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IAR and OAR updates
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-Distance and technology dependent transmission losses are calculated informing IAR and OAR.
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@maartenbrinkerink
maartenbrinkerink committed Oct 14, 2024
1 parent 0cc6b17 commit 206ad3b
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185 changes: 59 additions & 126 deletions workflow/scripts/osemosys_global/transmission/activity.py
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Original file line number Diff line number Diff line change
@@ -1,11 +1,52 @@
"""Function to calaculate activity related to transmission."""

"""Functions to calaculate activity related to transmission."""
from typing import Optional
import pandas as pd
from data import format_transmission_name

from data import(
calculate_transmission_distances,
get_years,
set_transmission_tech_groups
)

from utils import apply_dtypes

def activity_transmission(df_iar_base, df_oar_base, df_pw_prop, df_trn_efficiencies,
def set_transmission_losses(df_exist_corrected, df_planned_corrected,
centerpoints_dict, trn_param, SUBSEA_LINES):

# Set base df including calculated transmission distances per pathway to set efficiencies.
line_efficiency_dict = {}
acdc_conv_efficiency_dict = {}

# Get transmission parameters from config file.
for tech, tech_params in trn_param.items():
line_efficiency_dict[tech] = tech_params[2]
acdc_conv_efficiency_dict[tech] = tech_params[3]

# Assign technology groups to individual technologies.
tech_group_dict = set_transmission_tech_groups(df_exist_corrected,
df_planned_corrected,
centerpoints_dict,
trn_param,
SUBSEA_LINES)

# Set base df including calculated transmission distances per pathway.
eff_df = calculate_transmission_distances(df_exist_corrected, df_planned_corrected,
centerpoints_dict)[['TECHNOLOGY', 'distance']]

# Calculate technology and line specific transmission losses.
eff_df['tech_group'] = eff_df['TECHNOLOGY'].map(tech_group_dict)

for group in trn_param.keys():
eff_df.loc[eff_df['tech_group'] == group,
'VALUE'] = round((eff_df['distance'] / 1000) * line_efficiency_dict[
group] + acdc_conv_efficiency_dict[group],3)

# Convert losses into OAR format.
eff_df['VALUE'] = 1 - (eff_df['VALUE'] / 100)

return eff_df

def activity_transmission(df_iar_base, df_oar_base, df_eff,
start_year, end_year, region_name):

# #### Downstream Activity Ratios
Expand All @@ -28,69 +69,17 @@ def activity_transmission(df_iar_base, df_oar_base, df_pw_prop, df_trn_efficienc
df_oar_trn = df_iar_trn.copy()
df_oar_trn["FUEL"] = df_oar_trn["FUEL"].str[0:8] + "02"

# Build international transmission system from original input data, but for Line rather than Generator:
int_trn_cols = ["child_class", "child_object", "property", "value"]
df_int_trn = df_pw_prop[int_trn_cols]
df_int_trn = df_int_trn[df_int_trn["child_class"] == "Line"]

# For IAR and OAR we can drop the value:
df_int_trn = df_int_trn.drop(["child_class", "value"], axis=1)

# Create MofO column based on property:
# Build international transmission system from original input data, but for Line rather than Generator:
# Create MofO column:
df_int_trn = df_eff[['TECHNOLOGY']].copy()
df_int_trn["MODE_OF_OPERATION"] = 1
df_int_trn.loc[df_int_trn["property"] == "Min Flow", "MODE_OF_OPERATION"] = 2

# Use the child_object column to build the technology names:
df_int_trn["codes"] = df_int_trn["child_object"].str.split(pat="-")
df_int_trn = pd.concat([df_int_trn, df_int_trn.replace(int(1), int(2))])

