From 2bcbeaa72ded4f61286857899f7fa171aa60a59d Mon Sep 17 00:00:00 2001
From: Mirletz <heather.mirletz@nrel.gov>
Date: Tue, 24 Dec 2024 15:50:51 -0700
Subject: [PATCH] SIPS Repowering

Ran Resi case study, calculated mass demands and energy demands, and EROI.
---
 docs/dev/SIPS2025-CaseStudies.html  | 779 ++++++++++++++++++++++++++--
 docs/dev/SIPS2025-CaseStudies.ipynb | 549 ++++++++++++++++++--
 docs/dev/SIPS2025-CaseStudies.py    | 160 +++++-
 3 files changed, 1399 insertions(+), 89 deletions(-)

diff --git a/docs/dev/SIPS2025-CaseStudies.html b/docs/dev/SIPS2025-CaseStudies.html
index b4e0f97..778b8ad 100644
--- a/docs/dev/SIPS2025-CaseStudies.html
+++ b/docs/dev/SIPS2025-CaseStudies.html
@@ -14585,12 +14585,12 @@ <h1 id="SIPS:-QUIP-SERIES">SIPS: QUIP SERIES<a class="anchor-link" href="#SIPS:-
 </div>
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 <div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#setup</span>
@@ -14605,8 +14605,8 @@ <h1 id="SIPS:-QUIP-SERIES">SIPS: QUIP SERIES<a class="anchor-link" href="#SIPS:-
 
 <span class="n">cwd</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getcwd</span><span class="p">()</span> <span class="c1">#grabs current working directory</span>
 
-<span class="n">testfolder</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">Path</span><span class="p">()</span><span class="o">.</span><span class="n">resolve</span><span class="p">()</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">parent</span> <span class="o">/</span> <span class="s1">'PV_ICE'</span> <span class="o">/</span> <span class="s1">'TEMP'</span> <span class="o">/</span> <span class="s1">'MESC-NRELStdScens'</span><span class="p">)</span>
-<span class="n">inputfolder</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">Path</span><span class="p">()</span><span class="o">.</span><span class="n">resolve</span><span class="p">()</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">parent</span> <span class="o">/</span> <span class="s1">'PV_ICE'</span> <span class="o">/</span> <span class="s1">'baselines'</span><span class="o">/</span><span class="s1">'NRELStdScenarios'</span><span class="p">)</span>
+<span class="n">testfolder</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">Path</span><span class="p">()</span><span class="o">.</span><span class="n">resolve</span><span class="p">()</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">parent</span> <span class="o">/</span> <span class="s1">'PV_ICE'</span> <span class="o">/</span> <span class="s1">'TEMP'</span> <span class="o">/</span> <span class="s1">'SIPS-Repowering'</span><span class="p">)</span>
+<span class="c1">#inputfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines'/'NRELStdScenarios')</span>
 <span class="n">baselinesfolder</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">Path</span><span class="p">()</span><span class="o">.</span><span class="n">resolve</span><span class="p">()</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">parent</span> <span class="o">/</span><span class="s1">'PV_ICE'</span> <span class="o">/</span> <span class="s1">'baselines'</span><span class="p">)</span>
 <span class="n">supportMatfolder</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">Path</span><span class="p">()</span><span class="o">.</span><span class="n">resolve</span><span class="p">()</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">parent</span> <span class="o">/</span> <span class="s1">'PV_ICE'</span> <span class="o">/</span> <span class="s1">'baselines'</span> <span class="o">/</span> <span class="s1">'SupportingMaterial'</span><span class="p">)</span>
 <span class="c1">#altBaselinesfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'Energy_CellModuleTechCompare')</span>
@@ -14625,12 +14625,38 @@ <h1 id="SIPS:-QUIP-SERIES">SIPS: QUIP SERIES<a class="anchor-link" href="#SIPS:-
 </div>
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+<pre>Python version  3.11.5 | packaged by Anaconda, Inc. | (main, Sep 11 2023, 13:26:23) [MSC v.1916 64 bit (AMD64)]
+Pandas version  2.0.3
+pyplot  3.7.2
+PV_ICE version  
+</pre>
+</div>
+</div>
+
+</div>
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 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">sim1</span> <span class="o">=</span> <span class="n">PV_ICE</span><span class="o">.</span><span class="n">Simulation</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'SIPS'</span><span class="p">,</span> <span class="n">path</span><span class="o">=</span><span class="n">testfolder</span><span class="p">)</span>
@@ -14641,6 +14667,30 @@ <h1 id="SIPS:-QUIP-SERIES">SIPS: QUIP SERIES<a class="anchor-link" href="#SIPS:-
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+<pre>path = C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\TEMP\SIPS-Repowering
+Baseline folder directed to default:  C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\baselines
+</pre>
+</div>
+</div>
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@@ -14658,12 +14708,14 @@ <h2 id="Residential-Case-Study">Residential Case Study<a class="anchor-link" hre
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 <div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#c-Si</span>
 <span class="n">MATERIALS</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'glass'</span><span class="p">,</span><span class="s1">'aluminium_frames'</span><span class="p">,</span><span class="s1">'silver'</span><span class="p">,</span><span class="s1">'silicon'</span><span class="p">,</span> <span class="s1">'copper'</span><span class="p">,</span> <span class="s1">'encapsulant'</span><span class="p">,</span> <span class="s1">'backsheet'</span><span class="p">]</span>
-<span class="n">moduleFile</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">baselinesfolder</span><span class="p">,</span> <span class="s1">'baseline_modules_mass_US_updatedT50T90.csv'</span><span class="p">)</span>
+<span class="n">moduleFile_m</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">baselinesfolder</span><span class="p">,</span> <span class="s1">'baseline_modules_mass_US_updatedT50T90.csv'</span><span class="p">)</span>
+<span class="n">moduleFile_e</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">baselinesfolder</span><span class="p">,</span> <span class="s1">'baseline_modules_energy.csv'</span><span class="p">)</span>
+
 <span class="c1">#CdTe</span>
 <span class="c1">#MATERIALS_CdTe = ['glass_cdte','aluminium_frames_cdte', 'copper_cdte', 'encapsulant_cdte','cadmium','tellurium']</span>
 <span class="c1">#moduleFile_CdTe = os.path.join(baselinesfolder, 'baseline_modules_mass_US_CdTe.csv')</span>
@@ -14679,7 +14731,7 @@ <h2 id="Residential-Case-Study">Residential Case Study<a class="anchor-link" hre
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 <div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#residential system parameters</span>
@@ -14703,14 +14755,15 @@ <h2 id="Residential-Case-Study">Residential Case Study<a class="anchor-link" hre
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 <div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Residential case study</span>
-<span class="n">sim1</span><span class="o">.</span><span class="n">createScenario</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'Resi_keep'</span><span class="p">,</span> <span class="n">massmodulefile</span><span class="o">=</span><span class="n">moduleFile</span><span class="p">)</span> <span class="c1">#create the scenario, name and mod file attach</span>
+<span class="n">sim1</span><span class="o">.</span><span class="n">createScenario</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'Resi_keep'</span><span class="p">,</span> <span class="n">massmodulefile</span><span class="o">=</span><span class="n">moduleFile_m</span><span class="p">,</span> <span class="n">energymodulefile</span><span class="o">=</span><span class="n">moduleFile_e</span><span class="p">)</span> <span class="c1">#create the scenario, name and mod file attach</span>
 <span class="k">for</span> <span class="n">mat</span> <span class="ow">in</span> <span class="n">MATERIALS</span><span class="p">:</span>
-    <span class="n">materialfile</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">baselinesfolder</span><span class="p">,</span> <span class="s1">'baseline_material_mass_'</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="n">mat</span><span class="p">)</span><span class="o">+</span><span class="s1">'.csv'</span><span class="p">)</span>
-    <span class="n">sim1</span><span class="o">.</span><span class="n">scenario</span><span class="p">[</span><span class="s1">'Resi_keep'</span><span class="p">]</span><span class="o">.</span><span class="n">addMaterial</span><span class="p">(</span><span class="n">mat</span><span class="p">,</span> <span class="n">massmatfile</span><span class="o">=</span><span class="n">materialfile</span><span class="p">)</span> <span class="c1"># add all materials listed in MATERIALS</span>
+    <span class="n">materialfile_m</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">baselinesfolder</span><span class="p">,</span> <span class="s1">'baseline_material_mass_'</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="n">mat</span><span class="p">)</span><span class="o">+</span><span class="s1">'.csv'</span><span class="p">)</span>
+    <span class="n">materialfile_e</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">baselinesfolder</span><span class="p">,</span> <span class="s1">'baseline_material_energy_'</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="n">mat</span><span class="p">)</span><span class="o">+</span><span class="s1">'.csv'</span><span class="p">)</span>
+    <span class="n">sim1</span><span class="o">.</span><span class="n">scenario</span><span class="p">[</span><span class="s1">'Resi_keep'</span><span class="p">]</span><span class="o">.</span><span class="n">addMaterial</span><span class="p">(</span><span class="n">mat</span><span class="p">,</span> <span class="n">massmatfile</span><span class="o">=</span><span class="n">materialfile_m</span><span class="p">,</span> <span class="n">energymatfile</span><span class="o">=</span><span class="n">materialfile_e</span><span class="p">)</span> <span class="c1"># add all materials listed in MATERIALS</span>
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@@ -14723,14 +14776,15 @@ <h2 id="Residential-Case-Study">Residential Case Study<a class="anchor-link" hre
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 <div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Residential case study</span>
-<span class="n">sim1</span><span class="o">.</span><span class="n">createScenario</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'Resi_repower'</span><span class="p">,</span> <span class="n">massmodulefile</span><span class="o">=</span><span class="n">moduleFile</span><span class="p">)</span> <span class="c1">#create the scenario, name and mod file attach</span>
+<span class="n">sim1</span><span class="o">.</span><span class="n">createScenario</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'Resi_repower'</span><span class="p">,</span> <span class="n">massmodulefile</span><span class="o">=</span><span class="n">moduleFile_m</span><span class="p">,</span> <span class="n">energymodulefile</span><span class="o">=</span><span class="n">moduleFile_e</span><span class="p">)</span> <span class="c1">#create the scenario, name and mod file attach</span>
 <span class="k">for</span> <span class="n">mat</span> <span class="ow">in</span> <span class="n">MATERIALS</span><span class="p">:</span>
     <span class="n">materialfile</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">baselinesfolder</span><span class="p">,</span> <span class="s1">'baseline_material_mass_'</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="n">mat</span><span class="p">)</span><span class="o">+</span><span class="s1">'.csv'</span><span class="p">)</span>
-    <span class="n">sim1</span><span class="o">.</span><span class="n">scenario</span><span class="p">[</span><span class="s1">'Resi_repower'</span><span class="p">]</span><span class="o">.</span><span class="n">addMaterial</span><span class="p">(</span><span class="n">mat</span><span class="p">,</span> <span class="n">massmatfile</span><span class="o">=</span><span class="n">materialfile</span><span class="p">)</span> <span class="c1"># add all materials listed in MATERIALS</span>
+    <span class="n">materialfile_e</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">baselinesfolder</span><span class="p">,</span> <span class="s1">'baseline_material_energy_'</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="n">mat</span><span class="p">)</span><span class="o">+</span><span class="s1">'.csv'</span><span class="p">)</span>
+    <span class="n">sim1</span><span class="o">.</span><span class="n">scenario</span><span class="p">[</span><span class="s1">'Resi_repower'</span><span class="p">]</span><span class="o">.</span><span class="n">addMaterial</span><span class="p">(</span><span class="n">mat</span><span class="p">,</span> <span class="n">massmatfile</span><span class="o">=</span><span class="n">materialfile_m</span><span class="p">,</span> <span class="n">energymatfile</span><span class="o">=</span><span class="n">materialfile_e</span><span class="p">)</span> <span class="c1"># add all materials listed in MATERIALS</span>
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@@ -14759,15 +14813,15 @@ <h1 id="CdTeRamp_full">CdTeRamp_full<a class="anchor-link" href="#CdTeRamp_full"
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 <div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Modify the Scenario </span>
-<span class="n">sim1</span><span class="o">.</span><span class="n">modifyScenario</span><span class="p">(</span><span class="n">scenarios</span><span class="o">=</span><span class="s1">'Resi_keep'</span><span class="p">,</span><span class="n">stage</span><span class="o">=</span><span class="s1">'new_Installed_Capacity_[MW]'</span><span class="p">,</span> <span class="n">value</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="c1">#, start_year=) #</span>
+<span class="n">sim1</span><span class="o">.</span><span class="n">modifyScenario</span><span class="p">(</span><span class="n">scenarios</span><span class="o">=</span><span class="s1">'Resi_keep'</span><span class="p">,</span><span class="n">stage</span><span class="o">=</span><span class="s1">'new_Installed_Capacity_[MW]'</span><span class="p">,</span> <span class="n">value</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">start_year</span><span class="o">=</span><span class="mi">1995</span><span class="p">)</span> <span class="c1">#</span>
 <span class="c1">#sim1.modifyScenario(scenarios='NAME',stage='new_Installed_Capacity_[MW]', value=0.005, start_year=2015) #5kW system installed in 2015</span>
-<span class="n">sim1</span><span class="o">.</span><span class="n">scenario</span><span class="p">[</span><span class="s1">'Resi_keep'</span><span class="p">]</span><span class="o">.</span><span class="n">dataIn_m</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">2015</span><span class="p">,</span> <span class="s1">'new_Installed_Capacity_[MW]'</span><span class="p">]</span> <span class="o">=</span> <span class="n">resi_sys1_size</span>
-<span class="n">sim1</span><span class="o">.</span><span class="n">scenario</span><span class="p">[</span><span class="s1">'Resi_keep'</span><span class="p">]</span><span class="o">.</span><span class="n">dataIn_m</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">2015</span><span class="p">,</span> <span class="s1">'mod_degradation'</span><span class="p">]</span> <span class="o">=</span> <span class="n">resi_sys1_deg</span>
-<span class="n">sim1</span><span class="o">.</span><span class="n">scenario</span><span class="p">[</span><span class="s1">'Resi_keep'</span><span class="p">]</span><span class="o">.</span><span class="n">dataIn_m</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">2015</span><span class="p">,</span> <span class="s1">'mod_lifetime'</span><span class="p">]</span> <span class="o">=</span> <span class="n">resi_sys1_life</span>
+<span class="n">sim1</span><span class="o">.</span><span class="n">scenario</span><span class="p">[</span><span class="s1">'Resi_keep'</span><span class="p">]</span><span class="o">.</span><span class="n">dataIn_m</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">2015</span><span class="o">-</span><span class="mi">1995</span><span class="p">,</span> <span class="s1">'new_Installed_Capacity_[MW]'</span><span class="p">]</span> <span class="o">=</span> <span class="n">resi_sys1_size</span>
+<span class="n">sim1</span><span class="o">.</span><span class="n">scenario</span><span class="p">[</span><span class="s1">'Resi_keep'</span><span class="p">]</span><span class="o">.</span><span class="n">dataIn_m</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">2015</span><span class="o">-</span><span class="mi">1995</span><span class="p">,</span> <span class="s1">'mod_degradation'</span><span class="p">]</span> <span class="o">=</span> <span class="n">resi_sys1_deg</span>
+<span class="n">sim1</span><span class="o">.</span><span class="n">scenario</span><span class="p">[</span><span class="s1">'Resi_keep'</span><span class="p">]</span><span class="o">.</span><span class="n">dataIn_m</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">2015</span><span class="o">-</span><span class="mi">1995</span><span class="p">,</span> <span class="s1">'mod_lifetime'</span><span class="p">]</span> <span class="o">=</span> <span class="n">resi_sys1_life</span>
 </pre></div>
 
