Scaling

Project.toml

@@ -4,7 +4,7 @@ keywords = ["energy", "capacity", "JuMP", "optimization"]
 license = "MIT"
 desc = "Capacity Expansion Problem Formulation for Julia"
 author = ["Elias Kuepper <elias.kuepper@rwth-aachen.de>"]
-version = "0.1.3"
+version = "0.1.4"
 
 [deps]
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
@@ -19,3 +19,4 @@ StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 [compat]
 julia = "^1.0"
 JuMP = "^0.19"
+ClustForOpt = "^0.3.4"

data/CA_1/nodes.csv

@@ -1,3 +1,3 @@
 node,region,infrastruct,lat,lon,|,pv,wind,coal,oil,gas,bat_e,bat_in,bat_out,h2_e,h2_in,h2_out,trans
 california,CA,ex,36.782142,-119.415653,|,9857,5741,101,539,45321,115,98,98,0,0,0,0
-california,CA,lim,36.782142,-119.415653,|,1000000,1000000,100000,100000,100000,1000000,1000000,100000,100000000,1000000,1000000,0
+california,CA,lim,36.782142,-119.415653,|,1000000,1000000,100000,100000,100000,1000000,1000000,100000,10000000000,1000000,1000000,0

data/CA_14/TS/wind.csv

@@ -157799,3 +157799,12 @@ Time,year,CCT,CVA,ECA,ELU,FRE,LAX,NCT,NVA,PAC,SDG
 2017-12-31T13:00:00.0,2017,0.001,0,0,0.062,0.004,0,0,0.001,0.003,0.006
 2017-12-31T14:00:00.0,2017,0.001,0,0,0.044,0.001,0,0,0.005,0.005,0.001
 2017-12-31T15:00:00.0,2017,0.004,0.002,0,0.046,0,0.001,0.001,0.008,0.007,0
+2017-12-31T15:00:00.1,2017,0.004,0.002,0,0.046,0,0.001,0.001,0.008,0.007,0
+2017-12-31T15:00:00.2,2017,0.004,0.002,0,0.046,0,0.001,0.001,0.008,0.007,0
+2017-12-31T15:00:00.3,2017,0.004,0.002,0,0.046,0,0.001,0.001,0.008,0.007,0
+2017-12-31T15:00:00.4,2017,0.004,0.002,0,0.046,0,0.001,0.001,0.008,0.007,0
+2017-12-31T15:00:00.5,2017,0.004,0.002,0,0.046,0,0.001,0.001,0.008,0.007,0
+2017-12-31T15:00:00.6,2017,0.004,0.002,0,0.046,0,0.001,0.001,0.008,0.007,0
+2017-12-31T15:00:00.7,2017,0.004,0.002,0,0.046,0,0.001,0.001,0.008,0.007,0
+2017-12-31T15:00:00.8,2017,0.004,0.002,0,0.046,0,0.001,0.001,0.008,0.007,0
+2017-12-31T15:00:00.9,2017,0.004,0.002,0,0.046,0,0.001,0.001,0.008,0.007,0

data/CA_14/lines.csv

@@ -10,14 +10,3 @@ trans,line09,ELU,LAX,0,0,0,50000,AC_OHL,0,195,0
 trans,line11,ELU,SDG,0,0,0,50000,AC_OHL,0,166,0
 trans,line14,FRE,LAX,0,0,0,50000,AC_OHL,0,253,0
 trans,line20,NVA,PAC,0,0,0,50000,AC_OHL,0,53,0
-trans,line24,CVA,CCT,0,0,0,50000,AC_OHL,0,151,0
-trans,line25,FRE,CCT,0,0,0,50000,AC_OHL,0,145,0
-trans,line26,NCT,CCT,0,0,0,50000,AC_OHL,0,276,0
-trans,line27,FRE,CVA,0,0,0,50000,AC_OHL,0,217,0
-trans,line28,NCT,CVA,0,0,0,50000,AC_OHL,0,144,0
-trans,line29,NVA,CVA,0,0,0,50000,AC_OHL,0,301,0
-trans,line31,LAX,ECA,0,0,0,50000,AC_OHL,0,200,0
-trans,line32,LAX,ELU,0,0,0,50000,AC_OHL,0,195,0
-trans,line34,SDG,ELU,0,0,0,50000,AC_OHL,0,166,0
-trans,line37,LAX,FRE,0,0,0,50000,AC_OHL,0,253,0
-trans,line43,PAC,NVA,0,0,0,50000,AC_OHL,0,53,0

data/GER_1/nodes.csv

@@ -1,3 +1,3 @@
 node,infrastruct,region,lat,lon,|,pv,wind,coal,gas,oil,bat_e,bat_in,bat_out,h2_e,h2_in,h2_out,trans
 germany,ex,GER,51.167261,10.450738,|,32312,31827,45027,22370,7004,0,0,0,0,0,0,0
-germany,lim,GER,51.167261,10.450738,|,1000000,1000000,100000,100000,100000,1000000,1000000,1000000,100000000,1000000,1000000,0
+germany,lim,GER,51.167261,10.450738,|,1000000,1000000,100000,100000,100000,1000000,1000000,1000000,10000000000,1000000,1000000,0

data/GER_18/lines.csv

@@ -1,50 +1,34 @@
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docs/src/opt_cep.md

