Minor opt formulation fixes (#317)

Author: johnjasa

h2integrate/controllers/openloop_controllers.py

@@ -246,7 +246,8 @@ class DemandOpenLoopController(ControllerBaseClass):
                 the system.
         {resource_name}_missed_load (float): Missed load timeseries when demand exceeds supply.
             - Units: Defined in `resource_rate_units` (e.g., "kg/h").
-
+        total_{resource_name}_missed_load (float): Total missed load over the simulation period.
+            - Units: Defined in `resource_rate_units` (e.g., "kg").
     """
 
     def setup(self):
@@ -303,6 +304,12 @@ def setup(self):
             desc=f"{resource_name} missed load timeseries",
         )
 
+        self.add_output(
+            f"total_{resource_name}_missed_load",
+            units=self.config.resource_rate_units,
+            desc=f"Total {resource_name} missed load over the simulation period",
+        )
+
     def compute(self, inputs, outputs):
         """
         Compute the state of charge (SOC) and output flow based on demand and storage constraints.
@@ -383,3 +390,5 @@ def compute(self, inputs, outputs):
 
             # Record the missed load at the current time step
             missed_load_array[t] = max(0, (demand_t - output_array[t]))
+
+        outputs[f"total_{resource_name}_missed_load"] = np.sum(missed_load_array)

h2integrate/core/pose_optimization.py

@@ -356,7 +356,10 @@ def set_driver(self, opt_prob):
         return opt_prob
 
     def set_objective(self, opt_prob):
-        """Set merit figure. Each objective has its own scaling.  Check first for user override
+        """Set merit figure. Each objective has its own scaling.  Check first for user override.
+
+        The optimization is always minimizing the objective. If you wish to maximize the objective,
+        use a negative ref or scaler value in the config.
 
         Args:
             opt_prob (openmdao problem instance): openmdao problem instance for
@@ -413,11 +416,12 @@ def set_constraints(self, opt_prob):
             opt_prob (openmdao problem instance): openmdao problem instance for
                 current optimization problem edited to include constraint setup
         """
-        for technology, variables in self.config["constraints"].items():
-            for key, value in variables.items():
-                if value["flag"]:
-                    value.pop("flag")
-                    opt_prob.model.add_constraint(f"{technology}.{key}", **value)
+        if self.config.get("constraints", False):
+            for technology, variables in self.config["constraints"].items():
+                for key, value in variables.items():
+                    if value["flag"]:
+                        value.pop("flag")
+                        opt_prob.model.add_constraint(f"{technology}.{key}", **value)
 
     def set_recorders(self, opt_prob):
         """sets up a recorder for the openmdao problem as desired in the input yaml