first success run

ModelCases/MD_Excel_case/Data_PCM2035/notes.txt

@@ -0,0 +1,2 @@
+load2035 = load2022*1.15
+

Project.toml

@@ -6,6 +6,7 @@ version = "1.0.0-DEV"
 [deps]
 BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
+Cbc = "9961bab8-2fa3-5c5a-9d89-47fab24efd76"
 Clustering = "aaaa29a8-35af-508c-8bc3-b662a17a0fe5"
 Combinatorics = "861a8166-3701-5b0c-9a16-15d98fcdc6aa"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
@@ -18,6 +19,7 @@ Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 RecursiveArrayTools = "731186ca-8d62-57ce-b412-fbd966d074cd"
 SCIP = "82193955-e24f-5292-bf16-6f2c5261a85f"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
+XLSX = "fdbf4ff8-1666-58a4-91e7-1b58723a45e0"
 YAML = "ddb6d928-2868-570f-bddf-ab3f9cf99eb6"
 
 [compat]

src/HOPE.jl

@@ -3,12 +3,21 @@ module HOPE
 using JuMP 
 using DataFrames 
 using CSV
+using XLSX
 using LinearAlgebra
 using YAML
 using Dates
 using Clustering
 using Distances
 using Combinatorics
+using Statistics
+
+#using solver
+#using Gurobi
+#using CPLEX
+using Cbc
+using HiGHS
+#using Clp
 
 #include HOPE module scripts
 include("read_input_data.jl");		#read input data module

src/Read_input_data.jl

@@ -22,7 +22,7 @@ function aggregate_gendata_pcm(df)
 	:RU => mean,
 	:RD => mean)
 	rename!(agg_df, [Symbol("Pmax (MW)_sum"), Symbol("Pmin (MW)_sum"),Symbol("Cost (\$/MWh)_mean"),:EF_mean,:CC_mean,:FOR_mean,:RM_SPIN_mean,:RU_mean,:RD_mean] 
-	.=>  [Symbol("Pmax (MW)"), Symbol("Pmin (MW)"), Symbol("Cost (\$/MWh)"),:EF,:CC,:FOR,:RM_SPIN,:RU,:RD] )
+	.=>  [Symbol("Pmax (MW)"), Symbol("Pmin (MW)"), Symbol("Cost (\$/MWh)"),:EF,:CC,:FOR,:RM_SPIN,:RU,:RD])
 	return agg_df
 end
 
@@ -32,42 +32,42 @@ function load_data(config_set::Dict,path::AbstractString)
     if model_mode == "GTEP"                 #read data for generation and transmission expansion model
         input_data = Dict()
         println("Reading Input_Data Files for GTEP mode")
-        folderpath = path*Data_case
+        folderpath = joinpath(path,Data_case)
         files = readdir(folderpath)
         if any(endswith.(files, ".xlsx"))
             println("The directory $folderpath contains .xlsx file, then try to read input data from GTEP_input_total.xlsx")
             #xlsx_file = XLSX.readxlsx(path*Data_case*"GTEP_input_total.xlsx")
 
             #network
             println("Reading network")
-            input_data["Busdata"]=DataFrame(XLSX.readtable(folderpath*"GTEP_input_total.xlsx","busdata"))
-            input_data["Branchdata"]=DataFrame(XLSX.readtable(folderpath*"GTEP_input_total.xlsx","branchdata"))
+            input_data["Busdata"]=DataFrame(XLSX.readtable(joinpath(folderpath,"GTEP_input_total.xlsx"),"busdata"))
+            input_data["Branchdata"]=DataFrame(XLSX.readtable(joinpath(folderpath,"GTEP_input_total.xlsx"),"branchdata"))
             #technology
             println("Reading technology")
             if config_set["aggregated!"]==1
-                input_data["Gendata"] = aggregate_gendata_gtep(DataFrame(XLSX.readtable(folderpath*"GTEP_input_total.xlsx","gendata")))
+                input_data["Gendata"] = aggregate_gendata_gtep(DataFrame(XLSX.readtable(joinpath(folderpath,"GTEP_input_total.xlsx"),"gendata")))
             else
-                input_data["Gendata"]=DataFrame(XLSX.readtable(folderpath*"GTEP_input_total.xlsx","gendata"))
+                input_data["Gendata"]=DataFrame(XLSX.readtable(joinpath(folderpath,"GTEP_input_total.xlsx"),"gendata"))
             end 
 
