#!/usr/bin/env python
# coding: utf-8

# # Power Flow via PandaModels
# ### PandaModels.jl: Interfacing PowerModels with pandapower
# 
# This tutorial describes how to run the Power Flow via [PandaModels.jl](https://e2niee.github.io/PandaModels.jl/dev/) calling [PowerModels.jl](https://lanl-ansi.github.io/PowerModels.jl/stable/) package.
# Power Flow function in PowerModels is built based on [JuMP.jl](https://github.com/jump-dev/JuMP.jl) environment, thus same as OPF we need to set model parameter while calling PF function:
# 
# * "ACPPowerModel": a non-convex nonlinear AC power flow using complex voltages in polar coordinates
# * "ACRPowerModel": a non-convex nonlinear AC power flow using complex voltages in rectangular coordinates
# * "SOCWRPowerModel": a convex quadratic relaxation of the power flow problem
# * "DCPPowerModel": a linear DC approximation of the power flow problem
# * "DCMPPowerModel": a linear DC power flow model with polar voltage variables
# 
# Note: In order to compare the results of DC-PF between PowerModels and PYPOWER / matpower calculations,
#  you need to set the model to "DCMPPowerModel". This direct current model should be equal to the results of matpower calculations,
#   while model="DCPPowerModel" is the linearized model from AC model. For more information please check [here](https://lanl-ansi.github.io/PowerModels.jl/stable/formulation-details/#PowerModels.DCMPPowerModel).

# ### Let's get started
# 
# So here is an example of how it works. First, we create a simple grid in pandapower:

# In[3]:


import copy
import numpy as np
import pandapower as pp
import pandapower.networks as nw

net = nw.simple_four_bus_system()
net.trafo.loc[0, "shift_degree"] = 0.


# Then run ac power flow and get voltage angle and amplitude for buses:

# In[5]:


try:
    pp.runpm_pf(net, pm_model="ACPPowerModel", calculate_voltage_angles=True)
except Exception as err:
    print(err)


# Also, there more parameters and options that you can add as input while calling the Optimization Problem from PandaModles:
# 
# | parameter | description | type | default |
# | :--- | :--- | :---: | :--- |
# | correct_pm_network_data | checks if network data is correct. If not tries to correct it | bool | True |
# | silence | Suppresses information and warning messages output by PowerModels | bool | True |
# | pm_model | PowerModels.jl model to use | str | "ACPPowerModel" |
# | pm_solver | "main" solver| str | "ipopt" |
# | pm_mip_solver | mixed integer solver| str | "cbc" |
# | pm_nl_solver | nonlinear solver| str | "ipopt" |
# | pm_tol | default desired convergence tolerance for solver to use | float | 1e-8 |
# | pm_log_level | solver log level in power models | int | 0 |
# | delete_buffer_file | If True, the .json file used by PandaModels will be deleted after optimization. | bool | True
# 

# In[6]:


va_pm = copy.deepcopy(net.res_bus.va_degree)
vm_pm = copy.deepcopy(net.res_bus.vm_pu)


# run the powerflow from pypower:

# In[7]:


pp.runpp(net)


# In[8]:


va_pp = copy.deepcopy(net.res_bus.va_degree)
vm_pp = copy.deepcopy(net.res_bus.vm_pu)


# lets compare the results:

# In[9]:


np.allclose(va_pm, va_pp)


# In[10]:


np.allclose(vm_pm, vm_pp)


# In[ ]: