<no title> — BC Combined Modelling 202409 documentation

[1]:

# from bcnexus import constants as bcnexus_const
# from bcnexus.clews import model_structure

[2]:

import pandas as pd
import plotly.express as px
from pathlib import Path

# local packages

from bcnexus import utils

[3]:

storage_algorithm="Kotzur"
nexus_scenario='Base_CNZ'
timeslices=18
solver="gurobi"

[4]:

from bcnexus.clews.datapackage import GetDataPackage
nexus_results_root=Path(f'results/clews/Model_{storage_algorithm}_{nexus_scenario}/{timeslices}ts_csvs_{solver}')
result_pack=GetDataPackage(nexus_results_root)
result_pack.show

└> Loaded 29 CSV files from results/clews/Model_Kotzur_Base_CNZ/18ts_csvs_gurobi

[4]:

['AccumulatedNewCapacity',
 'AnnualEmissions',
 'AnnualFixedOperatingCost',
 'AnnualTechnologyEmission',
 'AnnualTechnologyEmissionByMode',
 'AnnualVariableOperatingCost',
 'CapitalInvestment',
 'Demand',
 'DiscountedSalvageValue',
 'DiscountedTechnologyEmissionsPenalty',
 'NewCapacity',
 'NewStorageCapacity',
 'ProductionByTechnology',
 'ProductionByTechnologyAnnual',
 'RateOfActivity',
 'RateOfProductionByTechnology',
 'RateOfProductionByTechnologyByMode',
 'RateOfUseByTechnology',
 'RateOfUseByTechnologyByMode',
 'SalvageValue',
 'SalvageValueStorage',
 'StorageLevelChronoDayStart',
 'StorageLevelDayTypeFinish',
 'StorageLevelYearStart',
 'TotalAnnualTechnologyActivityByMode',
 'TotalCapacityAnnual',
 'TotalTechnologyAnnualActivity',
 'TotalTechnologyModelPeriodActivity',
 'UseByTechnology']

[5]:

nexus_ts_plots={}
plot_categories = ['Climate', 'Land', 'Energy', 'Water', str(timeslices)]
nexus_plots = {category: {} for category in plot_categories}
nexus_plots[f'{timeslices}'] = nexus_ts_plots
plots = {'nexus': nexus_plots}

[6]:

from bcnexus.vis import plot_Climate

nexus_climate_plots={}
nexus_plots['Climate'] = nexus_climate_plots

nexus_climate_plots['emission_total']=plot_Climate.get_total_annual_emission(result_pack.get_dataframe('AnnualEmissions'), nexus_scenario)
nexus_climate_plots['emission_by_source']=plot_Climate.get_emission_from_fuels(result_pack.get_dataframe('AnnualTechnologyEmission'), nexus_scenario)
nexus_climate_plots['emission_by_sector']=plot_Climate.get_emission_from_sector(result_pack.get_dataframe('AnnualTechnologyEmission'), nexus_scenario)

[7]:

from bcnexus.vis import plot_Land
nexus_plots['Land'] = plot_Land.plot_landuse_for_clusters(result_pack.get_dataframe('RateOfProductionByTechnologyByMode'), nexus_scenario)

[8]:

from bcnexus.vis import plot_Energy
nexus_energy_plots={}
nexus_plots['Energy'] = nexus_energy_plots

nexus_energy_plots["Nexus_sectoral_consumption"] , nexus_energy_plots["Nexus_fuel_consumption"] = plot_Energy.plot_combined_stacked_energy_consumption(result_pack.get_dataframe('UseByTechnology'),
                                                                                                                                                       'gwh',
                                                                                                                                                       nexus_scenario)
nexus_energy_plots["Nexus_generation_from_fuels"]=plot_Energy.get_annual_generation_plot(result_pack.get_dataframe('ProductionByTechnology'),
                                                                                  nexus_scenario)
nexus_energy_plots["Nexus_capacity_investments"]=plot_Energy.get_capacity_plot(result_pack.get_dataframe('NewCapacity'),
                                                                               nexus_scenario,
                                                                               investment=True)
nexus_energy_plots["Nexus_capacity_total"]=plot_Energy.get_capacity_plot(result_pack.get_dataframe('TotalCapacityAnnual'),nexus_scenario,investment=False)
nexus_energy_plots["Nexus_power_generation_timeslices"]=plot_Energy.get_generation_timeseries_plot(result_pack.get_dataframe('RateOfUseByTechnology'),24,nexus_scenario)
nexus_energy_plots["Nexus_power_generation_annual"]=plot_Energy.get_annual_power_generation_plot(result_pack.get_dataframe('ProductionByTechnologyAnnual'),nexus_scenario)
nexus_energy_plots["Nexus_capital_investment_power"]=plot_Energy.get_capital_investments(result_pack.get_dataframe('CapitalInvestment'),nexus_scenario)