-            input_data["Estoragedata"]=DataFrame(XLSX.readtable(folderpath*"GTEP_input_total.xlsx","Estoragedata"))
+            input_data["Estoragedata"]=DataFrame(XLSX.readtable(joinpath(folderpath,"GTEP_input_total.xlsx"),"Estoragedata"))
             input_data["Gencostdata"]=input_data["Gendata"][:,Symbol("Cost (\$/MWh)")]
             #time series
             println("Reading time series")
-            input_data["Winddata"]=DataFrame(XLSX.readtable(folderpath*"GTEP_input_total.xlsx","WT_timeseries_regional"))
-            input_data["Solardata"]=DataFrame(XLSX.readtable(folderpath*"GTEP_input_total.xlsx","SPV_timeseries_regional"))
-            input_data["Loaddata"]=DataFrame(XLSX.readtable(folderpath*"GTEP_input_total.xlsx","Load_timeseries_regional"))
+            input_data["Winddata"]=DataFrame(XLSX.readtable(joinpath(folderpath,"GTEP_input_total.xlsx"),"WT_timeseries_regional"))
+            input_data["Solardata"]=DataFrame(XLSX.readtable(joinpath(folderpath,"GTEP_input_total.xlsx"),"SPV_timeseries_regional"))
+            input_data["Loaddata"]=DataFrame(XLSX.readtable(joinpath(folderpath,"GTEP_input_total.xlsx"),"Load_timeseries_regional"))
             input_data["NIdata"]=input_data["Loaddata"][:,"NI"]
             #candidate
             println("Reading resourc candidate")
-            input_data["Estoragedata_candidate"]=DataFrame(XLSX.readtable(folderpath*"GTEP_input_total.xlsx","Estoragedata_candidate"))
-            input_data["Linedata_candidate"]=DataFrame(XLSX.readtable(folderpath*"GTEP_input_total.xlsx","Linedata_candidate"))
-            input_data["Gendata_candidate"]=DataFrame(XLSX.readtable(folderpath*"GTEP_input_total.xlsx","Gendata_candidate"))
+            input_data["Estoragedata_candidate"]=DataFrame(XLSX.readtable(joinpath(folderpath,"GTEP_input_total.xlsx"),"Estoragedata_candidate"))
+            input_data["Linedata_candidate"]=DataFrame(XLSX.readtable(joinpath(folderpath,"GTEP_input_total.xlsx"),"Linedata_candidate"))
+            input_data["Gendata_candidate"]=DataFrame(XLSX.readtable(joinpath(folderpath,"GTEP_input_total.xlsx"),"Gendata_candidate"))
             #policies
             println("Reading polices")
-            input_data["CBPdata"]=DataFrame(XLSX.readtable(folderpath*"GTEP_input_total.xlsx","carbonpolices"))
+            input_data["CBPdata"]=DataFrame(XLSX.readtable(joinpath(folderpath,"GTEP_input_total.xlsx"),"carbonpolices"))
             #rpspolicydata=
-            input_data["RPSdata"]=DataFrame(XLSX.readtable(folderpath*"GTEP_input_total.xlsx","rpspolices"))
+            input_data["RPSdata"]=DataFrame(XLSX.readtable(joinpath(folderpath,"GTEP_input_total.xlsx"),"rpspolices"))
             println("xlsx Files Successfully Load From $folderpath")
 
