fix solve time for logical dispatch model

osier/models/dispatch.py

@@ -148,7 +148,7 @@ class DispatchModel():
     penalty : float
         The penalty applied to the objective function to eliminate
         simultaneous charging and discharging. Users may need to tune
-        this parameter. Default is 1e-4.
+        this parameter. Default is 1e-10.
     model_initialized : bool
         Indicates whether :attr:`DispatchModel.model` has been populated
         with equations yet. This is set to ``True`` after
@@ -205,7 +205,7 @@ def __init__(self,
                  oversupply=0.0,
                  undersupply=0.0,
                  verbosity=50,
-                 penalty=1e-4,
+                 penalty=1e-10,
                  power_units=MW,
                  curtailment=True,
                  allow_blackout=False,

osier/models/logic_dispatch.py

@@ -101,7 +101,7 @@ def _format_results(self):
                     t.charge_history).to_ndarray()
         data["Curtailment"] = np.array(
             [v if v <= 0 else 0 for v in self.covered_demand])
-        data["Shortfall"] = np.array(
+        data["LoadLoss"] = np.array(
             [v if v > 0 else 0 for v in self.covered_demand])
         self.results = pd.DataFrame(data)
         return
@@ -134,7 +134,7 @@ def solve(self):
                     if self.verbosity <= 20:
                         print(
                             ('solve failed -- '
-                            'too much overproduction '
+                            'too much supply '
                             '(no curtailment allowed)'))
                     raise ValueError
 

osier/models/model.py

@@ -41,10 +41,7 @@ def __init__(self,
         else:
             self.power_units = power_units
 
-        self.technology_list = synchronize_units(
-            technology_list,
-            unit_power=self.power_units,
-            unit_time=self.time_delta.units)
+        self.technology_list = technology_list
 
     @property
     def time_delta(self):

osier/utils.py

@@ -562,12 +562,18 @@ def n_mga(results_obj,
         raise ValueError
 
     pf = results_obj.F
+    xpf = results_obj.X
     try:
         n_inds, n_objs = pf.shape
     except ValueError:
         n_inds = pf.shape[0]
         n_objs = 1
 
+    try:
+        n_inds, n_vars = xpf.shape
+    except ValueError:
+        n_vars = xpf.shape[0]
+
     pop_size = results_obj.algorithm.pop_size
     n_gen = results_obj.algorithm.n_gen - 1
 
@@ -576,7 +582,7 @@ def n_mga(results_obj,
                            sense=sense)
 
     X_hist = np.array([hist.pop.get("X") for hist in results_obj.history]).reshape(
-        n_gen * pop_size, n_objs)
+        n_gen * pop_size, n_vars)
     F_hist = np.array([hist.pop.get("F") for hist in results_obj.history]).reshape(
         n_gen * pop_size, n_objs)
     try: