Remove graph dependency from src/constraints/consumer.jl

src/constraints/consumer.jl

@@ -12,29 +12,59 @@ add_consumer_constraints!(model,
 Adds the consumer asset constraints to the model.
 """
 
-function add_consumer_constraints!(model, constraints, graph, sets)
-    Ac = sets[:Ac]
-    incoming_flow_highest_in_out_resolution = constraints[:highest_in_out].expressions[:incoming]
-    outgoing_flow_highest_in_out_resolution = constraints[:highest_in_out].expressions[:outgoing]
+function add_consumer_constraints!(connection, model, constraints)
+    cons = constraints[:highest_in_out]
+
+    # TODO: Store the name of the table in the TulipaConstraint
+    table = _create_consumer_table(connection, "cons_highest_in_out")
 
     # - Balance constraint (using the lowest temporal resolution)
-    df = filter(:asset => ∈(Ac), constraints[:highest_in_out].indices; view = true)
     model[:consumer_balance] = [
-        @constraint(
-            model,
-            incoming_flow_highest_in_out_resolution[row.index] -
-            outgoing_flow_highest_in_out_resolution[row.index] -
-            profile_aggregation(
-                Statistics.mean,
-                graph[row.asset].rep_periods_profiles,
-                row.year,
-                row.year,
-                ("demand", row.rep_period),
-                row.time_block_start:row.time_block_end,
-                1.0,
-            ) * graph[row.asset].peak_demand[row.year] in
-            graph[row.asset].consumer_balance_sense,
-            base_name = "consumer_balance[$(row.asset),$(row.year),$(row.rep_period),$(row.time_block_start):$(row.time_block_end)]"
-        ) for row in eachrow(df)
+        begin
+            consumer_balance_sense = if ismissing(row.consumer_balance_sense)
+                MathOptInterface.EqualTo(0.0)
+            else
+                MathOptInterface.GreaterThan(0.0)
+            end
+            @constraint(
+                model,
+                incoming_flow - outgoing_flow - row.demand_agg * row.peak_demand in
+                consumer_balance_sense,
+                base_name = "consumer_balance[$(row.asset),$(row.year),$(row.rep_period),$(row.time_block_start):$(row.time_block_end)]"
+            )
+        end for (row, incoming_flow, outgoing_flow) in
+        zip(table, cons.expressions[:incoming], cons.expressions[:outgoing]) if
+        row.type == "consumer" # The length of the table must be the same as the expressions, so we filter here instead of earlier
     ]
 end
+
+function _create_consumer_table(connection, cons::String)
+    return DuckDB.query(
+        connection,
+        "SELECT
+            cons.*,
+            ANY_VALUE(asset.type) AS type,
+            ANY_VALUE(asset.consumer_balance_sense) AS consumer_balance_sense,
+            ANY_VALUE(asset_milestone.peak_demand) AS peak_demand,
+            COALESCE(MEAN(profile.value), 1) AS demand_agg
+        FROM $cons AS cons
+        LEFT JOIN asset
+            ON cons.asset = asset.asset
+        LEFT JOIN asset_milestone
+            ON cons.asset = asset_milestone.asset
+            AND cons.year = asset_milestone.milestone_year
+        LEFT JOIN assets_profiles
+            ON cons.asset = assets_profiles.asset
+            AND cons.year = assets_profiles.commission_year
+            AND assets_profiles.profile_type = 'demand'
+        LEFT JOIN profiles_rep_periods AS profile
+            ON profile.profile_name = assets_profiles.profile_name
+            AND cons.year = profile.year
+            AND cons.rep_period = profile.rep_period
+            AND cons.time_block_start <= profile.timestep
+            AND profile.timestep <= cons.time_block_end
+        GROUP BY cons.*
+        ORDER BY cons.index -- order is important
+        ",
+    )
+end

src/create-model.jl

@@ -40,7 +40,7 @@ function create_model!(energy_problem; kwargs...)
 end
 
 """
-    model = create_model(graph, representative_periods, dataframes, timeframe, groups; write_lp_file = false, enable_names = true)
+    model = create_model(connection, graph, representative_periods, dataframes, timeframe, groups; write_lp_file = false, enable_names = true)
 
 Create the energy model given the `graph`, `representative_periods`, dictionary of `dataframes` (created by [`construct_dataframes`](@ref)), timeframe, and groups.
 """
@@ -114,12 +114,7 @@ function create_model(
 
     @timeit to "add_energy_constraints!" add_energy_constraints!(model, constraints, graph)
 
-    @timeit to "add_consumer_constraints!" add_consumer_constraints!(
-        model,
-        constraints,
-        graph,
-        sets,
-    )
+    @timeit to "add_consumer_constraints!" add_consumer_constraints!(connection, model, constraints)
 
     @timeit to "add_storage_constraints!" add_storage_constraints!(
         model,

test/test-case-studies.jl

@@ -85,8 +85,16 @@ end
     dir = joinpath(INPUT_FOLDER, "Tiny")
     connection = DBInterface.connect(DuckDB.DB)
     _read_csv_folder(connection, dir)
+    DuckDB.execute( # Make it infeasible
+        connection,
+        "UPDATE asset_milestone
+            SET peak_demand = -1
+            WHERE
+                asset = 'demand'
+                AND milestone_year = 2030
+        ",
+    )
     energy_problem = EnergyProblem(connection)
-    energy_problem.graph["demand"].peak_demand[2030] = -1 # make it infeasible
     create_model!(energy_problem)
     @test_logs (:warn, "Model status different from optimal") solve_model!(energy_problem)
     @test energy_problem.termination_status == JuMP.INFEASIBLE