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diff --git a/Data/EnergyBalance/2019/Niue.csv b/Data/EnergyBalance/2019/Niue.csv
@@ -1,5 +1,5 @@
 Country (2019),Transactions(down)/Commodity(right),Primary Coal and Peat,Coal and Peat Products,Primary Oil,Oil Products,Natural Gas,Biofuels and Waste,Nuclear,Electricity,Heat,Total Energy,memo: Of which Renewables
-Niue,Primary production,---,---,---,---,---,1,---,*2,*16,*18,*18
+Niue,Primary production,---,---,---,---,---,1,---,*2,*15,*18,*18
 Niue,Imports,---,---,---,*110,---,---,---,---,---,*110,---
 Niue,Exports,---,---,---,---,---,---,---,---,---,---,---
 Niue,International marine bunkers,---,---,---,---,---,---,---,---,---,---,---
@@ -51,4 +51,4 @@ Niue,Agriculture  forestry and fishing,---,---,---,*1,---,---,---,---,---,*1,---
 Niue,Commerce and public services,---,---,---,*1,---,---,---,*5,---,*6,---
 Niue,Households,---,---,---,*2,---,*0,---,*5,*16,*23,*16
 Niue,Other consumption n.e.s,---,---,---,---,---,---,---,---,---,---,---
-Niue,Non-energy use,---,---,---,---,---,---,---,---,---,---,---
+Niue,Non-energy use,---,---,---,---,---,---,---,---,---,---,---
diff --git a/Data/EnergyBalance/2019/all_countries_df.csv b/Data/EnergyBalance/2019/all_countries_df.csv
@@ -582,7 +582,7 @@
 580,Micronesia,Households,0,0,0,0,0,4,0,61,0,64,4,0,0,4
 581,Micronesia,Other consumption n.e.s,0,0,0,45,0,11,0,0,0,56,11,0,45,56
 582,Micronesia,Non-energy use,0,0,0,92,0,0,0,0,0,92,0,0,92,92
-583,Niue,Primary production,0,0,0,0,0,1,0,2,16,18,18,0,0,17
+583,Niue,Primary production,0,0,0,0,0,1,0,2,15,18,18,0,0,17
 584,Niue,Imports,0,0,0,110,0,0,0,0,0,110,0,0,110,110
 585,Niue,Exports,0,0,0,0,0,0,0,0,0,0,0,0,0,0
 586,Niue,International marine bunkers,0,0,0,0,0,0,0,0,0,0,0,0,0,0

diff --git a/Data/SummaryTable.csv b/Data/SummaryTable.csv
@@ -1,20 +1,20 @@
-Country / Territory,Political Status,Land area (km2),Population,Area per person (m2),Population density (Person/km2),GDP ($Million),GDP Per Capita ($),Geographic Type,No. islands,No. inhab. island
-American Samoa,US territory,240,"45,443","5,176",193,658,14480,High islands + atolls,7,6
-Cook Islands,Independent. NZ-affiliated,180,"17,459","10,310",97,300,17183,High islands + atolls,15,13
-Federated States of Micronesia,Independent. US-affiliated,702,"104,468","6,720",149,390,3733,High islands + atolls,607,65
-Fiji,Independent,"18,376","929,276","19,775",51,"12,180",13107,High islands + atolls,330,110
-French Polynesia,French Territory,"3,521","275,918","12,761",78,"5,490",19897,High islands + atolls,118,67
-Guam,US territory,549,"168,801","3,252",307,6,34,High islands,1,1
-Kiribati,Independent,726,"119,000","6,101",164,270,2269,Atolls,33,21
-Marshall Islands,Independent. US-affiliated,720,"58,413","12,326",81,240,4109,Atolls,34,24
-Nauru,Independent,21,"10,670","1,968",508,150,14058,Raised coral island,1,1
-Niue,Independent. NZ-affiliated,258,"1,620","159,259",6,10,6235,Raised coral island,1,1
-Palau,Independent. US-affiliated,475,"17,907","26,526",38,320,17870,High islands + atolls,1,1
-PNG,Independent,"461,690","8,935,000","51,672",19,"38,170",4272,High islands + atolls,600,No data
-Solomon Islands,Independent,"29,785","652,857","45,623",22,"1,780",2726,High islands + atolls,992,347
-Tokelau,NZ territory,12,"1,500","8,000",125,8,5333,Atolls,3,3
-Tonga,Independent,696,"100,651","6,915",145,670,6657,High islands,169,36
-Tuvalu,Independent,26,"11,646","2,233",448,50,4293,Atolls,9,8
-Vanuatu,Independent,"12,189","307,815","39,598",25,930,3021,High islands + atolls,83,65