         else
@@ -76,107 +76,107 @@ function load_data(config_set::Dict,path::AbstractString)
             #network
             #Busdata=CSV.read("Data/busdata.csv",DataFrame)
             println("Reading network")
-            input_data["Busdata"]=CSV.read(path*Data_case*"busdata.csv",DataFrame) #110% Peak
-            input_data["Branchdata"]=CSV.read(path*Data_case*"branchdata.csv",DataFrame)
+            input_data["Busdata"]=CSV.read(joinpath(folderpath,"busdata.csv"),DataFrame) #110% Peak
+            input_data["Branchdata"]=CSV.read(joinpath(folderpath,"branchdata.csv"),DataFrame)
             #technology
             println("Reading technology")
             if config_set["aggregated!"]==1
-                input_data["Gendata"] = aggregate_gendata_gtep(CSV.read(path*Data_case*"gendata.csv",DataFrame))
+                input_data["Gendata"] = aggregate_gendata_gtep(CSV.read(joinpath(folderpath,"gendata.csv"),DataFrame))
             else
-                input_data["Gendata"]=CSV.read(path*Data_case*"gendata.csv",DataFrame)
+                input_data["Gendata"]=CSV.read(joinpath(folderpath,"gendata.csv"),DataFrame)
             end 
 
-            input_data["Estoragedata"]=CSV.read(path*Data_case*"Estoragedata.csv",DataFrame)
+            input_data["Estoragedata"]=CSV.read(joinpath(folderpath,"Estoragedata.csv"),DataFrame)
             input_data["Gencostdata"]=input_data["Gendata"][:,Symbol("Cost (\$/MWh)")]
             #time series
             println("Reading time series")
-            input_data["Winddata"]=CSV.read(path*Data_case*"WT_timeseries_regional.csv",DataFrame)
-            input_data["Solardata"]=CSV.read(path*Data_case*"SPV_timeseries_regional.csv",DataFrame)
-            input_data["Loaddata"]=CSV.read(path*Data_case*"Load_timeseries_regional.csv",DataFrame)
+            input_data["Winddata"]=CSV.read(joinpath(folderpath,"WT_timeseries_regional.csv"),DataFrame)
+            input_data["Solardata"]=CSV.read(joinpath(folderpath,"SPV_timeseries_regional.csv"),DataFrame)
+            input_data["Loaddata"]=CSV.read(joinpath(folderpath,"Load_timeseries_regional.csv"),DataFrame)
             input_data["NIdata"]=input_data["Loaddata"][:,"NI"]
             #candidate
             println("Reading resourc candidate")
-            input_data["Estoragedata_candidate"]=CSV.read(path*Data_case*"Estoragedata_candidate.csv",DataFrame)
-            input_data["Linedata_candidate"]=CSV.read(path*Data_case*"Linedata_candidate.csv",DataFrame)
-            input_data["Gendata_candidate"]=CSV.read(path*Data_case*"Gendata_candidate.csv",DataFrame)
+            input_data["Estoragedata_candidate"]=CSV.read(joinpath(folderpath,"Estoragedata_candidate.csv"),DataFrame)
+            input_data["Linedata_candidate"]=CSV.read(joinpath(folderpath,"Linedata_candidate.csv"),DataFrame)
+            input_data["Gendata_candidate"]=CSV.read(joinpath(folderpath,"Gendata_candidate.csv"),DataFrame)
             #policies
             println("Reading polices")
-            input_data["CBPdata"]=CSV.read(path*Data_case*"carbonpolices.csv",DataFrame)
+            input_data["CBPdata"]=CSV.read(joinpath(folderpath,"carbonpolices.csv"),DataFrame)
             #rpspolicydata=
-            input_data["RPSdata"]=CSV.read(path*Data_case*"rpspolices.csv",DataFrame)
-            println("CSV Files Successfully Load From $path")
+            input_data["RPSdata"]=CSV.read(joinpath(folderpath,"rpspolices.csv"),DataFrame)
+            println("CSV Files Successfully Load From $folderpath")
         end
 
 
 
     elseif model_mode == "PCM"          #read data for production cost model
         input_data = Dict()
         println("Reading Input_Data Files for PCM mode")
-        folderpath = path*Data_case
+        folderpath = joinpath(path*"/"*Data_case)
         files = readdir(folderpath)
         if any(endswith.(files, ".xlsx"))
             println("The directory $folderpath contains .xlsx file, then try to read input data from PCM_input_total.xlsx")
             #xlsx_file = XLSX.readxlsx(path*Data_case*"PCM_input_total.xlsx")
 
