# -*- coding: utf-8 -*-
"""
Create aggregates for analysing results of scenarios

"""
import numpy as np
import pandas as pd
import random
import matplotlib
import matplotlib.pyplot as plt
import itertools

in_fn = "C:/Users/theot/Dropbox (Sense.)/2018-19 EA TPM CBA (Shared)/Step 3/AoB model/Output/Demand_major_capex/"
out_fn="C:/Users/theot/Dropbox (Sense.)/2018-19 EA TPM CBA (Shared)/Step 3/AoB model/Output/Demand_major_capex/"

# DG costs  - assumed capital cost per MW and rate of growth
dg_capex_per_mw=733000
dg_capex_g = 0.93 

#Discount rate
r=1.06

# Demand results
rcpd = pd.read_csv(in_fn+"rcpd.csv") 
aob = pd.read_csv(in_fn+"aob.csv")
gen_rcpd = pd.read_csv(in_fn+"gen_rcpd.csv") 
gen_aob = pd.read_csv(in_fn+"gen_aob.csv")

# A. TRANSMISSION REVENUE
# (i) Load
load_revenue=rcpd[['bb','type','m_yr']].copy()
load_revenue['rcpd']=rcpd.pk_rev+rcpd.dg_rev+rcpd.sh_rev+rcpd.off_rev
load_revenue['aob']=aob.pk_rev+aob.dg_rev+aob.sh_rev+aob.off_rev
load_revenue['change']=load_revenue.aob-load_revenue.rcpd
load_revenue['percentage_change']=load_revenue.aob/load_revenue.rcpd-1
load_revenue.to_csv(out_fn+"load_revenue.csv")
load_tot_rev=load_revenue[['m_yr','rcpd','aob']].groupby(['m_yr']).sum().reset_index()

# (ii) Generation
gen_revenue=gen_rcpd[['bb','m_yr']].copy()
gen_revenue['gen_rcpd']=gen_rcpd.pk_rev+gen_rcpd.dg_rev+gen_rcpd.sh_rev+gen_rcpd.off_rev
gen_revenue['gen_aob']=gen_aob.pk_rev+gen_aob.dg_rev+gen_aob.sh_rev+gen_aob.off_rev
gen_revenue['change']=gen_revenue.gen_aob-gen_revenue.gen_rcpd
gen_revenue['percentage_change']=gen_revenue.gen_aob/gen_revenue.gen_rcpd-1
gen_revenue.to_csv(out_fn+"gen_revenue.csv")
gen_tot_rev = gen_revenue[['m_yr','gen_rcpd','gen_aob']].groupby(['m_yr']).sum().reset_index()

# Totals
total_revenue = load_tot_rev[['m_yr','rcpd','aob']]
total_revenue = total_revenue.merge(gen_tot_rev)
total_revenue.columns=['m_yr','load_rcpd','load_aob','gen_rcpd','gen_aob']
total_revenue['rcpd']=total_revenue.load_rcpd+total_revenue.gen_rcpd
total_revenue['aob']=total_revenue.load_aob+total_revenue.gen_aob
total_revenue.to_csv(out_fn+"total_revenue.csv")

# B. DEMAND VOLUMES

# (i) Demand
load_q=rcpd[['bb','type','m_yr']].copy()
load_q['rcpd']=rcpd.pk_q+rcpd.dg_q+rcpd.sh_q+rcpd.off_q
load_q['aob']=aob.pk_q+aob.dg_q+aob.sh_q+aob.off_q
load_q['change']=load_q.aob-load_q.rcpd
load_q['percentage_change']=load_q.aob/load_q.rcpd-1
load_tot_q=load_q[['m_yr','rcpd','aob']].groupby(['m_yr']).sum().reset_index()
load_tot_q['percentage_change']=load_tot_q.aob/load_tot_q.rcpd-1
load_q.to_csv(out_fn+"load.csv")
load_tot_q.to_csv(out_fn+"total_load.csv")

