Improve modeler tests (#2626)

- Added a modeler test with an int variable (have to used scip bc sirius
does not support this)
- Added the objective value to the output file
- Added test steps to test the objective value
- Added test steps to make it more simple to test variable values
- Added description to existing tests

---------

Signed-off-by: Peter Mitri <[email protected]>

src/solver/modeler/loadFiles/readLibraries.cpp

@@ -65,7 +65,8 @@ static Study::SystemModel::Library loadSingleLibrary(const fs::path& filePath)
     }
     catch (const std::runtime_error& e)
     {
-        logs.error() << "Error while converting this library yaml: " << filePath;
+        logs.error() << "Error while converting this library yaml: " << filePath << ": "
+                     << e.what();
         throw ErrorLoadingYaml(e.what());
     }
 }

src/solver/modeler/main.cpp

@@ -141,8 +141,9 @@ int main(int argc, const char** argv)
         case MipStatus::FEASIBLE:
             if (!parameters.noOutput)
             {
-                logs.info() << "Writing variables...";
+                logs.info() << "Writing objective & variable values...";
                 std::ofstream sol_out(outputPath / "solution.csv");
+                sol_out << "objective " << solution->getObjectiveValue() << std::endl;
                 for (const auto& [name, value]: solution->getOptimalValues())
                 {
                     sol_out << name << " " << value << std::endl;

src/tests/cucumber/features/modeler-features/epic2/us2.5.feature

@@ -1,36 +1,43 @@
 Feature: 2.5 - Pure modeler simple studies, with no ports and no timeseries
 
-  Scenario: 2.5.1: One model with one load and two generators, one timestamp
+  Scenario: 2.5.1: One model with one load and two generators, one timestep
     Given the study path is "modeler/epic2/us2.5/study_2.5.1"
     When I run antares modeler
     Then the simulation succeeds
+    And the objective value is 160
     And the optimal value of variable node1.gen1_p_0 is 80
     And the optimal value of variable node1.gen2_p_0 is 20
 
-  Scenario: 2.5.2: One model with one load and two generators (minP), three timestamps
+  Scenario: 2.5.2: One model with one load and two generators (minP), three timesteps
     Given the study path is "modeler/epic2/us2.5/study_2.5.2"
     When I run antares modeler
     Then the simulation succeeds
-    And the optimal value of variable node1.gen1_up_0 is 1
-    And the optimal value of variable node1.gen1_up_1 is 1
-    And the optimal value of variable node1.gen1_up_2 is 1
-    And the optimal value of variable node1.gen1_p_0 is 60
-    And the optimal value of variable node1.gen1_p_1 is 60
-    And the optimal value of variable node1.gen1_p_2 is 60
-    And the optimal value of variable node1.gen2_up_0 is 1
-    And the optimal value of variable node1.gen2_up_1 is 1
-    And the optimal value of variable node1.gen2_up_2 is 1
-    And the optimal value of variable node1.gen2_p_0 is 40
-    And the optimal value of variable node1.gen2_p_1 is 40
-    And the optimal value of variable node1.gen2_p_2 is 40
+    And the objective value is 810
+    And the optimal values of the variables are
+      | component | variable | timestep | value |
+      | node1     | gen1_up  | 0-2       | 1     |
+      | node1     | gen1_p   | 0-2       | 60    |
+      | node1     | gen1_up  | 0-2       | 1     |
+      | node1     | gen2_p   | 0-2       | 40    |
 
