Change the name convention (#139)

benchmark/benchmarks.jl

@@ -23,8 +23,8 @@ solution = optimise_investments(parameters, sets)
 SUITE["io"]["output"] = @benchmarkable begin
     save_solution_to_file(
         $OUTPUT_FOLDER,
-        $(sets.s_assets_investment),
+        $(sets.assets_investment),
         $(solution.v_investment),
-        $(parameters.p_unit_capacity),
+        $(parameters.unit_capacity),
     )
 end

src/io.jl

@@ -22,84 +22,84 @@ function create_parameters_and_sets_from_file(input_folder::AbstractString)
     rep_period_df = CSV.read(rep_period_file, DataFrames.DataFrame; header = 2)
 
     # Sets and subsets that depend on input data
-    A = s_assets = nodes_data_df[nodes_data_df.active.==true, :].name         #assets in the energy system that are active
-    Ap = s_assets_producer = nodes_data_df[nodes_data_df.type.=="producer", :].name  #producer assets in the energy system
-    Ac = s_assets_consumer = nodes_data_df[nodes_data_df.type.=="consumer", :].name  #consumer assets in the energy system
-    s_assets_investment = nodes_data_df[nodes_data_df.investable.==true, :].name #assets with investment method in the energy system
-    s_representative_periods = unique(nodes_profiles_df.rep_period_id)  #representative periods
-    s_time_steps = unique(nodes_profiles_df.time_step)   #time steps in the RP (e.g., hours)
+    A = assets = nodes_data_df[nodes_data_df.active.==true, :].name         #assets in the energy system that are active
+    Ap = assets_producer = nodes_data_df[nodes_data_df.type.=="producer", :].name  #producer assets in the energy system
+    Ac = assets_consumer = nodes_data_df[nodes_data_df.type.=="consumer", :].name  #consumer assets in the energy system
+    assets_investment = nodes_data_df[nodes_data_df.investable.==true, :].name #assets with investment method in the energy system
+    rep_periods = unique(nodes_profiles_df.rep_period_id)  #representative periods
+    time_steps = unique(nodes_profiles_df.time_step)   #time steps in the RP (e.g., hours)
 
     # Parameters for system
-    p_rp_weight = Dict((row.id) => row.weight for row in eachrow(rep_period_df)) #representative period weight [h]
+    rep_weight = Dict((row.id) => row.weight for row in eachrow(rep_period_df)) #representative period weight [h]
 
     # Parameters for assets
-    p_profile = Dict(
+    profile = Dict(
         (A[row.id], row.rep_period_id, row.time_step) => row.value for
         row in eachrow(nodes_profiles_df)
     ) # asset profile [p.u.]
 
     # Parameters for producers
-    p_variable_cost   = Dict{String,Float64}()
-    p_investment_cost = Dict{String,Float64}()
-    p_unit_capacity   = Dict{String,Float64}()
-    p_init_capacity   = Dict{String,Float64}()
+    variable_cost   = Dict{String,Float64}()
+    investment_cost = Dict{String,Float64}()
+    unit_capacity   = Dict{String,Float64}()
+    init_capacity   = Dict{String,Float64}()
     for row in eachrow(nodes_data_df)
         if row.name in Ap
-            p_variable_cost[row.name] = row.variable_cost
-            p_investment_cost[row.name] = row.investment_cost
-            p_unit_capacity[row.name] = row.capacity
-            p_init_capacity[row.name] = row.initial_capacity
+            variable_cost[row.name] = row.variable_cost
+            investment_cost[row.name] = row.investment_cost
+            unit_capacity[row.name] = row.capacity
+            init_capacity[row.name] = row.initial_capacity
         end
     end
 
     # Parameters for consumers
-    p_peak_demand = Dict{String,Float64}()
+    peak_demand = Dict{String,Float64}()
     for row in eachrow(nodes_data_df)
         if row.name in Ac
-            p_peak_demand[row.name] = row.peak_demand
+            peak_demand[row.name] = row.peak_demand
         end
     end
 
     params = (
-        p_init_capacity = p_init_capacity,
-        p_investment_cost = p_investment_cost,
-        p_peak_demand = p_peak_demand,
-        p_profile = p_profile,
-        p_rp_weight = p_rp_weight,
-        p_unit_capacity = p_unit_capacity,
-        p_variable_cost = p_variable_cost,
+        init_capacity = init_capacity,
+        investment_cost = investment_cost,
+        peak_demand = peak_demand,
+        profile = profile,
+        rep_weight = rep_weight,
+        unit_capacity = unit_capacity,
+        variable_cost = variable_cost,
     )
     sets = (
-        s_assets = s_assets,
-        s_assets_consumer = s_assets_consumer,
-        s_assets_investment = s_assets_investment,
-        s_assets_producer = s_assets_producer,
-        s_representative_periods = s_representative_periods,
-        s_time_steps = s_time_steps,
+        assets = assets,
+        assets_consumer = assets_consumer,
+        assets_investment = assets_investment,
+        assets_producer = assets_producer,
+        rep_periods = rep_periods,
+        time_steps = time_steps,
     )
 
     return params, sets
 end
 
 """
-    save_solution_to_file(output_file, v_investment, p_unit_capacity)
+    save_solution_to_file(output_file, v_investment, unit_capacity)
 
