-
Notifications
You must be signed in to change notification settings - Fork 1
/
functions.py
343 lines (263 loc) · 18.4 KB
/
functions.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
import pandas as pd
import numpy as np
from EnergyFlows import Country_List
def fetch_wind_PV_potential(Country):
df_p = pd.read_excel('Data/Potentials.xlsx')
Wind_pot = df_p.loc[2, Country] # GWh/MW/year
PV_pot = df_p.loc[0, Country] # GWh/MW/year
area = df_p.iloc[14][Country] # km2
coastline = df_p.iloc[13][Country]
return PV_pot,Wind_pot,area,coastline
def fetch_single_country_demand(Country,Year,Unit='GWh'):
"""This function calculates the final demand and non-RE transformation"""
df = pd.read_csv("Data/EnergyBalance/{}/all_countries_df.csv".format(Year))
df = df[df["Country ({})".format(Year)] == Country]
ElectricitySupply = df[df['Transactions(down)/Commodity(right)']=='Transformation']['Electricity'].values[0] #TJ
ofWhichRenewable_transformation = -df[df['Transactions(down)/Commodity(right)']=='Transformation']['memo: Of which Renewables'].values[0] #TJ # This is the input to power plants
renewable_transformation_efficiency = 0.35
non_RE_demand = ElectricitySupply - ofWhichRenewable_transformation * renewable_transformation_efficiency #TJ
electrification_efficiency_improvement = 0.4
final_consumption = df[df['Transactions(down)/Commodity(right)']=='Final consumption']['Total Energy'].values[0] #TJ#Does not include fuel for transformation. Only output of power plants and energy delivered to users
ofWhichRenewable_final_consumption = df[df['Transactions(down)/Commodity(right)']=='Final consumption']['memo: Of which Renewables'].values[0] #TJ # This is the input to power plants
final_consumption_electricity = df[df['Transactions(down)/Commodity(right)']=='Final consumption']['Electricity'].values[0]
"""
final demand is the non renewable part of the demand, assuming that all sectors are electrified
"""
final_demand = non_RE_demand + (final_consumption-final_consumption_electricity-ofWhichRenewable_final_consumption) * (1-electrification_efficiency_improvement)
df_pop = pd.read_csv('Data/Economic Indicators.csv')
population = df_pop[df_pop.Country == Country]['Population'].values[0]
net_zero_scenario_demand = 10 * population/1000 # GWh/year
net_zero_scenario_demand = net_zero_scenario_demand/0.277778 # to TJ
if Unit =='GWh':
final_demand = final_demand * 0.277778
non_RE_demand = non_RE_demand * 0.277778
net_zero_scenario_demand = net_zero_scenario_demand * 0.277778
return non_RE_demand,final_demand,net_zero_scenario_demand
def PV_area_single_country(Country,Year):
PV_pot, Wind_pot, area, coastline = fetch_wind_PV_potential(Country)
non_RE_demand,final_demand,net_zero_scenario_demand = fetch_single_country_demand(Country,Year)
PV_non_RE = 1.2 * non_RE_demand / PV_pot # MW
PV_final_demand = 1.2 * final_demand / PV_pot # MW
PV_net_zero = 1.2 * net_zero_scenario_demand / PV_pot # MW
PV_area_non_RE = PV_non_RE / (100) # 0.1kw/m2 # Converted to km2
PV_area_non_RE_per = 100 * PV_area_non_RE / area
PV_area_final_demand = PV_final_demand / (100) # 0.1kw/m2
PV_area_final_demand_per = 100 * PV_area_final_demand / area
PV_area_net_zero = PV_net_zero / (100) # 0.1kw/m2
PV_area_net_zero_per = 100 * PV_area_net_zero / area
return PV_area_non_RE,PV_area_final_demand,PV_area_net_zero,\
PV_area_non_RE_per,PV_area_final_demand_per, PV_area_net_zero_per
def Wind_area_single_country(Country,Year):
PV_pot, Wind_pot, area, coastline = fetch_wind_PV_potential(Country)
non_RE_demand,final_demand = fetch_single_country_demand(Country,Year)
