diff --git a/MAIN_Unbalanced_OPF_RUN.py b/MAIN_Unbalanced_OPF_RUN.py
index f30e189..e8ced2a 100644
--- a/MAIN_Unbalanced_OPF_RUN.py
+++ b/MAIN_Unbalanced_OPF_RUN.py
@@ -216,15 +216,15 @@ def range1(start, end, step): # like range but inlcuding the ending value
 
 #### Choose a NLP solver  - make sure its installed - some solvers won't be able to find a feasible or optimal solution - NOTE: with IPOPT as default, I've disabled "PV operational limits" constraint... you need another solver when including that
 # IPOPT
-optimizer = pyo.SolverFactory('ipopt') 
-optimizer.options["max_iter"] = 100000
-optimizer.options["linear_solver"] = 'mumps'
+#optimizer = pyo.SolverFactory('ipopt') 
+#optimizer.options["max_iter"] = 100000
+#optimizer.options["linear_solver"] = 'mumps'
 
 # KNITRO
-#optimizer = pyo.SolverFactory('knitroampl')
-#optimizer.options["par_numthreads"] = 5
-#optimizer.options["algorithm"] = 0 # Indicates which algorithm to use to solve the problem - 0 is auto [0-5]
-#optimizer.options["presolve"] = 1 # Determine whether or not to use the Knitro presolver to try to simplify the model by removing variables or constraints.
+optimizer = pyo.SolverFactory('knitroampl')
+optimizer.options["par_numthreads"] = 1
+optimizer.options["algorithm"] = 0 # Indicates which algorithm to use to solve the problem - 0 is auto [0-5]
+optimizer.options["presolve"] = 1 # Determine whether or not to use the Knitro presolver to try to simplify the model by removing variables or constraints.
 
 print('Solving OPF model...')
 Problem = optimizer.solve(instance,tee=True)
diff --git a/Main_packages/OPF_model_creator_v01.py b/Main_packages/OPF_model_creator_v01.py
index 00e2b46..bac8ced 100644
--- a/Main_packages/OPF_model_creator_v01.py
+++ b/Main_packages/OPF_model_creator_v01.py
@@ -166,7 +166,7 @@ def Q_flow_receiving_rule(model, l, s, t): # Q receiving flow
                 return -1.0 *(model.Vim[model.Lines_i[l],s,t] * model.Iflow_re[l,s,t] - model.Vre[model.Lines_i[l],s,t] * model.Iflow_im[l,s,t])  
             model.Q_flow_receiving = pyo.Expression(model.Lines,model.Phases_abc,model.time, rule=Q_flow_receiving_rule)
         # Losses
-        if 1==1:
+        if 1==0: # Only model the variables that you are using in the optimization problem (in objective function or constraints)
             # Losses for this time step per line and phase
             def P_losses_rule(model, l, s, t): # Active power losses Watts!
                 return model.P_flow_sending[l,s,t] + model.P_flow_receiving[l,s,t]
@@ -233,7 +233,7 @@ def Transformer_limit_rule(model, t):
             model.Transformer_limit_con = pyo.Constraint(model.time, rule=Transformer_limit_rule)  
     
     # PV operational limits
-    if 1==0: # I recommend using a different NLP solver than IPOPT if incorporating this constraint (e.g. knitro or modifiying IPOPT options) - otherwise, relax the constraint
+    if 1==1: # I recommend using a different NLP solver than IPOPT if incorporating this constraint (e.g. knitro or modifiying IPOPT options) - otherwise, relax the constraint
         def PV_inverter_limit_rule(model, pv, t):
             max_P =  PV_Gen_data[pv,'Profile'].loc[t]
             max_S = (max_P**2+ (max_P*math.tan(math.acos(min_Cosphi)))**2)**0.5