# If there are only two locations, then the node is XX
df_int_trn.loc[df_int_trn["codes"].str.len() == 2, "TECHNOLOGY"] = (
"TRN" + df_int_trn["codes"].str[0] + "XX" + df_int_trn["codes"].str[1] + "XX"
)
# If there are four locations, the node is already included
df_int_trn.loc[df_int_trn["codes"].str.len() == 4, "TECHNOLOGY"] = (
"TRN"
+ df_int_trn["codes"].str[0]
+ df_int_trn["codes"].str[1]
+ df_int_trn["codes"].str[2]
+ df_int_trn["codes"].str[3]
)
# If there are three items, and the last item is two characters, then the second item is an XX:
df_int_trn.loc[
(df_int_trn["codes"].str.len() == 3) & (df_int_trn["codes"].str[2].str.len() == 2),
"TECHNOLOGY",
] = (
"TRN"
+ df_int_trn["codes"].str[0]
+ "XX"
+ df_int_trn["codes"].str[1]
+ df_int_trn["codes"].str[2]
)
# If there are three items, and the last item is three characters, then the last item is an XX:
df_int_trn.loc[
(df_int_trn["codes"].str.len() == 3) & (df_int_trn["codes"].str[2].str.len() == 3),
"TECHNOLOGY",
] = (
"TRN"
+ df_int_trn["codes"].str[0]
+ df_int_trn["codes"].str[1]
+ df_int_trn["codes"].str[2]
+ "XX"
)

# Set the value (of either IAR or OAR) to 1
# Set the value (of either IAR or OAR) to 1 and add default columns.
df_int_trn["VALUE"] = 1
df_int_trn["REGION"] = region_name

df_int_trn = df_int_trn.drop(["property", "child_object", "codes"], axis=1)
df_int_trn["YEAR"] = start_year

# add in future years
df_int_trn_new = df_int_trn.copy()
df_int_trn_new["YEAR"] = [range(start_year + 1, end_year + 1)] * len(df_int_trn_new)
df_int_trn_new = df_int_trn_new.explode(column="YEAR")

df_int_trn = pd.concat([df_int_trn, df_int_trn_new]).reset_index(drop=True)
df_int_trn["YEAR"] = [get_years(start_year, end_year)] * len(df_int_trn)
df_int_trn = df_int_trn.explode('YEAR').reset_index(drop = True)

# Now create the input and output activity ratios
df_int_trn_oar = df_int_trn.copy()
Expand All @@ -114,41 +103,10 @@ def activity_transmission(df_iar_base, df_oar_base, df_pw_prop, df_trn_efficienc
"ELC" + df_int_trn_oar["TECHNOLOGY"].str[8:13] + "01"
)

# Drop unneeded columns
df_trn_efficiencies = df_trn_efficiencies.drop(
[
"Interface",
"KM distance",
"HVAC/HVDC/Subsea",
"Build Cost ($2010 in $000)",
"Build cost + FOM (3.5% of Capex per year)",
"Unnamed: 8",
"Interface is per MW",
"Unnamed: 10",
"Unnamed: 11",
"Unnamed: 12",
"Subsea lines",
"Unnamed: 14"
],
axis=1,
)

# Drop NaN values
df_trn_efficiencies = df_trn_efficiencies.dropna(subset=["From"])

# Create tech column from To and From Codes:
df_trn_efficiencies = format_transmission_name(df_trn_efficiencies)

# Rename column 'VALUES'
df_trn_efficiencies = df_trn_efficiencies.rename(columns={"Losses": "VALUE"})

# And adjust OAR values to be output amounts vs. losses:
df_trn_efficiencies['VALUE'] = 1.0 - df_trn_efficiencies['VALUE']

# and add values into OAR matrix
# Add line specific OAR values into matrix.
df_int_trn_oar = df_int_trn_oar.drop(["VALUE"], axis=1)
df_int_trn_oar = pd.merge(
df_int_trn_oar, df_trn_efficiencies, how="outer", on="TECHNOLOGY"
df_int_trn_oar, df_eff[['TECHNOLOGY', 'VALUE']], how="outer", on="TECHNOLOGY"
)

df_oar_trn_final = pd.concat(
Expand All @@ -157,40 +115,15 @@ def activity_transmission(df_iar_base, df_oar_base, df_pw_prop, df_trn_efficienc
df_oar_trn,
df_int_trn_oar,
]
).dropna()

