Genie leos dev

message_ix/model/MESSAGE/data_load.gms

@@ -97,6 +97,9 @@ Set rating_unrated(rating) ;
 rating_unrated(rating) = yes ;
 rating_unrated('unrated') = no ;
 
+* this is a temporary data for learning technologies. this need to be made automatic
+Set newtec(tec) / wind_ppl / ;
+
 *----------------------------------------------------------------------------------------------------------------------*
 * assignment and computation of MESSAGE-specific auxiliary parameters                                                  *
 *----------------------------------------------------------------------------------------------------------------------*

message_ix/model/MESSAGE/model_learningeos.gms

@@ -0,0 +1,68 @@
+Sets
+  size           'size'  / small, medium, large / ;
+
+Alias (size,size2);
+Parameters
+  cap_new2(node,newtec,year_all2)   'annual newly installed capacity'
+  rho(newtec) 'economy of scale parameter'
+         / wind_ppl      1 / #0.8
+  b(newtec)   'technology cost learning parameter'
+         / wind_ppl      0.9 / #0.9
+  u(size)   'unit size'
+         / small      5
+           medium     10
+           large      50     /
+  inv_cost0(node,newtec) 'initial capex'
+  n_unit0(newtec) 'initial number of unit'
+         / wind_ppl   100 /
+  u0(newtec) 'reference size'
+         / wind_ppl   5 / ;
+inv_cost0(node,newtec)=1500;
+
+scalar hist_length the length of historical periods;
+hist_length = card(year_all2) - card(model_horizon);
+
+*Table
+*  cap_new2(node,newtec,year_all2)   'annual newly installed capacity'
+*                         690      700     710     720
+*  Westeros.wind_ppl      100      10      0       0 ;
+
+Variables
+  N_unit(node,newtec,size,year_all2)  number of units for each size every year
+  CapexTec(node,newtec,year_all2)  capital cost in dollar per kW
+  Object            objective function ;
+
+Positive Variables
+  N_unit;
+
+Equations
+  Objective_inner        total investment cost
+  C_balance              installed capacity balance
+  Capex_estimate         estimating average capex
+  Annual_investment      annual investment cost
+;
+
+
+Objective_inner..        Object =e= sum((node,newtec,year_all2), CapexTec(node,newtec,year_all2)*cap_new2(node,newtec,year_all2)) ;
+C_balance(node,newtec,year_all2)..         sum(size, N_unit(node,newtec,size,year_all2)*u(size)) =e= cap_new2(node,newtec,year_all2) ;
+Capex_estimate(node,newtec,year_all2)..    CapexTec(node,newtec,year_all2)*cap_new2(node,newtec,year_all2) =g= sum(size,inv_cost0(node,newtec)
+                                              *N_unit(node,newtec,size,year_all2)*u(size)
+                                              *[(((sum((size2,year_all3)$(ord(year_all3) le ord(year_all2) and ord(year_all3) gt hist_length), N_unit(node,newtec,size2,year_all3))+n_unit0(newtec))/n_unit0(newtec))**(-b(newtec)))]
+                                              *[((u(size)/u0(newtec))**rho(newtec))]) ;
+Annual_investment(node,newtec,year_all2).. sum(year_all3$(ord(year_all3) le ord(year_all2) and ord(year_all3) gt hist_length), CapexTec(node,newtec,year_all3)*cap_new2(node,newtec,year_all3))
+                                           - sum(year_all3$(ord(year_all3) le (ord(year_all2)-1) and ord(year_all3) gt hist_length), CapexTec(node,newtec,year_all3)*cap_new2(node,newtec,year_all3)) =g= 0 ;
+
+model leaningeos / all /;
+
+$ontext
+
+Objective_inner..        Object =e= sum((node,newtec,year_all2), CapexTec(node,newtec,year_all2)*cap_new2(node,newtec,year_all2)) ;
+C_balance(node,newtec,year_all2)..         sum(size, N_unit(node,newtec,size,year_all2)*u(size)) =e= cap_new2(node,newtec,year_all2) ;
+Capex_estimate(node,newtec,year_all2)..    CapexTec(node,newtec,year_all2)*cap_new2(node,newtec,year_all2) =g= sum(size,inv_cost0(node,newtec)
+                                              *N_unit(node,newtec,size,year_all2)*u(size)
+                                              *[(((sum((size2,year_all3)$(ord(year_all3) le ord(year_all2) and ord(year_all3) gt hist_length), N_unit(node,newtec,size2,year_all3))+n_unit0(newtec))/n_unit0(newtec))**(-b(newtec)))]
+                                              *[((u(size)/u0(newtec))**rho(newtec))]) ;
+
+model leaningeos / all /;
+
+$offtext

message_ix/model/MESSAGE/model_setup.gms

@@ -84,3 +84,4 @@ $INCLUDE MESSAGE/scaling_investment_costs.gms
 *----------------------------------------------------------------------------------------------------------------------*
 
