Apply scaling checks on NMN Design Toolkit tests

CHANGELOG.md

@@ -1,6 +1,7 @@
 # [Unreleased] - 2024-10-07
 
 ## Added
+
 - Limit the available pipe classes connetced to heat/gas demand/producers
 - Add demands and sources to the topology object to have easy access to the connected pipes/cables later on
 - Heating and cooling example case added (2 heating demands, 1 cold demand, hot and cold producer, WKO as seasonal storage)
@@ -9,33 +10,39 @@
 - Test case: Head loss validation with pandapipes.
 - Example on ESDL file creation using pyESDL for the PoC Tutorial.
 - Electrolyzer specific power curve valley location specified optionally specified in ESDL.
-- Grow_workflow: Solver class created to allow the use of CPLEX as a solver for EndScenarioSizing classes. 
+- Grow_workflow: Solver class created to allow the use of CPLEX as a solver for EndScenarioSizing classes.
+- tests/test_runner.py: A custom test runner that collects range_data from from the scaling checks of tests models, as well as other detailed test run information.
+- tests\utils_test_scaling.py: Include functions for scaling checks.
 
 ## Changed
+
 - Cooling demand added to adapt_hourly_year_profile_to_day_averaged_with_hourly_peak_day (peak cooling day not used yet)
 - Impact on the way EndScenarioSizing problems in MESIDO are run: The calling of the different optimization problem classes has been split from the solver classes. In EndScenarioSizing classes, the HIGHS solver is the default and the calling functions also cather for other solvers by adding the keyword "solver_class" with the respective solver class.
 - Bugfix: No longer required to provide a power at the heating demands when a profile has been added.
 - Bugfix: Scaling fix on ATES temperature variable when temperature modelling not used.
 - Bugfix: Fix on nominals in electricity cables and gas pipes. Fix on nominals for nodes with logical links.
-
+- Tests: Include scaling checks in multiple tests listed in tests\README.txt
+
 ## Fixed
+
 - Bug fix: machine error/rounding with updating lower bound values in the grow_workflow after stage 1
 - documentation: heat physics tank storage
 - bug fix in grow_workflow (heat networks), changed minimum velocity from 0 to default value
 - addition to tests checking the minimum velocity setting in a workflow
 
 
 # [0.1.7] - 2024-08-23
- 
+
 ## Added
+
 - User feedback regarding demand type to be used in heat networks 
 - Functionality to connect assets with logical links 
 
 ## Changed
+
 - Increased time limit for stage 1, priority 1
 - Replaced Retry loop to connect all assets and apply the correct modifiers of the linked assets
-
-## Fixed
-- Error exit when stage 1, priority 1 was not successful
 
+## Fixed
 
+- Error exit when stage 1, priority 1 was not successful

tests/README.txt

@@ -16,7 +16,7 @@ General notes:
 
 ***************************************************************************************************
 
-Exsting test cases:
+Existing test cases:
 - What do we want to explain in description? General scenario? Or more detail like 1 year profile
 used etc?
 - Where is the warming up 3/4 test cases that have been mentioned by Ryvo and Sam - KvR check with Ryvo where there cases are?
@@ -663,3 +663,24 @@ r"""
 
 ***************************************************************************************************
 
+
+- Scaling checks
+
+    Scaling checks have been included in the following test functions:
+        test_absolute_heat
+        test_asset_is_realized
+        test_ates
+        test_airco
+        test_cold_demand
+        test_wko
+        test_end_scenario_sizing
+        test_gas_boiler
+        test_heat_network_head_loss_LINEARIZED_N_LINES_WEAK_INEQUALITY
+        test_heat_network_head_loss_LINEARIZED_N_LINES_EQUALITY
+        test_heat_loss
+        test_insulation_heatdemand
+        test_multi_commodity_simulator_priorities_el
+        test_network_simulator
+        test_pipe_class_var
+        test_1a
+        test_2a

tests/models/absolute_heat/src/example.py

@@ -14,7 +14,9 @@
 from rtctools.optimization.linearized_order_goal_programming_mixin import (
     LinearizedOrderGoalProgrammingMixin,
 )
-from rtctools.optimization.single_pass_goal_programming_mixin import SinglePassGoalProgrammingMixin
+from rtctools.optimization.single_pass_goal_programming_mixin import (
+    SinglePassGoalProgrammingMixin,
+)
 from rtctools.optimization.timeseries import Timeseries
 from rtctools.util import run_optimization_problem
 
