Business plan plot

Project.toml

@@ -9,28 +9,31 @@ DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 ExportAll = "ad2082ca-a69e-11e9-38fa-e96309a31fe4"
 FileIO = "5789e2e9-d7fb-5bc7-8068-2c6fae9b9549"
 Formatting = "59287772-0a20-5a39-b81b-1366585eb4c0"
-TheoryOfGames = "eb50afb4-6f20-4b37-9b66-473e668300bf"
+HiGHS = "87dc4568-4c63-4d18-b0c0-bb2238e4078b"
 InteractiveUtils = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
 JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
 LayeredLayouts = "f4a74d36-062a-4d48-97cd-1356bad1de4e"
 MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 SankeyPlots = "8fd88ec8-d95c-41fc-b299-05f2225f2cc5"
+StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd"
+TheoryOfGames = "eb50afb4-6f20-4b37-9b66-473e668300bf"
 XLSX = "fdbf4ff8-1666-58a4-91e7-1b58723a45e0"
 YAML = "ddb6d928-2868-570f-bddf-ab3f9cf99eb6"
 
 [compat]
-julia = "1"
 CSV = "0.10"
 DataFrames = "1"
 ExportAll = "0.1"
 FileIO = "1"
 Formatting = "0.4"
-TheoryOfGames = "0.1"
 JuMP = "1"
 LayeredLayouts = "0.2"
 MathOptInterface = "1.0"
 Plots = "1"
 SankeyPlots = "0.2.2"
+StatsPlots = "0.15"
+TheoryOfGames = "0.1"
 XLSX = "0.9"
-YAML = "0.4"
+YAML = "0.4"
+julia = "1"

examples/RunSingleModel.jl

@@ -45,6 +45,9 @@ save_summary(ECModel, output_file_combined)
 # Plot sankey plot of CO model
 plot_sankey(ECModel)
 
+# plot 20 years business plan of CO model
+business_plan_plot(ECModel)
+
 ## Model NC
 
 # create NonCooperative model
@@ -66,4 +69,7 @@ print_summary(NC_Model)
 save_summary(NC_Model, output_file_isolated)
 
 # plot Sankey plot of NC model
-plot_sankey(NC_Model)
+plot_sankey(NC_Model)
+
+# plot business plan of NC model
+#business_plan_plot(NC_Model)

src/ECModel.jl

@@ -842,6 +842,7 @@ function split_financial_terms(ECModel::AbstractEC, profit_distribution=nothing)
         NPV=NPV,
         CAPEX=CAPEX,
         OPEX=OPEX,
+        OEM = Ann_Maintenance,
         REP=Ann_Replacement,
         RV=Ann_Recovery,
         REWARD=Ann_reward,
@@ -852,6 +853,279 @@ function split_financial_terms(ECModel::AbstractEC, profit_distribution=nothing)
     )
 end
 
+""" TO BE IMPROVED
+split_yearly_financial_terms(ECModel::AbstractEC, profit_distribution)
+
+Function to describe the cost term distributions by all users for all years.
+
+Parameters
+----------
+- ECModel : AbstractEC
+    EnergyCommunity model
+- profit_distribution
+    Final objective function
+- user_set_financial
+    User set to be considered for the financial analysis
+
+Returns
+-------
+    The output value is a NamedTuple with the following elements
+    - NPV: the NPV of each user given the final profit_distribution adjustment
+    by game theory techniques
+    - CAPEX: the annualized CAPEX
+    - OPEX: the annualized operating costs (yearly maintenance and yearly peak and energy grid charges)
+    - REP: the annualized replacement costs
+    - RV: the annualized recovery charges
+    - REWARD: the annualized reward distribution by user
+    - PEAK: the annualized peak costs
+    - EN_SELL: the annualized revenues from energy sales
+    - EN_BUY: the annualized costs from energy consumption and buying
+    - EN_NET: the annualized net energy costs
+"""
+
+function split_yearly_financial_terms(ECModel::AbstractEC, profit_distribution=nothing)
+    gen_data = ECModel.gen_data
+
+    project_lifetime = field(gen_data, "project_lifetime")
+
+    get_value = (dense_axis, element) -> (element in axes(dense_axis)[1] ? dense_axis[element] : 0.0)
+    zero_if_negative = x->((x>=0) ? x : 0.0)
+
+    year_set = 0:project_lifetime
+
+    user_set_financial = [EC_CODE; get_user_set(ECModel)]
+
+    if isnothing(profit_distribution)
+        user_set = get_user_set(ECModel)
+        profit_distribution = JuMP.Containers.DenseAxisArray(
+            fill(0.0, length(user_set)),
+            user_set,
+        )
+    end
+
+    @assert termination_status(ECModel) != MOI.OPTIMIZE_NOT_CALLED
+
+    user_set = axes(profit_distribution)[1]
+    ann_factor = [1. ./((1 + field(gen_data, "d_rate")).^y) for y in year_set]
+
+    # Investment costs
+    CAPEX = JuMP.Containers.DenseAxisArray(
+        [(y == 0) ? sum(Float64[get_value(ECModel.results[:CAPEX_tot_us], u)]) : 0.0
+            for y in year_set, u in setdiff(user_set_financial, [EC_CODE])]
+           , year_set, user_set_financial
+    )
+    # Maintenance costs
+    Ann_Maintenance = JuMP.Containers.DenseAxisArray(
+        [get_value(ECModel.results[:C_OEM_tot_us], u)
+            for y in year_set, u in setdiff(user_set_financial, [EC_CODE])]
+        , year_set, user_set_financial
+    )
+    # Replacement costs
+    #The index is 1:20 so in the result should be proper changed. I'll open an issue
+    Ann_Replacement = JuMP.Containers.DenseAxisArray(
+            [(y == 10) ? get_value(ECModel.results[:C_REP_tot_us][y, :], u) : 0.0 
+                for y in year_set, u in setdiff(user_set_financial, [EC_CODE])]
+            , year_set, user_set_financial
+     )
+    # Recovery value
+    Ann_Recovery = JuMP.Containers.DenseAxisArray(
+        [(y == project_lifetime) ? (get_value(ECModel.results[:R_RV_tot_us][y, :], u)) : 0.0
+            for y in year_set, u in setdiff(user_set_financial, [EC_CODE])]
+                , year_set, user_set_financial
+    )
+    # Peak energy charges
+    Ann_peak_charges = JuMP.Containers.DenseAxisArray(
+        [get_value(ECModel.results[:C_Peak_tot_us], u)
+            for y in year_set, u in setdiff(user_set_financial, [EC_CODE])]
+                , year_set, user_set_financial
+    )
+    # Get revenes by selling energy and costs by buying or consuming energy
+    Ann_energy_revenues = JuMP.Containers.DenseAxisArray(
+        [(u in axes(ECModel.results[:R_Energy_us])[1]) ? sum(zero_if_negative.(ECModel.results[:R_Energy_us][u,:])) : 0.0
+            for y in year_set, u in setdiff(user_set_financial, [EC_CODE])]
+            , year_set, user_set_financial
+    )
+    Ann_energy_costs = JuMP.Containers.DenseAxisArray(
+        [(u in axes(ECModel.results[:R_Energy_us])[1]) ? sum(zero_if_negative.(.-(ECModel.results[:R_Energy_us][u, :]))) : 0.0
+        for y in year_set, u in setdiff(user_set_financial, [EC_CODE])]
+            , year_set, user_set_financial
+    )
+
+    Ann_energy_reward = JuMP.Containers.DenseAxisArray(
+        [ECModel == ECModel ? ECModel.results[:R_Reward_agg_tot] : 0.0
+        for y in year_set, u in [EC_CODE]]
+            , year_set, user_set_financial
+    )
+
+    # Total OPEX costs
+    # I think that I miss the reward here
+    Ann_ene_net_costs = Ann_energy_costs .- Ann_energy_revenues
+
+    OPEX = Ann_Maintenance .+ Ann_peak_charges .+ Ann_ene_net_costs 
+
+    # get NPV given the reward allocation
+    #=
+    Basically, what we may need to do is to create a proxy total discounted cost of all terms but NPV so to reproduce the old (CAPEX .+ OPEX .+ Ann_Replacement .- Ann_Recovery).
+    For example, something like:
+    (CAPEX .+ OPEX .+ Ann_Replacement .- Ann_Recovery).data * ann_factor (note that since they are matrix operation, there may be the need for some transpositions
+
+    Then, the resulting vector shall be a 1 column or 1 vector and we can create the equivalent 1D cost vector, so that we can do (total_discounted_reward = NPV .- new_vector).