-  Scenario: 2.5.3: Two libs, one timestamp
+  Scenario: 2.5.3: Two libs, one timestep
     Given the study path is "modeler/epic2/us2.5/study_2.5.3"
     When I run antares modeler
     Then the simulation succeeds
-    And the optimal value of variable node1.gen1_p_0 is 0
-    And the optimal value of variable node1.gen2_p_0 is 100
-    And the optimal value of variable node2.gen1_p_0 is 500
-    And the optimal value of variable node2.gen1_up_0 is 1
-    And the optimal value of variable node2.gen2_p_0 is 500
-    And the optimal value of variable node2.gen2_up_0 is 1
+    And the objective value is 15600
+    And the optimal values of the variables are
+      | component | variable | timestep | value |
+      | node1     | gen1_p   | 0         | 0     |
+      | node1     | gen2_p   | 0         | 100   |
+      | node2     | gen1_p   | 0         | 500   |
+      | node2     | gen1_up  | 0         | 1     |
+      | node2     | gen2_p   | 0         | 500   |
+      | node2     | gen2_up  | 0         | 1     |
+
+  Scenario: 2.5.4: Test with integer variable
+    Given the study path is "modeler/epic2/us2.5/study_2.5.4"
+    When I run antares modeler
+    Then the simulation succeeds
+    And the objective value is 540
+    And the optimal value of variable node1.gen_total_p_0 is 1000
+    And the optimal value of variable node1.gen_n_on_0 is 4

src/tests/cucumber/features/steps/common_steps/steps.py

@@ -4,10 +4,9 @@
 import pathlib
 
 from behave import *
-
 from common_steps.assertions import *
-from common_steps.simulator_utils import run_simulation
 from common_steps.modeler_utils import run_modeler
+from common_steps.simulator_utils import run_simulation
 
 from features.steps.common_steps.assertions import assert_double_close
 
@@ -133,4 +132,19 @@ def run_antares_modeler(context):
 
 @step('the optimal value of variable {var} is {value:g}')
 def modeler_var_optimal_value(context, var, value):
-    assert_double_close(value, context.moh.get_optimal_value(var), 1e-6)
+    assert_double_close(value, context.moh.get_optimal_value(var), 1e-6)
+
+
+@step('the objective value is {value:g}')
+def modeler_obj_value(context, value):
+    assert_double_close(value, context.moh.get_optimal_value("objective"), 1e-6)
+
+@step('the optimal values of the variables are')
+def modeler_var_optimal_value(context):
+    for row in context.table:
+        ts_array = row["timestep"].split("-")
+        ts_start = int(ts_array[0])
+        ts_end =  int(ts_array[1]) if len(ts_array) == 2 else ts_start
+        for ts in range(ts_start, ts_end + 1):
+            var_id = row["component"] + "." + row["variable"] + "_" + str(ts)
+            assert_double_close(float(row["value"]), context.moh.get_optimal_value(var_id), 1e-6)

src/tests/resources/modeler/epic2/us2.5/study_2.5.1/input/system.yml

@@ -1,6 +1,7 @@
 system:
   id: sys_example1
-  description: test system
+  description: one load of 100 MW, one cheap generator gen1 (max_p=80 MW, cost=0.5€/MWh), one expensive generator gen2  
+   (max_p=200 MW, cost=6€/MWh). Thus, gen1 must be used fully (80) and gen2 must complete (20), objective=80*0.5+20*6=160
   model-libraries: lib_example1
 
   components:

src/tests/resources/modeler/epic2/us2.5/study_2.5.2/input/system.yml

@@ -1,6 +1,8 @@
 system:
   id: sys_example2
-  description: test system
+  description: one load of 100 MW, one cheap generator gen1 (max_p=80 MW, cost=0.5€/MWh), one expensive generator gen2  
+   (min_p=40 MW, cost=6€/MWh). Thus, gen1 does not suffice, gen2 must be used at its min_p (40), and gen_1 must complete 
+   (60); on the 3 timestamps. Objective = 3 * (60 * 0.5 + 40 * 6) = 810
   model-libraries: lib_example2
 
   components:

src/tests/resources/modeler/epic2/us2.5/study_2.5.3/input/system.yml

@@ -1,6 +1,11 @@
 system:
   id: sys_example3
-  description: test system
+  description: in node1, gen is enough to fulfill load and is less expensive than gen1; thus gen1 must not be used (0)
+   and gen2 must be used at load level (100). Objective = 100 * 6 = 600 for node1.
+   In node2, load is 1000 MW and gen1 is cheap but not enough (max_p=650, cost=10€/MWh), and gen2 is large but more 
+   expensive (min_p=500, cost=20 €/MWh). Thus gen2 must be used at its minimum power (500) and gen1 should complete 
+   (500). Objective = 500 * 10 + 500 * 20 = 15000 for node2.
+   Total objective = 15600 for both nodes.
   model-libraries: lib_example1, lib_example2
 
   components:

src/tests/resources/modeler/epic2/us2.5/study_2.5.4/input/model-libraries/lib_example3.yml