-Samoa,Independent,"2,934","202,506","14,488",69,"1,280",6321,High islands,12,4
-New Caledonia,French Territory,"18,275","297,160","61,499",16,11,37,No Data,55,No data
+Country / Territory,Political Status,Land area (km2),Population,Area per person (m2),Population density (Person/km2),GDP Per Capita ($),Geographic Type,No. islands,No. inhab. island
+American Samoa,US territory,240,"45,443","5,176",193,14480,High islands + atolls,7,6
+Cook Islands,Independent. NZ-affiliated,180,"17,459","10,310",97,16700,High islands + atolls,15,13
+Micronesia,Independent. US-affiliated,702,"104,468","6,720",149,3500,High islands + atolls,607,65
+Fiji,Independent,"18,376","929,276","19,775",51,11000,High islands + atolls,330,110
+French Polynesia,French Territory,"3,521","275,918","12,761",78,17000,High islands + atolls,118,67
+Guam,US territory,549,"168,801","3,252",307,35600,High islands,1,1
+Kiribati,Independent,726,"119,000","6,101",164,2300,Atolls,33,21
+Marshall Islands,Independent. US-affiliated,720,"58,413","12,326",81,4000,Atolls,34,24
+Nauru,Independent,21,"10,670","1,968",508,13500,Raised coral island,1,1
+Niue,Independent. NZ-affiliated,258,"1,620","159,259",6,5800,Raised coral island,1,1
+Palau,Independent. US-affiliated,475,"17,907","26,526",38,17600,High islands + atolls,1,1
+PNG,Independent,"461,690","8,935,000","51,672",19,4200,High islands + atolls,600,No data
+Solomon Islands,Independent,"29,785","652,857","45,623",22,2500,High islands + atolls,992,347
+Tokelau,NZ territory,12,"1,500","8,000",125,60004,Atolls,3,3
+Tonga,Independent,696,"100,651","6,915",145,6400,High islands,169,36
+Tuvalu,Independent,26,"11,646","2,233",448,4400,Atolls,9,8
+Vanuatu,Independent,"12,189","307,815","39,598",25,2800,High islands + atolls,83,65
+Samoa,Independent,"2,934","202,506","14,488",69,6300,High islands,12,4
+New Caledonia,French Territory,"18,275","297,160","61,499",16,31100,No Data,55,No data
diff --git a/DecarbonizationFunctions.py b/DecarbonizationFunctions.py
@@ -1,18 +1,201 @@
 import pandas as pd
 import functions
-def calculate_community_battery_size(country,residential_battery_capacity,demand_scenario="Decarbonization", total_storage_days = 5):
-    demand = functions.fetch_single_country_demand(Country=country,Year=2019)
-    if demand_scenario == "World average":
-        demand = demand * 0.8
-    daily_average_demand = demand/365
-    total_storage_capacity_GWh = total_storage_days * daily_average_demand
-    community_storage_capacity = total_storage_capacity_GWh - residential_battery_capacity
+from EnergyFlows import Country_List
+def calculate_community_battery_size(demand,residential_battery_capacity,technical_pot,total_storage_days = 5):
+    average_daily_demand = demand/365 #GWH/day
+    if demand <= technical_pot:
+        total_storage_capacity_GWh = average_daily_demand * total_storage_days
+    elif demand > technical_pot:
+        total_storage_capacity_GWh = (average_daily_demand * total_storage_days)*(technical_pot/demand)
+    community_battery = max(total_storage_capacity_GWh - residential_battery_capacity,0)
 
-    return community_storage_capacity
+    return community_battery
 
+def calculate_demand(country,demand_scenario):
+    if demand_scenario == "Decarbonization":
+        demand = functions.fetch_single_country_demand(Country=country,Year=2019)[0]
+    elif demand_scenario == "Electrification":
+        demand = functions.fetch_single_country_demand(Country=country, Year=2019)[1]
+    elif demand_scenario == "Net_zero":
+        demand = functions.fetch_single_country_demand(Country=country, Year=2019)[2]
 
+    return demand
 
-def run_decarbonization_scenario():
-    pass
+def calculate_renewable_technical_potential(country,available_land,avaialble_coastline,avaialble_buildings=0.3,PV_size=2.5):
+    technical_potential_df  = functions.calculate_PV_Wind_potential(available_land=available_land, available_coastline=avaialble_coastline)
+    PV_technical_potential = technical_potential_df[technical_potential_df['Country'] == country]['PV_technical_GWh'].values[0]
+    Wind_technical_potential = technical_potential_df[technical_potential_df['Country'] == country]['Wind_technical_GWh'].values[0]
 
-calculate_community_battery_size("PNG",5)
+    rooftop_df = functions.calculate_rooftop_PV_potential(available_buildings=avaialble_buildings,PV_size=PV_size)
+    rooftop_potential = rooftop_df[rooftop_df['Country'] == country]['Generation_GWh'].values[0]
+    # print(PV_technical_potential,Wind_technical_potential,rooftop_potential)
+
+    total = PV_technical_potential + Wind_technical_potential + rooftop_potential
+    return {"PV_tech_GWh":PV_technical_potential,"Wind_tech_GWh":Wind_technical_potential,"Rooftop_GWh":rooftop_potential,"Total":total}
+
+def calculate_capacity_of_each_technology(country,dic_potential,demand):
+
+    rooftop_PV_GWh = min(dic_potential["Rooftop_GWh"],demand)
+    rooftop_PV_GWh = max(0,rooftop_PV_GWh)
+
+    large_PV_GWh = min(dic_potential["PV_tech_GWh"],demand-rooftop_PV_GWh)
+    large_PV_GWh = max(0,large_PV_GWh)
+
+    wind_GWh = min(dic_potential["Wind_tech_GWh"],demand-rooftop_PV_GWh-large_PV_GWh)
+    wind_GWh = max(0,wind_GWh)
+
+    df_potentials = pd.read_excel('Data/Potentials.xlsx')
+    PV_pot = df_potentials.iloc[0, 2:] #GWh/MW/year
+    Wind_pot =df_potentials.iloc[2, 2:] #GWh/MW/year
+
+    PV_pot = PV_pot[country]
+    Wind_pot = Wind_pot[country]
+    # print(PV_pot[country])
+
+    rooftop_MW = rooftop_PV_GWh/PV_pot #MW
+    large_PV_MW = large_PV_GWh/PV_pot #MW
+
+    wind_MW = wind_GWh/Wind_pot
+
+    total_GWh = wind_GWh + large_PV_GWh + rooftop_PV_GWh
+
+    community_battery = calculate_community_battery_size(demand=demand,residential_battery_capacity = rooftop_MW*2/1000,technical_pot=total_GWh,total_storage_days=5 )
+
+    return {"Rooftop_MW":rooftop_MW,"Large_PV_MW":large_PV_MW,"Wind_MW":wind_MW,"residential_battery_MWh":rooftop_MW*2,"total_GWh":total_GWh,"Community_battery_GWh":community_battery}
+
+
+def create_yearly_df(country,decarb_year,capacity_dic,cost_dic,diesel_price,inflation_rate, discount_rate):
+    from datetime import datetime
+    now = datetime.now().year
+    number_of_years = decarb_year - 2022
+    year_list = []
+    installation_df = pd.DataFrame()
+
+    for i in range(0, 31):
+        now += 1
+        year_list.append(now)
+
+    # community_battery = calculate_community_battery_size(demand=)
+    installation_df['Year'] = year_list
+    installation_df['rooftop_MW'] = capacity_dic["Rooftop_MW"]/number_of_years
+    installation_df['resid_battery_MW'] = installation_df['rooftop_MW'] * 2
+    installation_df['PV_MW'] = capacity_dic["Large_PV_MW"]/number_of_years
+    installation_df['wind_MW'] = capacity_dic["Wind_MW"]/number_of_years
+    installation_df["Community_battery_GWh"] = capacity_dic['Community_battery_GWh']/number_of_years
+
+    installation_df.loc[number_of_years:,'rooftop_MW'] = 0
+    installation_df.loc[number_of_years:,'resid_battery_MW'] = 0