+    Then, we can do total_discounted_reward ./(sum(act_factor) - 1) and this should be a 1D vector of the yearly reward allocation by user, that can be exploded into 2D by simply duplicating the entries.
+
+    =#
+    NPV = JuMP.Containers.DenseAxisArray(
+        [get_value(profit_distribution, u)
+            for u in setdiff(user_set_financial, [EC_CODE])]
+                , user_set_financial
+    )
+
+    # Total reward
+    # This is the total discounted cost of all terms but NPV. I think that this must be improved
+    total_discounted_cost= CAPEX .+ OPEX .+ Ann_Replacement .- Ann_Recovery
+
+    #=Ann_reward = JuMP.Containers.DenseAxisArray(
+        [(NPV[u] .- sum(total_discounted_cost[:,u]))/(sum(ann_factor) - 1) for y in year_set, u in setdiff(user_set_financial, [EC_CODE])]
+            , year_set, user_set_financial
+    )=#
+
+    return (
+        NPV=NPV,
+        CAPEX=CAPEX,
+        OPEX=OPEX,
+        OEM = Ann_Maintenance,
+        REP = Ann_Replacement,
+        RV = Ann_Recovery,
+        REWARD = Ann_energy_reward,
+        PEAK = Ann_peak_charges,
+        EN_SELL = Ann_energy_revenues,
+        EN_CONS = Ann_energy_costs,
+        year_set = year_set
+    )
+end
+
+"""
+    business_plan(ECModel::AbstractEC, profit_distribution)
+
+Function to describe the cost term distributions by all users for all years.
+
+Parameters
+----------
+- ECModel : AbstractEC
+    EnergyCommunity model
+- profit_distribution
+    Final objective function
+- user_set_financial
+    User set to be considered for the financial analysis
+
+Returns
+-------
+    The output value is a NamedTuple with the following elements
+    - df_business
+        Dataframe with the business plan information
+"""
+
+function business_plan(ECModel::AbstractEC,profit_distribution=nothing, user_set_financial=nothing)
+    gen_data = ECModel.gen_data
+
+    project_lifetime = field(gen_data, "project_lifetime")
+
+    if isnothing(user_set_financial)
+        user_set_financial = get_user_set(ECModel)
+    end
+
+    # Create a vector of years from 2023 to (2023 + project_lifetime)
+    gen_data = ECModel.gen_data
+    project_lifetime = field(gen_data, "project_lifetime")
+
+    business_plan = split_yearly_financial_terms(ECModel)
+    year_set = business_plan.year_set
+
+    # Create an empty DataFrame
+    df_business = DataFrame(Year = Int[], CAPEX = Float64[], OEM = Float64[], EN_SELL = Float64[], EN_CONS = EN_SELL = Float64[], PEAK = Float64[], REP = Float64[], 
+    REWARD = Float64[], RV = Float64[])
+    for i in year_set
+        Year = 0 + year_set[i+1]
+        CAPEX = sum(business_plan.CAPEX[i, :])
+        OEM = sum(business_plan.OEM[i, :])
+        EN_SELL = sum(business_plan.EN_SELL[i, :])
+        EN_CONS = sum(business_plan.EN_CONS[i, :])
+        PEAK = sum(business_plan.PEAK[i, :])
+        REP = sum(business_plan.REP[i, :])
+        REWARD = sum(business_plan.REWARD[i, :])
+        RV = sum(business_plan.RV[i, :])
+        push!(df_business, (Year, CAPEX, OEM,  EN_SELL, EN_CONS, PEAK, REP, REWARD, RV))
+    end
+
+    return df_business
+end
+
+"""
+    business_plan_plot(ECModel::AbstractEC, profit_distribution)
+
+Function to describe the cost term distributions by all users for all years.
+
+Parameters
+----------
+- ECModel : AbstractEC
+    EnergyCommunity model
+- df_business
+    Dataframe with the business plan information
+
+Returns
+-------
+    The output value is a plot with the business plan information
+"""
+
+function business_plan_plot(ECModel::AbstractEC, df_business=nothing,
+    xlabel="Year",
+    ylabel="Amount [k€]",
+    title="Business Plan Over 20 Years",
+    legend=:bottomright,
+    color=:auto,
+    xrotation=45,
+    bar_width=0.6,
+    grid=false,
+    framestyle=:box,
+    barmode=:stack,
+    scaling_factor = 0.001,
+    kwargs...)
+
+    if df_business === nothing
+        df_business = business_plan(ECModel)
+    end
+
+    # Define the plot structure
+    plot_struct = Dict(
+        "CAPEX" => [(-1, :CAPEX)],
+        "OEM" => [(-1, :OEM), (-1, :PEAK)],
+        "Repl. and Recovery" => [(-1, :REP), (+1, :RV)],
+        "Energy expences" => [(-1, :EN_CONS), (+1, :EN_SELL)],
+        "Reward" => [(+1, :REWARD)],
+    )
+
+    # Extract the year from the DataFrame
+    years = df_business.Year
+
+    bar_labels = collect(keys(plot_struct))
+    bar_data = [sum(tup[1] .* df_business[!, tup[2]] .* scaling_factor for tup in plot_struct[l]) for l in bar_labels]
+
+    # Create a bar plot
+    p = bar(years, bar_data,
+            label=bar_labels,
+            xlabel=xlabel, ylabel=ylabel,
+            title=title,
+            legend=legend,
+            color=color,
+            xrotation=xrotation,
+            bar_width=bar_width,
+            grid=grid,
+            framestyle=framestyle,
+            barmode=barmode,
+            )
+
+    return p
+end
+
 function EnergyCommunity.split_financial_terms(ECModel::AbstractEC, profit_distribution::Dict)
     return split_financial_terms(
         ECModel,

src/EnergyCommunity.jl

@@ -8,6 +8,7 @@ module EnergyCommunity
     using MathOptInterface
     using Base.Iterators
     using TheoryOfGames
+    using StatsPlots
     # import ECharts
     import SankeyPlots
     import CSV