modify genx so that it can be run on Tsinghua Desktop

Project.toml

@@ -4,50 +4,42 @@ authors = ["Jesse Jenkins", "Nestor Sepulveda", "Dharik Mallapragada", "Aaron Sc
 version = "0.3.1"
 
 [deps]
-BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
+CPLEX = "a076750e-1247-5638-91d2-ce28b192dca0"
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
 Cbc = "9961bab8-2fa3-5c5a-9d89-47fab24efd76"
 Clp = "e2554f3b-3117-50c0-817c-e040a3ddf72d"
 Clustering = "aaaa29a8-35af-508c-8bc3-b662a17a0fe5"
 Combinatorics = "861a8166-3701-5b0c-9a16-15d98fcdc6aa"
-#CPLEX = "a076750e-1247-5638-91d2-ce28b192dca0"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
 Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
 Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
-#Gurobi = "2e9cd046-0924-5485-92f1-d5272153d98b"
 HiGHS = "87dc4568-4c63-4d18-b0c0-bb2238e4078b"
 JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"
 OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 RecursiveArrayTools = "731186ca-8d62-57ce-b412-fbd966d074cd"
-SCIP = "82193955-e24f-5292-bf16-6f2c5261a85f"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 YAML = "ddb6d928-2868-570f-bddf-ab3f9cf99eb6"
 
 [compat]
-BenchmarkTools = "1.3.1"
 CSV = "0.10.4"
 Cbc = "1.0.1"
 Clp = "1.0.1"
-Clustering = "0.14.2"
+Clustering = "0.14.2, 0.15"
 Combinatorics = "1.0.2"
-#CPLEX ="0.6.1"
 DataFrames = "1.3.4"
 DataStructures = "0.18.13"
 Distances = "0.10.7"
-#Gurobi ="0.9.14"
 HiGHS = "1.1.4"
 JuMP = "1.1.1"
 MathOptInterface = "1.6.1"
-OrdinaryDiffEq = "6.20.0"
 RecursiveArrayTools = "2.31.2"
-SCIP = "0.11.3"
 Statistics = "1.4.0"
-StatsBase = "0.33.21"
+StatsBase = "0.33.21, 0.34"
 YAML = "0.4.7"
 julia = "1"
 

src/GenX.jl

@@ -47,19 +47,19 @@ using Clustering
 using Distances
 using Combinatorics
 
-using OrdinaryDiffEq
+# using OrdinaryDiffEq
 using Random
 using RecursiveArrayTools
 using Statistics
 
 # Uncomment if Gurobi or CPLEX active license and installations are there and the user intends to use either of them
 
-#using CPLEX
+using CPLEX
 #using Gurobi
 #using CPLEX
 #using MOI
 #using SCIP
-using BenchmarkTools
+# using BenchmarkTools
 using HiGHS
 using Clp
 using Cbc

src/load_inputs/load_generators_variability.jl

@@ -31,12 +31,20 @@ function load_generators_variability(setup::Dict, path::AbstractString, inputs_g
 	end
 
 	# Reorder DataFrame to R_ID order (order provided in Generators_data.csv)
-	select!(gen_var, [:Time_Index; Symbol.(inputs_genvar["VARIABILITY"])])
+	variability_names = inputs_genvar["VARIABILITY"]
+	existing_variability = names(gen_var)
+	temp = zeros(inputs_genvar["T"], inputs_genvar["G"])
+	for g = 1: inputs_genvar["G"]
+		r = variability_names[g]
+		location = findfirst(x -> x == r, existing_variability)
+		temp[:, g] = Vector{Float64}(gen_var[:, location])
+    end
+	# temp = select(gen_var, [:Time_Index; Symbol.(inputs_genvar["VARIABILITY"])])
 
 	# Maximum power output and variability of each energy resource
-	inputs_genvar["pP_Max"] = transpose(Matrix{Float64}(gen_var[1:inputs_genvar["T"],2:(inputs_genvar["G"]+1)]))
+	inputs_genvar["pP_Max"] = transpose(temp)
 
+	return inputs_genvar
 	println("Generators_variability.csv Successfully Read!")
 
-	return inputs_genvar
 end