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pipeline.jl
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pipeline.jl
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### A Pluto.jl notebook ###
# v0.19.42
using Markdown
using InteractiveUtils
# ╔═╡ 6876e975-7525-4139-803b-7bc7d939d6b5
using CSV, DataFrames, DuckDB, JuMP, Plots, PlutoUI , TulipaClustering , TulipaEnergyModel , TulipaIO, SparseArrays
# ╔═╡ 5e8526b5-f13c-45a5-9d61-8acdfa014f50
begin
DF = DataFrames
DDB = DuckDB
PUI = PlutoUI
TC = TulipaClustering
TEM = TulipaEnergyModel
TIO = TulipaIO
end
# ╔═╡ a46312ec-7cc7-432b-8965-a82f4a163a4b
PUI.TableOfContents(depth=4)
# ╔═╡ 75094888-27c7-430c-8a77-38d104b5b389
md"""
# Pipeline example
This pipeline will show how to use the Tulipa ecosystem to read data, cluster the profiles, create and solve the energy model problem, and plot the result.
We should aim to use `TulipaIO/DuckDB` whenever we need to manipulate data in this file.
!!! danger "WIP"
This is a work in progress.
"""
# ╔═╡ e4688e71-118a-40e6-a060-18acaf062cac
md"""
## Step 1 - Read (some of) the data
The first step in the pipeline is to read the relevant data.
Some of the information required to the model will be read (or created) later.
!!! warning
The data used here is based on the existing Norse data. When we use a more realistic data source, this section might change heavily.
"""
# ╔═╡ 29b2fb1f-dfa6-4360-90b0-0c01d1ad435b
md"""
### Step 1.1 - What data is expected now
At the current state of the pipeline, we are expecting the following data to be available (although not everything will be used yet):
**Graph data (or geographical data)**: What are the assets and how they connect.
It is not important where they are on a map, but rather their abstract representation.
The nodes of this graph are the **assets** and the edges are the **flows**.
**Assets and flows metadata**: Relevant information for each of the assets and for each of the flows. For instance, the capacity of the flow between nodes A and B.
**Profile data per type**: Profiles that will used (and possibly reused) in assets and flows. These are named and organized according to their types (e.g., availability, demand).
**Assets and flows profiles**: Link the assets and flows to profiles types and names. E.g., asset A uses profile P1 for demand.
"""
# ╔═╡ f6a9c905-cf01-40be-8ad3-71288b5b4929
md"""
### Step 1.2 - Reading data from CSV folder
"""
# ╔═╡ 3bd01539-abac-44ae-8b17-eccb22d1c966
md"""
Reading all CSV files from the `data` folder. Rerun below whenever there are file changes.
"""
# ╔═╡ 38f7f8cb-9f64-47be-8d9e-91f24091c341
begin
connection = DBInterface.connect(DDB.DB)
TIO.read_csv_folder(connection, "data")
# Inspect initial tables
TIO.show_tables(connection)
end
# ╔═╡ 84ff13ef-4b9a-4396-a37c-770d82ee5933
md"""
#### Graph and metadata
Both the graph and the metadata are stored in `assets_data` and `flows_data`.
"""
# ╔═╡ f3e981dc-9647-455f-84a1-b623dc522fff
TIO.get_table(connection, "assets_data")
# ╔═╡ 9114c0bf-4848-4365-9333-b275ef8fd285
TIO.get_table(connection, "flows_data")
# ╔═╡ 6ad02eab-d565-47f4-9468-6182f64fc93a
md"""
#### Profiles data
These profiles include the values of an year hourly.
These values were obtained from the representative period mapping and profiles by extending them and then adding Normal noise to it.
"""
# ╔═╡ 59c4f23d-55e5-4375-b9b1-d08b9c5a40d0
TIO.get_table(connection, "profiles")
# ╔═╡ 1af1109f-dc99-4d67-bcfc-d4647aa25c1b
DDB.execute(connection, "SELECT DISTINCT ON(profile_name) * FROM profiles") |> DataFrame
# ╔═╡ e9f8d407-0265-4783-b63d-934ff7a0cc7b
DDB.execute(connection, "SELECT * FROM profiles WHERE profile_name = 'availability-Asgard_Solar'") |> DataFrame
# ╔═╡ 652457bd-5391-4ef5-b714-32eebbd15cbb
Plots.plot(
DDB.execute(connection, "SELECT value FROM profiles WHERE profile_name = 'demand-Midgard_E_demand'") |> DataFrame |> df -> df.value
)
# ╔═╡ 9ebf99c7-4296-4541-95f0-0a5b84c2488a
md"""
#### Assets and flows profile linking
This information is not used at all before clustering, although it links to the current profiles. It will be used as is when the profiles are clustered by just pointing to the equivalent representative period profile.
"""
# ╔═╡ 5a82498f-777d-4610-945b-c93bd0e06ffe
TIO.get_table(connection, "assets_profiles")
# ╔═╡ 46cde502-2a6d-4c8c-aba7-2c76bc03c3ef
TIO.get_table(connection, "flows_profiles")
# ╔═╡ b874f098-aa50-4d93-8c42-56126f6f6153
md"""
## Step 2 - TulipaClustering
The basic idea here it to take the 8760 periods of the profiles, split into 365 equal periods of 24h, then represent each of these periods using a number of representative periods.
### Step 2.1 - Clustering
The representatives are computed using the whole profiles information, so all profiles need to be in the same table.
!!! tip "TODO"
Does it make sense to make the `split_into_periods!` function execute at the table level? It would run before creating the dataframes, and thus hopefully it would be faster.
[TC #36](https://github.com/TulipaEnergy/TulipaClustering.jl/issues/36)
"""
# ╔═╡ 9cd57f33-9906-4f29-af51-b30a688cade6
begin
period_duration = 24 # will be used later
tc_df = TIO.get_table(connection, "profiles")
TC.split_into_periods!(tc_df; period_duration = period_duration)
nothing
end
# ╔═╡ 8ace55c3-9ac9-4c25-b4d5-364e5d33dd1b
# Example asset
DF.subset(tc_df, :profile_name => DF.ByRow(==("availability-Asgard_Solar")))
# ╔═╡ df1a421e-7d54-48a5-9f91-14175e0fd596
# Example asset
DF.subset(
tc_df,
:profile_name => DF.ByRow(==("availability-Asgard_Solar")),
:period => DF.ByRow(==(1)),
)
# ╔═╡ 2d123f6b-1d94-425a-b37b-acc71e035519
begin
num_rep_periods = 5
clustering_result = TC.find_representative_periods(tc_df, num_rep_periods)
nothing
end
# ╔═╡ e0a815ad-de49-459e-b5e3-72e9418a9283
clustering_result.weight_matrix
# ╔═╡ c409d91a-0938-40d0-88dc-9df4fd8a980f
clustering_result.profiles
# ╔═╡ 7970c2ca-c828-4f83-99e3-18e4ed816b62
DF.subset(
clustering_result.profiles,
:profile_name => DF.ByRow(==("availability-Asgard_Solar"))
)
# ╔═╡ 8e5d990c-8d56-47ee-b1df-b6d375586241
DF.subset(
clustering_result.profiles,
:profile_name => DF.ByRow(==("availability-Asgard_Solar")),
:rep_period => DF.ByRow(==(1)),
)
# ╔═╡ 202cca05-e73b-43ed-a126-a52d43516741
md"""
### Step 2.2 - Export to TEM format
We are splitting the profile type and name for TulipaEnergyModel.
This is necessary in the current implementation of TC and TEM.
"""
# ╔═╡ 775aa0ac-01f9-4313-a634-8ac9abc40963
TC.write_clustering_result_to_tables(connection, clustering_result)
# ╔═╡ a65fcec7-f09b-40e3-9147-ab3d9ead504a
TIO.show_tables(connection)
# ╔═╡ cbaa11c1-4b0f-462d-ab80-e2e653113769
TIO.get_table(connection, "rep_periods_data")
# ╔═╡ 197f49e9-9602-45c5-aa03-eb6bdd6c607b
TIO.get_table(connection, "rep_periods_mapping")
# ╔═╡ f419e9fe-962c-4377-b56c-41b6bb3483e4
TIO.get_table(connection, "profiles_rep_periods")
# ╔═╡ 6b73622b-ad36-45eb-8005-bc23c9555f68
md"""
## Step 3 - TulipaEnergyModel
We finally start with TEM. In addition to all data that was used so far, we need data that can only be computed after we have the representative periods (or at least the splitting of the initial periods).
"""
# ╔═╡ 1ad97d2f-1716-47ea-a300-406d270fbe6f
md"""
### Step 3.1 - User provided info
Here is how to provide the additional information.
"""
# ╔═╡ bbcca6dd-45d1-4d70-a0a3-cd91b8a1570b
md"""
#### Timeframe (optional)
The timeframe (the year) was divided into $(div(365 * 24, period_duration)) periods of $period_duration h. These periods are represented by $num_rep_periods representative periods.
Most of the constraints apply within a representative period, but some apply to the "outer" periods, i.e., the 365 periods.
For some of these, you might need additional profiles.
In this section you can inform the timeframe profiles.
!!! tip "Optional"
This information is optional
!!! tip "TODO"
Add some example timeframe profiles.
"""
# ╔═╡ 10dbc445-3e9b-45a6-a67a-3469faf0106d
# let
# DDB.register_data_frame(
# connection,
# DataFrame(
# :asset => String[],
# :profile_type => String[],
# :profile_name => String[]
# ),
# "assets_timeframe_profiles",
# )
# end
# ╔═╡ dc52d2f3-6f24-4dd8-ac38-09bc94afe012
md"""
#### Partitions (optional)
By default, all variables and constraints are defined in an hourly resolution, i.e., the representative period is partitioned uniformly with 1 timestep per hour.
However, sometimes we want to set a different resolution to a flow, or a different resolution to balance constraints in an asset.
For that, we can add information to the tables below.
We can change the partitioning of the representative period in the given situations:
- The partitioning of representative periods in an asset.