      </div>
@@ -14780,19 +14834,19 @@ <h1 id="CdTeRamp_full">CdTeRamp_full<a class="anchor-link" href="#CdTeRamp_full"
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[8]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Modify the Scenario </span>
-<span class="n">sim1</span><span class="o">.</span><span class="n">modifyScenario</span><span class="p">(</span><span class="n">scenarios</span><span class="o">=</span><span class="s1">'Resi_repower'</span><span class="p">,</span><span class="n">stage</span><span class="o">=</span><span class="s1">'new_Installed_Capacity_[MW]'</span><span class="p">,</span> <span class="n">value</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="c1">#, start_year=) #</span>
+<span class="n">sim1</span><span class="o">.</span><span class="n">modifyScenario</span><span class="p">(</span><span class="n">scenarios</span><span class="o">=</span><span class="s1">'Resi_repower'</span><span class="p">,</span><span class="n">stage</span><span class="o">=</span><span class="s1">'new_Installed_Capacity_[MW]'</span><span class="p">,</span> <span class="n">value</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">start_year</span><span class="o">=</span><span class="mi">1995</span><span class="p">)</span> <span class="c1">#</span>
 <span class="c1">#sim1.modifyScenario(scenarios='NAME',stage='new_Installed_Capacity_[MW]', value=0.005, start_year=2015) #5kW system installed in 2015</span>
-<span class="n">sim1</span><span class="o">.</span><span class="n">scenario</span><span class="p">[</span><span class="s1">'Resi_repower'</span><span class="p">]</span><span class="o">.</span><span class="n">dataIn_m</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">2015</span><span class="p">,</span> <span class="s1">'new_Installed_Capacity_[MW]'</span><span class="p">]</span> <span class="o">=</span> <span class="n">resi_sys1_size</span>
-<span class="n">sim1</span><span class="o">.</span><span class="n">scenario</span><span class="p">[</span><span class="s1">'Resi_repower'</span><span class="p">]</span><span class="o">.</span><span class="n">dataIn_m</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">2015</span><span class="p">,</span> <span class="s1">'mod_degradation'</span><span class="p">]</span> <span class="o">=</span> <span class="n">resi_sys1_deg</span>
-<span class="n">sim1</span><span class="o">.</span><span class="n">scenario</span><span class="p">[</span><span class="s1">'Resi_repower'</span><span class="p">]</span><span class="o">.</span><span class="n">dataIn_m</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">2015</span><span class="p">,</span> <span class="s1">'mod_lifetime'</span><span class="p">]</span> <span class="o">=</span> <span class="n">resi_sys1_life_repower</span>
+<span class="n">sim1</span><span class="o">.</span><span class="n">scenario</span><span class="p">[</span><span class="s1">'Resi_repower'</span><span class="p">]</span><span class="o">.</span><span class="n">dataIn_m</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">2015</span><span class="o">-</span><span class="mi">1995</span><span class="p">,</span> <span class="s1">'new_Installed_Capacity_[MW]'</span><span class="p">]</span> <span class="o">=</span> <span class="n">resi_sys1_size</span>
+<span class="n">sim1</span><span class="o">.</span><span class="n">scenario</span><span class="p">[</span><span class="s1">'Resi_repower'</span><span class="p">]</span><span class="o">.</span><span class="n">dataIn_m</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">2015</span><span class="o">-</span><span class="mi">1995</span><span class="p">,</span> <span class="s1">'mod_degradation'</span><span class="p">]</span> <span class="o">=</span> <span class="n">resi_sys1_deg</span>
+<span class="n">sim1</span><span class="o">.</span><span class="n">scenario</span><span class="p">[</span><span class="s1">'Resi_repower'</span><span class="p">]</span><span class="o">.</span><span class="n">dataIn_m</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">2015</span><span class="o">-</span><span class="mi">1995</span><span class="p">,</span> <span class="s1">'mod_lifetime'</span><span class="p">]</span> <span class="o">=</span> <span class="n">resi_sys1_life_repower</span>
 
-<span class="n">sim1</span><span class="o">.</span><span class="n">scenario</span><span class="p">[</span><span class="s1">'Resi_repower'</span><span class="p">]</span><span class="o">.</span><span class="n">dataIn_m</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">2024</span><span class="p">,</span> <span class="s1">'new_Installed_Capacity_[MW]'</span><span class="p">]</span> <span class="o">=</span> <span class="n">resi_sys2_size</span>
-<span class="n">sim1</span><span class="o">.</span><span class="n">scenario</span><span class="p">[</span><span class="s1">'Resi_repower'</span><span class="p">]</span><span class="o">.</span><span class="n">dataIn_m</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">2024</span><span class="p">,</span> <span class="s1">'mod_degradation'</span><span class="p">]</span> <span class="o">=</span> <span class="n">resi_sys2_deg</span>
-<span class="n">sim1</span><span class="o">.</span><span class="n">scenario</span><span class="p">[</span><span class="s1">'Resi_repower'</span><span class="p">]</span><span class="o">.</span><span class="n">dataIn_m</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">2024</span><span class="p">,</span> <span class="s1">'mod_lifetime'</span><span class="p">]</span> <span class="o">=</span> <span class="n">resi_sys2_life</span>
+<span class="n">sim1</span><span class="o">.</span><span class="n">scenario</span><span class="p">[</span><span class="s1">'Resi_repower'</span><span class="p">]</span><span class="o">.</span><span class="n">dataIn_m</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">2024</span><span class="o">-</span><span class="mi">1995</span><span class="p">,</span> <span class="s1">'new_Installed_Capacity_[MW]'</span><span class="p">]</span> <span class="o">=</span> <span class="n">resi_sys2_size</span>
+<span class="n">sim1</span><span class="o">.</span><span class="n">scenario</span><span class="p">[</span><span class="s1">'Resi_repower'</span><span class="p">]</span><span class="o">.</span><span class="n">dataIn_m</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">2024</span><span class="o">-</span><span class="mi">1995</span><span class="p">,</span> <span class="s1">'mod_degradation'</span><span class="p">]</span> <span class="o">=</span> <span class="n">resi_sys2_deg</span>
+<span class="n">sim1</span><span class="o">.</span><span class="n">scenario</span><span class="p">[</span><span class="s1">'Resi_repower'</span><span class="p">]</span><span class="o">.</span><span class="n">dataIn_m</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">2024</span><span class="o">-</span><span class="mi">1995</span><span class="p">,</span> <span class="s1">'mod_lifetime'</span><span class="p">]</span> <span class="o">=</span> <span class="n">resi_sys2_life</span>
 </pre></div>
 
      </div>
@@ -14800,12 +14854,69 @@ <h1 id="CdTeRamp_full">CdTeRamp_full<a class="anchor-link" href="#CdTeRamp_full"
 </div>
 </div>
 