 Saves the solution variable v_investment to a file "investments.csv" inside `output_file`.
 The format of each row is `a,v,p*v`, where `a` is the asset indexing `v_investment`, `v`
-is corresponding `v_investment` value, and `p` is the corresponding `p_unit_capacity` value.
+is corresponding `v_investment` value, and `p` is the corresponding `unit_capacity` value.
 """
 function save_solution_to_file(
     output_folder,
-    s_assets_investment,
+    assets_investment,
     v_investment,
-    p_unit_capacity,
+    unit_capacity,
 )
     # Writing the investment results to a CSV file
     output_file = joinpath(output_folder, "investments.csv")
     output_table = DataFrame(;
-        a = s_assets_investment,
-        InstalUnits = [v_investment[a] for a in s_assets_investment],
-        InstalCap_MW = [p_unit_capacity[a] * v_investment[a] for a in s_assets_investment],
+        a = assets_investment,
+        InstalUnits = [v_investment[a] for a in assets_investment],
+        InstalCap_MW = [unit_capacity[a] * v_investment[a] for a in assets_investment],
     )
     CSV.write(output_file, output_table)
 

src/model.jl

@@ -7,13 +7,13 @@ Create and solve the model using the `graph` structure, the parameters and sets.
 """
 function optimise_investments(graph, params, sets; verbose = false)
     # Sets unpacking
-    A = sets.s_assets
-    Ac = sets.s_assets_consumer
-    # Ap = sets.s_assets_producer
-    Ai = sets.s_assets_investment
+    A = sets.assets
+    Ac = sets.assets_consumer
+    # Ap = sets.assets_producer
+    Ai = sets.assets_investment
     F = [(A[e.src], A[e.dst]) for e in edges(graph)]
-    K = sets.s_time_steps
-    RP = sets.s_representative_periods
+    K = sets.time_steps
+    RP = sets.rep_periods
 
     # Model
     model = Model(HiGHS.Optimizer)
@@ -27,15 +27,15 @@ function optimise_investments(graph, params, sets; verbose = false)
     e_investment_cost = @expression(
         model,
         sum(
-            params.p_investment_cost[a] * params.p_unit_capacity[a] * v_investment[a]
-            for a in Ai
+            params.investment_cost[a] * params.unit_capacity[a] * v_investment[a] for
+            a in Ai
         )
     )
 
     e_variable_cost = @expression(
         model,
         sum(
-            params.p_rp_weight[rp] * params.p_variable_cost[a] * v_flow[f, rp, k] for
+            params.rep_weight[rp] * params.variable_cost[a] * v_flow[f, rp, k] for
             a in A, f in F, rp in RP, k in K if f[1] == a
         )
     )
@@ -49,16 +49,16 @@ function optimise_investments(graph, params, sets; verbose = false)
         model,
         c_balance[a in Ac, rp in RP, k in K],
         sum(v_flow[f, rp, k] for f in F if f[2] == a) ==
-        params.p_profile[a, rp, k] * params.p_peak_demand[a]
+        params.profile[a, rp, k] * params.peak_demand[a]
     )
 
     # - maximum generation
     @constraint(
         model,
         c_max_prod[a in Ai, f in F, rp in RP, k in K; f[1] == a],
         v_flow[f, rp, k] <=
-        get(params.p_profile, (a, rp, k), 1.0) *
-        (params.p_init_capacity[a] + params.p_unit_capacity[a] * v_investment[a])
+        get(params.profile, (a, rp, k), 1.0) *
+        (params.init_capacity[a] + params.unit_capacity[a] * v_investment[a])
     )
 
     # print lp file

test/runtests.jl

@@ -16,9 +16,9 @@ const OUTPUT_FOLDER = joinpath(@__DIR__, "outputs")
     @test solution.objective_value ≈ 269238.43825 atol = 1e-5
     save_solution_to_file(
         OUTPUT_FOLDER,
-        sets.s_assets_investment,
+        sets.assets_investment,
         solution.v_investment,
-        parameters.p_unit_capacity,
+        parameters.unit_capacity,
     )
 end