# -*- coding: utf-8 -*-
"""
Created on Wed Nov  6 07:40:01 2019

Simulate battery operation using 3 years of trading data (volumes and prices)
And linear programme for optimising operation.

@author: theot
"""
import numpy as np
import pandas as pd
import os

# Working directory
os.chdir("XXX")

# Import optimisation function
from ChargeDischargeUncertainty import BatteryCycleUncertain 

# Input data directory
input_dir = "XXXX"

# Get Data
data_all=pd.read_csv(input_dir+"PriceAndVolume_2010_to_2019_RCPD_N_100.csv")

# Data slice
data=data_all[(data_all['p_yr']>2016)].copy()

# Convert dates to datetimes
data['TP']=pd.to_datetime(data['TP'],format='%Y-%m-%d %H:%M:%S')
data['date']=pd.to_datetime(data['date'],format='%Y-%m-%d')

# Dates in data
dates=np.array(data.date.dt.strftime('%Y-%m-%d').unique())

# Add values for expected prices - with uncertainty about demand
df_minRCPD=data[(data.rcpd==1)].groupby(['pz','pz.name','p_yr']).X.min().reset_index()
df_minRCPD['RCPDerr']=df_minRCPD.X*0.98
data=pd.merge(data,df_minRCPD[['pz','p_yr','RCPDerr']],on=['pz','p_yr'],how='left')
data['E.Pt']=np.where(data.X>data.RCPDerr,(data['IC.rate']*1000)/data.rcpd_n,0)
data['E.P']=data['P.e']+data['E.Pt']*2 #NB: Adjustment to transmission charge unit cost because energy prices are in $/MWh while transmission charges are $/MWhh

# Given the year and region, loop over dates in dates and concatenate results
capacities = np.array([1,2,5,10,20,50,100,200,300,400,500])

#List to collect results
daily_cycles=[]

for m, mw_val in enumerate(capacities):
    for d, day in enumerate(dates):
        if d==0:
            newS0_uni=0
            newS0_lni=0
            newS0_usi=0
            newS0_lsi=0
        else:
            newS0_uni=day_cyc_uni["Stored"].iloc[-1]
            newS0_lni=day_cyc_lni["Stored"].iloc[-1]
            newS0_usi=day_cyc_usi["Stored"].iloc[-1]
            newS0_lsi=day_cyc_lsi["Stored"].iloc[-1]
        #UNI    
        day_data_uni = data[(data.date==day)&(data["pz.name"]=="UNI")].copy()
        minRCPD_uni=df_minRCPD[(df_minRCPD.p_yr==day_data_uni["p_yr"].iloc[0])&(df_minRCPD['pz.name']=="UNI")].RCPDerr.min()
        minRCPDActual_uni=df_minRCPD[(df_minRCPD.p_yr==day_data_uni["p_yr"].iloc[0])&(df_minRCPD['pz.name']=="UNI")].X.min()
        day_cyc_uni = BatteryCycleUncertain(df=day_data_uni,MW=mw_val,S0=newS0_uni,DC=True,minPk=minRCPD_uni,minActual=minRCPDActual_uni)
        day_cyc_uni['X1']=day_cyc_uni['X']-day_cyc_uni['Q'] # Grid demand with battery charging and discharging
        day_cyc_uni['PeakCharge']="Yes"
        day_cyc_uni['PZ']="UNI"
        day_cyc_uni['Capacity']=mw_val # Battery capacity
        daily_cycles.append(day_cyc_uni)
        #LNI
        day_data_lni = data[(data.date==day)&(data["pz.name"]=="LNI")].copy()
        minRCPD_lni=df_minRCPD[(df_minRCPD.p_yr==day_data_lni["p_yr"].iloc[0])&(df_minRCPD['pz.name']=="LNI")].RCPDerr.min()
        minRCPDActual_lni=df_minRCPD[(df_minRCPD.p_yr==day_data_lni["p_yr"].iloc[0])&(df_minRCPD['pz.name']=="LNI")].X.min()
        day_cyc_lni = BatteryCycleUncertain(df=day_data_lni,MW=mw_val,S0=newS0_lni,DC=True,minPk=minRCPD_lni,minActual=minRCPDActual_lni)
        day_cyc_lni['X1']=day_cyc_lni['X']-day_cyc_lni['Q'] # Grid demand with battery charging and discharging
        day_cyc_lni['PeakCharge']="Yes"
        day_cyc_lni['PZ']="LNI"
        day_cyc_lni['Capacity']=mw_val # Battery capacity
        daily_cycles.append(day_cyc_lni)
        #USI
        day_data_usi = data[(data.date==day)&(data["pz.name"]=="USI")].copy()
        minRCPD_usi=df_minRCPD[(df_minRCPD.p_yr==day_data_usi["p_yr"].iloc[0])&(df_minRCPD['pz.name']=="USI")].RCPDerr.min()
        minRCPDActual_usi=df_minRCPD[(df_minRCPD.p_yr==day_data_usi["p_yr"].iloc[0])&(df_minRCPD['pz.name']=="USI")].X.min()
        day_cyc_usi = BatteryCycleUncertain(df=day_data_usi,MW=mw_val,S0=newS0_usi,DC=True,minPk=minRCPD_usi,minActual=minRCPDActual_usi)
        day_cyc_usi['X1']=day_cyc_usi['X']-day_cyc_usi['Q'] # Grid demand with battery charging and discharging
        day_cyc_usi['PeakCharge']="Yes"
        day_cyc_usi['PZ']="USI"
        day_cyc_usi['Capacity']=mw_val # Battery capacity
        daily_cycles.append(day_cyc_usi)
        #LSI
        day_data_lsi = data[(data.date==day)&(data["pz.name"]=="LSI")].copy()
        minRCPD_lsi=df_minRCPD[(df_minRCPD.p_yr==day_data_lsi["p_yr"].iloc[0])&(df_minRCPD['pz.name']=="LSI")].RCPDerr.min()
        minRCPDActual_lsi=df_minRCPD[(df_minRCPD.p_yr==day_data_lsi["p_yr"].iloc[0])&(df_minRCPD['pz.name']=="LSI")].X.min()
        day_cyc_lsi = BatteryCycleUncertain(df=day_data_lsi,MW=mw_val,S0=newS0_lsi,DC=True,minPk=minRCPD_lsi,minActual=minRCPDActual_uni)
        day_cyc_lsi['X1']=day_cyc_lsi['X']-day_cyc_lsi['Q'] # Grid demand with battery charging and discharging
        day_cyc_lsi['PeakCharge']="Yes"
        day_cyc_lsi['PZ']="LSI"
        day_cyc_lsi['Capacity']=mw_val # Battery capacity
        daily_cycles.append(day_cyc_lsi)
        print((m+(d/(len(dates))))/len(capacities))
    
daily_cycles = pd.concat(daily_cycles)
daily_cycles.to_csv("daily_cycles_variable_MW_uncertain.csv")