Wind_MW_non_RE = 1.2 * non_RE_demand / Wind_pot
Wind_MW_final = 1.2 * final_demand / Wind_pot
percentage_of_coastline_final = ((Wind_MW_final * 100/1.5)*0.25)/coastline
percentage_of_coastline_non_RE = ((Wind_MW_non_RE * 100/1.5)*0.25)/coastline
return percentage_of_coastline_final,percentage_of_coastline_non_RE
def fetch_all_countries_demand(Year,Unit='GWh',Use="Analysis"):
df = pd.read_csv("Data/EnergyBalance/{}/all_countries_df.csv".format(Year))
# final_demand = df[df['Transactions(down)/Commodity(right)']=='Total energy supply']['Total Energy'].values
Countries = df. iloc[:, 1].unique()
ElectricitySupply = df[df['Transactions(down)/Commodity(right)']=='Electricity Plants']['Electricity'].values #TJ
ofWhichRenewable_transformation = -df[df['Transactions(down)/Commodity(right)']=='Electricity Plants']['memo: Of which Renewables'].values #TJ # This is the input to power plants
renewable_transformation_efficiency = 0.35
transformed_elec_from_renewables = ofWhichRenewable_transformation * renewable_transformation_efficiency
non_RE_elec = ElectricitySupply - transformed_elec_from_renewables #TJ
if Use == 'Analysis':
electrification_efficiency_improvement = 0.4
final_consumption = df[df['Transactions(down)/Commodity(right)']=='Final consumption']['Total Energy'].values #TJ#Does not include fuel for transformation. Only output of power plants and energy delivered to users
ofWhichRenewable_final_consumption = df[df['Transactions(down)/Commodity(right)']=='Final consumption']['memo: Of which Renewables'].values #TJ # This is the input to power plants
final_consumption_electricity = df[df['Transactions(down)/Commodity(right)']=='Final consumption']['Electricity'].values
total_demand = non_RE_elec + (final_consumption-final_consumption_electricity-ofWhichRenewable_final_consumption) * (1-electrification_efficiency_improvement)
#previously
if Use == 'SummaryPlot':
total_demand = df[df['Transactions(down)/Commodity(right)']=='Total energy supply']['Total Energy'].values #TJ#Total energy entering the country(oil and renewables)
renewables_in_total = df[df['Transactions(down)/Commodity(right)']=='Total energy supply']['memo: Of which Renewables'].values #TJ
renewable_electricity_primary = df[df['Transactions(down)/Commodity(right)']=='Primary production']['Electricity'].values #TJ
renewable_electricity = renewable_electricity_primary + transformed_elec_from_renewables
imports_oil = df[df['Transactions(down)/Commodity(right)']=='Imports']['All Oil'].values
all_imports = df[df['Transactions(down)/Commodity(right)']=='Imports']['Total Energy'].values
Int_marine_oil = df[df['Transactions(down)/Commodity(right)']=='International marine bunkers']['All Oil'].values
Int_avi_oil = df[df['Transactions(down)/Commodity(right)']=='International aviation bunkers']['All Oil'].values
Int_marine_total = df[df['Transactions(down)/Commodity(right)'] == 'International marine bunkers']['Total Energy'].values
Int_avi_total = df[df['Transactions(down)/Commodity(right)'] == 'International aviation bunkers']['Total Energy'].values
exports_total = df[df['Transactions(down)/Commodity(right)'] == 'Exports']['Total Energy'].values
net_imports_total = all_imports + exports_total #+ Int_marine_total + Int_avi_total
transformation = -df[df['Transactions(down)/Commodity(right)']=='Electricity CHP & Heat Plants']['All Oil'].values
transformation_losses = - df[df['Transactions(down)/Commodity(right)']=='Electricity CHP & Heat Plants']['Total Energy'].values
wolrd_per_capita_use = pd.read_csv('Data/worldinData/per-capita-energy-use.csv')
