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gas_network: use IGGIELGN scigrid dataset

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This commit is contained in:
Fabian Neumann 2021-11-13 16:48:08 +01:00
parent b39a852d3b
commit 4cd964b3bd
4 changed files with 78 additions and 87 deletions
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8
Snakefile
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@ -85,20 +85,24 @@ if config["sector"]["gas_network"]:
"IGGIELGN_LNGs.geojson",
"IGGIELGN_BorderPoints.geojson",
"IGGIELGN_Productions.geojson",
"IGGIELGN_PipeSegments.geojson",
]
rule retrieve_gas_infrastructure_data:
output: expand("data/gas_network/scigrid-gas/data/{files}", files=datafiles)
script: 'scripts/retrieve_gas_infrastructure_data.py'
rule build_gas_network:
input:
gas_network="data/gas_network/gas_network_dataset.csv"
gas_network="data/gas_network/scigrid-gas/data/IGGIELGN_PipeSegments.geojson"
output:
cleaned_gas_network="resources/gas_network.csv"
resources: mem_mb=4000
script: "scripts/build_gas_network.py"
rule build_gas_input_locations:
input:
lng="data/gas_network/scigrid-gas/data/IGGIELGN_LNGs.geojson",
@ -112,6 +116,7 @@ if config["sector"]["gas_network"]:
resources: mem_mb=2000,
script: "scripts/build_gas_input_locations.py"
rule cluster_gas_network:
input:
cleaned_gas_network="resources/gas_network.csv",
@ -122,6 +127,7 @@ if config["sector"]["gas_network"]:
resources: mem_mb=4000
script: "scripts/cluster_gas_network.py"
gas_infrastructure = {**rules.cluster_gas_network.output, **rules.build_gas_input_locations.output}
else:
gas_infrastructure = {}

147
scripts/build_gas_network.py
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@ -1,38 +1,15 @@
"""
Preprocess gas network based on data from:
[1] the SciGRID Gas project
(https://www.gas.scigrid.de/)
[2] ENTSOG capacity map
(https://www.entsog.eu/sites/default/files/2019-10/Capacities%20for%20Transmission%20Capacity%20Map%20RTS008_NS%20-%20DWH_final.xlsx)
"""
"""Preprocess gas network based on data from bthe SciGRID Gas project (https://www.gas.scigrid.de/)."""
import logging
logger = logging.getLogger(__name__)
import re
import json
import pandas as pd
import geopandas as gpd
from shapely.geometry import Point
from pypsa.geo import haversine_pts
def string2list(string, with_none=True):
"""Convert string format to a list."""
if with_none:
p2 = re.compile('None')
string = p2.sub('\"None\"', string)
else:
p = re.compile('(?<!\\\\)\'')
string = p.sub('\"', string)
return json.loads(string)
def diameter2capacity(pipe_diameter_mm):
def diameter_to_capacity(pipe_diameter_mm):
"""Calculate pipe capacity in MW based on diameter in mm.
20 inch (500 mm) 50 bar -> 1.5 GW CH4 pipe capacity (LHV)
@ -65,74 +42,80 @@ def diameter2capacity(pipe_diameter_mm):
return a3 + m3 * pipe_diameter_mm
def find_terminal_points(df):
latlon = []
for attr in ["lat", "long"]:
s = df[attr].apply(string2list)
s = s.apply(lambda x: [x[0], x[-1]])
latlon.append(pd.DataFrame(s.to_list(),
columns=[f"{attr}0", f"{attr}1"]
))
latlon = pd.concat(latlon, axis=1)
points = latlon.apply(
lambda x: {
"point0": Point(x.long0, x.lat0),
"point1": Point(x.long1, x.lat1)
},
axis=1,
result_type='expand'
)
return pd.concat([df, points], axis=1)
def process_gas_network_data(fn):
df = pd.read_csv(fn, sep=',')
df = find_terminal_points(df)
to_drop = ["name", "source_id", "country_code", "node_id",
"long", "lat", "lat_mean", "long_mean", "num_compressor"]
def load_dataset(fn):
df = gpd.read_file(fn)
param = df.param.apply(pd.Series)
method = df.method.apply(pd.Series)[["diameter_mm", "max_cap_M_m3_per_d"]]
method.columns = method.columns + "_method"
df = pd.concat([df, param, method], axis=1)
to_drop = ["param", "uncertainty", "method", "tags"]
to_drop = df.columns.intersection(to_drop)
df.drop(to_drop, axis=1, inplace=True)
return df
def prepare_dataset(
df,
length_factor=1.5,
correction_threshold_length=4,
correction_threshold_p_nom=8,
bidirectional_below=10
):
# extract start and end from LineString
df["point0"] = df.geometry.apply(lambda x: Point(x.coords[0]))
df["point1"] = df.geometry.apply(lambda x: Point(x.coords[-1]))
conversion_factor = 437.5 # MCM/day to MWh/h
df["p_nom"] = df.max_cap_M_m3_per_d * conversion_factor
# for inferred diameters, assume 500 mm rather than 900 mm (more conservative)
df.loc[df.diameter_mm_method != 'raw', "diameter_mm"] = 500.
keep = ["name", "diameter_mm", "is_H_gas", "is_bothDirection",
"length_km", "p_nom", "max_pressure_bar",
"start_year", "point0", "point1", "geometry"]
to_rename = {
"is_bothDirection": "bidirectional",
"is_H_gas": "H_gas",
"start_year": "build_year",
"length_km": "length",
"Capacity_GWh_h": "p_nom_data",
"id": "tags",
}
df.rename(columns=to_rename, inplace=True)
df = df[keep].rename(columns=to_rename)
df.bidirectional = df.bidirectional.astype(bool)
df.H_gas = df.H_gas.astype(bool)
# convert from GWh/h to MW
df.p_nom_data *= 1e3
# short lines below 10 km are assumed to be bidirectional
short_lines = df["length"] < bidirectional_below
df.loc[short_lines, "bidirectional"] = True
# for pipes with missing diameter, assume 500 mm
df.loc[df.diameter_mm.isna(), "diameter_mm"] = 500.
# for nord stream and small pipelines take original capacity data
# otherwise inferred values from pipe diameter
df["p_nom"] = df.diameter_mm.map(diameter2capacity)
df.p_nom.update(
df.p_nom_data.where((df.diameter_mm < 500) | (df.max_pressure_bar == 220))
)
# correct all capacities that deviate correction_threshold factor
# to diameter-based capacities, unless they are NordStream pipelines
# also all capacities below 0.5 GW are now diameter-based capacities
df["p_nom_diameter"] = df.diameter_mm.apply(diameter_to_capacity)
ratio = df.p_nom / df.p_nom_diameter
not_nordstream = df.max_pressure_bar < 220
df.p_nom.update(df.p_nom_diameter.where(
(df.p_nom <= 500) |
((ratio > correction_threshold_p_nom) & not_nordstream) |
((ratio < 1 / correction_threshold_p_nom) & not_nordstream)
))
# lines which have way too discrepant line lengths
# get assigned haversine length * length factor
df["length_haversine"] = df.apply(
lambda p: 1.5 * haversine_pts([p.point0.x, p.point1.y], [p.point1.x, p.point1.y]),
axis=1
lambda p: length_factor * haversine_pts(
[p.point0.x, p.point1.y],
[p.point1.x, p.point1.y]
), axis=1
)
df.length.update(df.length_haversine.where(df.length.isna()))
ratio = df.eval("length / length_haversine")
df["length"].update(df.length_haversine.where(
(df["length"] < 20) |
(ratio > correction_threshold_length) |
(ratio < 1 / correction_threshold_length)
))
return df
@ -145,6 +128,8 @@ if __name__ == "__main__":
logging.basicConfig(level=snakemake.config['logging_level'])
gas_network = process_gas_network_data(snakemake.input.gas_network)
gas_network = load_dataset(snakemake.input.gas_network)
gas_network = prepare_dataset(gas_network)
gas_network.to_csv(snakemake.output.cleaned_gas_network)

4
scripts/cluster_gas_network.py
View File

@ -70,12 +70,12 @@ def aggregate_parallel_pipes(df):
'bus0': 'first',
'bus1': 'first',
"p_nom": 'sum',
"p_nom_data": 'sum',
"p_nom_diameter": 'sum',
"max_pressure_bar": "mean",
"build_year": "mean",
"diameter_mm": "mean",
"length": 'mean',
'tags': ' '.join,
'name': ' '.join,
"p_min_pu": 'min',
}
return df.groupby(df.index).agg(strategies)

4
scripts/prepare_sector_network.py
View File

@ -1115,7 +1115,7 @@ def add_storage_and_grids(n, costs):
p_nom_min=gas_pipes.p_nom_min,
length=gas_pipes.length,
capital_cost=gas_pipes.capital_cost,
tags=gas_pipes.tags,
tags=gas_pipes.name,
carrier="gas pipeline",
lifetime=costs.at['CH4 (g) pipeline', 'lifetime']
)
@ -1190,7 +1190,7 @@ def add_storage_and_grids(n, costs):
p_nom_extendable=True,
length=h2_pipes.length,
capital_cost=costs.at['H2 (g) pipeline repurposed', 'fixed'] * h2_pipes.length,
tags=h2_pipes.tags,
tags=h2_pipes.name,
carrier="H2 pipeline retrofitted",
lifetime=costs.at['H2 (g) pipeline repurposed', 'lifetime']
)