+    installation_df.loc[number_of_years:,'PV_MW'] = 0
+    installation_df.loc[number_of_years:,'wind_MW'] = 0
+    installation_df.loc[number_of_years:,"Community_battery_GWh"] = 0
+
+    # costs are $/W - 1000000/MW
+    # The output is #$
+    installation_df['installation_Cost'] = (installation_df['rooftop_MW'] * cost_dic['rooftop'] +
+                                            installation_df['resid_battery_MW'] *cost_dic['resid_battery'] +
+                                            installation_df['Community_battery_GWh']*1000 *cost_dic['resid_battery'] +
+                                            installation_df['PV_MW'] * cost_dic['large_PV'] +\
+                                           installation_df['wind_MW'] * cost_dic['wind'])*1000000#Convert to $/MW #in the cost dic, are costs are $/W
+
+
+    installation_df['avoided_demand_GWh'] = capacity_dic["total_GWh"]/number_of_years
+    installation_df.loc[number_of_years:,'avoided_demand_GWh'] = 0
+
+    diesel_efficiency = 0.4
+
+    diesel_generatio = 2.5 #kWh/Liter
+    installation_df["avoided_diesel_liter"] = installation_df["avoided_demand_GWh"] / (2.5/1000000)
+    installation_df["cumulative_avoided_diesel_liter"] = installation_df["avoided_diesel_liter"].cumsum(axis=0)
+    installation_df["avoided_diesel_savings"] = installation_df["cumulative_avoided_diesel_liter"] * diesel_price
+
+    if country =="New Caledonia":
+        #coal price is 400 USD/Tonne
+        # 47% demand is met by diesel, and 53% by coal
+        # 0.00814 GWh energy in one tonne coal.
+        installation_df["avoided_diesel_liter"] = installation_df["avoided_diesel_liter"]*0.47
+        installation_df["avoided_coal_tonne"] = (installation_df["avoided_demand_GWh"]*0.53)/(0.00814 * 0.35)
+
+        installation_df["cumulative_avoided_diesel_liter"] = installation_df["avoided_diesel_liter"].cumsum(axis=0)
+        installation_df["cumulative_avoided_coal_tonne"] = installation_df["avoided_coal_tonne"].cumsum(axis=0)
+
+        installation_df["avoided_diesel_savings"] = installation_df["cumulative_avoided_diesel_liter"] * diesel_price
+        installation_df["avoided_coal_savings"] = installation_df["cumulative_avoided_coal_tonne"] * 400
+        installation_df["avoided_diesel_savings"] = installation_df["avoided_diesel_savings"] + installation_df["avoided_coal_savings"]
+
+    installation_df['Cumulative_avoided_cost'] = installation_df['avoided_diesel_savings'].cumsum(axis=0)
+
+    inflation_rate = inflation_rate/100
+    discount_rate = discount_rate/100
+
+    for i, row in installation_df.iterrows():
+        installation_df.at[i, 'Cumulative_avoided_cost'] = installation_df.at[i, 'Cumulative_avoided_cost'] * ((1+inflation_rate)/(1+discount_rate))**i
+        installation_df.at[i, 'installation_Cost'] = installation_df.at[i, 'installation_Cost'] * ((1+inflation_rate)/(1+discount_rate))**i
+
+    installation_df['Annual_Net_saving'] = installation_df['Cumulative_avoided_cost'] - installation_df['installation_Cost']  # $MM
+    installation_df["Cumulative_net_saving"] = installation_df['Annual_Net_saving'].cumsum(axis=0)
+
+    return installation_df
+    # demand_df['Net_saving_discounted'] = 0
+    # demand_df['Emission_red_saving_discounted'] = 0
+    # inflation_rate = inflation_rate/100
+    # discount_rate = discount_rate/100
+
+def calculate_diesel_price(country):
+    diesel_df = pd.read_csv("Data/Diesel.csv")
+    if country in diesel_df["Country"].to_list():
+        print("Hi")
