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master conflict solve
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@maartenbrinkerink
maartenbrinkerink committed Dec 4, 2024
2 parents 5cd162b + 7b4eaf4 commit 19f0356
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Showing 2 changed files with 38 additions and 43 deletions.
7 changes: 4 additions & 3 deletions config/config.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -70,14 +70,15 @@ user_defined_capacity_transmission:
# capacity,
# build_year,
# first_year_of_expansion,
# final_year_of_expansion,
# build_rate_per_year,
# capex_cost,
# annual_fixed_O&M_cost,
# variable_O&M_cost,
# efficiency]
# Per technology, only the capacity and build_year
# parameters can deviate from each other. For all other
# parameters only the last entry will be used.
# Per technology, only the capacity, build_year, first_year_of_expansion,
# final_year_of_expansion and build_rate_per_year parameters can deviate
# from each other. For all other parameters only the last entry will be used.

# Existing (GTD)
trn1: [TRNIDNJWIDNNU, 0.4, 2020, 2030, 3, 527, 18.5, 4, 95.3]
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74 changes: 34 additions & 40 deletions workflow/scripts/osemosys_global/transmission/user_defined_capacity.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
"""Function to integrate user defined capacities for transmission."""

import pandas as pd
import numpy as np
import itertools

from data import get_years
Expand All @@ -12,23 +13,25 @@ def set_user_defined_capacity_trn(tech_capacity_trn, op_life_dict,
start_year, end_year, region_name):

techCapacity_trn = []
build_year_dict = {}
first_year_expansion_dict = {}
final_year_expansion_dict = {}
build_rate_dict = {}
capex_dict = {}
var_dict = {}
fix_dict = {}
build_year_dict = {}
efficiency_dict = {}

for idx, tech_params in tech_capacity_trn.items():
techCapacity_trn.append([idx, tech_params[0], tech_params[1], tech_params[2]])
build_year_dict[idx] = tech_params[2]
first_year_expansion_dict[idx] = tech_params[3]
build_rate_dict[idx] = tech_params[4]
capex_dict[idx] = tech_params[5]
fix_dict[idx] = tech_params[6]
var_dict[idx] = tech_params[7]
efficiency_dict[idx] = tech_params[8]
final_year_expansion_dict[idx] = tech_params[4]
build_rate_dict[idx] = tech_params[5]
capex_dict[idx] = tech_params[6]
fix_dict[idx] = tech_params[7]
var_dict[idx] = tech_params[8]
efficiency_dict[idx] = tech_params[9]

tech_capacity_trn_df = pd.DataFrame(techCapacity_trn,
columns=['idx', 'TECHNOLOGY', 'VALUE', 'YEAR'])
Expand All @@ -38,47 +41,36 @@ def set_user_defined_capacity_trn(tech_capacity_trn, op_life_dict,
df_min_cap_inv = pd.concat([df_min_cap_invest, tech_capacity_trn_df])
df_min_cap_inv.drop_duplicates(inplace=True)

max_cap_techs_df = pd.DataFrame(list(itertools.product(list(tech_capacity_trn_df['idx']),
get_years(start_year, end_year))
),
columns = ['idx',
'YEAR']
)

max_cap_techs_df['REGION'], max_cap_techs_df['VALUE'] = region_name, ''

max_cap_techs_df = pd.merge(max_cap_techs_df, df_min_cap_inv[['TECHNOLOGY', 'idx']],
how='left',
on=['idx'])

max_cap_techs_df = max_cap_techs_df[['idx', 'REGION', 'TECHNOLOGY', 'YEAR', 'VALUE']]
max_cap_techs_df = pd.DataFrame(columns = ['idx', 'REGION', 'TECHNOLOGY', 'YEAR', 'VALUE'])

for idx, tech_params in tech_capacity_trn.items():
max_cap_techs_df.loc[max_cap_techs_df['idx'] == idx, 'BUILD_YEAR'] = build_year_dict.get(idx)
max_cap_techs_df.loc[max_cap_techs_df['idx'] == idx, 'FIRST_YEAR'] = first_year_expansion_dict.get(idx)
max_cap_techs_df.loc[max_cap_techs_df['idx'] == idx, 'MAX_BUILD'] = build_rate_dict.get(idx)

max_cap_techs_df.loc[(max_cap_techs_df['YEAR']>=max_cap_techs_df['FIRST_YEAR']),
'VALUE'] = max_cap_techs_df['MAX_BUILD']
data = pd.DataFrame(list(itertools.product([tech_capacity_trn_df['idx']],
[region_name],
[tech_params[0]],
get_years(first_year_expansion_dict.get(idx),
final_year_expansion_dict.get(idx)),
[build_rate_dict.get(idx)]
)),
columns = ['idx', 'REGION', 'TECHNOLOGY', 'YEAR', 'VALUE'])

with pd.option_context("future.no_silent_downcasting", True):
max_cap_techs_df['VALUE'] = max_cap_techs_df[
'VALUE'].replace(r'^\s*$', 0, regex = True).infer_objects(copy=False)
if max_cap_techs_df.empty:
max_cap_techs_df = data
else:
max_cap_techs_df = pd.concat([max_cap_techs_df, data]).reset_index(drop = True)

max_cap_techs_df = max_cap_techs_df[['REGION',
'TECHNOLOGY',
'YEAR',
'VALUE']]
max_cap_techs_df = max_cap_techs_df[['REGION','TECHNOLOGY','YEAR','VALUE']]

for tech in max_cap_techs_df['TECHNOLOGY'].unique():
for year in get_years(start_year, end_year):
if not year in max_cap_techs_df.loc[max_cap_techs_df['TECHNOLOGY'] ==
tech]['YEAR'].values:

max_cap_techs_df.loc[max_cap_techs_df.shape[0]] = region_name, tech, year, 0

# Append existing TotalAnnualMaxCapacityInvestment data with MAX_BUILD
df_max_cap_inv = pd.concat([df_max_cap_invest, max_cap_techs_df]).drop_duplicates()
df_max_cap_inv = pd.concat([df_max_cap_invest, max_cap_techs_df])

# Print TotalAnnualMaxCapacityInvestment.csv with MAX_BUILD
df_max_cap_inv.drop_duplicates(subset=['REGION',
'TECHNOLOGY',
'YEAR'],
keep='last',
inplace=True)
df_max_cap_inv.drop_duplicates(subset=['REGION', 'TECHNOLOGY','YEAR'], keep='last', inplace=True)

# For technologies with start year before model start year, add to ResidualCapacity
df_res_cap_ud = df_min_cap_inv.copy().loc[df_min_cap_inv.copy()['YEAR'] < min(get_years(start_year, end_year))]
Expand Down Expand Up @@ -135,6 +127,8 @@ def set_user_defined_capacity_trn(tech_capacity_trn, op_life_dict,
(df_max_cap_inv['YEAR'] == row['YEAR']), 'VALUE'] = df_max_cap_inv.loc[(
df_max_cap_inv['TECHNOLOGY'] == row['TECHNOLOGY']) &
(df_max_cap_inv['YEAR'] == row['YEAR']), 'VALUE'] + row['VALUE']

df_max_cap_inv.to_csv('temp5.csv')

# Add IAR and OAR for custom technologies
tech_list = list(tech_capacity_trn_df['TECHNOLOGY'].unique())
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