execution_count": 45,
"metadata": {},
"output_type": "execute_result"
},
@@ -445,7 +447,7 @@
},
{
"cell_type": "code",
- "execution_count": 72,
+ "execution_count": 46,
"id": "5b55fdcd-bfc4-4742-a365-8816b5cc38a9",
"metadata": {},
"outputs": [
@@ -470,13 +472,13 @@
" \n",
" \n",
" Keep_[MWh] \n",
- " Repower_[MWh] \n",
+ " Keep \n",
+ " Repower \n",
" \n",
" \n",
" \n",
" \n",
- " Energy_Gen \n",
+ " Energy Gen [MWh] \n",
" 148.307821 \n",
" 365.792917 \n",
" \n",
@@ -485,11 +487,11 @@
""
],
"text/plain": [
- " Keep_[MWh] Repower_[MWh]\n",
- "Energy_Gen 148.307821 365.792917"
+ " Keep Repower\n",
+ "Energy Gen [MWh] 148.307821 365.792917"
]
},
- "execution_count": 72,
+ "execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
@@ -497,14 +499,14 @@
"source": [
"cumu_energy_gen = sim1_energyGen.cumsum().loc[[2060]]\n",
"cumu_energy_gen_MWh = cumu_energy_gen/1e6\n",
- "cumu_energy_gen_MWh.columns = ['Keep_[MWh]', 'Repower_[MWh]']\n",
- "cumu_energy_gen_MWh.index=['Energy_Gen']\n",
+ "cumu_energy_gen_MWh.columns = ['Keep', 'Repower']\n",
+ "cumu_energy_gen_MWh.index=['Energy Gen [MWh]']\n",
"cumu_energy_gen_MWh"
]
},
{
"cell_type": "code",
- "execution_count": 73,
+ "execution_count": 47,
"id": "245df34e-ddc9-416c-9eb3-512ab8d8341c",
"metadata": {},
"outputs": [],
@@ -516,7 +518,7 @@
},
{
"cell_type": "code",
- "execution_count": 75,
+ "execution_count": 48,
"id": "cce5ad74-8191-4b71-b4f1-dbea5c280317",
"metadata": {},
"outputs": [
@@ -541,46 +543,46 @@
" \n",
" \n",
" Keep_[MWh] \n",
- " Repower_[MWh] \n",
+ " Keep \n",
+ " Repower \n",
" \n",
" \n",
" \n",
" \n",
- " Energy_Gen \n",
+ " Energy Gen [MWh] \n",
" 148.307821 \n",
" 365.792917 \n",
" \n",
" \n",
- " Energy_Demand \n",
+ " Energy Demand [MWh] \n",
" 22.049576 \n",
- " 35.809028 \n",
+ " 37.518583 \n",
" \n",
" \n",
"\n",
"
"
],
"text/plain": [
- " Keep_[MWh] Repower_[MWh]\n",
- "Energy_Gen 148.307821 365.792917\n",
- "Energy_Demand 22.049576 35.809028"
+ " Keep Repower\n",
+ "Energy Gen [MWh] 148.307821 365.792917\n",
+ "Energy Demand [MWh] 22.049576 37.518583"
]
},
- "execution_count": 75,
+ "execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cumu_energy_MWh = cumu_energy_gen_MWh.copy()\n",
- "cumu_energy_MWh.loc['Energy_Demand','Keep_[MWh]'] = energydemand_keep\n",
- "cumu_energy_MWh.loc['Energy_Demand','Repower_[MWh]'] = energydemand_repower\n",
+ "cumu_energy_MWh.loc['Energy Demand [MWh]','Keep'] = energydemand_keep\n",
+ "cumu_energy_MWh.loc['Energy Demand [MWh]','Repower'] = energydemand_repower\n",
"cumu_energy_MWh"
]
},
{
"cell_type": "code",
- "execution_count": 78,
+ "execution_count": 52,
"id": "6319d6ed-18d2-4869-8d66-4a307731c6d5",
"metadata": {},
"outputs": [
@@ -605,51 +607,51 @@
" \n",
" "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(sim1_yearly.loc[:,'VirginStock_Module_SIPS_Resi_keep_[Tonnes]'], label = 'matdemand_keep')\n",
+ "plt.plot(sim1_yearly.loc[:,'VirginStock_Module_SIPS_Resi_repower_[Tonnes]'], ls='--', label = 'matdemand_repower')\n",
+ "\n",
+ "plt.plot(sim1_yearly.loc[:,'WasteAll_Module_SIPS_Resi_keep_[Tonnes]'], label = 'waste_keep')\n",
+ "plt.plot(sim1_yearly.loc[:,'WasteAll_Module_SIPS_Resi_repower_[Tonnes]'], ls='--', label='waste_repower')\n",
+ "\n",
+ "plt.ylim(0,0.5)\n",
+ "plt.ylabel('Material Demand and Wastes [tonnes]')\n",
+ "plt.legend()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "id": "720d84de-79ee-45bc-976f-4d2261852ea4",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Keep Repower\n",
+ "Energy Gen [MWh] 148.31 365.79\n",
+ "Energy Demand [MWh] 22.05 37.52\n",
+ "EROI 6.73 9.75\n",
+ "Virgin Material Demands [tonnes] 0.41 0.84\n",
+ "Wastes [tonnes] 0.39 0.81"
+ ]
+ },
+ "execution_count": 56,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "cumu_metrics = cumu_energy_MWh.copy()\n",
+ "cumu_metrics.loc['Virgin Material Demands [tonnes]','Keep'] = modmass_keep\n",
+ "cumu_metrics.loc['Virgin Material Demands [tonnes]','Repower'] = modmass_repower\n",
+ "cumu_metrics.loc['Wastes [tonnes]', 'Keep'] = modwaste_keep\n",
+ "cumu_metrics.loc['Wastes [tonnes]', 'Repower'] = modwaste_repower\n",
+ "\n",
+ "round(cumu_metrics,2)"
+ ]
},
{
"cell_type": "markdown",
diff --git a/docs/dev/SIPS2025-CaseStudies.py b/docs/dev/SIPS2025-CaseStudies.py
index a442903..cb03f9a 100644
--- a/docs/dev/SIPS2025-CaseStudies.py
+++ b/docs/dev/SIPS2025-CaseStudies.py
@@ -87,7 +87,7 @@
#Residential case study
sim1.createScenario(name='Resi_repower', massmodulefile=moduleFile_m, energymodulefile=moduleFile_e) #create the scenario, name and mod file attach
for mat in MATERIALS:
- materialfile = os.path.join(baselinesfolder, 'baseline_material_mass_'+str(mat)+'.csv')
+ materialfile_m = os.path.join(baselinesfolder, 'baseline_material_mass_'+str(mat)+'.csv')
materialfile_e = os.path.join(baselinesfolder, 'baseline_material_energy_'+str(mat)+'.csv')
sim1.scenario['Resi_repower'].addMaterial(mat, massmatfile=materialfile_m, energymatfile=materialfile_e) # add all materials listed in MATERIALS
@@ -163,7 +163,7 @@
sim1_allenergy, sim1_energyGen, sim1_energyDemands_all = sim1.aggregateEnergyResults()
-# In[19]:
+# In[44]:
decommisioned = sim1_yearly.filter(like='Decommisioned')
@@ -173,7 +173,7 @@
plt.ylabel('Decommissioned Capacity [kW_dc]')
-# In[15]:
+# In[45]:
plt.plot(sim1_energyGen/1e6)
@@ -182,17 +182,17 @@
plt.ylabel('Annual Energy Generation [MWh]')
-# In[72]:
+# In[46]:
cumu_energy_gen = sim1_energyGen.cumsum().loc[[2060]]
