Implement line rating
This commit is contained in:
Philipp Glaum
parent
47ab49e86b
commit
8febb73bf1
18
Snakefile
18
Snakefile
@ -215,12 +215,25 @@ if 'hydro' in config['renewable'].keys():
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resources: mem=5000
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script: 'scripts/build_hydro_profile.py'
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if config['lines'].get('line_rating', False):
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rule build_line_rating:
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input:
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base_network="networks/base.nc",
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cutout="cutouts/" + config["lines"]['cutout'] + ".nc"
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output:
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output="resources/line_rating.nc"
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log: "logs/build_line_rating.log"
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benchmark: "benchmarks/build_line_rating"
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threads: ATLITE_NPROCESSES
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resources: mem=ATLITE_NPROCESSES * 1000
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script: "scripts/build_line_rating.py"
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rule add_electricity:
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input:
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base_network='networks/base.nc',
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base_network = "networks/base.nc",
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tech_costs=COSTS,
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regions="resources/regions_onshore.geojson",
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line_rating="resources/line_rating.nc" if config['lines'].get('line_rating', False) else None,
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powerplants='resources/powerplants.csv',
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hydro_capacities='data/bundle/hydro_capacities.csv',
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geth_hydro_capacities='data/geth2015_hydro_capacities.csv',
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@ -397,5 +410,4 @@ rule plot_p_nom_max:
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input: input_plot_p_nom_max
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output: "results/plots/elec_s{simpl}_cum_p_nom_max_{clusts}_{techs}_{country}.{ext}"
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log: "logs/plot_p_nom_max/elec_s{simpl}_{clusts}_{techs}_{country}_{ext}.log"
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script: "scripts/plot_p_nom_max.py"
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script: "scripts/plot_p_nom_max.py"
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265
config.yaml
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265
config.yaml
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@ -0,0 +1,265 @@
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# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
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#
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# SPDX-License-Identifier: CC0-1.0
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version: 0.4.0
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tutorial: true
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logging:
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level: INFO
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format: '%(levelname)s:%(name)s:%(message)s'
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summary_dir: results
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scenario:
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simpl: ['']
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ll: ['copt']
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clusters: [5]
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opts: [Co2L-24H]
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countries: ['DE']
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clustering:
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simplify:
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to_substations: false # network is simplified to nodes with positive or negative power injection (i.e. substations or offwind connections)
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snapshots:
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start: "2013-03-01"
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end: "2013-03-07"
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closed: 'left' # end is not inclusive
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enable:
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prepare_links_p_nom: false
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retrieve_databundle: true
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build_cutout: true
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retrieve_cutout: false
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build_natura_raster: false
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retrieve_natura_raster: true
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custom_busmap: false
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electricity:
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voltages: [220., 300., 380.]
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co2limit: 100.e+6
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extendable_carriers:
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Generator: [OCGT]
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StorageUnit: [] #battery, H2
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Store: [battery, H2]
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Link: []
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max_hours:
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battery: 6
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H2: 168
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powerplants_filter: false # use pandas query strings here, e.g. Country not in ['Germany']
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custom_powerplants: false # use pandas query strings here, e.g. Country in ['Germany']
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conventional_carriers: [coal, CCGT] # [nuclear, oil, OCGT, CCGT, coal, lignite, geothermal, biomass]
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atlite:
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nprocesses: 4
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cutouts:
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europe-2013-era5-tutorial:
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module: era5
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x: [4., 15.]
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y: [46., 56.]
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time: ["2013-03", "2013-03"]
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renewable:
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onwind:
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cutout: europe-2013-era5-tutorial
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resource:
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method: wind
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turbine: Vestas_V112_3MW
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capacity_per_sqkm: 3 # ScholzPhd Tab 4.3.1: 10MW/km^2
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# correction_factor: 0.93
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corine:
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# Scholz, Y. (2012). Renewable energy based electricity supply at low costs:
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# development of the REMix model and application for Europe. ( p.42 / p.28)
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grid_codes: [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
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24, 25, 26, 27, 28, 29, 31, 32]
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distance: 1000
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distance_grid_codes: [1, 2, 3, 4, 5, 6]
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natura: true
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potential: simple # or conservative
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clip_p_max_pu: 1.e-2
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offwind-ac:
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cutout: europe-2013-era5-tutorial
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resource:
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method: wind
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turbine: NREL_ReferenceTurbine_5MW_offshore
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capacity_per_sqkm: 3
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# correction_factor: 0.93
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corine: [44, 255]
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natura: true
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max_shore_distance: 30000
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potential: simple # or conservative
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clip_p_max_pu: 1.e-2
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offwind-dc:
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cutout: europe-2013-era5-tutorial
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resource:
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method: wind
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turbine: NREL_ReferenceTurbine_5MW_offshore
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# ScholzPhd Tab 4.3.1: 10MW/km^2
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capacity_per_sqkm: 3
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# correction_factor: 0.93
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corine: [44, 255]
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natura: true
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min_shore_distance: 30000
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potential: simple # or conservative
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clip_p_max_pu: 1.e-2
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solar:
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cutout: europe-2013-era5-tutorial
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resource:
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method: pv
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panel: CSi
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orientation:
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slope: 35.
