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Implement solution saving and exporting for refactored code
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@abelsiqueira
abelsiqueira committed Jan 15, 2025
1 parent 8322b5c commit a373345
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6 changes: 4 additions & 2 deletions Project.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -18,14 +18,15 @@ OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
TOML = "fa267f1f-6049-4f14-aa54-33bafae1ed76"
Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
TimerOutputs = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f"
TulipaIO = "7b3808b7-0819-42d4-885c-978ba173db11"

[compat]
CSV = "0.10"
DataFrames = "1"
DuckDB = "0.10, ~1.0" # ~1.0 until they fix https://github.com/duckdb/duckdb/issues/13911
DuckDB_jll = "0.10, ~1.0" # DuckDB 1.0.0 still allows DuckDB_jll 1.1.0
DuckDB = "0.10, ~1.0"
DuckDB_jll = "0.10, ~1.0"
Graphs = "1.8"
HiGHS = "1"
JuMP = "1"
Expand All @@ -35,6 +36,7 @@ OrderedCollections = "1"
SparseArrays = "1"
Statistics = "1"
TOML = "1"
Tables = "1.12.0"
TimerOutputs = "0.5"
TulipaIO = "0.1, 0.2"
julia = "1.10"
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166 changes: 30 additions & 136 deletions src/io.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -320,158 +320,52 @@ end
save_solution_to_file(output_folder, energy_problem)
Saves the solution from `energy_problem` in CSV files inside `output_file`.
Notice that this assumes that the solution has been computed by [`save_solution!`](@ref).
"""
function save_solution_to_file(output_folder, energy_problem::EnergyProblem)
if !energy_problem.solved
error("The energy_problem has not been solved yet.")
end
save_solution_to_file(output_folder, energy_problem.graph, energy_problem.solution)
save_solution_to_file(
output_folder,
energy_problem.db_connection,
energy_problem.variables,
energy_problem.constraints,
)
return
end

"""
save_solution_to_file(output_file, graph, solution)
save_solution_to_file(output_file, connection, variables, constraints)
Saves the solution in CSV files inside `output_folder`.
The following files are created:
- `assets-investment.csv`: The format of each row is `a,v,p*v`, where `a` is the asset name,
`v` is the corresponding asset investment value, and `p` is the corresponding
capacity value. Only investable assets are included.
- `assets-investments-energy.csv`: The format of each row is `a,v,p*v`, where `a` is the asset name,
`v` is the corresponding asset investment value on energy, and `p` is the corresponding
energy capacity value. Only investable assets with a `storage_method_energy` set to `true` are included.
- `flows-investment.csv`: Similar to `assets-investment.csv`, but for flows.
- `flows.csv`: The value of each flow, per `(from, to)` flow, `rp` representative period
and `timestep`. Since the flow is in power, the value at a timestep is equal to the value
at the corresponding time block, i.e., if flow[1:3] = 30, then flow[1] = flow[2] = flow[3] = 30.
- `storage-level.csv`: The value of each storage level, per `asset`, `rp` representative period,
and `timestep`. Since the storage level is in energy, the value at a timestep is a
proportional fraction of the value at the corresponding time block, i.e., if level[1:3] = 30,
then level[1] = level[2] = level[3] = 10.
Notice that this assumes that the solution has been computed by [`save_solution!`](@ref).
"""
function save_solution_to_file(output_folder, graph, solution)
output_file = joinpath(output_folder, "assets-investments.csv")
output_table = DataFrame(;
asset = String[],
year = Int[],
InstalUnits = Float64[],
InstalCap_MW = Float64[],
)

for ((y, a), investment) in solution.assets_investment
capacity = graph[a].capacity
push!(output_table, (a, y, investment, capacity * investment))
end
CSV.write(output_file, output_table)

output_file = joinpath(output_folder, "assets-investments-energy.csv")
output_table = DataFrame(;
asset = String[],
year = Int[],
InstalEnergyUnits = Float64[],
InstalEnergyCap_MWh = Float64[],
)