@@ -83,6 +83,34 @@ Pkg.add("Clp")
 using Clp
 optimizer=Clp.Optimizer
 ```
+
+## Scaling
+The package features the scaling of variables and equations. Scaling variables, which are used in the numerical model, to `0.01 ≤ x ≤ 100` and scaling equations to `3⋅x = 1` instead of `3000⋅x = 1000` improves the shape of the optimization space and significantly reduces the computational time used to solve the numerical model.
+
+The values are only scaled within the numerical model formulation, where we call the variable `VAR`, but the values are unscaled in the solution, which we call `real-VAR`. The following logic is used to scale the variables:
+`real-VAR [EUR, USD, MW, or MWh] = scale[:VAR] ⋅ VAR`
+`  0.01 ≤ VAR  ≤ 100`
+`⇔ 0.01 ≤ real-VAR / scale[:VAR] ≤ 100`
+
+The equations are scaled with the scaling parameter of the first variable, which is `scale[:COST]` in the following example:
+`  scale[:COST]⋅COST = 10⋅scale[:CAP]⋅CAP`
+`⇔              COST = 10⋅(scale[:CAP]/scale[:COST])⋅CAP`
+
+### Change scaling parameters
+Changing the scaling parameters is useful if the data you use represents a much smaller or bigger energy system than the ones representing Germany and California provided in this package Determine the right scaling parameters by checking the real-values of COST, CAP, GEN... (real-VAR) in a solution using your data. Select the scaling parameters to match the following:
+`0.01 ≤ real-VAR / scale[:VAR] ≤ 100`
+Create a dictionary with the new scaling parameters for EACH variable and include it as the optional `scale` input to overwrite the default scale in `run_opt`:
+```julia
+scale=Dict{Symbol,Int}(:COST => 1e9, :CAP => 1e3, :GEN => 1e3, :SLACK => 1e3, :INTRASTOR => 1e3, :INTERSTOR => 1e6, :FLOW => 1e3, :TRANS =>1e3, :LL => 1e6, :LE => 1e9)
+scale_result = run_opt(ts_clust_data,cep_data,optimizer;scale=scale)
+```
+
+### Adding another variable
+- Extend the default `scale`-dictionary in the `src/optim_problems/run_opt`-file to include the new variable as well.
+- Include the new variable in the problem formulation in the `src/optim_problems/opt_cep`-file. Reformulate the equations by dividing them by the scaling parameter of the first variable, which is `scale[:COST]` in the following example:
+`  scale[:COST]⋅COST = 10⋅scale[:CAP]⋅CAP                  + 100`
+`⇔              COST = 10⋅(scale[:CAP]/scale[:COST])⋅CAP   + 100/scale[:COST]`
+
 ## Opt Result - A closer look
 ```@docs
 OptResult

examples/workflow_example_cep.jl

@@ -2,8 +2,8 @@
 using CapacityExpansion
 using Clp
 ## LOAD DATA ##
-state="GER_18" # or "GER_18" or "CA_1" or "TX_1"
-years=[2015] #2016 works for GER_1 and CA_1, GER_1 can also be used with 2006 to 2016 and, GER_18 is 2015 TX_1 is 2008
+state="GER_1" # or "GER_18" or "CA_1" or "TX_1"
+years=[2016] #2016 works for GER_1 and CA_1, GER_1 can also be used with 2006 to 2016 and, GER_18 is 2015 TX_1 is 2008
 # laod ts-data
 ts_input_data = load_timeseries_data_provided(state;T=24, years=years) #CEP
 # load cep-data
@@ -50,3 +50,11 @@ design_variables=get_cep_design_variables(design_result)
 
 # Use the design variable results for the operational run
 operation_result = run_opt(ts_input_data,cep_data,design_result.opt_config,design_variables,optimizer;lost_el_load_cost=1e6,lost_CO2_emission_cost=700)
+
+# Change scaling parameters
+# Changing the scaling parameters is useful if the data you use represents a much smaller or bigger energy system than the ones representing Germany and California provided in this package
+# Determine the right scaling parameters by checking the "real" values of COST, CAP, GEN... (real-VAR) in a solution using your data. Select the scaling parameters to match the following:
+#   0.01 ≤ VAR  ≤ 100,     real-VAR = scale[:VAR] ⋅ VAR
+# ⇔ 0.01 ≤ real-VAR / scale[:VAR] ≤ 100
+scale=Dict{Symbol,Int}(:COST => 1e9, :CAP => 1e3, :GEN => 1e3, :SLACK => 1e3, :INTRASTOR => 1e3, :INTERSTOR => 1e6, :FLOW => 1e3, :TRANS =>1e3, :LL => 1e6, :LE => 1e9)
+co2_result = run_opt(ts_clust_data.best_results,cep_data,optimizer;scale=scale, co2_limit=50)