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azimuth: 180.
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capacity_per_sqkm: 1.7 # ScholzPhd Tab 4.3.1: 170 MW/km^2
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# Determined by comparing uncorrected area-weighted full-load hours to those
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# published in Supplementary Data to
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# Pietzcker, Robert Carl, et al. "Using the sun to decarbonize the power
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# sector: The economic potential of photovoltaics and concentrating solar
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# power." Applied Energy 135 (2014): 704-720.
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correction_factor: 0.854337
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corine: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
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14, 15, 16, 17, 18, 19, 20, 26, 31, 32]
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natura: true
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potential: simple # or conservative
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clip_p_max_pu: 1.e-2
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lines:
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types:
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220.: "Al/St 240/40 2-bundle 220.0"
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300.: "Al/St 240/40 3-bundle 300.0"
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380.: "Al/St 240/40 4-bundle 380.0"
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s_max_pu: 0.7
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s_nom_max: .inf
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length_factor: 1.25
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under_construction: 'zero' # 'zero': set capacity to zero, 'remove': remove, 'keep': with full capacity
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cutout: "europe-2013-era5-tutorial"
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line_rating: true
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links:
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p_max_pu: 1.0
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p_nom_max: .inf
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include_tyndp: true
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under_construction: 'zero' # 'zero': set capacity to zero, 'remove': remove, 'keep': with full capacity
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transformers:
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x: 0.1
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s_nom: 2000.
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type: ''
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load:
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power_statistics: True # only for files from <2019; set false in order to get ENTSOE transparency data
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interpolate_limit: 3 # data gaps up until this size are interpolated linearly
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time_shift_for_large_gaps: 1w # data gaps up until this size are copied by copying from
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manual_adjustments: true # false
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scaling_factor: 1.0
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costs:
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year: 2030
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discountrate: 0.07 # From a Lion Hirth paper, also reflects average of Noothout et al 2016
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USD2013_to_EUR2013: 0.7532 # [EUR/USD] ECB: https://www.ecb.europa.eu/stats/exchange/eurofxref/html/eurofxref-graph-usd.en.html
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marginal_cost:
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solar: 0.01
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onwind: 0.015
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offwind: 0.015
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H2: 0.
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battery: 0.
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emission_prices: # in currency per tonne emission, only used with the option Ep
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co2: 0.
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solving:
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options:
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formulation: kirchhoff
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load_shedding: false
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noisy_costs: true
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min_iterations: 1
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max_iterations: 1
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clip_p_max_pu: 0.01
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skip_iterations: false
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track_iterations: false
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solver:
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name: cbc
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plotting:
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map:
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figsize: [7, 7]
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boundaries: [-10.2, 29, 35, 72]
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p_nom:
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bus_size_factor: 5.e+4
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linewidth_factor: 3.e+3
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costs_max: 800
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costs_threshold: 1
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energy_max: 15000.
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energy_min: -10000.
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energy_threshold: 50.
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vre_techs: ["onwind", "offwind-ac", "offwind-dc", "solar", "ror"]
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conv_techs: ["OCGT", "CCGT", "Nuclear", "Coal"]
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storage_techs: ["hydro+PHS", "battery", "H2"]
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load_carriers: ["AC load"]
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AC_carriers: ["AC line", "AC transformer"]
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link_carriers: ["DC line", "Converter AC-DC"]
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tech_colors:
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"onwind" : "#235ebc"
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"onshore wind" : "#235ebc"
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'offwind' : "#6895dd"
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'offwind-ac' : "#6895dd"
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'offshore wind' : "#6895dd"
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'offshore wind ac' : "#6895dd"
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'offwind-dc' : "#74c6f2"
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'offshore wind dc' : "#74c6f2"
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"hydro" : "#08ad97"
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"hydro+PHS" : "#08ad97"
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"PHS" : "#08ad97"
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"hydro reservoir" : "#08ad97"
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'hydroelectricity' : '#08ad97'
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"ror" : "#4adbc8"
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"run of river" : "#4adbc8"
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'solar' : "#f9d002"
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'solar PV' : "#f9d002"
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'solar thermal' : '#ffef60'
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'biomass' : '#0c6013'
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'solid biomass' : '#06540d'
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'biogas' : '#23932d'
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'waste' : '#68896b'
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'geothermal' : '#ba91b1'
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"OCGT" : "#d35050"
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"gas" : "#d35050"
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"natural gas" : "#d35050"
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"CCGT" : "#b20101"
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"nuclear" : "#ff9000"
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"coal" : "#707070"
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"lignite" : "#9e5a01"
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"oil" : "#262626"
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"H2" : "#ea048a"
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"hydrogen storage" : "#ea048a"
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"battery" : "#b8ea04"
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"Electric load" : "#f9d002"
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"electricity" : "#f9d002"
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"lines" : "#70af1d"