# (ii) Generation quantites
gen_q=gen_rcpd[['bb','type','m_yr']].copy()
gen_q['rcpd']=gen_rcpd.pk_q+gen_rcpd.dg_q+gen_rcpd.sh_q+gen_rcpd.off_q
gen_q['aob']=gen_aob.pk_q+gen_aob.dg_q+gen_aob.sh_q+gen_aob.off_q
gen_q['change']=gen_q.aob-gen_q.rcpd
gen_q['percentage_change']=gen_q.aob/gen_q.rcpd-1
gen_tot_q=gen_q[['m_yr','rcpd','aob']].groupby(['m_yr']).sum().reset_index()
gen_tot_q['percentage_change']=gen_tot_q.aob/gen_tot_q.rcpd-1
gen_q.to_csv(out_fn+"generation.csv")
gen_tot_q.to_csv(out_fn+"total_generation.csv")

# C. EXPENDITURE

load_e=rcpd[['bb','type','m_yr']].copy()
load_e['rcpd']=rcpd.pk_e+rcpd.dg_e+rcpd.sh_e+rcpd.off_e
load_e['aob']=aob.pk_e+aob.dg_e+aob.sh_e+aob.off_e
load_e['change']=load_e.aob-load_e.rcpd
load_e['percentage_change']=load_e.aob/load_e.rcpd-1
load_e.to_csv(out_fn+"expenditure.csv")
load_tot_e=load_e[['m_yr','rcpd','aob']].groupby(['m_yr']).sum().reset_index()
load_tot_e.to_csv(out_fn+"total_expenditure.csv")

# Average prices - inclusive of transmission costs
load_p=rcpd[['bb','type','m_yr']].copy()
load_p['rcpd']=load_e.rcpd/load_q.rcpd
load_p['aob']=load_e.aob/load_q.aob
load_p['change']=load_p.aob-load_p.rcpd
load_p['percentage_change']=load_p.aob/load_p.rcpd-1
load_tot_p=pd.DataFrame(load_tot_e['m_yr'].copy())
load_tot_p['rcpd']=load_tot_e.rcpd/load_tot_q.rcpd
load_tot_p['aob']=load_tot_e.aob/load_tot_q.aob
load_tot_p['change']=load_tot_p.aob-load_tot_p.rcpd
load_tot_p['percentage_change']=load_tot_p.aob/load_tot_p.rcpd-1
load_p.to_csv(out_fn+"average_prices.csv")
load_tot_p.to_csv(out_fn+"total_average_prices.csv")

# DG investment
dg=pd.DataFrame(load_tot_e['m_yr'].copy())
dg['rcpd_mw']=rcpd.groupby(['m_yr'])['dg_max'].sum().reset_index()['dg_max']
dg['rcpd_inv']=dg.rcpd_mw.diff()*(dg_capex_per_mw*(dg_capex_g**(dg.m_yr-2017)))
dg['rcpd_q']=rcpd.groupby(['m_yr'])['dg_q'].sum().reset_index()['dg_q']
dg['aob_mw']=aob.groupby(['m_yr'])['dg_max'].sum().reset_index()['dg_max']
dg['aob_inv']=dg.aob_mw.diff()*(dg_capex_per_mw*(dg_capex_g**(dg.m_yr-2017)))
dg['aob_q']=aob.groupby(['m_yr'])['dg_q'].sum().reset_index()['dg_q']
dg['mw_change']=dg.aob_mw-dg.rcpd_mw
dg['mw_percentage_change']=dg.aob_mw/dg.rcpd_mw-1
dg['mw_cost']=dg_capex_per_mw*(dg_capex_g**(dg.m_yr-2017))
dg['inv_change']=dg.aob_inv-dg.rcpd_inv
dg['pv_inv_change']=dg.inv_change*(1/(r**(dg.m_yr-2019)))
dg.to_csv(out_fn+'total_dg.csv')
dg.pv_inv_change.sum()