wolrd_per_capita_use = wolrd_per_capita_use.sort_values('Year', ascending=False).drop_duplicates(['Entity'])
wolrd_average_per_capita_use = wolrd_per_capita_use['Primary energy consumption per capita (kWh/person)'].mean()
df_pop = pd.read_csv('Data/Economic Indicators.csv')
df_pop.rename(columns={'Country': 'Entity'}, inplace=True)
df_pop['average_scenario_demand_GWh'] = 0.8 * df_pop[
"Population"] * wolrd_average_per_capita_use / 1000000 # GWh/year
world_average_demand = df_pop['average_scenario_demand_GWh']/0.277778 # it is calculated on GWh > convert to TJ
Net_zero_scenario_demand_GWh = np.array((df_pop["Population"] * 10 / 1000)/0.277778) #10MWh/person/year scenario >Tj
if Unit == "GWh":
non_RE_elec = non_RE_elec * 0.277778
total_demand = total_demand * 0.277778
imports_oil = imports_oil * 0.277778
Int_marine_oil = Int_marine_oil * 0.277778
Int_avi_oil = Int_avi_oil * 0.277778
transformation = transformation * 0.277778
transformation_losses = transformation_losses * 0.277778
renewables_in_total = renewables_in_total * 0.277778
renewable_electricity = renewable_electricity * 0.277778
all_imports = all_imports * 0.277778
world_average_demand = world_average_demand * 0.277778
exports_total = exports_total * 0.277778
Int_avi_total = Int_avi_total * 0.277778
Int_marine_total = Int_marine_total * 0.277778
net_imports_total = net_imports_total * 0.277778
Net_zero_scenario_demand_GWh = Net_zero_scenario_demand_GWh * 0.277778
df_demand = pd.DataFrame()
df_demand['Country'] = Countries
df_demand['Non-RE'] = non_RE_elec
df_demand['Total'] = total_demand
df_demand['World_average_demand'] = world_average_demand.round(0)
df_demand['10MWh/person_GWh'] = Net_zero_scenario_demand_GWh.round(0)
return [Countries,total_demand,imports_oil,Int_marine_oil,Int_avi_oil,transformation,
transformation_losses,renewables_in_total,
renewable_electricity,non_RE_elec,all_imports,world_average_demand,
Int_marine_total,Int_avi_total,exports_total,net_imports_total,Net_zero_scenario_demand_GWh]
def all_countries_cross_comparison_unstats(Year,Unit,Use):
summary_df = pd.DataFrame()
population= pd.read_csv('Data/Economic Indicators.csv')
df = pd.read_csv("Data/EnergyBalance/{}/all_countries_df.csv".format(Year))
summary_list = fetch_all_countries_demand(2019,Unit=Unit,Use=Use)
summary_df['Country'] = summary_list[0]
summary_df['Total_demand'] = summary_list[1]
summary_df['Oil imports'] = summary_list[2]
summary_df['int marine'] = -summary_list[3]
summary_df['int aviation'] = -summary_list[4]
summary_df['Transformation'] = summary_list[5]
summary_df['transformation_losses'] = summary_list[6]
summary_df['renewables_in_total'] = summary_list[7]
summary_df['renewable_electricity'] = summary_list[8]
summary_df['total imports'] = summary_list[10]
summary_df['World_average_demand'] = summary_list[11].round(0)
summary_df['Renewables/Total_demand'] = 100 * summary_df['renewables_in_total']/summary_df['Total_demand']
summary_df['Renewables/Total_demand']=summary_df['Renewables/Total_demand'].round(1)
summary_df['Renewables/Total_imports'] = 100 * summary_df['renewables_in_total']/summary_df['total imports']
summary_df['Renewables/Total_imports']=summary_df['Renewables/Total_imports'].round(1)
summary_df['Renewables/capita'] = (summary_df['renewables_in_total']/population['Population'])*1000 #Tj or GWh
summary_df['Renewables/capita'] = summary_df['Renewables/capita'].round(1)
summary_df['marine_to_import'] = 100 * summary_df['int marine']/summary_df['Oil imports']