- The partitioning of representative periods in a flow;
- The partitioning of the timeframe periods in an asset.
!!! tip "TODO"
Add an example of partitioning
"""
# ╔═╡ 6adb5dba-6a2a-44e3-b1e9-98e6f40beae4
# let
# DDB.register_data_frame(
# connection,
# DataFrame(
# :asset => String[],
# :rep_period => Int[],
# :specification => String[],
# :partition => String[],
# ),
# "assets_rep_periods_partitions",
# )
# DDB.register_data_frame(
# connection,
# DataFrame(
# :from_asset => String[],
# :to_asset => String[],
# :rep_period => Int[],
# :specification => String[],
# :partition => String[],
# ),
# "flows_rep_periods_partitions",
# )
# DDB.register_data_frame(
# connection,
# DataFrame(
# :asset => String[],
# :specification => String[],
# :partition => String[],
# ),
# "assets_timeframe_partitions",
# )
# end
# ╔═╡ 05ab44cd-7f47-4a57-9124-d5d77dcca529
md"""
### Step 3.2 - Creating and solving the model
To finally create the model and solve it, we use the `EnergyProblem` structure.
"""
# ╔═╡ 3349642c-c466-4443-a75e-93a477ce4f17
begin
energy_problem = TEM.EnergyProblem(connection)
TEM.create_model!(energy_problem)
TEM.solve_model!(energy_problem)
energy_problem
end
# ╔═╡ c9a0cf5c-9b3f-44d5-9c9e-fc2124107497
JuMP.solution_summary(energy_problem.model)
# ╔═╡ 30c724b9-a9ad-43e0-ab52-d63cbdd9f4cb
energy_problem.termination_status
# ╔═╡ a029d632-9541-4211-afea-c53fe2bf2908
md"""
## Step 4 - Plotting the solution
!!! tip "TODO"
What should be plotted? Use TulipaPlots?
"""
# ╔═╡ 638aad2d-2c40-475c-81de-67696e4fef0c
md"""
---
## Behind the scenes
"""
# ╔═╡ f0f49ee6-e3ab-4103-b7a8-eabda6830745
begin
function _nicename(filename)
filename = replace(filename, "profiles-rep-periods-" => "")
filename = replace(filename, "-" => "_")
filename, _ = splitext(filename)
filename
end
function _read_csv(filename)
CSV.read(joinpath("Norse-from-TEM", filename), DataFrame; header=2)
end
end
# ╔═╡ 657371f9-30a1-4b27-89e0-ba120dcca2eb
# begin
# # For debugging
# _rp_data = _read_csv("rep-periods-data.csv")
# _rp_map = _read_csv("rep-periods-mapping.csv")
# _profiles = Dict(
# _nicename(filename) => _read_csv(filename)
# for filename in readdir("Norse-from-TEM") if startswith("profiles-rep")(filename)
# )
# # End debugging
# nothing
# end
# ╔═╡ 3bd6c7aa-ea9d-4e95-9c98-d2ed245f8600
let
df_TC_profiles = DataFrame(
:profile_name => String[],
:timestep => Int[],
:value => Float64[],
)
filename = "profiles-rep-periods.csv"
df = _read_csv(filename)
profile_names = unique(df.profile_name)
rp_map = _read_csv("rep-periods-mapping.csv")
for profile_name in profile_names
value = clamp.(vcat([
DF.subset(
df,
:profile_name => DF.ByRow(==(profile_name)),
:rep_period => DF.ByRow(==(rp)),
).value
for rp in rp_map.rep_period
]...) + 0.01 * randn(365 * 24), 0, 10)
append!(df_TC_profiles,
DataFrame(
:profile_name => profile_name,
:timestep => 1:365 * 24,
:value => value,
)
)
end
filename = joinpath("data", "profiles.csv")
open(filename, "w") do io
println(io, ",,p.u.")
end
CSV.write(
filename,
df_TC_profiles,
append=true,
writeheader=true,
)
end
# ╔═╡ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
[deps]
CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
DuckDB = "d2f5444f-75bc-4fdf-ac35-56f514c445e1"
JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
PlutoUI = "7f904dfe-b85e-4ff6-b463-dae2292396a8"
SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
TulipaClustering = "314fac8b-c762-4aa3-9d12-851379729163"
TulipaEnergyModel = "5d7bd171-d18e-45a5-9111-f1f11ac5d04d"
TulipaIO = "7b3808b7-0819-42d4-885c-978ba173db11"
[compat]
CSV = "~0.10.14"
DataFrames = "~1.6.1"
DuckDB = "~1.0.0"
JuMP = "~1.22.2"
Plots = "~1.40.4"
PlutoUI = "~0.7.59"
TulipaClustering = "~0.2.0"
TulipaEnergyModel = "~0.9.1"
TulipaIO = "~0.2.0"
"""
# ╔═╡ 00000000-0000-0000-0000-000000000002
PLUTO_MANIFEST_TOML_CONTENTS = """
# This file is machine-generated - editing it directly is not advised
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manifest_format = "2.0"
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[deps.ColorVectorSpace.extensions]
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