cumu_energy_gen_MWh = cumu_energy_gen/1e6
-cumu_energy_gen_MWh.columns = ['Keep_[MWh]', 'Repower_[MWh]']
-cumu_energy_gen_MWh.index=['Energy_Gen']
+cumu_energy_gen_MWh.columns = ['Keep', 'Repower']
+cumu_energy_gen_MWh.index=['Energy Gen [MWh]']
cumu_energy_gen_MWh
-# In[73]:
+# In[47]:
#sum the energy demands
@@ -200,24 +200,24 @@
energydemand_repower = sim1_energyDemands_all.filter(like='repower').sum(axis=1).sum()/1e6 #MWh
-# In[75]:
+# In[48]:
cumu_energy_MWh = cumu_energy_gen_MWh.copy()
-cumu_energy_MWh.loc['Energy_Demand','Keep_[MWh]'] = energydemand_keep
-cumu_energy_MWh.loc['Energy_Demand','Repower_[MWh]'] = energydemand_repower
+cumu_energy_MWh.loc['Energy Demand [MWh]','Keep'] = energydemand_keep
+cumu_energy_MWh.loc['Energy Demand [MWh]','Repower'] = energydemand_repower
cumu_energy_MWh
-# In[78]:
+# In[52]:
#EROI
-cumu_energy_MWh.loc['EROI'] = cumu_energy_MWh.loc['Energy_Gen']/cumu_energy_MWh.loc['Energy_Demand']
+cumu_energy_MWh.loc['EROI'] = cumu_energy_MWh.loc['Energy Gen [MWh]']/cumu_energy_MWh.loc['Energy Demand [MWh]']
cumu_energy_MWh
-# In[50]:
+# In[53]:
#material demands
@@ -225,10 +225,38 @@
modmass_repower = sim1_yearly.loc[:,'VirginStock_Module_SIPS_Resi_repower_[Tonnes]'].sum()
-# In[ ]:
+# In[54]:
+
+
+#wastes
+modwaste_keep = sim1_cumu.iloc[-1]['WasteAll_Module_SIPS_Resi_keep_[Tonnes]']
+modwaste_repower = sim1_cumu.iloc[-1]['WasteAll_Module_SIPS_Resi_repower_[Tonnes]']
+
+
+# In[55]:
+
+
+plt.plot(sim1_yearly.loc[:,'VirginStock_Module_SIPS_Resi_keep_[Tonnes]'], label = 'matdemand_keep')
+plt.plot(sim1_yearly.loc[:,'VirginStock_Module_SIPS_Resi_repower_[Tonnes]'], ls='--', label = 'matdemand_repower')
+
+plt.plot(sim1_yearly.loc[:,'WasteAll_Module_SIPS_Resi_keep_[Tonnes]'], label = 'waste_keep')
+plt.plot(sim1_yearly.loc[:,'WasteAll_Module_SIPS_Resi_repower_[Tonnes]'], ls='--', label='waste_repower')
+
+plt.ylim(0,0.5)
+plt.ylabel('Material Demand and Wastes [tonnes]')
+plt.legend()
+
+
+# In[56]:
+cumu_metrics = cumu_energy_MWh.copy()
+cumu_metrics.loc['Virgin Material Demands [tonnes]','Keep'] = modmass_keep
+cumu_metrics.loc['Virgin Material Demands [tonnes]','Repower'] = modmass_repower
+cumu_metrics.loc['Wastes [tonnes]', 'Keep'] = modwaste_keep
+cumu_metrics.loc['Wastes [tonnes]', 'Repower'] = modwaste_repower
+round(cumu_metrics,2)
# # Commercial Case Study
| \n", + " | Keep | \n", + "Repower | \n", + "
|---|---|---|
| Energy Gen [MWh] | \n", + "148.31 | \n", + "365.79 | \n", + "
| Energy Demand [MWh] | \n", + "22.05 | \n", + "37.52 | \n", + "
| EROI | \n", + "6.73 | \n", + "9.75 | \n", + "
| Virgin Material Demands [tonnes] | \n", + "0.41 | \n", + "0.84 | \n", + "
| Wastes [tonnes] | \n", + "0.39 | \n", + "0.81 | \n", + "