@@ -48,7 +50,9 @@ def __init__(self, state: str, target: Timeseries):
         self.function_nominal = np.median(target.values)
 
     def function(
-        self, optimization_problem: CollocatedIntegratedOptimizationProblem, ensemble_member: int
+        self,
+        optimization_problem: CollocatedIntegratedOptimizationProblem,
+        ensemble_member: int,
     ) -> ca.MX:
         """
         This function returns the state to which will be tried to match to the target.
@@ -89,7 +93,9 @@ def __init__(self, source: str):
         self.function_nominal = 1e6
 
     def function(
-        self, optimization_problem: CollocatedIntegratedOptimizationProblem, ensemble_member: int
+        self,
+        optimization_problem: CollocatedIntegratedOptimizationProblem,
+        ensemble_member: int,
     ) -> ca.MX:
         """
         This function returns the state variable to which should to be matched to the target

tests/models/basic_buffer/src/model_heat.py

@@ -66,10 +66,18 @@ def __init__(self):
         )
 
         self.add_variable(
-            HeatPipe, "pipe1a_cold", length=170.365, diameter=0.15, **cold_pipe_modifiers
+            HeatPipe,
+            "pipe1a_cold",
+            length=170.365,
+            diameter=0.15,
+            **cold_pipe_modifiers,
         )
         self.add_variable(
-            HeatPipe, "pipe1b_cold", length=309.635, diameter=0.15, **cold_pipe_modifiers
+            HeatPipe,
+            "pipe1b_cold",
+            length=309.635,
+            diameter=0.15,
+            **cold_pipe_modifiers,
         )
         self.add_variable(HeatPipe, "pipe4a_cold", length=5, diameter=0.15, **cold_pipe_modifiers)
         self.add_variable(HeatPipe, "pipe4b_cold", length=15, diameter=0.15, **cold_pipe_modifiers)

tests/models/basic_source_and_demand/src/model_heat.py

@@ -34,7 +34,10 @@ def __init__(self):
         )
 
         self.add_variable(
-            HeatDemand, "demand", HeatIn=dict(Heat=dict(max=5e5)), **supply_return_modifiers
+            HeatDemand,
+            "demand",
+            HeatIn=dict(Heat=dict(max=5e5)),
+            **supply_return_modifiers,
         )
 
         self.add_variable(

tests/models/basic_source_and_demand/src/qth_minimize_temperatures.py

@@ -11,7 +11,9 @@
 from rtctools.optimization.csv_mixin import CSVMixin
 from rtctools.optimization.goal_programming_mixin import Goal
 from rtctools.optimization.homotopy_mixin import HomotopyMixin
-from rtctools.optimization.single_pass_goal_programming_mixin import SinglePassGoalProgrammingMixin
+from rtctools.optimization.single_pass_goal_programming_mixin import (
+    SinglePassGoalProgrammingMixin,
+)
 from rtctools.util import run_optimization_problem
 
 if __name__ == "__main__":

tests/models/electricity_cable_topology/src/example.py

@@ -15,7 +15,9 @@
 from rtctools.optimization.linearized_order_goal_programming_mixin import (
     LinearizedOrderGoalProgrammingMixin,
 )
-from rtctools.optimization.single_pass_goal_programming_mixin import SinglePassGoalProgrammingMixin
+from rtctools.optimization.single_pass_goal_programming_mixin import (
+    SinglePassGoalProgrammingMixin,
+)
 from rtctools.optimization.timeseries import Timeseries
 from rtctools.util import run_optimization_problem
 
