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

# # Distribution Generation Curtailment with OPF
# This is an introduction on how to use the pandapower optimal power flow for calculation optimal distributed generation curtailment.
# 
# ## Example Network
# 
# We use the four bus example network from the basic OPF tutorial:
# 
# <img src="pics/example_opf.png" width="50%">
# 
# We first create this network in pandapower:

# In[100]:


import pandapower as pp
net = pp.create_empty_network()

#create buses
bus1 = pp.create_bus(net, vn_kv=220., min_vm_pu=1.0, max_vm_pu=1.02)
bus2 = pp.create_bus(net, vn_kv=110., min_vm_pu=1.0, max_vm_pu=1.02)
bus3 = pp.create_bus(net, vn_kv=110., min_vm_pu=1.0, max_vm_pu=1.02)
bus4 = pp.create_bus(net, vn_kv=110., min_vm_pu=1.0, max_vm_pu=1.02)

#create 220/110 kV transformer
pp.create_transformer(net, bus1, bus2, std_type="100 MVA 220/110 kV", max_loading_percent=100)

#create 110 kV lines
pp.create_line(net, bus2, bus3, length_km=70., std_type='149-AL1/24-ST1A 110.0', max_loading_percent=100)
pp.create_line(net, bus3, bus4, length_km=50., std_type='149-AL1/24-ST1A 110.0', max_loading_percent=100)
pp.create_line(net, bus4, bus2, length_km=40., std_type='149-AL1/24-ST1A 110.0', max_loading_percent=100)

#create loads
pp.create_load(net, bus2, p_mw=60, controllable=False)
pp.create_load(net, bus3, p_mw=70, controllable=False)
pp.create_load(net, bus4, p_mw=10, controllable=False)

#create generators
eg = pp.create_ext_grid(net, bus1)
g0 = pp.create_gen(net, bus3, p_mw=80, min_p_mw=0, max_p_mw=80,  vm_pu=1.01, controllable=True)
g1 = pp.create_gen(net, bus4, p_mw=100, min_p_mw=0, max_p_mw=100, vm_pu=1.01, controllable=True)


# In[102]:


pp.create_poly_cost(net, 0, 'gen', cp1_eur_per_mw=-1)
pp.create_poly_cost(net, 1, 'gen', cp1_eur_per_mw=-1)
pp.create_poly_cost(net, 0, 'ext_grid', cp1_eur_per_mw=0)
pp.runopp(net, verbose=True)


# Because of the negative costs, the OPF now maximizes power generation at the generators, which is constrained by their maximum power:

# In[103]:


pd.concat([net.res_gen.p_mw, net.gen.min_p_mw, net.gen.max_p_mw], axis=1)


# While gen 1 is operating at the limit, gen 0 is below the maximum output. Apparently the generator can not reach its maximum output without violating at least one power flow constraint. Let's check on the constraints.
# 
# The line and transformer constraints are not reached:

# In[104]:


pd.concat([net.line.max_loading_percent, net.res_line.loading_percent], axis=1)


# In[105]:


pd.concat([net.trafo.max_loading_percent, net.res_trafo.loading_percent], axis=1)


# But the voltage constraints are:

# In[106]:


import pandas as pd
pd.concat([net.res_bus.vm_pu, net.bus.min_vm_pu, net.bus.max_vm_pu], axis=1)


# Obviously the voltage profile was the limiting factor for the generator feed-in. If we relax this constraint a little bit:

# In[107]:


net.bus["max_vm_pu"] = 1.05
pp.runopp(net)


# We see an increased feed-in of the generators:

# In[108]:


pd.concat([net.res_gen.p_mw, net.gen.min_p_mw, net.gen.max_p_mw], axis=1)


# In[109]:


net.res_bus