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[9]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#extend the analysis period for full energy calc</span>
+<span class="n">sim1</span><span class="o">.</span><span class="n">trim_Years</span><span class="p">(</span><span class="mi">2010</span><span class="p">,</span><span class="mi">2060</span><span class="p">)</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>Trimming and extending  Resi_keep
+Resi_keep glass : Data trimmed for Energy, years now encompass  2010  to  2060
+Resi_keep aluminium_frames : Data trimmed for Energy, years now encompass  2010  to  2060
+Resi_keep silver : Data trimmed for Energy, years now encompass  2010  to  2060
+Resi_keep silicon : Data trimmed for Energy, years now encompass  2010  to  2060
+Resi_keep copper : Data trimmed for Energy, years now encompass  2010  to  2060
+Resi_keep encapsulant : Data trimmed for Energy, years now encompass  2010  to  2060
+Resi_keep backsheet : Data trimmed for Energy, years now encompass  2010  to  2060
+Resi_keep backsheet : Data trimmed for Mass, years now encompass  2010  to  2060
+Trimming and extending  Resi_repower
+Resi_repower glass : Data trimmed for Energy, years now encompass  2010  to  2060
+Resi_repower aluminium_frames : Data trimmed for Energy, years now encompass  2010  to  2060
+Resi_repower silver : Data trimmed for Energy, years now encompass  2010  to  2060
+Resi_repower silicon : Data trimmed for Energy, years now encompass  2010  to  2060
+Resi_repower copper : Data trimmed for Energy, years now encompass  2010  to  2060
+Resi_repower encapsulant : Data trimmed for Energy, years now encompass  2010  to  2060
+Resi_repower backsheet : Data trimmed for Energy, years now encompass  2010  to  2060
+Resi_repower backsheet : Data trimmed for Mass, years now encompass  2010  to  2060
+</pre>
+</div>
+</div>
+
+</div>
+
+</div>
+
 </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
 <div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[10]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#do we also need a reuse scenario where both systems are used for full life? - wouldn't it just be additive?</span>
@@ -14816,6 +14927,614 @@ <h1 id="CdTeRamp_full">CdTeRamp_full<a class="anchor-link" href="#CdTeRamp_full"
 </div>
 </div>
 
