From 1481e06ddb0ee513b0f4b6861f0ccb1715fe6300 Mon Sep 17 00:00:00 2001
From: Robert Pietzcker <pietzcker@pik-potsdam.de>
Date: Wed, 11 Mar 2020 23:21:01 +0100
Subject: [PATCH] Use the updated PV investment costs for 2020, and reduce
floor cost assumptions
---
core/bounds.gms | 6 +++---
core/datainput.gms | 13 ++++++++++++-
core/equations.gms | 24 ++++++++++++------------
core/input/generisdata_tech.prn | 4 ++--
core/input/generisdata_tech_SSP1.prn | 4 ++--
core/input/generisdata_tech_SSP5.prn | 8 ++++----
6 files changed, 35 insertions(+), 24 deletions(-)
diff --git a/core/bounds.gms b/core/bounds.gms
index b84c883..c69a62c 100755
--- a/core/bounds.gms
+++ b/core/bounds.gms
@@ -287,12 +287,12 @@ loop(te$(sameas(te,"spv") OR sameas(te,"csp") OR sameas(te,"wind")),
vm_cap.lo("2015",regi,te,"1") = 0.95 * p_histCap("2015",regi,te)$(p_histCap("2015",regi,te) gt 1e-10);
vm_cap.up("2015",regi,te,"1") = 1.05 * p_histCap("2015",regi,te)$(p_histCap("2015",regi,te) gt 1e-10);
*additional bound on 2020 expansion: at least yearly as much as in 2016,2017 average
- vm_deltaCap.lo("2020",regi,te,"1") = (p_histCap("2017",regi,te)-p_histCap("2015",regi,te))/2;
+ vm_deltaCap.lo("2020",regi,te,"1") = (p_histCap("2018",regi,te)-p_histCap("2015",regi,te))/3;
);
vm_cap.up("2015",regi,"csp",'1') = 1e-5 + 1.05 * vm_cap.lo("2015",regi,"csp","1"); !! allow offset of 10MW even for countries with no CSP installations to help the solver
-*RR* set lower bounds to spv installed capacity in 2020 to reflect the massive deployment in recent years to 2017 historical values plus a conservative estimation of expected additional deployment until 2020 (+10% per year).
-vm_cap.lo("2020",regi,"spv","1")$(p_histCap("2017",regi,"spv")) = p_histCap("2017",regi,"spv")*(1+0.1)**3;
+*RR* set lower bounds to spv installed capacity in 2020 to reflect the massive deployment in recent years to 2018 historical values plus a conservative estimation of expected additional deployment until 2020 (+10% per year).
+vm_cap.lo("2020",regi,"spv","1")$(p_histCap("2018",regi,"spv")) = p_histCap("2018",regi,"spv")*(1+0.1)**2;
*CB* additional upper bound on 2020 deployment: doubling of historic rate plus 2 GW for solar and wind, and 500 MW for CSP
loop(regi,
diff --git a/core/datainput.gms b/core/datainput.gms
index a46dd2b..ab6bb1f 100644
--- a/core/datainput.gms
+++ b/core/datainput.gms
@@ -753,7 +753,7 @@ loop(ttot$(ttot.val ge 2005),
p_adj_coeff(ttot,regi,"gasftcrec") = 0.8;
p_adj_coeff(ttot,regi,"coalftrec") = 0.6;
p_adj_coeff(ttot,regi,"coalftcrec") = 0.8;
- p_adj_coeff(ttot,regi,"spv") = 0.05;
+ p_adj_coeff(ttot,regi,"spv") = 0.1;
p_adj_coeff(ttot,regi,"wind") = 0.1;
p_adj_coeff(ttot,regi,"dac") = 0.8;
p_adj_coeff(ttot,regi,'apCarH2T') = 1.0;
@@ -874,6 +874,10 @@ pm_data(regi,"floorcost",teLearn(te)) = pm_data(regi,"inco0",te) - pm_data(regi,
*** In case regionally differentiated investment costs should be used the corresponding entries are revised:
$if %cm_techcosts% == "REG" pm_data(regi,"inco0",teRegTechCosts) = p_inco0("2015",regi,teRegTechCosts);
