# -*- coding: utf-8 -*-
"""
Analyse revenue and prices, checking total revenue is collected 
and looking for decomposition of revenue.

With RCPD, revenue is AC to load and DC to south island generation

With AoB, revenue is AC and DC to load and generation according to benefits
from existing and future assets. Plus load pays for a residual that declines
through time

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

# Objective, function that takes two files as arguments and returns a file of
# analysis for revenue

# 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")

# DG investment
rcpd_mw=rcpd.groupby(['m_yr','dg_lrmc_g','dg_ds'])['dg_max'].sum().reset_index()
rcpd_mw=rcpd_mw.rename(index=str, columns={"dg_max": "rcpd_mw"})

rcpd_q=rcpd.groupby(['m_yr','dg_lrmc_g','dg_ds'])['dg_q'].sum().reset_index()
rcpd_q=rcpd_q.rename(index=str, columns={"dg_q": "rcpd_q"})

rcpd_dg = pd.merge(rcpd_mw,rcpd_q)

aob_mw=aob.groupby(['m_yr','dg_lrmc_g','dg_ds'])['dg_max'].sum().reset_index()
aob_mw=aob_mw.rename(index=str, columns={"dg_max": "aob_mw"})

aob_q=aob.groupby(['m_yr','dg_lrmc_g','dg_ds'])['dg_q'].sum().reset_index()
aob_q=aob_q.rename(index=str, columns={"dg_q": "aob_q"})

aob_dg = pd.merge(aob_mw,aob_q)

dg = pd.merge(rcpd_dg,aob_dg)
chk=dg.groupby(['dg_lrmc_g','dg_ds'])['rcpd_mw'].diff()

dg = dg.sort_values(by=['m_yr'])
dg['rcpd_dmw']=dg.groupby(['dg_lrmc_g','dg_ds'])['rcpd_mw'].diff()
dg['rcpd_inv']=dg.rcpd_dmw*(dg_capex_per_mw*(dg.dg_lrmc_g**(dg.m_yr-2017)))
#dg['rcpd_inv']=dg.rcpd_mw.diff()*(dg_capex_per_mw*(dg_capex_g**(dg.m_yr-2017)))
dg['aob_dmw']=dg.groupby(['dg_lrmc_g','dg_ds'])['aob_mw'].diff()
dg['aob_inv']=dg.aob_dmw*(dg_capex_per_mw*(dg.dg_lrmc_g**(dg.m_yr-2017)))

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.dg_lrmc_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()