diff --git a/scripts/script_debug_optim.py b/scripts/script_debug_optim.py
index 2f2dbb15..d5f4dcc1 100644
--- a/scripts/script_debug_optim.py
+++ b/scripts/script_debug_optim.py
@@ -69,9 +69,11 @@
     }
     
     # Set special debug cases
+    optim_conf.update({'lp_solver': 'PULP_CBC_CMD'}) # set the name of the linear programming solver that will be used. Options are 'PULP_CBC_CMD', 'GLPK_CMD' and 'COIN_CMD'. 
+    optim_conf.update({'lp_solver_path': 'empty'})  # set the path to the LP solver, COIN_CMD default is /usr/bin/cbc
     optim_conf.update({'treat_def_as_semi_cont': [True, True]})
     optim_conf.update({'set_def_constant': [False, False]})
-    optim_conf.update({'P_deferrable_nom': [[500.0, 1000.0, 1000.0, 500.0], 750.0]})
+    optim_conf.update({'P_deferrable_nom': [[500.0, 100.0, 100.0, 500.0], 750.0]})
     
     optim_conf.update({'set_use_battery': False})
     optim_conf.update({'set_nocharge_from_grid': False})
diff --git a/src/emhass/optimization.py b/src/emhass/optimization.py
index 6905808f..03fbf0f2 100644
--- a/src/emhass/optimization.py
+++ b/src/emhass/optimization.py
@@ -174,14 +174,14 @@ def perform_optimization(self, data_opt: pd.DataFrame, P_PV: np.array, P_load: n
         P_deferrable = []
         P_def_bin1 = []
         for k in range(self.optim_conf['num_def_loads']):
+            if type(self.optim_conf['P_deferrable_nom'][k]) == list:
+                upBound = np.max(self.optim_conf['P_deferrable_nom'][k])
+            else:
+                upBound = self.optim_conf['P_deferrable_nom'][k]
             if self.optim_conf['treat_def_as_semi_cont'][k]:
                 P_deferrable.append({(i):plp.LpVariable(cat='Continuous',
                                                         name="P_deferrable{}_{}".format(k, i)) for i in set_I})
             else:
-                if type(self.optim_conf['P_deferrable_nom'][k]) == list:
-                    upBound = np.max(self.optim_conf['P_deferrable_nom'][k])
-                else:
-                    upBound = self.optim_conf['P_deferrable_nom'][k]
                 P_deferrable.append({(i):plp.LpVariable(cat='Continuous',
                                                         lowBound=0, upBound=upBound,
                                                         name="P_deferrable{}_{}".format(k, i)) for i in set_I})
@@ -334,26 +334,26 @@ def create_matrix(input_list, n):
                     return matrix
                 matrix = create_matrix(power_sequence, n-sequence_length)
                 y = plp.LpVariable.dicts(f"y{k}", (i for i in range(len(matrix))), cat='Binary')
-                constraints.update({f"SingleValueConstraint_{i}" :
+                constraints.update({f"single_value_constraint_{k}" :
                     plp.LpConstraint(
                         e = plp.lpSum(y[i] for i in range(len(matrix))) - 1,
                         sense = plp.LpConstraintEQ,
                         rhs = 0)
                     })
-                # constraints.update({f"pdef{k}_sumconstraint_{i}" :
-                #     plp.LpConstraint(
-                #         e = plp.lpSum(P_deferrable[k][i] for i in set_I) - np.sum(power_sequence),
-                #         sense = plp.LpConstraintEQ,
-                #         rhs = 0)
-                #     })
-                constraints.update({f"pdef{k}_positiveconstraint_{i}" :
+                constraints.update({f"pdef{k}_sumconstraint_{i}" :
+                    plp.LpConstraint(
+                        e = plp.lpSum(P_deferrable[k][i] for i in set_I) - np.sum(power_sequence),
+                        sense = plp.LpConstraintEQ,
+                        rhs = 0)
+                    })
+                constraints.update({f"pdef{k}_positive_constraint_{i}" :
                     plp.LpConstraint(
                         e = P_deferrable[k][i],
                         sense = plp.LpConstraintGE,
                         rhs = 0)
                     for i in set_I})
                 for num, mat in enumerate(matrix):
-                    constraints.update({f"pdef{k}_valueconstraint_{num}_{i}" :
+                    constraints.update({f"pdef{k}_value_constraint_{num}_{i}" :
                         plp.LpConstraint(
                             e = P_deferrable[k][i] - mat[i]*y[num],
                             sense = plp.LpConstraintEQ,