+loop(teRegTechCosts$(sameas(teRegTechCosts,"spv") ),
+$if %cm_techcosts% == "REG" pm_data(regi,"inco0",teRegTechCosts) = p_inco0("2020",regi,teRegTechCosts);
+);
+
$if %cm_techcosts% == "REG" pm_data(regi,"incolearn",teLearn(te)) = pm_data(regi,"inco0",te) - pm_data(regi,"floorcost",te) ;
@@ -889,12 +893,19 @@ Execute_Loadpoint 'input' p_capCum = vm_capCum.l;
*** in case the technologies did not exist in the gdx, set to a non-zero value
p_capCum(t,regi,te)$( NOT p_capCum(t,regi,te)) = sm_eps;
+display p_capCum;
+*RP overwrite p_capCum by exogenous values for 2020
+p_capCum("2020",regi,"spv") = 0.6 / card(regi2); !! roughly 600GW in 2020
+display p_capCum;
pm_data(regi,"learnMult_woFC",teLearn(te)) = pm_data(regi,"incolearn",te)/sum(regi2,(pm_data(regi2,"ccap0",te))**(pm_data(regi,"learnExp_woFC",te)));
*RP* adjust parameter learnMult_woFC to take floor costs into account
$if %cm_techcosts% == "GLO" pm_data(regi,"learnMult_wFC",teLearn(te)) = pm_data(regi,"incolearn",te)/(sum(regi2,pm_data(regi2,"ccap0",te))**pm_data(regi,"learnExp_wFC",te));
*NB* this is the correction of the original parameter calibration
$if %cm_techcosts% == "REG" pm_data(regi,"learnMult_wFC",teLearn(te)) = pm_data(regi,"incolearn",te)/(sum(regi2,p_capCum("2015",regi2,te))**pm_data(regi,"learnExp_wFC",te));
+loop(teRegTechCosts$(sameas(teRegTechCosts,"spv") ),
+$if %cm_techcosts% == "REG" pm_data(regi,"learnMult_wFC",teLearn(te)) = pm_data(regi,"incolearn",te)/(sum(regi2,p_capCum("2020",regi2,te))**pm_data(regi,"learnExp_wFC",te));
+);
***parameter calculation for global level, that regional values can gradually converge to
fm_dataglob("learnMult_wFC",teLearn(te)) = fm_dataglob("incolearn",te)/(fm_dataglob("ccap0",te) **fm_dataglob("learnExp_wFC", te));
diff --git a/core/equations.gms b/core/equations.gms
index 36fb20a..4f8c653 100644
--- a/core/equations.gms
+++ b/core/equations.gms
@@ -422,35 +422,35 @@ q_costTeCapital(t,regi,teLearn) ..
** fm_dataglob("learnExp_wFC",teLearn)
)
)$( t.val le 2005 )
-*** special treatment for 2010: start divergence of regional values by using a
-*** 50/50-split global 2005 to regional 2015 in order to phase-in the observed 2015 regional
+*** special treatment for 2010, 2015: start divergence of regional values by using a
+*** t-split of global 2005 to regional 2020 in order to phase-in the observed 2020 regional
*** variation from input-data
- + ( 0.5 * fm_dataglob("learnMult_wFC",teLearn)
- * ( sum(regi2, vm_capCum("2005",regi2,teLearn))
- + pm_capCumForeign("2005",regi,teLearn)
+ + ( (2020 - t.val)/15 * fm_dataglob("learnMult_wFC",teLearn)
+ * ( sum(regi2, vm_capCum(t,regi2,teLearn))
+ + pm_capCumForeign(t,regi,teLearn)
)
** fm_dataglob("learnExp_wFC",teLearn)
- + 0.5 * pm_data(regi,"learnMult_wFC",teLearn)
- * ( sum(regi2, vm_capCum("2015",regi2,teLearn))
- + pm_capCumForeign("2015",regi,teLearn)
+ + (t.val - 2005)/15 * pm_data(regi,"learnMult_wFC",teLearn)