             #network
             println("Reading network")
-            input_data["Busdata"]=DataFrame(XLSX.readtable(folderpath*"PCM_input_total.xlsx","busdata"))
-            input_data["Branchdata"]=DataFrame(XLSX.readtable(folderpath*"PCM_input_total.xlsx","branchdata"))
+            input_data["Busdata"]=DataFrame(XLSX.readtable(joinpath(folderpath,"PCM_input_total.xlsx"),"busdata"))
+            input_data["Branchdata"]=DataFrame(XLSX.readtable(joinpath(folderpath,"PCM_input_total.xlsx"),"branchdata"))
             #technology
             println("Reading technology")
             if config_set["aggregated!"]==1
-                input_data["Gendata"] = aggregate_gendata_pcm(DataFrame(XLSX.readtable(folderpath*"PCM_input_total.xlsx","gendata")))
+                input_data["Gendata"] = aggregate_gendata_pcm(DataFrame(XLSX.readtable(joinpath(folderpath,"PCM_input_total.xlsx"),"gendata")))
             else
-                input_data["Gendata"]=DataFrame(XLSX.readtable(folderpath*"PCM_input_total.xlsx","gendata"))
+                input_data["Gendata"]=DataFrame(XLSX.readtable(joinpath(folderpath,"PCM_input_total.xlsx"),"gendata"))
             end 
 
-            input_data["Estoragedata"]=DataFrame(XLSX.readtable(folderpath*"PCM_input_total.xlsx","Estoragedata"))
+            input_data["Estoragedata"]=DataFrame(XLSX.readtable(joinpath(folderpath,"PCM_input_total.xlsx"),"Estoragedata"))
             input_data["Gencostdata"]=input_data["Gendata"][:,Symbol("Cost (\$/MWh)")]
 
 
             #time series
             println("Reading time series")
-            input_data["Winddata"]=DataFrame(XLSX.readtable(folderpath*"PCM_input_total.xlsx","WT_timeseries_regional"))
-            input_data["Solardata"]=DataFrame(XLSX.readtable(folderpath*"PCM_input_total.xlsx","SPV_timeseries_regional"))
-            input_data["Loaddata"]=DataFrame(XLSX.readtable(folderpath*"PCM_input_total.xlsx","Load_timeseries_regional"))
+            input_data["Winddata"]=DataFrame(XLSX.readtable(joinpath(folderpath,"PCM_input_total.xlsx"),"WT_timeseries_regional"))
+            input_data["Solardata"]=DataFrame(XLSX.readtable(joinpath(folderpath,"PCM_input_total.xlsx"),"SPV_timeseries_regional"))
+            input_data["Loaddata"]=DataFrame(XLSX.readtable(joinpath(folderpath*"PCM_input_total.xlsx"),"Load_timeseries_regional"))
             input_data["NIdata"]=input_data["Loaddata"][:,"NI"]
 
             #policies
             println("Reading polices")
-            input_data["CBPdata"]=DataFrame(XLSX.readtable(folderpath*"PCM_input_total.xlsx","carbonpolices"))
+            input_data["CBPdata"]=DataFrame(XLSX.readtable(joinpath(folderpath,"PCM_input_total.xlsx"),"carbonpolices"))
             #rpspolicydata=
-            input_data["RPSdata"]=DataFrame(XLSX.readtable(folderpath*"PCM_input_total.xlsx","rpspolices"))
+            input_data["RPSdata"]=DataFrame(XLSX.readtable(joinpath(folderpath,"PCM_input_total.xlsx"),"rpspolices"))
             println("xlsx Files Successfully Load From $folderpath")
         else
             println("No xlsx file found in the directory $folderpath, try to read data from .csv files")
 