summary_df['aviation_to_import'] = 100 * summary_df['int aviation']/summary_df['Oil imports']
summary_df['transformation_to_import'] = 100 * summary_df['Transformation']/summary_df['Oil imports']
summary_df['transformation_losses_to_import'] = 100 * summary_df['transformation_losses']/summary_df['Oil imports']
summary_df['road'] = 100 * df[df['Transactions(down)/Commodity(right)']=='Road']['All Oil'].values/summary_df['Oil imports']
summary_df['rail'] = 100 * df[df['Transactions(down)/Commodity(right)']=='Rail']['All Oil'].values/summary_df['Oil imports']
summary_df['Domestic aviation'] = 100 * df[df['Transactions(down)/Commodity(right)']=='Domestic aviation']['All Oil'].values/summary_df['Oil imports']
summary_df['Domestic navigation'] = 100 * df[df['Transactions(down)/Commodity(right)']=='Domestic navigation']['All Oil'].values/summary_df['Oil imports']
summary_df['Pipeline transport'] = 100 * df[df['Transactions(down)/Commodity(right)']=='Pipeline transport']['All Oil'].values/summary_df['Oil imports']
summary_df['transport n.e.s'] = 100 * df[df['Transactions(down)/Commodity(right)']=='Transport n.e.s']['All Oil'].values/summary_df['Oil imports']
summary_df['road_real'] = df[df['Transactions(down)/Commodity(right)'] == 'Road']['All Oil'].values
summary_df['rail_real'] = df[df['Transactions(down)/Commodity(right)'] == 'Rail']['All Oil'].values
summary_df['Domestic aviation_real'] = df[df['Transactions(down)/Commodity(right)'] == 'Domestic aviation'][
'All Oil'].values
summary_df['Domestic navigation_real'] = df[df['Transactions(down)/Commodity(right)'] == 'Domestic navigation'][
'All Oil'].values
summary_df['Pipeline transport_real'] = df[df['Transactions(down)/Commodity(right)'] == 'Pipeline transport'][
'All Oil'].values
summary_df['transport n.e.s_real'] = df[df['Transactions(down)/Commodity(right)'] == 'Transport n.e.s'][
'All Oil'].values
return summary_df
def Update_UNstats_database(year):
import os
Country_List = ['Samoa', 'Nauru', 'Vanuatu', 'Palau', 'Kiribati', 'Cook Islands', 'Solomon Islands', 'Tonga',
'New Caledonia', 'French Polynesia', 'Micronesia', 'Niue', 'Tuvalu', 'PNG', 'Fiji']
all_countries_df = pd.DataFrame()
for country in Country_List:
df = pd.read_csv("Data/EnergyBalance/{}/{}.csv".format(year,country))
if country == Country_List[0]:
all_countries_df = df
elif country != Country_List[0]:
all_countries_df = all_countries_df.append(df,ignore_index=True)
all_countries_df.replace({"---": 0}, inplace=True)
all_countries_df = all_countries_df.replace({'\*': ''},regex=True)
c_list = all_countries_df.columns
for i in c_list[2:]:
all_countries_df[i] = all_countries_df[i].astype(float)
all_countries_df['All Coal'] = all_countries_df['Primary Coal and Peat'] + all_countries_df['Coal and Peat Products']
all_countries_df['All Oil'] = all_countries_df['Primary Oil'] + all_countries_df['Oil Products']
all_countries_df['All Inputs'] = all_countries_df['All Coal'] + all_countries_df['All Oil'] + all_countries_df['Natural Gas'] +\
all_countries_df['Biofuels and Waste'] + all_countries_df['Nuclear'] +all_countries_df['Heat']
all_countries_df.replace('Micronesia (Federated States of)', 'Micronesia',inplace=True)
all_countries_df.replace('Papua New Guinea', 'PNG',inplace=True)
all_countries_df.to_csv("Data/EnergyBalance/{}/all_countries_df.csv".format(year))
def calculate_PV_Wind_potential(available_land = 0.01,available_coastline = 0.1):
df_technical_potential = pd.DataFrame()
df = pd.read_excel('Data/Potentials.xlsx')
countries = Country_List
Wind_pot = df.iloc[2, 2:] # GWh/MW/year
PV_pot = df.iloc[0, 2:] # GWh/MW/year
