opf: Allow extra_postprocessing function for reading shadow prices

It is called after solving and results are extracted. It can be used
to get the values of shadow prices for constraints not normally
extracted.

pypsa/opf.py

@@ -1128,7 +1128,8 @@ def define_linear_objective(network,snapshots):
 
     l_objective(model,objective)
 
-def extract_optimisation_results(network, snapshots, formulation="angles", free_pyomo=True):
+def extract_optimisation_results(network, snapshots, formulation="angles", free_pyomo=True,
+                                 extra_postprocessing=None):
 
     if isinstance(snapshots, pd.DatetimeIndex) and _pd_version < '0.18.0':
         # Work around pandas bug #12050 (https://github.com/pydata/pandas/issues/12050)
@@ -1150,6 +1151,7 @@ def extract_optimisation_results(network, snapshots, formulation="angles", free_
     model = network.model
 
     duals = pd.Series(list(model.dual.values()), index=pd.Index(list(model.dual.keys())))
+
     if free_pyomo:
         model.dual.clear()
 
@@ -1319,6 +1321,10 @@ def get_shadows(constraint, multiind=True):
             network.generators_t.status.loc[snapshots,fixed_committable_gens_i] = \
                 get_values(model.generator_status).unstack(0)
 
+    if extra_postprocessing is not None:
+        extra_postprocessing(network, snapshots, duals)
+
+
 def network_lopf_build_model(network, snapshots=None, skip_pre=False,
                              formulation="angles", ptdf_tolerance=0.):
     """
@@ -1417,7 +1423,8 @@ def network_lopf_prepare_solver(network, solver_name="glpk", solver_io=None):
 
     return network.opt
 
-def network_lopf_solve(network, snapshots=None, formulation="angles", solver_options={}, keep_files=False, free_memory={'pyomo'}):
+def network_lopf_solve(network, snapshots=None, formulation="angles", solver_options={}, keep_files=False,
+                       free_memory={'pyomo'},extra_postprocessing=None):
     """
     Solve linear optimal power flow for a group of snapshots and extract results.
 
@@ -1440,6 +1447,11 @@ def network_lopf_solve(network, snapshots=None, formulation="angles", solver_opt
         Any subset of {'pypsa', 'pyomo'}. Allows to stash `pypsa` time-series
         data away while the solver runs (as a pickle to disk) and/or free
         `pyomo` data after the solution has been extracted.
+    extra_postprocessing : callable function
+        This function must take three arguments
+        `extra_postprocessing(network,snapshots,duals)` and is called after
+        the model has solved and the results are extracted. It allows the user to
+        extract further information about the solution, such as additional shadow prices.
 
     Returns
     -------
@@ -1473,11 +1485,13 @@ def network_lopf_solve(network, snapshots=None, formulation="angles", solver_opt
     if status == "ok" and termination_condition == "optimal":
         logger.info("Optimization successful")
         extract_optimisation_results(network, snapshots, formulation,
-                                     free_pyomo='pyomo' in free_memory)
+                                     free_pyomo='pyomo' in free_memory,
+                                     extra_postprocessing=extra_postprocessing)
     elif status == "warning" and termination_condition == "other":
         logger.warn("WARNING! Optimization might be sub-optimal. Writing output anyway")
         extract_optimisation_results(network, snapshots, formulation,
-                                     free_pyomo='pyomo' in free_memory)
+                                     free_pyomo='pyomo' in free_memory,
+                                     extra_postprocessing=extra_postprocessing)
     else:
         logger.error("Optimisation failed with status %s and terminal condition %s"
               % (status, termination_condition))
@@ -1487,7 +1501,7 @@ def network_lopf_solve(network, snapshots=None, formulation="angles", solver_opt
 def network_lopf(network, snapshots=None, solver_name="glpk", solver_io=None,
                  skip_pre=False, extra_functionality=None, solver_options={},
                  keep_files=False, formulation="angles", ptdf_tolerance=0.,
-                 free_memory={}):
+                 free_memory={},extra_postprocessing=None):
     """
     Linear optimal power flow for a group of snapshots.
 
@@ -1526,6 +1540,11 @@ def network_lopf(network, snapshots=None, solver_name="glpk", solver_io=None,
         Any subset of {'pypsa', 'pyomo'}. Allows to stash `pypsa` time-series
         data away while the solver runs (as a pickle to disk) and/or free
         `pyomo` data after the solution has been extracted.
+    extra_postprocessing : callable function
+        This function must take three arguments
+        `extra_postprocessing(network,snapshots,duals)` and is called after
+        the model has solved and the results are extracted. It allows the user to
+        extract further information about the solution, such as additional shadow prices.
 
     Returns
     -------
@@ -1545,4 +1564,5 @@ def network_lopf(network, snapshots=None, solver_name="glpk", solver_io=None,
 
     return network_lopf_solve(network, snapshots, formulation=formulation,
                               solver_options=solver_options,
-                              keep_files=keep_files, free_memory=free_memory)
+                              keep_files=keep_files, free_memory=free_memory,
+                              extra_postprocessing=extra_postprocessing)