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"transmission lines" : "#70af1d"
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"AC-AC" : "#70af1d"
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"AC line" : "#70af1d"
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"links" : "#8a1caf"
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"HVDC links" : "#8a1caf"
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"DC-DC" : "#8a1caf"
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"DC link" : "#8a1caf"
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nice_names:
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OCGT: "Open-Cycle Gas"
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CCGT: "Combined-Cycle Gas"
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offwind-ac: "Offshore Wind (AC)"
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offwind-dc: "Offshore Wind (DC)"
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onwind: "Onshore Wind"
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solar: "Solar"
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PHS: "Pumped Hydro Storage"
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hydro: "Reservoir & Dam"
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battery: "Battery Storage"
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H2: "Hydrogen Storage"
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lines: "Transmission Lines"
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ror: "Run of River"
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@ -539,6 +539,10 @@ def estimate_renewable_capacities(n, tech_map=None):
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.where(lambda s: s>0.1, 0.)) # only capacities above 100kW
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n.generators.loc[tech_i, 'p_nom_min'] = n.generators.loc[tech_i, 'p_nom']
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def attach_line_rating(n):
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if snakemake.config["lines"]["line_rating"]:
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s_max=xr.open_dataarray(snakemake.input.line_rating).to_pandas().transpose()
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n.lines_t.s_max_pu=s_max/n.lines.loc[s_max.columns,:]['s_nom'] #only considers overhead lines
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def add_nice_carrier_names(n, config=None):
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if config is None: config = snakemake.config
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@ -575,9 +579,11 @@ if __name__ == "__main__":
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attach_hydro(n, costs, ppl)
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attach_extendable_generators(n, costs, ppl)
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estimate_renewable_capacities(n)
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attach_OPSD_renewables(n)
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update_p_nom_max(n)
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attach_line_rating(n)
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add_nice_carrier_names(n)
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89
scripts/build_line_rating.py
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89
scripts/build_line_rating.py
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@ -0,0 +1,89 @@
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# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
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#
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# SPDX-License-Identifier: MIT
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# coding: utf-8
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"""
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Adds dynamic line rating timeseries to the base network.
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Relevant Settings
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-----------------
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.. code:: yaml
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lines_t:
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s_max_pu
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.. seealso::
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Documentation of the configuration file ``config.yaml`
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Inputs
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------
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- ``data/cutouts``:
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- ``networks/base.nc``: confer :ref:`base`
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Outputs
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-------
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- ``networks/base_with_line_rating.nc``
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Description
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-----------
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The rule :mod:`build_line_rating` calculates the line rating for transmission lines.
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The line rating provides the maximal capacity of a transmission line considering the heat exchange with the environment.
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The folloing heat gains and losses are considered:
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- heat gain through resistive losses
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- heat gain trough solar radiation
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- heat loss through radiation of the trasnmission line
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- heat loss through forced convection with wind
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- heat loss through natural convection
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With a heat balance considering the maximum temperature threshold of the tranmission line,
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the maximal possible capacity factor "s_max_pu" for each transmission line at each time step is calculated.
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"""
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import logging
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from _helpers import configure_logging
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import pypsa
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import pandas as pd
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import numpy as np
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import geopandas as gpd
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from shapely.geometry import Point, LineString as Line
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import atlite
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import xarray as xr
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def calculate_line_rating(n):
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relevant_lines=n.lines[(n.lines['underground']==False) & (n.lines['under_construction']==False)]
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buses = relevant_lines[["bus0", "bus1"]].values
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x = n.buses.x
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y = n.buses.y
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shapes = [Line([Point(x[b0], y[b0]), Point(x[b1], y[b1])]) for (b0, b1) in buses]
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shapes = gpd.GeoSeries(shapes, index=relevant_lines.index)
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cutout = atlite.Cutout(snakemake.input.cutout)
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if relevant_lines.r_pu.eq(0).all():
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#Overwrite standard line resistance with line resistance obtained from line type
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relevant_lines["r_pu"]=relevant_lines.join(n.line_types["r_per_length"], on=["type"])['r_per_length']/1000 #in meters
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Imax=cutout.line_rating(shapes, relevant_lines.r_pu)
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da = xr.DataArray(data=np.sqrt(3) * Imax * relevant_lines["v_nom"].values.reshape(-1,1) * relevant_lines["num_parallel"].values.reshape(-1,1)/1e3, #in mW
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attrs=dict(description="Maximal possible power in MW for given line considering line rating"))
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return da
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if __name__ == "__main__":
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if 'snakemake' not in globals():
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from _helpers import mock_snakemake
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snakemake = mock_snakemake('build_line_rating', network='elec', simpl='',
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clusters='6', ll='copt', opts='Co2L-24H')
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configure_logging(snakemake)
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n = pypsa.Network(snakemake.input.base_network)
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da=calculate_line_rating(n)
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da.to_netcdf(snakemake.output[0])
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