for ((y, a), energy_units_investmented) in solution.assets_investment_energy
energy_capacity = graph[a].capacity_storage_energy
push!(
output_table,
(a, y, energy_units_investmented, energy_capacity * energy_units_investmented),
function save_solution_to_file(output_folder, connection, variables, constraints)
# Save each variable
for (name, var) in variables
if length(var.container) == 0
continue
end
output_file = joinpath(output_folder, "var_$name.csv")
DuckDB.execute(
connection,
"COPY $(var.table_name) TO '$output_file' (HEADER, DELIMITER ',')",
)
end
CSV.write(output_file, output_table)

output_file = joinpath(output_folder, "flows-investments.csv")
output_table = DataFrame(;
from_asset = String[],
to_asset = String[],
year = Int[],
InstalUnits = Float64[],
InstalCap_MW = Float64[],
)

for ((y, (u, v)), investment) in solution.flows_investment
capacity = graph[u, v].capacity
push!(output_table, (u, v, y, investment, capacity * investment))
# Save each constraint
for (name, cons) in constraints
if cons.num_rows == 0
continue
end

output_file = joinpath(output_folder, "cons_$name.csv")
DuckDB.execute(
connection,
"COPY $(cons.table_name) TO '$output_file' (HEADER, DELIMITER ',')",
)
end
CSV.write(output_file, output_table)

#=
In both cases below, we select the relevant columns from the existing dataframes,
then, we append the solution column.
After that, we transform and flatten, by rows, the time block values into a long version.
I.e., if a row shows `timesteps_block = 3:5` and `value = 30`, then we transform into
three rows with values `timestep = [3, 4, 5]` and `value` equal to 30 / 3 for storage,
or 30 for flows.
=#

# TODO: Fix all output
# output_file = joinpath(output_folder, "flows.csv")
# output_table = DataFrames.select(
# dataframes[:flows],
# :from,
# :to,
# :year,
# :rep_period,
# :timesteps_block => :timestep,
# )
# output_table.value = solution.flow
# output_table = DataFrames.flatten(
# DataFrames.transform(
# output_table,
# [:timestep, :value] =>
# DataFrames.ByRow(
# (timesteps_block, value) -> begin # transform each row using these two columns
# n = length(timesteps_block)
# (timesteps_block, Iterators.repeated(value, n)) # e.g., (3:5, [30, 30, 30])
# end,
# ) => [:timestep, :value],
# ),
# [:timestep, :value], # flatten, e.g., [(3, 30), (4, 30), (5, 30)]
# )
# output_table |> CSV.write(output_file)

# output_file = joinpath(output_folder, "storage-level-intra-rp.csv")
# output_table = DataFrames.select(
# dataframes[:storage_level_rep_period],
# :asset,
# :rep_period,
# :timesteps_block => :timestep,
# )
# output_table.value = solution.storage_level_rep_period
# if !isempty(output_table.asset)
# output_table = DataFrames.combine(DataFrames.groupby(output_table, :asset)) do subgroup
# _check_initial_storage_level!(subgroup, graph)
# _interpolate_storage_level!(subgroup, :timestep)
# end
# end
# output_table |> CSV.write(output_file)

# output_file = joinpath(output_folder, "storage-level-inter-rp.csv")
# output_table =
# DataFrames.select(dataframes[:storage_level_over_clustered_year], :asset, :periods_block => :period)
# output_table.value = solution.storage_level_over_clustered_year
# if !isempty(output_table.asset)
# output_table = DataFrames.combine(DataFrames.groupby(output_table, :asset)) do subgroup
# _check_initial_storage_level!(subgroup, graph)
# _interpolate_storage_level!(subgroup, :period)
# end
# end
# output_table |> CSV.write(output_file)
#
# output_file = joinpath(output_folder, "max-energy-inter-rp.csv")
# output_table =
# DataFrames.select(dataframes[:max_energy_over_clustered_year], :asset, :periods_block => :period)
# output_table.value = solution.max_energy_over_clustered_year
# output_table |> CSV.write(output_file)
#
# output_file = joinpath(output_folder, "min-energy-inter-rp.csv")
# output_table =
# DataFrames.select(dataframes[:min_energy_over_clustered_year], :asset, :periods_block => :period)
# output_table.value = solution.min_energy_over_clustered_year
# output_table |> CSV.write(output_file)

return
end
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