@@ -49,7 +51,9 @@ def __init__(self, state: str, target: Timeseries):
         self.function_nominal = np.median(target.values)
 
     def function(
-        self, optimization_problem: CollocatedIntegratedOptimizationProblem, ensemble_member: int
+        self,
+        optimization_problem: CollocatedIntegratedOptimizationProblem,
+        ensemble_member: int,
     ) -> ca.MX:
         """
         This function returns the state to which will be tried to match to the target.
@@ -90,7 +94,9 @@ def __init__(self, cable: str):
         self.function_nominal = 1.0
 
     def function(
-        self, optimization_problem: CollocatedIntegratedOptimizationProblem, ensemble_member: int
+        self,
+        optimization_problem: CollocatedIntegratedOptimizationProblem,
+        ensemble_member: int,
     ) -> ca.MX:
         """
         This function returns the state variable to which should to be matched to the target

tests/models/emerge/src/example.py

@@ -21,7 +21,9 @@
 from rtctools.optimization.linearized_order_goal_programming_mixin import (
     LinearizedOrderGoalProgrammingMixin,
 )
-from rtctools.optimization.single_pass_goal_programming_mixin import SinglePassGoalProgrammingMixin
+from rtctools.optimization.single_pass_goal_programming_mixin import (
+    SinglePassGoalProgrammingMixin,
+)
 
 
 class MaxHydrogenProduction(Goal):
@@ -45,7 +47,9 @@ def __init__(self, source: str):
         self.source = source
 
     def function(
-        self, optimization_problem: CollocatedIntegratedOptimizationProblem, ensemble_member: int
+        self,
+        optimization_problem: CollocatedIntegratedOptimizationProblem,
+        ensemble_member: int,
     ) -> ca.MX:
         """
         This function returns the state variable to be minimized.
@@ -83,14 +87,14 @@ def __init__(self, source: str):
         self.source = source
 
     def function(
-        self, optimization_problem: CollocatedIntegratedOptimizationProblem, ensemble_member: int
+        self,
+        optimization_problem: CollocatedIntegratedOptimizationProblem,
+        ensemble_member: int,
     ) -> ca.MX:
-
         return -optimization_problem.state(f"{self.source}.Electricity_source")
 
 
 class MaxRevenue(Goal):
-
     priority = 1
 
     order = 1
@@ -110,7 +114,9 @@ def __init__(self, asset_name: str):
         self.asset_name = asset_name
 
     def function(
-        self, optimization_problem: CollocatedIntegratedOptimizationProblem, ensemble_member: int
+        self,
+        optimization_problem: CollocatedIntegratedOptimizationProblem,
+        ensemble_member: int,
     ) -> ca.MX:
         """
         This function returns the state variable to be minimized.
@@ -129,7 +135,6 @@ def function(
 
 
 class MinCost(Goal):
-
     priority = 1
 
     order = 1
@@ -142,9 +147,10 @@ def __init__(self, asset_name: str):
         self.asset_name = asset_name
 
     def function(
-        self, optimization_problem: CollocatedIntegratedOptimizationProblem, ensemble_member: int
+        self,
+        optimization_problem: CollocatedIntegratedOptimizationProblem,
+        ensemble_member: int,
     ) -> ca.MX:
-
         return optimization_problem.extra_variable(
             f"{self.asset_name}__fixed_operational_cost", ensemble_member
         ) + optimization_problem.extra_variable(
@@ -195,7 +201,6 @@ def __init__(self, *args, **kwargs):
     #     return goals
 
     def goals(self):
-
         goals = super().goals().copy()
 
         for asset_name in [
@@ -319,7 +324,6 @@ def post(self):
 
 
 if __name__ == "__main__":
-
     tic = time.time()
     # for _ in range(10):
     #     elect = run_optimization_problem(

tests/models/gas_pipe_topology/src/example.py

@@ -14,7 +14,9 @@
 from rtctools.optimization.linearized_order_goal_programming_mixin import (
     LinearizedOrderGoalProgrammingMixin,
 )
-from rtctools.optimization.single_pass_goal_programming_mixin import SinglePassGoalProgrammingMixin
+from rtctools.optimization.single_pass_goal_programming_mixin import (
+    SinglePassGoalProgrammingMixin,
+)
 from rtctools.optimization.timeseries import Timeseries
 from rtctools.util import run_optimization_problem
 
