gas_network: use IGGIELGN scigrid dataset
This commit is contained in:
Fabian Neumann
parent
b39a852d3b
commit
4cd964b3bd
@ -85,20 +85,24 @@ if config["sector"]["gas_network"]:
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"IGGIELGN_LNGs.geojson",
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"IGGIELGN_BorderPoints.geojson",
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"IGGIELGN_Productions.geojson",
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"IGGIELGN_PipeSegments.geojson",
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]
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rule retrieve_gas_infrastructure_data:
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output: expand("data/gas_network/scigrid-gas/data/{files}", files=datafiles)
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script: 'scripts/retrieve_gas_infrastructure_data.py'
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rule build_gas_network:
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input:
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gas_network="data/gas_network/gas_network_dataset.csv"
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gas_network="data/gas_network/scigrid-gas/data/IGGIELGN_PipeSegments.geojson"
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output:
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cleaned_gas_network="resources/gas_network.csv"
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resources: mem_mb=4000
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script: "scripts/build_gas_network.py"
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rule build_gas_input_locations:
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input:
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lng="data/gas_network/scigrid-gas/data/IGGIELGN_LNGs.geojson",
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@ -112,6 +116,7 @@ if config["sector"]["gas_network"]:
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resources: mem_mb=2000,
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script: "scripts/build_gas_input_locations.py"
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rule cluster_gas_network:
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input:
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cleaned_gas_network="resources/gas_network.csv",
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@ -122,6 +127,7 @@ if config["sector"]["gas_network"]:
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resources: mem_mb=4000
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script: "scripts/cluster_gas_network.py"
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gas_infrastructure = {**rules.cluster_gas_network.output, **rules.build_gas_input_locations.output}
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else:
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gas_infrastructure = {}
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@ -1,38 +1,15 @@
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"""
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Preprocess gas network based on data from:
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[1] the SciGRID Gas project
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(https://www.gas.scigrid.de/)
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[2] ENTSOG capacity map
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(https://www.entsog.eu/sites/default/files/2019-10/Capacities%20for%20Transmission%20Capacity%20Map%20RTS008_NS%20-%20DWH_final.xlsx)
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"""
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"""Preprocess gas network based on data from bthe SciGRID Gas project (https://www.gas.scigrid.de/)."""
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import logging
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logger = logging.getLogger(__name__)
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import re
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import json
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import pandas as pd
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import geopandas as gpd
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from shapely.geometry import Point
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from pypsa.geo import haversine_pts
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def string2list(string, with_none=True):
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"""Convert string format to a list."""
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if with_none:
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p2 = re.compile('None')
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string = p2.sub('\"None\"', string)
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else:
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p = re.compile('(?<!\\\\)\'')
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string = p.sub('\"', string)
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return json.loads(string)
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def diameter2capacity(pipe_diameter_mm):
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def diameter_to_capacity(pipe_diameter_mm):
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"""Calculate pipe capacity in MW based on diameter in mm.
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20 inch (500 mm) 50 bar -> 1.5 GW CH4 pipe capacity (LHV)
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@ -65,75 +42,81 @@ def diameter2capacity(pipe_diameter_mm):
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return a3 + m3 * pipe_diameter_mm
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def find_terminal_points(df):
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latlon = []
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for attr in ["lat", "long"]:
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s = df[attr].apply(string2list)
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s = s.apply(lambda x: [x[0], x[-1]])
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latlon.append(pd.DataFrame(s.to_list(),
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columns=[f"{attr}0", f"{attr}1"]
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))
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latlon = pd.concat(latlon, axis=1)
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points = latlon.apply(
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lambda x: {
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"point0": Point(x.long0, x.lat0),
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"point1": Point(x.long1, x.lat1)
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},
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axis=1,
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result_type='expand'
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)
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return pd.concat([df, points], axis=1)
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def process_gas_network_data(fn):
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df = pd.read_csv(fn, sep=',')
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df = find_terminal_points(df)
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to_drop = ["name", "source_id", "country_code", "node_id",
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"long", "lat", "lat_mean", "long_mean", "num_compressor"]
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def load_dataset(fn):
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df = gpd.read_file(fn)
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param = df.param.apply(pd.Series)
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method = df.method.apply(pd.Series)[["diameter_mm", "max_cap_M_m3_per_d"]]
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method.columns = method.columns + "_method"
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df = pd.concat([df, param, method], axis=1)
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to_drop = ["param", "uncertainty", "method", "tags"]
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to_drop = df.columns.intersection(to_drop)
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df.drop(to_drop, axis=1, inplace=True)
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return df
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def prepare_dataset(
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df,
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length_factor=1.5,
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correction_threshold_length=4,
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correction_threshold_p_nom=8,
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bidirectional_below=10
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):
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# extract start and end from LineString
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df["point0"] = df.geometry.apply(lambda x: Point(x.coords[0]))
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df["point1"] = df.geometry.apply(lambda x: Point(x.coords[-1]))
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conversion_factor = 437.5 # MCM/day to MWh/h
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df["p_nom"] = df.max_cap_M_m3_per_d * conversion_factor
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# for inferred diameters, assume 500 mm rather than 900 mm (more conservative)
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df.loc[df.diameter_mm_method != 'raw', "diameter_mm"] = 500.