coastline = df.iloc[13,2:]
area = df.iloc[14,2:]
arable = df.iloc[4, 2:]
crops = df.iloc[5, 2:]
pasture = df.iloc[6, 2:]
forested = df.iloc[7, 2:]
other = df.iloc[8, 2:]
Technical_PV_area = (available_land * pasture/100 + available_land * arable/100) * area
Theoretical_PV_GWh = PV_pot * area * 0.1 * 1000 * 0.8 #GWh
Theoretical_wind_GWh = Wind_pot * coastline * (1.5/0.25) * 0.8 # GWh
Technical_PV_GWh = PV_pot * Technical_PV_area * 0.1 * 1000 * 0.8 #GWh
Technical_wind_GWh = Theoretical_wind_GWh * available_coastline
Theoretical_PV_GW = area * 1000 * 0.1/1000
Technical_PV_GW = Technical_PV_area * 1000 * 0.1 / 1000
Theoretical_wind_GW = (1.5/0.25) * coastline/1000
Technical_wind_GW = Theoretical_wind_GW * available_coastline
Technical_wind_GW = Technical_wind_GW.astype(float)
Technical_wind_GW = Technical_wind_GW.round(decimals=2)
Theoretical_PV_GWh = Theoretical_PV_GWh.astype(int)
Theoretical_wind_GWh = Theoretical_wind_GWh.astype(float)
Theoretical_wind_GWh = Theoretical_wind_GWh.round(decimals= 1)
Theoretical_PV_GW = Theoretical_PV_GW.astype(int)
Theoretical_wind_GW = Theoretical_wind_GW.astype(float)
Theoretical_wind_GW = Theoretical_wind_GW.round(decimals= 1)
Technical_PV_GW = Technical_PV_GW.astype(float)
Technical_PV_GW = Technical_PV_GW.round(decimals=2)
Technical_PV_GWh = Technical_PV_GWh.astype(float)
Technical_PV_GWh = Technical_PV_GWh.round(decimals=1)
Technical_wind_GWh = Technical_wind_GWh.astype(float)
Technical_wind_GWh = Technical_wind_GWh.round(decimals=1)
df_technical_potential['Country'] = countries
df_technical_potential['PV_pot'] = PV_pot.values
df_technical_potential['Wind_pot'] = Wind_pot.values
df_technical_potential['Theoretical_PV_GWh'] = Theoretical_PV_GWh.values
df_technical_potential['Theoretical_wind_GWh'] = Theoretical_wind_GWh.values
df_technical_potential['Theoretical_PV_GW'] = Theoretical_PV_GW.values
df_technical_potential['Theoretical_wind_GW'] = Theoretical_wind_GW.values
df_technical_potential['PV_technical_GWh'] = Technical_PV_GWh.values
df_technical_potential['Wind_technical_GWh'] = Technical_wind_GWh.values
df_technical_potential['PV_technical_GW'] = Technical_PV_GW.values
df_technical_potential['Wind_technical_GW'] = Technical_wind_GW.values
df_technical_potential['sum_of_wind_and_solar_GWh'] = df_technical_potential['PV_technical_GWh'] + df_technical_potential['Wind_technical_GWh']
return df_technical_potential
def calculate_rooftop_PV_potential(available_buildings = 0.3,PV_size = 2.5):
import pandas as pd
rooftop_df = pd.DataFrame()
df = pd.read_csv('Data/Rooftop Potential.csv')
Countries = df['Country']
Population = df['Population']
Household_size = df['Household size']
solar_radiation = df['Potential of Av.PV gen (GWh/MW/year)']
number_of_homes = available_buildings*Population/Household_size # only those homes with rooftop PV potential
number_of_homes = number_of_homes.round(0)
rooftop_capacity_MW = number_of_homes * PV_size/1000
rooftop_capacity_MW = rooftop_capacity_MW.round(1)
rooftop_PV_generation_GWh = rooftop_capacity_MW * solar_radiation
rooftop_PV_generation_GWh = rooftop_PV_generation_GWh.astype(float)
rooftop_PV_generation_GWh = rooftop_PV_generation_GWh.round(decimals=1)
rooftop_df['Country'] = Countries
rooftop_df['Capacity_MW'] = rooftop_capacity_MW
rooftop_df['avaialble_homes'] = number_of_homes
rooftop_df['Household_size'] = Household_size
rooftop_df['Population'] = Population
rooftop_df['Generation_GWh'] = rooftop_PV_generation_GWh
return rooftop_df
if __name__ == "__main__":
Update_UNstats_database(2020)