[9]:

nexus_energy_plots["Nexus_power_generation_annual"]

[10]:

df = utils.add_power_tech_labels(result_pack.get_dataframe('CapitalInvestment'),'capacity')
df.power_techs_labels.unique()

[10]:

array([nan, 'Wind', 'Battery Storage', 'Solar'], dtype=object)

[11]:

nexus_energy_plots["Nexus_capital_investment_power"]

[12]:

result_pack.get_dataframe('CapitalInvestment').TECHNOLOGY.unique()

[12]:

array(['DEMAGRGWTBC1', 'DEMAGRSURBC1', 'DEMCOMBIO', 'DEMCOMELCB02',
       'DEMCOMRPP', 'DEMCOMNGS', 'DEMINDBIO', 'DEMINDDSL', 'DEMINDELCB02',
       'DEMINDNGS', 'DEMPUBGWTBC1', 'DEMPUBSURBC1', 'DEMPWRBIO',
       'DEMPWRHYD', 'DEMPWRSOL', 'DEMPWRWND', 'DEMRESBIO', 'DEMRESELCB02',
       'DEMRESRPP', 'DEMRESNGS', 'DEMTRABIO', 'DEMTRADSL', 'DEMTRAELCB02',
       'DEMTRAGSL', 'DEMTRAHFO', 'DEMTRAJFL', 'DEMTRALPG', 'DEMTRANGS',
       'IMPDSL', 'IMPGSL', 'IMPHFO', 'IMPJFL', 'IMPRPP', 'IMPLPG',
       'LNDAGRBC1C01', 'LNDAGRBC1C02', 'LNDAGRBC1C03', 'LNDAGRBC1C04',
       'LNDAGRBC1C05', 'LNDAGRBC1C06', 'LNDAGRBC1C07', 'LNDALFHRBC1',
       'LNDBARBC1', 'LNDBLTBC1', 'LNDFORBC1', 'LNDGRSBC1', 'LNDMAIHIBC1',
       'LNDOATHIBC1', 'LNDOTHHIBC1', 'LNDPEAHIBC1', 'LNDPTWHIBC1',
       'LNDRAPHIBC1', 'LNDRYEHIBC1', 'LNDWATBC1', 'LNDWHEHRBC1',
       'MINLNDBC1', 'MINNGS', 'MINPRCBC1', 'PWRTRNB01', 'PWRWNDB11',
       'RNWBIO', 'RNWHYD', 'RNWSOL', 'RNWWND', 'BATTERY_STORAGE',
       'PWRSOLB02', 'PWRWNDB12', 'PWRWNDB13', 'PWRWNDB14', 'HDG02',
       'HDG01', 'CCS01', 'PWRSOLB04', 'PWRSOLB07', 'PWRWNDB15',
       'PWRWNDB16', 'PWRWNDB17', 'PWRWNDB18', 'PWRWNDB19', 'PWRWNDB21',
       'PWRWNDB25', 'PWRWNDB27'], dtype=object)

[13]:

data=result_pack.get_dataframe('CapitalInvestment')

[ ]:

[ ]:

[14]:

data_pwr = data.loc[data['TECHNOLOGY'].str.startswith('PWR')].copy()
data_pwr.loc[:, 'TECHNOLOGY_CODE'] = data_pwr['TECHNOLOGY'].str[:6]
data_pwr.loc[:, 'TECHNOLOGY_LABEL'] = data_pwr['TECHNOLOGY_CODE'].map(bcnexus_const.legend_labels)
data_pwr
data_pwr_grouped_data = data_pwr.groupby(['TECHNOLOGY_LABEL','TECHNOLOGY_CODE', 'YEAR', 'REGION']).sum().reset_index()

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[14], line 3
      1 data_pwr = data.loc[data['TECHNOLOGY'].str.startswith('PWR')].copy()
      2 data_pwr.loc[:, 'TECHNOLOGY_CODE'] = data_pwr['TECHNOLOGY'].str[:6]
----> 3 data_pwr.loc[:, 'TECHNOLOGY_LABEL'] = data_pwr['TECHNOLOGY_CODE'].map(bcnexus_const.legend_labels)
      4 data_pwr
      5 data_pwr_grouped_data = data_pwr.groupby(['TECHNOLOGY_LABEL','TECHNOLOGY_CODE', 'YEAR', 'REGION']).sum().reset_index()