+ * ( sum(regi2, vm_capCum(t,regi2,teLearn))
+ + pm_capCumForeign(t,regi,teLearn)
)
** pm_data(regi,"learnExp_wFC",teLearn)
- )$( t.val eq 2010 )
+ )$( (t.val gt 2005) AND (t.val lt 2020) )
*** assuming linear convergence of regional learning curves to global values until 2050
- + ( (pm_ttot_val(t) - 2015) / 35 * fm_dataglob("learnMult_wFC",teLearn)
+ + ( (pm_ttot_val(t) - 2020) / 30 * fm_dataglob("learnMult_wFC",teLearn)
* ( sum(regi2, vm_capCum(t,regi2,teLearn))
+ pm_capCumForeign(t,regi,teLearn)
)
** fm_dataglob("learnExp_wFC",teLearn)
- + (2050 - pm_ttot_val(t)) / 35 * pm_data(regi,"learnMult_wFC",teLearn)
+ + (2050 - pm_ttot_val(t)) / 30 * pm_data(regi,"learnMult_wFC",teLearn)
* ( sum(regi2, vm_capCum(t,regi2,teLearn))
+ pm_capCumForeign(t,regi,teLearn)
)
** pm_data(regi,"learnExp_wFC",teLearn)
- )$( t.val ge 2015 AND t.val le 2050 )
+ )$( t.val ge 2020 AND t.val le 2050 )
*** globally harmonized costs after 2050
+ ( fm_dataglob("learnMult_wFC",teLearn)
diff --git a/core/input/generisdata_tech.prn b/core/input/generisdata_tech.prn
index ab3f8dc..b0af633 100644
--- a/core/input/generisdata_tech.prn
+++ b/core/input/generisdata_tech.prn
@@ -70,9 +70,9 @@ inco0 3600 1200 2700 2400 5160
constrTme 4 1 5 1 1 1 3
mix0
eta 1.00 1.00 1.00 1.00 1.00 1.00 1.00
-omf 0.04 0.06 0.025 0.02 0.015 0.025
+omf 0.04 0.06 0.025 0.02 0.020 0.025
lifetime 30 25 130 25 30 25 30
-incolearn 1500 4910 8760
+incolearn 1500 5060 8760
ccap0 0.06 0.005 0.0002
learn 0.108 0.207 0.103
luse 0.09 0.021
diff --git a/core/input/generisdata_tech_SSP1.prn b/core/input/generisdata_tech_SSP1.prn
index b8a1370..590a735 100644
--- a/core/input/generisdata_tech_SSP1.prn
+++ b/core/input/generisdata_tech_SSP1.prn
@@ -70,9 +70,9 @@ inco0 3600 1200 2700 2400 5160
constrTme 4 1 5 1 1 1 3
mix0
eta 1.00 1.00 1.00 1.00 1.00 1.00 1.00
-omf 0.04 0.06 0.025 0.02 0.015 0.025
+omf 0.04 0.06 0.025 0.02 0.020 0.025
lifetime 30 25 130 25 30 25 30
-incolearn 1700 5010 8960
+incolearn 1700 5110 8960
ccap0 0.06 0.005 0.0002
learn 0.108 0.207 0.103
luse 0.09 0.021
diff --git a/core/input/generisdata_tech_SSP5.prn b/core/input/generisdata_tech_SSP5.prn
index bef0dfb..1b7d0c8 100644
--- a/core/input/generisdata_tech_SSP5.prn
+++ b/core/input/generisdata_tech_SSP5.prn
@@ -70,9 +70,9 @@ inco0 3600 1200 2700 2400 5160
constrTme 4 1 5 1 1 1 3
mix0
eta 1.00 1.00 1.00 1.00 1.00 1.00 1.00
-omf 0.04 0.06 0.025 0.02 0.015 0.025
+omf 0.04 0.06 0.025 0.02 0.020 0.025
lifetime 30 25 130 25 30 25 30
-incolearn 1300 4460 8290
+incolearn 1300 4910 8290
ccap0 0.06 0.005 0.0002
learn 0.084 0.17 0.08
luse 0.09 0.021
@@ -136,12 +136,12 @@ omv 36 76
lifetime 40 40
+ storspv storwind storcsp
-inco0 10800 4180 3310
+inco0 18000 7500 5400
mix0 0.00 0.00 0.00
eta 0.67 0.78 0.72
omf 0.02 0.02 0.02
lifetime 25 25 25
-incolearn 7490 2880 2300
+incolearn 12000 4800 3800
ccap0 0.00005 0.00005 0.00005
learn 0.10 0.10 0.10
--
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