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keep = ["name", "diameter_mm", "is_H_gas", "is_bothDirection",
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"length_km", "p_nom", "max_pressure_bar",
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"start_year", "point0", "point1", "geometry"]
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to_rename = {
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"is_bothDirection": "bidirectional",
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"is_H_gas": "H_gas",
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"start_year": "build_year",
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"length_km": "length",
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"Capacity_GWh_h": "p_nom_data",
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"id": "tags",
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}
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df.rename(columns=to_rename, inplace=True)
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df = df[keep].rename(columns=to_rename)
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df.bidirectional = df.bidirectional.astype(bool)
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df.H_gas = df.H_gas.astype(bool)
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# convert from GWh/h to MW
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df.p_nom_data *= 1e3
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# short lines below 10 km are assumed to be bidirectional
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short_lines = df["length"] < bidirectional_below
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df.loc[short_lines, "bidirectional"] = True
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# for pipes with missing diameter, assume 500 mm
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df.loc[df.diameter_mm.isna(), "diameter_mm"] = 500.
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# for nord stream and small pipelines take original capacity data
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# otherwise inferred values from pipe diameter
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df["p_nom"] = df.diameter_mm.map(diameter2capacity)
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df.p_nom.update(
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df.p_nom_data.where((df.diameter_mm < 500) | (df.max_pressure_bar == 220))
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)
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# correct all capacities that deviate correction_threshold factor
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# to diameter-based capacities, unless they are NordStream pipelines
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# also all capacities below 0.5 GW are now diameter-based capacities
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df["p_nom_diameter"] = df.diameter_mm.apply(diameter_to_capacity)
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ratio = df.p_nom / df.p_nom_diameter
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not_nordstream = df.max_pressure_bar < 220
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df.p_nom.update(df.p_nom_diameter.where(
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(df.p_nom <= 500) |
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((ratio > correction_threshold_p_nom) & not_nordstream) |
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((ratio < 1 / correction_threshold_p_nom) & not_nordstream)
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))
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# lines which have way too discrepant line lengths
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# get assigned haversine length * length factor
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df["length_haversine"] = df.apply(
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lambda p: 1.5 * haversine_pts([p.point0.x, p.point1.y], [p.point1.x, p.point1.y]),
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axis=1
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lambda p: length_factor * haversine_pts(
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[p.point0.x, p.point1.y],
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[p.point1.x, p.point1.y]
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), axis=1
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)
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ratio = df.eval("length / length_haversine")
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df["length"].update(df.length_haversine.where(
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(df["length"] < 20) |
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(ratio > correction_threshold_length) |
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(ratio < 1 / correction_threshold_length)
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))
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df.length.update(df.length_haversine.where(df.length.isna()))
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return df
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@ -145,6 +128,8 @@ if __name__ == "__main__":
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logging.basicConfig(level=snakemake.config['logging_level'])
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gas_network = process_gas_network_data(snakemake.input.gas_network)
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gas_network = load_dataset(snakemake.input.gas_network)
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gas_network = prepare_dataset(gas_network)
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gas_network.to_csv(snakemake.output.cleaned_gas_network)
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@ -70,12 +70,12 @@ def aggregate_parallel_pipes(df):
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'bus0': 'first',
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'bus1': 'first',
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"p_nom": 'sum',
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"p_nom_data": 'sum',
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"p_nom_diameter": 'sum',
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"max_pressure_bar": "mean",
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"build_year": "mean",
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"diameter_mm": "mean",
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"length": 'mean',
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'tags': ' '.join,
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'name': ' '.join,
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"p_min_pu": 'min',
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}
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return df.groupby(df.index).agg(strategies)
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@ -1115,7 +1115,7 @@ def add_storage_and_grids(n, costs):
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p_nom_min=gas_pipes.p_nom_min,
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length=gas_pipes.length,
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capital_cost=gas_pipes.capital_cost,
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tags=gas_pipes.tags,
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tags=gas_pipes.name,
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carrier="gas pipeline",
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lifetime=costs.at['CH4 (g) pipeline', 'lifetime']
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)
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@ -1190,7 +1190,7 @@ def add_storage_and_grids(n, costs):
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p_nom_extendable=True,
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length=h2_pipes.length,
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capital_cost=costs.at['H2 (g) pipeline repurposed', 'fixed'] * h2_pipes.length,
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tags=h2_pipes.tags,
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tags=h2_pipes.name,
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carrier="H2 pipeline retrofitted",
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lifetime=costs.at['H2 (g) pipeline repurposed', 'lifetime']
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)
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