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[11]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">sim1</span><span class="o">.</span><span class="n">scenario</span><span class="p">[</span><span class="s1">'Resi_keep'</span><span class="p">]</span><span class="o">.</span><span class="n">dataIn_m</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s1">'year'</span><span class="p">],</span> 
+         <span class="n">sim1</span><span class="o">.</span><span class="n">scenario</span><span class="p">[</span><span class="s1">'Resi_keep'</span><span class="p">]</span><span class="o">.</span><span class="n">dataIn_m</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s1">'new_Installed_Capacity_[MW]'</span><span class="p">])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">sim1</span><span class="o">.</span><span class="n">scenario</span><span class="p">[</span><span class="s1">'Resi_repower'</span><span class="p">]</span><span class="o">.</span><span class="n">dataIn_m</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s1">'year'</span><span class="p">],</span> 
+         <span class="n">sim1</span><span class="o">.</span><span class="n">scenario</span><span class="p">[</span><span class="s1">'Resi_repower'</span><span class="p">]</span><span class="o">.</span><span class="n">dataIn_m</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s1">'new_Installed_Capacity_[MW]'</span><span class="p">],</span> <span class="n">ls</span><span class="o">=</span><span class="s1">'--'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mf">0.01</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Deployed [MW_dc]'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s1">'Keep'</span><span class="p">,</span><span class="s1">'Repower'</span><span class="p">])</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[11]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>&lt;matplotlib.legend.Legend at 0x1bd577ff450&gt;</pre>
+</div>
+
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
+<img 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"
+class="
+"
+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[12]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sim1</span><span class="o">.</span><span class="n">calculateFlows</span><span class="p">()</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>&gt;&gt;&gt;&gt; Calculating Material Flows &lt;&lt;&lt;&lt;
+
+Working on Scenario:  Resi_keep
+********************
+Finished Area+Power Generation Calculations
+==&gt; Working on Material :  glass
+Recycled surplus End of Sim for Mat  glass  Scenario  Resi_keep  =  6.923027231999998e-05  tonnes.
+==&gt; Working on Material :  aluminium_frames
+Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  Resi_keep  =  0.0012234953067535056  tonnes.
+==&gt; Working on Material :  silver
+==&gt; Working on Material :  silicon
+==&gt; Working on Material :  copper
+==&gt; Working on Material :  encapsulant
+==&gt; Working on Material :  backsheet
+Working on Scenario:  Resi_repower
+********************
+Finished Area+Power Generation Calculations
+==&gt; Working on Material :  glass
+==&gt; Working on Material :  aluminium_frames
+==&gt; Working on Material :  silver
+==&gt; Working on Material :  silicon
+==&gt; Working on Material :  copper
+==&gt; Working on Material :  encapsulant
+==&gt; Working on Material :  backsheet
+
+
+&gt;&gt;&gt;&gt; Calculating Energy Flows &lt;&lt;&lt;&lt;
+
+Working on Scenario:  Resi_keep
+********************
+==&gt; Working on Energy for Material :  glass
+==&gt; Working on Energy for Material :  aluminium_frames
+==&gt; Working on Energy for Material :  silver
+==&gt; Working on Energy for Material :  silicon
+==&gt; Working on Energy for Material :  copper
+==&gt; Working on Energy for Material :  encapsulant
+==&gt; Working on Energy for Material :  backsheet
+Working on Scenario:  Resi_repower
+********************
+==&gt; Working on Energy for Material :  glass
+==&gt; Working on Energy for Material :  aluminium_frames
+==&gt; Working on Energy for Material :  silver
+==&gt; Working on Energy for Material :  silicon
+==&gt; Working on Energy for Material :  copper
+==&gt; Working on Energy for Material :  encapsulant
+==&gt; Working on Energy for Material :  backsheet
+</pre>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[13]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sim1_yearly</span><span class="p">,</span> <span class="n">sim1_cumu</span> <span class="o">=</span> <span class="n">sim1</span><span class="o">.</span><span class="n">aggregateResults</span><span class="p">()</span>
+<span class="n">sim1_allenergy</span><span class="p">,</span> <span class="n">sim1_energyGen</span><span class="p">,</span> <span class="n">sim1_energyDemands_all</span> <span class="o">=</span> <span class="n">sim1</span><span class="o">.</span><span class="n">aggregateEnergyResults</span><span class="p">()</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2263: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands
+C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2263: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands
+</pre>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[19]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">decommisioned</span> <span class="o">=</span> <span class="n">sim1_yearly</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">like</span><span class="o">=</span><span class="s1">'Decommisioned'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">decommisioned</span><span class="o">*</span><span class="mi">1000</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s1">'Keep'</span><span class="p">,</span><span class="s1">'Repower'</span><span class="p">])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">14</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Decommissioned Capacity [kW_dc]'</span><span class="p">)</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[19]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>Text(0, 0.5, &#39;Decommissioned Capacity [kW_dc]&#39;)</pre>
+</div>
+
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
+<img 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"
+class="
+"
+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[15]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">sim1_energyGen</span><span class="o">/</span><span class="mf">1e6</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s1">'Keep'</span><span class="p">,</span><span class="s1">'Repower'</span><span class="p">])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mf">12.0</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Annual Energy Generation [MWh]'</span><span class="p">)</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[15]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>Text(0, 0.5, &#39;Annual Energy Generation [MWh]&#39;)</pre>
+</div>
+
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
+<img 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"
+class="
+"
+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[72]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cumu_energy_gen</span> <span class="o">=</span> <span class="n">sim1_energyGen</span><span class="o">.</span><span class="n">cumsum</span><span class="p">()</span><span class="o">.</span><span class="n">loc</span><span class="p">[[</span><span class="mi">2060</span><span class="p">]]</span>
+<span class="n">cumu_energy_gen_MWh</span> <span class="o">=</span> <span class="n">cumu_energy_gen</span><span class="o">/</span><span class="mf">1e6</span>
+<span class="n">cumu_energy_gen_MWh</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Keep_[MWh]'</span><span class="p">,</span> <span class="s1">'Repower_[MWh]'</span><span class="p">]</span>
+<span class="n">cumu_energy_gen_MWh</span><span class="o">.</span><span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'Energy_Gen'</span><span class="p">]</span>
+<span class="n">cumu_energy_gen_MWh</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[72]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Keep_[MWh]</th>
+      <th>Repower_[MWh]</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>Energy_Gen</th>
+      <td>148.307821</td>
+      <td>365.792917</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[73]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#sum the energy demands</span>
+<span class="n">energydemand_keep</span> <span class="o">=</span> <span class="n">sim1_energyDemands_all</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">like</span><span class="o">=</span><span class="s1">'keep'</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">/</span><span class="mf">1e6</span> <span class="c1">#MWh</span>
+<span class="n">energydemand_repower</span> <span class="o">=</span> <span class="n">sim1_energyDemands_all</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">like</span><span class="o">=</span><span class="s1">'repower'</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">/</span><span class="mf">1e6</span> <span class="c1">#MWh</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[75]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cumu_energy_MWh</span> <span class="o">=</span> <span class="n">cumu_energy_gen_MWh</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+<span class="n">cumu_energy_MWh</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">'Energy_Demand'</span><span class="p">,</span><span class="s1">'Keep_[MWh]'</span><span class="p">]</span> <span class="o">=</span> <span class="n">energydemand_keep</span>
+<span class="n">cumu_energy_MWh</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">'Energy_Demand'</span><span class="p">,</span><span class="s1">'Repower_[MWh]'</span><span class="p">]</span> <span class="o">=</span> <span class="n">energydemand_repower</span>
+<span class="n">cumu_energy_MWh</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[75]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Keep_[MWh]</th>
+      <th>Repower_[MWh]</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>Energy_Gen</th>
+      <td>148.307821</td>
+      <td>365.792917</td>
+    </tr>
+    <tr>
+      <th>Energy_Demand</th>
+      <td>22.049576</td>
+      <td>35.809028</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[78]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#EROI</span>
+<span class="n">cumu_energy_MWh</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">'EROI'</span><span class="p">]</span> <span class="o">=</span> <span class="n">cumu_energy_MWh</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">'Energy_Gen'</span><span class="p">]</span><span class="o">/</span><span class="n">cumu_energy_MWh</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">'Energy_Demand'</span><span class="p">]</span>
+<span class="n">cumu_energy_MWh</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[78]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Keep_[MWh]</th>
+      <th>Repower_[MWh]</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>Energy_Gen</th>
+      <td>148.307821</td>
+      <td>365.792917</td>
+    </tr>
+    <tr>
+      <th>Energy_Demand</th>
+      <td>22.049576</td>
+      <td>35.809028</td>
+    </tr>
+    <tr>
+      <th>EROI</th>
+      <td>6.726108</td>
+      <td>10.215103</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[50]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#material demands</span>
+<span class="n">modmass_keep</span> <span class="o">=</span> <span class="n">sim1_yearly</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">2015</span><span class="p">,</span><span class="s1">'VirginStock_Module_SIPS_Resi_keep_[Tonnes]'</span><span class="p">]</span>
+<span class="n">modmass_repower</span> <span class="o">=</span> <span class="n">sim1_yearly</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span><span class="s1">'VirginStock_Module_SIPS_Resi_repower_[Tonnes]'</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span> 
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
 <div class="jp-Cell-inputWrapper">
diff --git a/docs/dev/SIPS2025-CaseStudies.ipynb b/docs/dev/SIPS2025-CaseStudies.ipynb
index 87e55ef..69e0682 100644
--- a/docs/dev/SIPS2025-CaseStudies.ipynb
+++ b/docs/dev/SIPS2025-CaseStudies.ipynb
@@ -11,10 +11,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "51c46971-8cdf-4836-8418-0469123f3ab9",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python version  3.11.5 | packaged by Anaconda, Inc. | (main, Sep 11 2023, 13:26:23) [MSC v.1916 64 bit (AMD64)]\n",
+      "Pandas version  2.0.3\n",
+      "pyplot  3.7.2\n",
+      "PV_ICE version  \n"
+     ]
+    }
+   ],
    "source": [
     "#setup\n",
     "import numpy as np\n",
@@ -28,8 +39,8 @@
     "\n",
     "cwd = os.getcwd() #grabs current working directory\n",
     "\n",
-    "testfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP' / 'MESC-NRELStdScens')\n",
-    "inputfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines'/'NRELStdScenarios')\n",
+    "testfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP' / 'SIPS-Repowering')\n",
+    "#inputfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines'/'NRELStdScenarios')\n",
     "baselinesfolder = str(Path().resolve().parent.parent /'PV_ICE' / 'baselines')\n",
     "supportMatfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'SupportingMaterial')\n",
     "#altBaselinesfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'Energy_CellModuleTechCompare')\n",
@@ -45,10 +56,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "613f82f3-f393-43dc-9386-0b9329ab0804",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "path = C:\\Users\\hmirletz\\Documents\\GitHub\\PV_ICE\\PV_ICE\\TEMP\\SIPS-Repowering\n",
+      "Baseline folder directed to default:  C:\\Users\\hmirletz\\Documents\\GitHub\\PV_ICE\\PV_ICE\\baselines\n"
+     ]
+    }
+   ],
    "source": [
     "sim1 = PV_ICE.Simulation(name='SIPS', path=testfolder)"
    ]
@@ -64,14 +84,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "93a52417-0815-46cc-8b4f-c86fc2b6e556",
    "metadata": {},
    "outputs": [],
    "source": [
     "#c-Si\n",
     "MATERIALS = ['glass','aluminium_frames','silver','silicon', 'copper', 'encapsulant', 'backsheet']\n",
-    "moduleFile = os.path.join(baselinesfolder, 'baseline_modules_mass_US_updatedT50T90.csv')\n",
+    "moduleFile_m = os.path.join(baselinesfolder, 'baseline_modules_mass_US_updatedT50T90.csv')\n",
+    "moduleFile_e = os.path.join(baselinesfolder, 'baseline_modules_energy.csv')\n",
+    "\n",
     "#CdTe\n",
     "#MATERIALS_CdTe = ['glass_cdte','aluminium_frames_cdte', 'copper_cdte', 'encapsulant_cdte','cadmium','tellurium']\n",
     "#moduleFile_CdTe = os.path.join(baselinesfolder, 'baseline_modules_mass_US_CdTe.csv')"
@@ -79,7 +101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "5ee5a3e3-29a3-4eea-94c5-e5ae75fb1da6",
    "metadata": {},
    "outputs": [],
@@ -97,30 +119,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "efedbc4b-0216-48aa-a884-dbc8ce255975",
    "metadata": {},
    "outputs": [],
    "source": [
     "#Residential case study\n",
-    "sim1.createScenario(name='Resi_keep', massmodulefile=moduleFile) #create the scenario, name and mod file attach\n",
+    "sim1.createScenario(name='Resi_keep', 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",
-    "    sim1.scenario['Resi_keep'].addMaterial(mat, massmatfile=materialfile) # add all materials listed in MATERIALS"
+    "    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_keep'].addMaterial(mat, massmatfile=materialfile_m, energymatfile=materialfile_e) # add all materials listed in MATERIALS"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "7be01ab2-6824-4687-a1b8-23b68dbeb4ac",
    "metadata": {},
    "outputs": [],
    "source": [
     "#Residential case study\n",
-    "sim1.createScenario(name='Resi_repower', massmodulefile=moduleFile) #create the scenario, name and mod file attach\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",
-    "    sim1.scenario['Resi_repower'].addMaterial(mat, massmatfile=materialfile) # add all materials listed in MATERIALS"
+    "    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"
    ]
   },
   {
@@ -138,48 +162,511 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "61014a62-9b16-431d-9c5e-3aa2f19acef6",
    "metadata": {},
    "outputs": [],
    "source": [
     "#Modify the Scenario \n",
-    "sim1.modifyScenario(scenarios='Resi_keep',stage='new_Installed_Capacity_[MW]', value=0)#, start_year=) #\n",
+    "sim1.modifyScenario(scenarios='Resi_keep',stage='new_Installed_Capacity_[MW]', value=0, start_year=1995) #\n",
     "#sim1.modifyScenario(scenarios='NAME',stage='new_Installed_Capacity_[MW]', value=0.005, start_year=2015) #5kW system installed in 2015\n",
-    "sim1.scenario['Resi_keep'].dataIn_m.loc[2015, 'new_Installed_Capacity_[MW]'] = resi_sys1_size\n",
-    "sim1.scenario['Resi_keep'].dataIn_m.loc[2015, 'mod_degradation'] = resi_sys1_deg\n",
-    "sim1.scenario['Resi_keep'].dataIn_m.loc[2015, 'mod_lifetime'] = resi_sys1_life\n"
+    "sim1.scenario['Resi_keep'].dataIn_m.loc[2015-1995, 'new_Installed_Capacity_[MW]'] = resi_sys1_size\n",
+    "sim1.scenario['Resi_keep'].dataIn_m.loc[2015-1995, 'mod_degradation'] = resi_sys1_deg\n",
+    "sim1.scenario['Resi_keep'].dataIn_m.loc[2015-1995, 'mod_lifetime'] = resi_sys1_life\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "5ac8fa8a-7c2b-40ee-8b64-5c1e34594d6f",
    "metadata": {},
    "outputs": [],
    "source": [
     "#Modify the Scenario \n",
-    "sim1.modifyScenario(scenarios='Resi_repower',stage='new_Installed_Capacity_[MW]', value=0)#, start_year=) #\n",
+    "sim1.modifyScenario(scenarios='Resi_repower',stage='new_Installed_Capacity_[MW]', value=0, start_year=1995) #\n",
     "#sim1.modifyScenario(scenarios='NAME',stage='new_Installed_Capacity_[MW]', value=0.005, start_year=2015) #5kW system installed in 2015\n",
-    "sim1.scenario['Resi_repower'].dataIn_m.loc[2015, 'new_Installed_Capacity_[MW]'] = resi_sys1_size\n",
-    "sim1.scenario['Resi_repower'].dataIn_m.loc[2015, 'mod_degradation'] = resi_sys1_deg\n",
-    "sim1.scenario['Resi_repower'].dataIn_m.loc[2015, 'mod_lifetime'] = resi_sys1_life_repower\n",
+    "sim1.scenario['Resi_repower'].dataIn_m.loc[2015-1995, 'new_Installed_Capacity_[MW]'] = resi_sys1_size\n",
+    "sim1.scenario['Resi_repower'].dataIn_m.loc[2015-1995, 'mod_degradation'] = resi_sys1_deg\n",
+    "sim1.scenario['Resi_repower'].dataIn_m.loc[2015-1995, 'mod_lifetime'] = resi_sys1_life_repower\n",
     "\n",
-    "sim1.scenario['Resi_repower'].dataIn_m.loc[2024, 'new_Installed_Capacity_[MW]'] = resi_sys2_size\n",
-    "sim1.scenario['Resi_repower'].dataIn_m.loc[2024, 'mod_degradation'] = resi_sys2_deg\n",
-    "sim1.scenario['Resi_repower'].dataIn_m.loc[2024, 'mod_lifetime'] = resi_sys2_life"
+    "sim1.scenario['Resi_repower'].dataIn_m.loc[2024-1995, 'new_Installed_Capacity_[MW]'] = resi_sys2_size\n",
+    "sim1.scenario['Resi_repower'].dataIn_m.loc[2024-1995, 'mod_degradation'] = resi_sys2_deg\n",
+    "sim1.scenario['Resi_repower'].dataIn_m.loc[2024-1995, 'mod_lifetime'] = resi_sys2_life"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
+   "id": "4e2d9061-0112-4364-af82-fe4f8fc8110f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trimming and extending  Resi_keep\n",
+      "Resi_keep glass : Data trimmed for Energy, years now encompass  2010  to  2060\n",
+      "Resi_keep aluminium_frames : Data trimmed for Energy, years now encompass  2010  to  2060\n",
+      "Resi_keep silver : Data trimmed for Energy, years now encompass  2010  to  2060\n",
+      "Resi_keep silicon : Data trimmed for Energy, years now encompass  2010  to  2060\n",