@@ -0,0 +1,43 @@
+library:
+  id: lib_example3
+  description: test model library
+
+  models:
+    - id: node_genCluster_oneLoad
+      description: A simple node with a generator cluster and one load, to test integer variables
+      parameters:
+        - id: load
+          time-dependent: false
+          scenario-dependent: false
+        - id: gen_max_p
+          time-dependent: false
+          scenario-dependent: false
+        - id: gen_min_p
+          time-dependent: false
+          scenario-dependent: false
+        - id: gen_cluster_size
+          time-dependent: false
+          scenario-dependent: false
+        - id: gen_prop_cost
+          time-dependent: false
+          scenario-dependent: false
+        - id: gen_fixed_cost
+          time-dependent: false
+          scenario-dependent: false
+      variables:
+        - id: gen_total_p
+          lower-bound: 0
+          upper-bound: gen_max_p * gen_cluster_size
+          variable-type: continuous
+        - id: gen_n_on
+          lower-bound: 0
+          upper-bound: gen_cluster_size
+          variable-type: integer
+      constraints:
+        - id: respect_min_p
+          expression: gen_total_p >= gen_n_on * gen_min_p
+        - id: respect_max_p
+          expression: gen_total_p <= gen_n_on * gen_max_p
+        - id: balance
+          expression: gen_total_p = load
+      objective: gen_total_p * gen_prop_cost + gen_n_on * gen_fixed_cost

src/tests/resources/modeler/epic2/us2.5/study_2.5.4/input/system.yml

@@ -0,0 +1,29 @@
+system:
+  id: sys_example4
+  description: a node with up to 10 similar generators (min_p=150, max_p=300, cost = 0.5 €/MWh + 10€/hour of up time).
+   Load is of 1000MW; thus 4 generators should be used. Objective = 1000*0.5 + 4*10 = 540.
+  model-libraries: lib_example3
+
+  components:
+  - id: node1
+    model: lib_example3.node_genCluster_oneLoad
+    scenario-group: sg
+    parameters:
+    - id: load
+      type: constant
+      value: 1000
+    - id: gen_min_p
+      type: constant
+      value: 150
+    - id: gen_max_p
+      type: constant
+      value: 300
+    - id: gen_cluster_size
+      type: constant
+      value: 10
+    - id: gen_prop_cost
+      type: constant
+      value: 0.5
+    - id: gen_fixed_cost
+      type: constant
+      value: 10

src/tests/resources/modeler/epic2/us2.5/study_2.5.4/output/problem.lp

@@ -0,0 +1,20 @@
+\ Generated by MPModelProtoExporter
+\   Name             : 
+\   Format           : Free
+\   Constraints      : 3
+\   Variables        : 2
+\     Binary         : 0
+\     Integer        : 1
+\     Continuous     : 1
+Minimize
+ Obj: +10 node1.gen_n_on_0 +0.5 node1.gen_total_p_0 
+Subject to
+ node1.balance_0: +1 node1.gen_total_p_0  = 1000
+ node1.respect_max_p_0: -300 node1.gen_n_on_0 +1 node1.gen_total_p_0  <= -0
+ node1.respect_min_p_0: -150 node1.gen_n_on_0 +1 node1.gen_total_p_0  >= -0
+Bounds
+ 0 <= node1.gen_n_on_0 <= 10
+ 0 <= node1.gen_total_p_0 <= 3000
+Generals
+ node1.gen_n_on_0
+End

src/tests/resources/modeler/epic2/us2.5/study_2.5.4/parameters.yml

@@ -0,0 +1,6 @@
+solver: scip
+solver-logs: false
+solver-parameters: 
+no-output: false
+first-time-step: 0
+last-time-step: 0