             println("Reading network")
-            input_data["Busdata"]=CSV.read(path*Data_case*"busdata.csv",DataFrame) #110% Peak
-            input_data["Branchdata"]=CSV.read(path*Data_case*"branchdata.csv",DataFrame)
+            input_data["Busdata"]=CSV.read(joinpath(folderpath,"busdata.csv"),DataFrame)
+            input_data["Branchdata"]=CSV.read(joinpath(folderpath,"branchdata.csv"),DataFrame)
             #technology
             println("Reading technology")
             if config_set["aggregated!"]==1
-                input_data["Gendata"] = aggregate_gendata_pcm(CSV.read(path*Data_case*"gendata.csv",DataFrame))
+                input_data["Gendata"] = aggregate_gendata_pcm(CSV.read(joinpath(folderpath,"gendata.csv"),DataFrame))
             else
-                input_data["Gendata"]=CSV.read(path*Data_case*"gendata.csv",DataFrame)
+                input_data["Gendata"]=CSV.read(joinpath(folderpath,"gendata.csv"),DataFrame)
             end 
 
-            input_data["Estoragedata"]=CSV.read(path*Data_case*"Estoragedata.csv",DataFrame)
+            input_data["Estoragedata"]=CSV.read(joinpath(folderpath,"Estoragedata.csv"),DataFrame)
             input_data["Gencostdata"]=input_data["Gendata"][:,Symbol("Cost (\$/MWh)")]
 
 
             #time series
             println("Reading time series")
-            input_data["Winddata"]=CSV.read(path*Data_case*"WT_timeseries_regional.csv",DataFrame)
-            input_data["Solardata"]=CSV.read(path*Data_case*"SPV_timeseries_regional.csv",DataFrame)
-            input_data["Loaddata"]=CSV.read(path*Data_case*"Load_timeseries_regional.csv",DataFrame)
+            input_data["Winddata"]=CSV.read(joinpath(folderpath,"WT_timeseries_regional.csv"),DataFrame)
+            input_data["Solardata"]=CSV.read(joinpath(folderpath,"SPV_timeseries_regional.csv"),DataFrame)
+            input_data["Loaddata"]=CSV.read(joinpath(folderpath,"Load_timeseries_regional.csv"),DataFrame)
             input_data["NIdata"]=input_data["Loaddata"][:,"NI"]
 
             #policies
             println("Reading polices")
-            input_data["CBPdata"]=CSV.read(path*Data_case*"carbonpolices.csv",DataFrame)
+            input_data["CBPdata"]=CSV.read(joinpath(folderpath,"carbonpolices.csv"),DataFrame)
             #rpspolicydata=
-            input_data["RPSdata"]=CSV.read(path*Data_case*"rpspolices.csv",DataFrame)
+            input_data["RPSdata"]=CSV.read(joinpath(folderpath,"rpspolices.csv"),DataFrame)
             println("CSV Files Successfully Load From $folderpath")
         end   
     end

src/run.jl

@@ -1,8 +1,10 @@
 function run_hope(case::AbstractString)
+	#wk_dir = @__DIR__
+	#path = joinpath(wk_dir,case)
 	path = case
-	outpath = case*"/output/"
+	outpath = path*"/output/"
 	#Set model configuration 
-	config_set = YAML.load(open("Settings/HOPE_model_settings.yml"))
+	config_set = YAML.load(open(case*"/Settings/HOPE_model_settings.yml"))
 
 	#Set solver configuration
 	optimizer =  initiate_solver(config_set["solver"])

src/write_output.jl

@@ -1,6 +1,15 @@
+function mkdir_overwrite(path::AbstractString)
+    if isdir(path)
+        rm(path; force=true, recursive=true)
+        println("'output' folder exists, will be overwrite now!")
+    end
+    mkdir(path)
+end
+
 function write_output(outpath::AbstractString,config_set::Dict, input_data::Dict, model::Model)
-	mkdir(outpath, exist_ok=true)
+	mkdir_overwrite(outpath)
     model_mode = config_set["model_mode"]
+    println("HOPE model ("*model_mode*" mode) is successfully solved!")
     if model_mode == "GTEP"
         ##read input for print	
         Gendata = input_data["Gendata"]
@@ -187,5 +196,5 @@ function write_output(outpath::AbstractString,config_set::Dict, input_data::Dict
         CSV.write(joinpath(outpath, "es_power_hourly.csv"), P_es_df, writeheader=true)
 
     end
-	return
+	println("Write solved results in the folder 'output' DONE!")
 end