Fixed OPF formulation issue when some constraints turn out to be 0==0

README.md

@@ -26,7 +26,7 @@ for the future generations.
 ## Installation
 
 GridCal is a software made in the Python programming language. 
-Therefore, it needs a python interpreter installed in your operative system. 
+Therefore, it needs a Python interpreter installed in your operative system. 
 
 ### Standalone setup
 

requirements.txt

@@ -4,7 +4,7 @@ networkx>=2.1
 pandas>=1.1
 xlwt>=1.3.0
 xlrd>=1.1.0
-ortools>=9.0.0
+ortools>=9.8.0
 matplotlib>=2.1.1
 qtconsole>=4.3.1
 pyDOE>=0.3.8

src/GridCalEngine/Core/Compilers/circuit_to_newton_pa.py

@@ -1187,19 +1187,18 @@ def get_newton_pa_linear_opf_options(opf_opt: "OptimalPowerFlowOptions",
                      bs.TimeGrouping.Monthly: npa.TimeGrouping.Monthly,
                      bs.TimeGrouping.Hourly: npa.TimeGrouping.Hourly}
 
-    opt = npa.LinearOpfOptions(solver=solver_dict[opf_opt.mip_solver])
-    # opt.solver = solver_dict[opf_opt.mip_solver]
-    opt.time_grouping = grouping_dict[opf_opt.time_grouping]
-    opt.unit_commitment = False
-    opt.compute_flows = opf_opt.zonal_grouping == ZonalGrouping.NoGrouping
+    opt = npa.LinearOpfOptions(solver=solver_dict[opf_opt.mip_solver],
+                               grouping=grouping_dict[opf_opt.grouping],
+                               unit_commitment=opf_opt.unit_commitment,
+                               compute_flows=opf_opt.zonal_grouping == ZonalGrouping.NoGrouping,
+                               pf_options=get_newton_pa_pf_options(pf_opt))
+
     opt.check_with_power_flow = False
     opt.add_contingencies = opf_opt.consider_contingencies
     opt.skip_generation_limits = opf_opt.skip_generation_limits
     opt.maximize_area_exchange = opf_opt.maximize_flows
-    opt.unit_commitment = opf_opt.unit_commitment
     opt.use_ramp_constraints = False
     opt.lodf_threshold = opf_opt.lodf_tolerance
-    opt.pf_options = get_newton_pa_pf_options(pf_opt)
 
     if opf_opt.areas_from is not None:
         opt.areas_from = [area_dict[e] for e in opf_opt.areas_from]

src/GridCalEngine/Core/Devices/editable_device.py

@@ -19,7 +19,7 @@
 import numpy as np
 from typing import List, Dict, AnyStr, Any, Optional, Union, Type, Tuple
 from GridCalEngine.enumerations import DeviceType, TimeFrame, BuildStatus, WindingsConnection, TransformerControlType, ConverterControlType
-
+from GridCalEngine.basic_structures import Vec, IntVec, BoolVec
 
 class GCProp:
     """
@@ -392,6 +392,21 @@ def set_profile_values(self, t):
             profile = getattr(self, self.properties_with_profile[magnitude])
             setattr(self, magnitude, profile[t])
 
+    def get_profile(self, magnitude: str) -> Union[Vec, IntVec, BoolVec]:
+        """
+        Get the profile of a property name
+        :param magnitude: name of the property
+        :return: Profile object
+        """
+
+        # try to get the profile name
+        profile_name = self.properties_with_profile.get(magnitude, None)
+
+        if profile_name is None:
+            return None
+        else:
+            return getattr(self, profile_name)
+
     def get_properties_dict(self, version=3):
         """
 

src/GridCalEngine/Simulations/ContingencyAnalysis/contingency_analysis_ts_driver.py

@@ -294,7 +294,7 @@ def run(self) -> None:
         """
         Run contingency analysis time series
         """
-        start = time.time()
+        self.tic()
 
         if self.engine == bs.EngineType.GridCal:
             self.results = self.run_contingency_analysis()
@@ -307,5 +307,4 @@ def run(self) -> None:
             # default to GridCal mode
             self.results = self.run_contingency_analysis()
 
-        end = time.time()
-        self.elapsed = end - start
+        self.toc()

src/GridCalEngine/Simulations/NTC/ntc_ts_driver.py

@@ -420,11 +420,10 @@ def run(self):
 
         :return:
         """
-        start = time.time()
+        self.tic()
 
         self.opf()
         self.progress_text.emit('Done!')
 
-        end = time.time()
-        self.results.elapsed = end - start
+        self.toc()