+      "Resi_keep copper : Data trimmed for Energy, years now encompass  2010  to  2060\n",
+      "Resi_keep encapsulant : Data trimmed for Energy, years now encompass  2010  to  2060\n",
+      "Resi_keep backsheet : Data trimmed for Energy, years now encompass  2010  to  2060\n",
+      "Resi_keep backsheet : Data trimmed for Mass, years now encompass  2010  to  2060\n",
+      "Trimming and extending  Resi_repower\n",
+      "Resi_repower glass : Data trimmed for Energy, years now encompass  2010  to  2060\n",
+      "Resi_repower aluminium_frames : Data trimmed for Energy, years now encompass  2010  to  2060\n",
+      "Resi_repower silver : Data trimmed for Energy, years now encompass  2010  to  2060\n",
+      "Resi_repower silicon : Data trimmed for Energy, years now encompass  2010  to  2060\n",
+      "Resi_repower copper : Data trimmed for Energy, years now encompass  2010  to  2060\n",
+      "Resi_repower encapsulant : Data trimmed for Energy, years now encompass  2010  to  2060\n",
+      "Resi_repower backsheet : Data trimmed for Energy, years now encompass  2010  to  2060\n",
+      "Resi_repower backsheet : Data trimmed for Mass, years now encompass  2010  to  2060\n"
+     ]
+    }
+   ],
+   "source": [
+    "#extend the analysis period for full energy calc\n",
+    "sim1.trim_Years(2010,2060)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
    "id": "6268306d-58bc-4065-9810-0871f6871744",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "#do we also need a reuse scenario where both systems are used for full life? - wouldn't it just be additive?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e791648b-0ccb-47aa-8b84-fcd97b12394c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x1bd577ff450>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(sim1.scenario['Resi_keep'].dataIn_m.loc[:, 'year'], \n",
+    "         sim1.scenario['Resi_keep'].dataIn_m.loc[:, 'new_Installed_Capacity_[MW]'])\n",
+    "plt.plot(sim1.scenario['Resi_repower'].dataIn_m.loc[:, 'year'], \n",
+    "         sim1.scenario['Resi_repower'].dataIn_m.loc[:, 'new_Installed_Capacity_[MW]'], ls='--')\n",
+    "plt.ylim(0,0.01)\n",
+    "plt.ylabel('Deployed [MW_dc]')\n",
+    "plt.legend(['Keep','Repower'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "828595fe-ad41-40a2-9c90-7e59e4968a35",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      ">>>> Calculating Material Flows <<<<\n",
+      "\n",
+      "Working on Scenario:  Resi_keep\n",
+      "********************\n",
+      "Finished Area+Power Generation Calculations\n",
+      "==> Working on Material :  glass\n",
+      "Recycled surplus End of Sim for Mat  glass  Scenario  Resi_keep  =  6.923027231999998e-05  tonnes.\n",
+      "==> Working on Material :  aluminium_frames\n",
+      "Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  Resi_keep  =  0.0012234953067535056  tonnes.\n",
+      "==> Working on Material :  silver\n",
+      "==> Working on Material :  silicon\n",
+      "==> Working on Material :  copper\n",
+      "==> Working on Material :  encapsulant\n",
+      "==> Working on Material :  backsheet\n",
+      "Working on Scenario:  Resi_repower\n",
+      "********************\n",
+      "Finished Area+Power Generation Calculations\n",
+      "==> Working on Material :  glass\n",
+      "==> Working on Material :  aluminium_frames\n",
+      "==> Working on Material :  silver\n",
+      "==> Working on Material :  silicon\n",
+      "==> Working on Material :  copper\n",
+      "==> Working on Material :  encapsulant\n",
+      "==> Working on Material :  backsheet\n",
+      "\n",
+      "\n",
+      ">>>> Calculating Energy Flows <<<<\n",
+      "\n",
+      "Working on Scenario:  Resi_keep\n",
+      "********************\n",
+      "==> Working on Energy for Material :  glass\n",
+      "==> Working on Energy for Material :  aluminium_frames\n",
+      "==> Working on Energy for Material :  silver\n",
+      "==> Working on Energy for Material :  silicon\n",
+      "==> Working on Energy for Material :  copper\n",
+      "==> Working on Energy for Material :  encapsulant\n",
+      "==> Working on Energy for Material :  backsheet\n",
+      "Working on Scenario:  Resi_repower\n",
+      "********************\n",
+      "==> Working on Energy for Material :  glass\n",
+      "==> Working on Energy for Material :  aluminium_frames\n",
+      "==> Working on Energy for Material :  silver\n",
+      "==> Working on Energy for Material :  silicon\n",
+      "==> Working on Energy for Material :  copper\n",
+      "==> Working on Energy for Material :  encapsulant\n",
+      "==> Working on Energy for Material :  backsheet\n"
+     ]
+    }
+   ],
+   "source": [
+    "sim1.calculateFlows()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "2e42289d-aded-4577-92c4-65ec1855d329",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\hmirletz\\Documents\\GitHub\\PV_ICE\\PV_ICE\\main.py:2263: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands\n",
+      "C:\\Users\\hmirletz\\Documents\\GitHub\\PV_ICE\\PV_ICE\\main.py:2263: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  scende_demands.loc[:,colname] = scende_demands.sum(axis=1) #sums module and material energy demands\n"
+     ]
+    }
+   ],
+   "source": [
+    "sim1_yearly, sim1_cumu = sim1.aggregateResults()\n",
+    "sim1_allenergy, sim1_energyGen, sim1_energyDemands_all = sim1.aggregateEnergyResults()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "6db33248-50a3-4274-a1b3-a9ced04a20e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Decommissioned Capacity [kW_dc]')"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "decommisioned = sim1_yearly.filter(like='Decommisioned')\n",
+    "plt.plot(decommisioned*1000)\n",
+    "plt.legend(['Keep','Repower'])\n",
+    "plt.ylim(0,14)\n",
+    "plt.ylabel('Decommissioned Capacity [kW_dc]')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "eedd27d0-17da-4752-8479-1191cd246ead",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Annual Energy Generation [MWh]')"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(sim1_energyGen/1e6)\n",
+    "plt.legend(['Keep','Repower'])\n",
+    "plt.ylim(0,12.0)\n",
+    "plt.ylabel('Annual Energy Generation [MWh]')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "5b55fdcd-bfc4-4742-a365-8816b5cc38a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Keep_[MWh]</th>\n",
+       "      <th>Repower_[MWh]</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Energy_Gen</th>\n",
+       "      <td>148.307821</td>\n",
+       "      <td>365.792917</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Keep_[MWh]  Repower_[MWh]\n",
+       "Energy_Gen  148.307821     365.792917"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "245df34e-ddc9-416c-9eb3-512ab8d8341c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#sum the energy demands\n",
+    "energydemand_keep = sim1_energyDemands_all.filter(like='keep').sum(axis=1).sum()/1e6 #MWh\n",
+    "energydemand_repower = sim1_energyDemands_all.filter(like='repower').sum(axis=1).sum()/1e6 #MWh"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "cce5ad74-8191-4b71-b4f1-dbea5c280317",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Keep_[MWh]</th>\n",
+       "      <th>Repower_[MWh]</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Energy_Gen</th>\n",
+       "      <td>148.307821</td>\n",
+       "      <td>365.792917</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Energy_Demand</th>\n",
+       "      <td>22.049576</td>\n",
+       "      <td>35.809028</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               Keep_[MWh]  Repower_[MWh]\n",
+       "Energy_Gen     148.307821     365.792917\n",
+       "Energy_Demand   22.049576      35.809028"
+      ]
+     },
+     "execution_count": 75,
+     "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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "6319d6ed-18d2-4869-8d66-4a307731c6d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Keep_[MWh]</th>\n",
+       "      <th>Repower_[MWh]</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Energy_Gen</th>\n",
+       "      <td>148.307821</td>\n",
+       "      <td>365.792917</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Energy_Demand</th>\n",
+       "      <td>22.049576</td>\n",
+       "      <td>35.809028</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>EROI</th>\n",
+       "      <td>6.726108</td>\n",
+       "      <td>10.215103</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "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"
+      ]
+     },
+     "execution_count": 78,
+     "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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "77030757-12bb-4e0e-834e-6a23fbd59d8c",
    "metadata": {},
    "outputs": [],
    "source": [
-    "#do we also need a reuse scenario where both systems are used for full life? - wouldn't it just be additive?"
+    "#material demands\n",
+    "modmass_keep = sim1_yearly.loc[2015,'VirginStock_Module_SIPS_Resi_keep_[Tonnes]']\n",
+    "modmass_repower = sim1_yearly.loc[:,'VirginStock_Module_SIPS_Resi_repower_[Tonnes]'].sum()\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "720d84de-79ee-45bc-976f-4d2261852ea4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "id": "b229bce9-496d-4f55-b8a1-2fd1158b3a75",
diff --git a/docs/dev/SIPS2025-CaseStudies.py b/docs/dev/SIPS2025-CaseStudies.py
index 1cd669a..a442903 100644
--- a/docs/dev/SIPS2025-CaseStudies.py
+++ b/docs/dev/SIPS2025-CaseStudies.py
@@ -4,7 +4,7 @@
 # # SIPS: QUIP SERIES
 # This journal explores the case studies of the SIPS 2025 project understanding the implications of repowering and module reuse. This compliments analyses conducted in SAM and PV Watts. Below are the Residential, Commercial, and Utility scale case studies
 