 $INCLUDE MESSAGE/model_core.gms
+$INCLUDE MESSAGE/model_learningeos.gms

message_ix/model/MESSAGE/model_solve.gms

@@ -64,6 +64,81 @@ EMISSION_CONSTRAINT.m(node,type_emission,type_tec,type_year)$(
 %AUX_BOUNDS% AUX_ACT_BOUND_UP(node,tec,year_all,year_all2,mode,time)$( ACT.l(node,tec,year_all,year_all2,mode,time) > 0 AND
 %AUX_BOUNDS%    ACT.l(node,tec,year_all,year_all2,mode,time) = %AUX_BOUND_VALUE% ) = yes ;
 
+
+
+elseif %foresight% = -1,
+***
+* Recursive-dynamic and myopic model
+* ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+* For the myopic and rolling-horizon models, loop over horizons and iteratively solve the model, keeping the decision
+* variables from prior periods fixed.
+* This option is selected by setting the GAMS global variable ``%foresight%`` to a value greater than 0,
+* where the value represents the number of years that the model instance is considering when iterating over the periods
+* of the optimization horizon.
+*
+* Loop over :math:`\hat{y} \in Y`, solving
+*
+* .. math::
+*     \min_x \ OBJ = \sum_{y \in \hat{Y}(\hat{y})} OBJ_y(x_y) \\
+*     \text{s.t. } x_{y'} = x_{y'}^* \quad \forall \ y' < y
+*
+* where :math:`\hat{Y}(\hat{y}) = \{y \in Y | \ |\hat{y}| - |y| < optimization\_horizon \}` and
+* :math:`x_{y'}^*` is the optimal value of :math:`x_{y'}` in iteration :math:`|y'|` of the iterative loop.
+*
+* The advantage of this implementation is that there is no need to 'store' the optimal values of all decision
+* variables in additional reporting parameters - the last model solve automatically includes the results over the
+* entire model horizon and can be imported via the ixmp interface.
+***
+    year(year_all) = no ;
+    year(model_horizon) = yes ;
+    LOOP(year_all$( model_horizon(year_all) ),
+
+*             year(model_horizon) = yes ;
+             year4(year_all2)$((ord(year_all2) < ord(year_all))) = yes ;
+*
+             option threads = 4 ;
+             Solve MESSAGE_LP using LP minimizing OBJ ;
+*            write model status summary
+             status('perfect_foresight','modelstat') = MESSAGE_LP.modelstat ;
+             status('perfect_foresight','solvestat') = MESSAGE_LP.solvestat ;
+             status('perfect_foresight','resUsd')    = MESSAGE_LP.resUsd ;
+             status('perfect_foresight','objEst')    = MESSAGE_LP.objEst ;
+             status('perfect_foresight','objVal')    = MESSAGE_LP.objVal ;
+
+*            write an error message if model did not solve to optimality
+             IF( NOT ( MESSAGE_LP.modelstat = 1 OR MESSAGE_LP.modelstat = 8 ),
+                 put_utility 'log' /'+++ MESSAGEix did not solve to optimality - run is aborted, no output produced! +++ ' ;
+                 ABORT "MESSAGEix did not solve to optimality!"
+             ) ;
+
+*            passing CAP_NEW values to update cap_new2 data for unit and size optimization
+             cap_new2(node,newtec,year_all2) = CAP_NEW.l(node,newtec,year_all2);
+             display cap_new2;
+
+             solve leaningeos using nlp minimizing Object;
+*            passing CapexTec values to update inv_cost data for MESSAGE optimization
+             inv_cost(node,newtec,year_all) = CapexTec.l(node,newtec,year_all);
+
+             Execute_unload 'learningeos.gdx',N_unit,CapexTec,cap_new2, CAP_NEW.l, cap_new2;;
+
+* fix all variables of the current iteration period 'year_all' to the optimal levels
+        EXT.fx(node,commodity,grade,year4) =  EXT.l(node,commodity,grade,year4) ;
+        CAP_NEW.fx(node,tec,year4) = CAP_NEW.l(node,tec,year4) ;
+        CAP.fx(node,tec,year4,year4) = CAP.l(node,tec,year4,year4) ;
+        ACT.fx(node,tec,year4,year4,mode,time) = ACT.l(node,tec,year4,year4,mode,time) ;
+        CAP_NEW_UP.fx(node,tec,year4) = CAP_NEW_UP.l(node,tec,year4) ;
+        CAP_NEW_LO.fx(node,tec,year4) = CAP_NEW_LO.l(node,tec,year4) ;
+        ACT_UP.fx(node,tec,year4,time) = ACT_UP.l(node,tec,year4,time) ;
+        ACT_LO.fx(node,tec,year4,time) = ACT_LO.l(node,tec,year4,time) ;
+
+
+             Display year,year4,year_all,model_horizon,CapexTec.l,hist_length ;
+    ) ; # end of the recursive-dynamic loop for technology cost learning and economy of scale module
+
+
+
+
+
 else
 ***
 * Recursive-dynamic and myopic model
@@ -93,41 +168,53 @@ else
     LOOP(year_all$( model_horizon(year_all) ),
 