+        diesel_price = diesel_df[diesel_df['Country'] == country]['Tax included'].values[0]
+    else:
+        diesel_price = diesel_df['Tax included'].mean()
+
+    diesel_price = diesel_price-20 #20c less than retails price
+    diesel_price = diesel_price/100 # convert to $ from cents
+
+    print(diesel_price)
+    return diesel_price
+
+
+def run_decarbonization_scenario(cost_scenario,country_list,demand_scenario="Decarbonization",available_land=0.02, avaialble_coastline=0.1,avaialble_buildings=0.3,PV_size=2.5,decarb_year=2030):
+    # demand_scenario = ["Decarbonization","Electrification","Net_zero"]
+    costs= {"optimistic":{"rooftop":3,"resid_battery":4,"large_PV":3,"wind":3},"pessimistic":{"rooftop":4.5,"resid_battery":4,"large_PV":4.5,"wind":6}}
+    all_countries_result = pd.DataFrame()
+    all_countries_result['Technology'] =["Rooftop_MW", "Large_PV_MW","Wind_MW","Residential_battery_MWh","Community_battery_GWh","total_GWh",'Payback period (years)']
+    # : rooftop_MW, : large_PV_MW, : wind_MW, : total_GWh, : community_battery
+    # cost_dic = {"rooftop":4.5,"resid_battery":4,"large_PV":4.5,"wind":6}#Pessimistic
+    # cost_dic = {"rooftop":3,"resid_battery":4,"large_PV":3,"wind":3}#Optimistic
+    cost_dic = costs[cost_scenario]
+    for country in country_list:
+        diesel_price = calculate_diesel_price(country)
+        pot = calculate_renewable_technical_potential(country, available_land=available_land, avaialble_coastline=avaialble_coastline,avaialble_buildings=avaialble_buildings,PV_size=PV_size)
+        demand = calculate_demand(country, demand_scenario)
+        capacity_dic = calculate_capacity_of_each_technology(country, pot, demand)
+        final_df = create_yearly_df(country=country,decarb_year=decarb_year,capacity_dic=capacity_dic,cost_dic=cost_dic,diesel_price=diesel_price,inflation_rate=3,discount_rate=7)
+        final_df.to_csv("Results/{}/Simulation_result_{}.csv".format(demand_scenario,country))
+
+        payback_period = final_df[final_df.Cumulative_net_saving < 0].index.values.max()
+
+        all_countries_result[country] = [capacity_dic["Rooftop_MW"],
+                                         capacity_dic["Large_PV_MW"],
+                                         capacity_dic["Wind_MW"],
+                                         capacity_dic["residential_battery_MWh"],
+                                         capacity_dic["Community_battery_GWh"],
+                                         capacity_dic["total_GWh"],
+                                         payback_period
+                                         ]
+
+    all_countries_result.reset_index(drop=True,inplace=True)
+    # all_countries_result = all_countries_result.pivot(columns="Technology",index=all_countries_result.columns)[all_countries_result.columns]
+
+    all_countries_result.to_excel("Results/{}/{}_simulation_result_{}_wind_{}_PV_{}.xlsx".format(demand_scenario,cost_scenario,demand_scenario,avaialble_coastline,available_land))
+    # print(all_countries_result.head())
+    return final_df
+
+for cost_scenario in ["optimistic",'pessimistic']:
+    for demand_sceanrio in ['Decarbonization',"Electrification","Net_zero"]:
+        run_decarbonization_scenario(cost_scenario=cost_scenario,country_list=Country_List,
+                             demand_scenario=demand_sceanrio,available_land=0.1,
+                             avaialble_coastline=0,avaialble_buildings=0.3,
+                             PV_size=2.5,decarb_year=2030)
+
+
+#check community battery