# Select columns for final output table
df_oar_trn_final = df_oar_trn_final[['REGION',
'TECHNOLOGY',
'FUEL',
'MODE_OF_OPERATION',
'YEAR',
'VALUE',]]
)

df_iar_trn_final = pd.concat(
[
df_iar_base,
df_iar_trn,
df_int_trn_iar,
]
).dropna()


# Select columns for final output table
df_iar_trn_final = df_iar_trn_final[['REGION',
'TECHNOLOGY',
'FUEL',
'MODE_OF_OPERATION',
'YEAR',
'VALUE',]]

df_oar_trn_final.drop_duplicates(subset=['REGION',
'TECHNOLOGY',
'FUEL',
'MODE_OF_OPERATION',
'YEAR'],
keep='last',
inplace=True)
)

return df_iar_trn_final, df_oar_trn_final

Expand Down Expand Up @@ -218,8 +151,8 @@ def activity_transmission_limit(cross_border_trade, df_oar_trn_final):

return df_crossborder_final


def create_trn_dist_capacity_activity(*dfs: pd.DataFrame, value: Optional[float] = 31.536, region: Optional[str] = "GLOBAL") -> pd.DataFrame:
def create_trn_dist_capacity_activity(*dfs: pd.DataFrame, value: Optional[float] = 31.536,
region: Optional[str] = "GLOBAL") -> pd.DataFrame:
"""Creates tranmission and distribution capacity to activity unit data
Inputs are any number of dataframes with a TECHNOLOGY colums.
Expand All @@ -234,4 +167,4 @@ def create_trn_dist_capacity_activity(*dfs: pd.DataFrame, value: Optional[float]
for tech in set(techs):
data.append([region, tech, value])

return pd.DataFrame(data, columns=["REGION", "TECHNOLOGY", "VALUE"])
return pd.DataFrame(data, columns=["REGION", "TECHNOLOGY", "VALUE"])
25 changes: 14 additions & 11 deletions workflow/scripts/osemosys_global/transmission/main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -31,11 +31,11 @@
from data import(
format_gtd_existing,
format_gtd_planned,
correct_gtd_data,
set_transmission_tech_groups
correct_gtd_data
)

from activity import(
set_transmission_losses,
activity_transmission,
activity_transmission_limit,
create_trn_dist_capacity_activity
Expand All @@ -49,7 +49,9 @@

from user_defined_capacity import set_user_defined_capacity_trn

from sets import create_set_from_iterators, get_unique_fuels, get_unique_technologies
from sets import(create_set_from_iterators,
get_unique_fuels,
get_unique_technologies)

def main(
plexos_prop: pd.DataFrame,
Expand Down Expand Up @@ -93,18 +95,18 @@ def main(
start_year, end_year,
region_name, SUBSEA_LINES)

# Calculate technology and transmission pathway specific transmission losses.
trn_eff = set_transmission_losses(gtd_exist_corrected, gtd_planned_corrected,
centerpoints_dict, transmission_parameters, SUBSEA_LINES)

# Set activity ratios for transmission.
iar_trn, oar_trn = activity_transmission(iar_base, oar_base,
plexos_prop, interface_data,
start_year, end_year,
region_name)


trn_eff, start_year,
end_year, region_name)

# Adjust activity limits if cross border trade is not allowed following user config.
df_crossborder_final = activity_transmission_limit(cross_border_trade, oar_trn)



# Set operational life for transmission.
df_op_life_trn_final = set_op_life_transmission(iar_trn, oar_trn,
default_op_life, op_life_base, region_name)
Expand Down Expand Up @@ -156,7 +158,8 @@ def main(

# assign capacity to activity unit to transmission + distribution techs
cap_activity_trn = create_trn_dist_capacity_activity(iar_trn, oar_trn)
cap_activity = pd.concat([capact_base, cap_activity_trn]).drop_duplicates(subset=["REGION", "TECHNOLOGY"], keep="last")
cap_activity = pd.concat([capact_base, cap_activity_trn]
).drop_duplicates(subset=["REGION", "TECHNOLOGY"], keep="last")

# OUTPUT CSV's

Expand Down

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