 * include all past periods and future periods including the period where the %foresight% is reached
-        year(year_all) = yes ;
+*             year(year_all) = yes ;
+             year(year_all2)$( ORD(year_all2) < (ORD(year_all) + %foresight%) ) = yes ;
+             year4(year_all2)$((ord(year_all2) < ord(year_all))) = yes ;
 
 * reset the investment cost scaling parameter
-        year(year_all2)$( ORD(year_all2) > ORD(year_all)
-            AND duration_period_sum(year_all,year_all2) < %foresight% ) = yes ;
+*             year(year_all2)$( ORD(year_all2) > ORD(year_all)
+*                 AND duration_period_sum(year_all,year_all2) < %foresight% ) = yes ;
 
-* write a status update and time elapsed to the log file, solve the model
-        put_utility 'log' /'+++ Solve the recursive-dynamic version of MESSAGEix - iteration ' year_all.tl:0 '  +++ ' ;
-        $$INCLUDE includes/aux_computation_time.gms
-        Solve MESSAGE_LP using LP minimizing OBJ ;
 
-* write model status summary
-        status(year_all,'modelstat') = MESSAGE_LP.modelstat ;
-        status(year_all,'solvestat') = MESSAGE_LP.solvestat ;
-        status(year_all,'resUsd')    = MESSAGE_LP.resUsd ;
-        status(year_all,'objEst')    = MESSAGE_LP.objEst ;
-        status(year_all,'objVal')    = MESSAGE_LP.objVal ;
-
-* write an error message AND ABORT THE SOLVE LOOP if model did not solve to optimality
-        IF( NOT ( MESSAGE_LP.modelstat = 1 OR MESSAGE_LP.modelstat = 8 ),
-            put_utility 'log' /'+++ MESSAGEix did not solve to optimality - run is aborted, no output produced! +++ ' ;
-            ABORT "MESSAGEix did not solve to optimality!"
-        ) ;
+             option threads = 4 ;
+             Solve MESSAGE_LP using LP minimizing OBJ ;
+*            write model status summary
+             status('perfect_foresight','modelstat') = MESSAGE_LP.modelstat ;
+             status('perfect_foresight','solvestat') = MESSAGE_LP.solvestat ;
+             status('perfect_foresight','resUsd')    = MESSAGE_LP.resUsd ;
+             status('perfect_foresight','objEst')    = MESSAGE_LP.objEst ;
+             status('perfect_foresight','objVal')    = MESSAGE_LP.objVal ;
+
+*            write an error message if model did not solve to optimality
+             IF( NOT ( MESSAGE_LP.modelstat = 1 OR MESSAGE_LP.modelstat = 8 ),
+                 put_utility 'log' /'+++ MESSAGEix did not solve to optimality - run is aborted, no output produced! +++ ' ;
+                 ABORT "MESSAGEix did not solve to optimality!"
+             ) ;
+
+             IF(%learningmode% = 1,
+*            passing CAP_NEW values to update cap_new2 data for unit and size optimization
+                 cap_new2(node,newtec,year_all2) = CAP_NEW.l(node,newtec,year_all2);
+                 display cap_new2;
 