NameError: name 'bcnexus_const' is not defined

[ ]:

data_pwr_grouped_data

	TECHNOLOGY_LABEL	TECHNOLOGY_CODE	YEAR	REGION	TECHNOLOGY	VALUE	power_techs	power_techs_labels
0	Solar	PWRSOL	2030	REGION1	PWRSOLB02	154.198720	PWRSOL	Solar
1	Solar	PWRSOL	2041	REGION1	PWRSOLB04PWRSOLB07	196.447620	PWRSOLPWRSOL	SolarSolar
2	Solar	PWRSOL	2042	REGION1	PWRSOLB04	46.023512	PWRSOL	Solar
3	Solar	PWRSOL	2043	REGION1	PWRSOLB04	41.921152	PWRSOL	Solar
4	Solar	PWRSOL	2044	REGION1	PWRSOLB07	180.242341	PWRSOL	Solar
5	Solar	PWRSOL	2045	REGION1	PWRSOLB04PWRSOLB07	923.025077	PWRSOLPWRSOL	SolarSolar
6	Solar	PWRSOL	2046	REGION1	PWRSOLB04	714.000511	PWRSOL	Solar
7	Solar	PWRSOL	2050	REGION1	PWRSOLB04	78.831701	PWRSOL	Solar
8	Transmission Grid	PWRTRN	2021	REGION1	PWRTRNB01	0.082407	0	0
9	Transmission Grid	PWRTRN	2022	REGION1	PWRTRNB01	0.087942	0	0
10	Transmission Grid	PWRTRN	2023	REGION1	PWRTRNB01	0.092708	0	0
11	Transmission Grid	PWRTRN	2024	REGION1	PWRTRNB01	0.095800	0	0
12	Transmission Grid	PWRTRN	2025	REGION1	PWRTRNB01	0.099011	0	0
13	Transmission Grid	PWRTRN	2026	REGION1	PWRTRNB01	0.102372	0	0
14	Transmission Grid	PWRTRN	2027	REGION1	PWRTRNB01	0.105132	0	0
15	Transmission Grid	PWRTRN	2028	REGION1	PWRTRNB01	0.108612	0	0
16	Transmission Grid	PWRTRN	2029	REGION1	PWRTRNB01	0.113077	0	0
17	Transmission Grid	PWRTRN	2030	REGION1	PWRTRNB01	0.116846	0	0
18	Transmission Grid	PWRTRN	2031	REGION1	PWRTRNB01	0.119784	0	0
19	Transmission Grid	PWRTRN	2032	REGION1	PWRTRNB01	0.124479	0	0
20	Transmission Grid	PWRTRN	2033	REGION1	PWRTRNB01	0.129068	0	0
21	Transmission Grid	PWRTRN	2034	REGION1	PWRTRNB01	0.131598	0	0
22	Transmission Grid	PWRTRN	2035	REGION1	PWRTRNB01	0.134036	0	0
23	Transmission Grid	PWRTRN	2036	REGION1	PWRTRNB01	0.136388	0	0
24	Transmission Grid	PWRTRN	2037	REGION1	PWRTRNB01	0.138601	0	0
25	Transmission Grid	PWRTRN	2038	REGION1	PWRTRNB01	0.140688	0	0
26	Transmission Grid	PWRTRN	2039	REGION1	PWRTRNB01	0.142723	0	0
27	Transmission Grid	PWRTRN	2040	REGION1	PWRTRNB01	0.144651	0	0
28	Transmission Grid	PWRTRN	2041	REGION1	PWRTRNB01	0.146480	0	0
29	Transmission Grid	PWRTRN	2042	REGION1	PWRTRNB01	0.145630	0	0
30	Transmission Grid	PWRTRN	2043	REGION1	PWRTRNB01	0.144049	0	0
31	Transmission Grid	PWRTRN	2044	REGION1	PWRTRNB01	0.143604	0	0
32	Transmission Grid	PWRTRN	2045	REGION1	PWRTRNB01	0.142651	0	0
33	Transmission Grid	PWRTRN	2046	REGION1	PWRTRNB01	0.141284	0	0
34	Transmission Grid	PWRTRN	2047	REGION1	PWRTRNB01	0.141284	0	0
35	Transmission Grid	PWRTRN	2048	REGION1	PWRTRNB01	0.141284	0	0
36	Transmission Grid	PWRTRN	2049	REGION1	PWRTRNB01	0.141284	0	0
37	Transmission Grid	PWRTRN	2050	REGION1	PWRTRNB01	0.141284	0	0
38	Wind	PWRWND	2031	REGION1	PWRWNDB11PWRWNDB12	693.760946	PWRWNDPWRWND	WindWind
39	Wind	PWRWND	2032	REGION1	PWRWNDB13PWRWNDB14PWRWNDB15PWRWNDB16PWRWNDB17P...	1962.937424	PWRWNDPWRWNDPWRWNDPWRWNDPWRWNDPWRWNDPWRWND	WindWindWindWindWindWindWind
40	Wind	PWRWND	2045	REGION1	PWRWNDB18PWRWNDB21PWRWNDB25	212.799367	PWRWNDPWRWNDPWRWND	WindWindWind
41	Wind	PWRWND	2046	REGION1	PWRWNDB27	8914.138107	PWRWND	Wind
42	Wind	PWRWND	2047	REGION1	PWRWNDB27	506.072191	PWRWND	Wind