-# In[ ]:
+# In[1]:
 
 
 #setup
@@ -19,8 +19,8 @@
 
 cwd = os.getcwd() #grabs current working directory
 
-testfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP' / 'MESC-NRELStdScens')
-inputfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines'/'NRELStdScenarios')
+testfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP' / 'SIPS-Repowering')
+#inputfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines'/'NRELStdScenarios')
 baselinesfolder = str(Path().resolve().parent.parent /'PV_ICE' / 'baselines')
 supportMatfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'SupportingMaterial')
 #altBaselinesfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'Energy_CellModuleTechCompare')
@@ -34,7 +34,7 @@
 print("PV_ICE version ", PV_ICE.__version__)
 
 
-# In[ ]:
+# In[2]:
 
 
 sim1 = PV_ICE.Simulation(name='SIPS', path=testfolder)
@@ -43,18 +43,20 @@
 # ## Residential Case Study
 # This case study considers a residential system where the PV system is not at technical end of life (installed 2015), but the roof needs replacing in 2024. The owner has the option of keeping the old system or replacing the PV system.
 
-# In[ ]:
+# In[3]:
 
 
 #c-Si
 MATERIALS = ['glass','aluminium_frames','silver','silicon', 'copper', 'encapsulant', 'backsheet']
-moduleFile = os.path.join(baselinesfolder, 'baseline_modules_mass_US_updatedT50T90.csv')
+moduleFile_m = os.path.join(baselinesfolder, 'baseline_modules_mass_US_updatedT50T90.csv')
+moduleFile_e = os.path.join(baselinesfolder, 'baseline_modules_energy.csv')
+
 #CdTe
 #MATERIALS_CdTe = ['glass_cdte','aluminium_frames_cdte', 'copper_cdte', 'encapsulant_cdte','cadmium','tellurium']
 #moduleFile_CdTe = os.path.join(baselinesfolder, 'baseline_modules_mass_US_CdTe.csv')
 
 
-# In[ ]:
+# In[4]:
 
 
 #residential system parameters
@@ -68,24 +70,26 @@
 resi_sys2_life = 30 #years of life
 
 
-# In[ ]:
+# In[5]:
 
 
 #Residential case study
-sim1.createScenario(name='Resi_keep', massmodulefile=moduleFile) #create the scenario, name and mod file attach
+sim1.createScenario(name='Resi_keep', 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')
-    sim1.scenario['Resi_keep'].addMaterial(mat, massmatfile=materialfile) # add all materials listed in MATERIALS
+    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_keep'].addMaterial(mat, massmatfile=materialfile_m, energymatfile=materialfile_e) # add all materials listed in MATERIALS
 
 
-# In[ ]:
+# In[6]:
 
 
 #Residential case study
-sim1.createScenario(name='Resi_repower', massmodulefile=moduleFile) #create the scenario, name and mod file attach
+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')
-    sim1.scenario['Resi_repower'].addMaterial(mat, massmatfile=materialfile) # add all materials listed in MATERIALS
+    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
 
 
 # idx_temp = pd.RangeIndex(start=2024,stop=2051,step=1) #create the index
@@ -95,38 +99,138 @@
 # CdTeRamp_full = round(CdTeRamp.interpolate(),0)
 # #CdTeRamp_full
 
-# In[ ]:
+# In[7]:
 
 
 #Modify the Scenario 
-sim1.modifyScenario(scenarios='Resi_keep',stage='new_Installed_Capacity_[MW]', value=0)#, start_year=) #
+sim1.modifyScenario(scenarios='Resi_keep',stage='new_Installed_Capacity_[MW]', value=0, start_year=1995) #
 #sim1.modifyScenario(scenarios='NAME',stage='new_Installed_Capacity_[MW]', value=0.005, start_year=2015) #5kW system installed in 2015
-sim1.scenario['Resi_keep'].dataIn_m.loc[2015, 'new_Installed_Capacity_[MW]'] = resi_sys1_size
-sim1.scenario['Resi_keep'].dataIn_m.loc[2015, 'mod_degradation'] = resi_sys1_deg
-sim1.scenario['Resi_keep'].dataIn_m.loc[2015, 'mod_lifetime'] = resi_sys1_life
+sim1.scenario['Resi_keep'].dataIn_m.loc[2015-1995, 'new_Installed_Capacity_[MW]'] = resi_sys1_size
+sim1.scenario['Resi_keep'].dataIn_m.loc[2015-1995, 'mod_degradation'] = resi_sys1_deg
+sim1.scenario['Resi_keep'].dataIn_m.loc[2015-1995, 'mod_lifetime'] = resi_sys1_life
 
 
-# In[ ]:
+# In[8]:
 
 
 #Modify the Scenario 
-sim1.modifyScenario(scenarios='Resi_repower',stage='new_Installed_Capacity_[MW]', value=0)#, start_year=) #
+sim1.modifyScenario(scenarios='Resi_repower',stage='new_Installed_Capacity_[MW]', value=0, start_year=1995) #
 #sim1.modifyScenario(scenarios='NAME',stage='new_Installed_Capacity_[MW]', value=0.005, start_year=2015) #5kW system installed in 2015
-sim1.scenario['Resi_repower'].dataIn_m.loc[2015, 'new_Installed_Capacity_[MW]'] = resi_sys1_size
-sim1.scenario['Resi_repower'].dataIn_m.loc[2015, 'mod_degradation'] = resi_sys1_deg
-sim1.scenario['Resi_repower'].dataIn_m.loc[2015, 'mod_lifetime'] = resi_sys1_life_repower
+sim1.scenario['Resi_repower'].dataIn_m.loc[2015-1995, 'new_Installed_Capacity_[MW]'] = resi_sys1_size
+sim1.scenario['Resi_repower'].dataIn_m.loc[2015-1995, 'mod_degradation'] = resi_sys1_deg
+sim1.scenario['Resi_repower'].dataIn_m.loc[2015-1995, 'mod_lifetime'] = resi_sys1_life_repower
 
-sim1.scenario['Resi_repower'].dataIn_m.loc[2024, 'new_Installed_Capacity_[MW]'] = resi_sys2_size
-sim1.scenario['Resi_repower'].dataIn_m.loc[2024, 'mod_degradation'] = resi_sys2_deg
-sim1.scenario['Resi_repower'].dataIn_m.loc[2024, 'mod_lifetime'] = resi_sys2_life
+sim1.scenario['Resi_repower'].dataIn_m.loc[2024-1995, 'new_Installed_Capacity_[MW]'] = resi_sys2_size
+sim1.scenario['Resi_repower'].dataIn_m.loc[2024-1995, 'mod_degradation'] = resi_sys2_deg
+sim1.scenario['Resi_repower'].dataIn_m.loc[2024-1995, 'mod_lifetime'] = resi_sys2_life
 
 
-# In[ ]:
+# In[9]:
+
+
+#extend the analysis period for full energy calc
+sim1.trim_Years(2010,2060)
+
+
+# In[10]:
 
 
 #do we also need a reuse scenario where both systems are used for full life? - wouldn't it just be additive?
 
 
+# In[11]:
+
+
+plt.plot(sim1.scenario['Resi_keep'].dataIn_m.loc[:, 'year'], 
+         sim1.scenario['Resi_keep'].dataIn_m.loc[:, 'new_Installed_Capacity_[MW]'])
+plt.plot(sim1.scenario['Resi_repower'].dataIn_m.loc[:, 'year'], 
+         sim1.scenario['Resi_repower'].dataIn_m.loc[:, 'new_Installed_Capacity_[MW]'], ls='--')
+plt.ylim(0,0.01)
+plt.ylabel('Deployed [MW_dc]')
+plt.legend(['Keep','Repower'])
+
+
+# In[12]:
+
+
+sim1.calculateFlows()
+
+
+# In[13]:
+
+
+sim1_yearly, sim1_cumu = sim1.aggregateResults()
+sim1_allenergy, sim1_energyGen, sim1_energyDemands_all = sim1.aggregateEnergyResults()
+
+
+# In[19]:
+
+
+decommisioned = sim1_yearly.filter(like='Decommisioned')
+plt.plot(decommisioned*1000)
+plt.legend(['Keep','Repower'])
+plt.ylim(0,14)
+plt.ylabel('Decommissioned Capacity [kW_dc]')
+
+
+# In[15]:
+
+
+plt.plot(sim1_energyGen/1e6)
+plt.legend(['Keep','Repower'])
+plt.ylim(0,12.0)
+plt.ylabel('Annual Energy Generation [MWh]')
+
+
+# In[72]:
+
+
+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
+
+
+# In[73]:
+
+
+#sum the energy demands
+energydemand_keep = sim1_energyDemands_all.filter(like='keep').sum(axis=1).sum()/1e6 #MWh
+energydemand_repower = sim1_energyDemands_all.filter(like='repower').sum(axis=1).sum()/1e6 #MWh
+
+
+# In[75]:
+
+
+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
+
+
+# In[78]:
+
+
+#EROI
+cumu_energy_MWh.loc['EROI'] = cumu_energy_MWh.loc['Energy_Gen']/cumu_energy_MWh.loc['Energy_Demand']
+cumu_energy_MWh
+
+
+# In[50]:
+
+
+#material demands
+modmass_keep = sim1_yearly.loc[2015,'VirginStock_Module_SIPS_Resi_keep_[Tonnes]']
+modmass_repower = sim1_yearly.loc[:,'VirginStock_Module_SIPS_Resi_repower_[Tonnes]'].sum()
+
+
+# In[ ]:
+
+
+
+
+
 # # Commercial Case Study
 # This case study considers a set of commerical PV installations.