+                 solve leaningeos using nlp minimizing Object;
+*            passing CapexTec values to update inv_cost data for MESSAGE optimization
+                 inv_cost(node,newtec,year_all) = CapexTec.l(node,newtec,year_all);
+
+                 Execute_unload 'learningeos.gdx',N_unit,CapexTec,cap_new2, CAP_NEW.l, cap_new2;;
+                 );
 * fix all variables of the current iteration period 'year_all' to the optimal levels
-        EXT.fx(node,commodity,grade,year_all) =  EXT.l(node,commodity,grade,year_all) ;
-        CAP_NEW.fx(node,tec,year_all) = CAP_NEW.l(node,tec,year_all) ;
-        CAP.fx(node,tec,year_all2,year_all)$( map_period(year_all2,year_all) ) = CAP.l(node,tec,year_all,year_all2) ;
-        ACT.fx(node,tec,year_all2,year_all,mode,time)$( map_period(year_all2,year_all) )
-            = ACT.l(node,tec,year_all2,year_all,mode,time) ;
-        CAP_NEW_UP.fx(node,tec,year_all) = CAP_NEW_UP.l(node,tec,year_all) ;
-        CAP_NEW_LO.fx(node,tec,year_all) = CAP_NEW_LO.l(node,tec,year_all) ;
-        ACT_UP.fx(node,tec,year_all,time) = ACT_UP.l(node,tec,year_all,time) ;
-        ACT_LO.fx(node,tec,year_all,time) = ACT_LO.l(node,tec,year_all,time) ;
+        EXT.fx(node,commodity,grade,year4) =  EXT.l(node,commodity,grade,year4) ;
+        CAP_NEW.fx(node,tec,year4) = CAP_NEW.l(node,tec,year4) ;
+        CAP.fx(node,tec,year4,year4) = CAP.l(node,tec,year4,year4) ;
+        ACT.fx(node,tec,year4,year4,mode,time) = ACT.l(node,tec,year4,year4,mode,time) ;
+        CAP_NEW_UP.fx(node,tec,year4) = CAP_NEW_UP.l(node,tec,year4) ;
+        CAP_NEW_LO.fx(node,tec,year4) = CAP_NEW_LO.l(node,tec,year4) ;
+        ACT_UP.fx(node,tec,year4,time) = ACT_UP.l(node,tec,year4,time) ;
+        ACT_LO.fx(node,tec,year4,time) = ACT_LO.l(node,tec,year4,time) ;
+
 
+             Display year,year4,year_all,model_horizon,CapexTec.l,hist_length ;
     ) ; # end of the recursive-dynamic loop
 
 ) ; # end of if statement for the selection betwen perfect-foresight or recursive-dynamic model

message_ix/model/MESSAGE/sets_maps_def.gms

@@ -140,24 +140,26 @@ $ONEMPTY
 ***
 
 Sets
-    node            world - regions - countries - grid cells
-    commodity       resources - electricity - water - land availability - etc.
-    level           levels of the reference energy system or supply chain ( primary - secondary - ... - useful )
-    sector          sectors (for integration with MACRO)
-    grade           grades of extraction of raw materials
-    tec             technologies
-    mode            modes of operation
-    emission        greenhouse gases - pollutants - etc.
-    land_scenario   scenarios of land use (for land-use model emulator)
-    land_type       types of land use
-    year_all        years (over entire model horizon)
-    year (year_all) years included in a model instance (for myopic or rolling-horizon optimization)
-    time            subannual time periods (seasons - days - hours)
-    shares          share constraint relations
-    relation        generic linear relations
-    lvl_spatial     hierarchical levels of spatial resolution
-    lvl_temporal    hierarchical levels of temporal resolution
-    rating          identifies the 'quality' of the renewable energy potential (bins acc. to Sullivan)
+    node                    world - regions - countries - grid cells
+    commodity               resources - electricity - water - land availability - etc.
+    level                   levels of the reference energy system or supply chain ( primary - secondary - ... - useful )
+    sector                  sectors (for integration with MACRO)
+    grade                   grades of extraction of raw materials
+    tec                     technologies
+    mode                    modes of operation
+    emission                greenhouse gases - pollutants - etc.
+    land_scenario           scenarios of land use (for land-use model emulator)
+    land_type               types of land use
+    year_all                years (over entire model horizon)
+    year (year_all)         years included in a model instance (for myopic or rolling-horizon optimization)
+    year4 (year_all)        years included in a model instance (for myopic or rolling-horizon optimization)
+    year_hist (year_all)    historical year
+    time                    subannual time periods (seasons - days - hours)
+    shares                  share constraint relations
+    relation                generic linear relations
+    lvl_spatial             hierarchical levels of spatial resolution
+    lvl_temporal            hierarchical levels of temporal resolution
+    rating                  identifies the 'quality' of the renewable energy potential (bins acc. to Sullivan)
 ;
 
 * definition of aliases

message_ix/model/MESSAGE_master.gms

@@ -58,6 +58,11 @@ $SETGLOBAL macromode "none"
 * rolling horizon (period-by-period, recursive-dynamic with limited foresight - 'number of years of foresight'
 $SETGLOBAL foresight "0"
 
+** for recursive dynamic approach, this is to activate/deactivate technology learning module **
+* deactivate technology learning - 0
+* activate technology learning - 1
+$SETGLOBAL learningmode "0"
+
 ** add a comment and name extension for model report files (e.g. run-specific info, calibration notes) - optional **
 $SETGLOBAL comment ""