[ ]:

import plotly.express as px

# Create a bar plot with custom legend colors
# custom_colors = bcnexus_const.custom_colors
fig = px.bar(data_pwr_grouped_data, x='YEAR', y='VALUE', color='TECHNOLOGY_LABEL',
             labels={'VALUE': 'Mill $'}, title='Bar Plot of VALUE over Years')



fig.show()

[ ]:

end_use_fuels_set

{'BIO',
 'COA',
 'CRU',
 'DSL',
 'ELCB02',
 'GEO',
 'GSL',
 'HDG',
 'HFO',
 'HYD',
 'JFL',
 'KER',
 'LPG',
 'NGS',
 'RPP',
 'SOL',
 'URN',
 'WND'}

[ ]:

import re

# Create a regex pattern from the set items
pattern = '|'.join(map(re.escape, end_use_fuels_set))

# Filter rows where FUEL contains any of the set items
data2 = data[~data['FUEL'].str.contains(pattern, regex=True)]

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
File ~/miniconda3/envs/bc_combined_modelling/lib/python3.12/site-packages/pandas/core/indexes/base.py:3805, in Index.get_loc(self, key)
   3804 try:
-> 3805     return self._engine.get_loc(casted_key)
   3806 except KeyError as err:

File index.pyx:167, in pandas._libs.index.IndexEngine.get_loc()

File index.pyx:196, in pandas._libs.index.IndexEngine.get_loc()

File pandas/_libs/hashtable_class_helper.pxi:7081, in pandas._libs.hashtable.PyObjectHashTable.get_item()

File pandas/_libs/hashtable_class_helper.pxi:7089, in pandas._libs.hashtable.PyObjectHashTable.get_item()

KeyError: 'FUEL'

The above exception was the direct cause of the following exception:

KeyError                                  Traceback (most recent call last)
Cell In[17], line 7
      4 pattern = '|'.join(map(re.escape, end_use_fuels_set))
      6 # Filter rows where FUEL contains any of the set items
----> 7 data2 = data[~data['FUEL'].str.contains(pattern, regex=True)]

File ~/miniconda3/envs/bc_combined_modelling/lib/python3.12/site-packages/pandas/core/frame.py:4102, in DataFrame.__getitem__(self, key)
   4100 if self.columns.nlevels > 1:
   4101     return self._getitem_multilevel(key)
-> 4102 indexer = self.columns.get_loc(key)
   4103 if is_integer(indexer):
   4104     indexer = [indexer]

File ~/miniconda3/envs/bc_combined_modelling/lib/python3.12/site-packages/pandas/core/indexes/base.py:3812, in Index.get_loc(self, key)
   3807     if isinstance(casted_key, slice) or (
   3808         isinstance(casted_key, abc.Iterable)
   3809         and any(isinstance(x, slice) for x in casted_key)
   3810     ):
   3811         raise InvalidIndexError(key)
-> 3812     raise KeyError(key) from err
   3813 except TypeError:
   3814     # If we have a listlike key, _check_indexing_error will raise
   3815     #  InvalidIndexError. Otherwise we fall through and re-raise
   3816     #  the TypeError.
   3817     self._check_indexing_error(key)

KeyError: 'FUEL'

[ ]:

data3=data2[data2['TECHNOLOGY'].str.startswith('LND')]

[ ]:

bcnexus_const.name_mapping.get('FUEL')

[ ]:

import plotly.express as px

# Map legend labels using bcnexus_const.name_mapping
data3['FUEL_LABEL'] = data3['FUEL'].map(bcnexus_const.name_mapping.get('FUEL'))
fig = px.bar(data3, x='YEAR', y='VALUE', color='FUEL_LABEL', title='Bar Plot of Fuel vs Year')
fig.show()

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