@@ -48,7 +50,9 @@ def __init__(self, state: str, target: Timeseries):
         self.function_nominal = np.median(target.values)
 
     def function(
-        self, optimization_problem: CollocatedIntegratedOptimizationProblem, ensemble_member: int
+        self,
+        optimization_problem: CollocatedIntegratedOptimizationProblem,
+        ensemble_member: int,
     ) -> ca.MX:
         """
         This function returns the state to which will be tried to match to the target.
@@ -89,7 +93,9 @@ def __init__(self, pipe: str):
         self.function_nominal = 1.0e3
 
     def function(
-        self, optimization_problem: CollocatedIntegratedOptimizationProblem, ensemble_member: int
+        self,
+        optimization_problem: CollocatedIntegratedOptimizationProblem,
+        ensemble_member: int,
     ) -> ca.MX:
         """
         This function returns the state variable to which should to be matched to the target

tests/models/heat_exchange/src/run_heat_exchanger.py

@@ -10,10 +10,14 @@
 from rtctools.optimization.collocated_integrated_optimization_problem import (
     CollocatedIntegratedOptimizationProblem,
 )
-from rtctools.optimization.goal_programming_mixin import Goal, GoalProgrammingMixin
+from rtctools.optimization.goal_programming_mixin import Goal
 from rtctools.optimization.linearized_order_goal_programming_mixin import (
     LinearizedOrderGoalProgrammingMixin,
 )
+from rtctools.optimization.single_pass_goal_programming_mixin import (
+    CachingQPSol,
+    SinglePassGoalProgrammingMixin,
+)
 from rtctools.util import run_optimization_problem
 
 
@@ -49,6 +53,7 @@ def path_goals(self):
     def solver_options(self):
         options = super().solver_options()
         options["solver"] = "highs"
+        self._qpsol = CachingQPSol()
         return options
 
 
@@ -72,7 +77,7 @@ class HeatProblem(
     _GoalsAndOptions,
     PhysicsMixin,
     LinearizedOrderGoalProgrammingMixin,
-    GoalProgrammingMixin,
+    SinglePassGoalProgrammingMixin,
     ESDLMixin,
     CollocatedIntegratedOptimizationProblem,
 ):
@@ -91,17 +96,12 @@ def energy_system_options(self):
 
         return options
 
-    def solver_options(self):
-        options = super().solver_options()
-        options["solver"] = "highs"
-        return options
-
 
 class HeatProblemTvarSecondary(
     _GoalsAndOptions,
     PhysicsMixin,
     LinearizedOrderGoalProgrammingMixin,
-    GoalProgrammingMixin,
+    SinglePassGoalProgrammingMixin,
     ESDLMixin,
     CollocatedIntegratedOptimizationProblem,
 ):
@@ -158,7 +158,7 @@ class HeatProblemTvar(
     _GoalsAndOptions,
     PhysicsMixin,
     LinearizedOrderGoalProgrammingMixin,
-    GoalProgrammingMixin,
+    SinglePassGoalProgrammingMixin,
     ESDLMixin,
     CollocatedIntegratedOptimizationProblem,
 ):
@@ -210,7 +210,7 @@ class HeatProblemTvarDisableHEX(
     _GoalsAndOptions,
     PhysicsMixin,
     LinearizedOrderGoalProgrammingMixin,
-    GoalProgrammingMixin,
+    SinglePassGoalProgrammingMixin,
     ESDLMixin,
     CollocatedIntegratedOptimizationProblem,
 ):

tests/models/multiple_gas_carriers/src/run_multiple_gas_carriers.py

@@ -31,7 +31,9 @@ def __init__(self, state: str, target: Timeseries):
         self.function_nominal = np.median(target.values)
 
     def function(
-        self, optimization_problem: CollocatedIntegratedOptimizationProblem, ensemble_member: int
+        self,
+        optimization_problem: CollocatedIntegratedOptimizationProblem,
+        ensemble_member: int,
     ):
         return optimization_problem.state(self.state)