Fix prepare_sector_network to reproduce old sector prenetworks
from pypsa-eur sector branch
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Tom Brown
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README.md
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README.md
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# PyPSA-Eur-Sec: A Sector-Coupled Open Optimisation Model of the European Energy System
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PyPSA-Eur-Sec builds on the electricity generation and transmission
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model [PyPSA-Eur](https://github.com/PyPSA/pypsa-eur) to add demand
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and supply for the following sectors: transport, space and water
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heating, biomass, industry and industrial feedstocks. This completes
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the energy system and includes all greenhouse gas emitters except
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waste management, agriculture, forestry and land use.
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PyPSA-Eur-Sec includes PyPSA-Eur as a
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[snakemake](https://snakemake.readthedocs.io/en/stable/index.html)
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[subworkflow](https://snakemake.readthedocs.io/en/stable/snakefiles/modularization.html#snakefiles-sub-workflows). PyPSA-Eur-Sec
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uses PyPSA-Eur to build the clustered transmission model along with
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wind, solar PV and hydroelectricity potentials and time series. Then
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PyPSA-Eur-Sec adds other conventional generators, storage units and
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the additional sectors.
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Currently the scripts to solve and process the resulting PyPSA models
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are also included in PyPSA-Eur-Sec, although they could in future be
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better integrated with the corresponding scripts in PyPSA-Eur.
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# Installation
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First install [PyPSA-Eur](https://github.com/PyPSA/pypsa-eur) and all
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its dependencies.
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## Data requirements
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JRC-IDEES, JRC biomass potentials, .... EEA emissions.
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18
Snakefile
18
Snakefile
@ -6,7 +6,8 @@ wildcard_constraints:
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simpl="[a-zA-Z0-9]*",
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clusters="[0-9]+m?",
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sectors="[+a-zA-Z0-9]+",
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opts="[-+a-zA-Z0-9]*"
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opts="[-+a-zA-Z0-9]*",
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sector_opts="[-+a-zA-Z0-9]*"
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@ -23,7 +24,7 @@ rule test_script:
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rule prepare_sector_networks:
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input:
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expand(config['results_dir'] + config['run'] + "/prenetworks/elec_s{simpl}_{clusters}_lv{lv}_{opts}.nc",
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expand(config['results_dir'] + config['run'] + "/prenetworks/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}.nc",
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**config['scenario'])
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@ -141,13 +142,16 @@ rule build_industrial_demand:
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rule prepare_network:
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rule prepare_sector_network:
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input:
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network=config['results_dir'] + config['run'] + '/prenetworks/{network}_s{simpl}_{clusters}.nc',
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network=pypsaeur('networks/{network}_s{simpl}_{clusters}_lv{lv}_{opts}.nc'),
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energy_totals_name='data/energy_totals.csv',
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co2_totals_name='data/co2_totals.csv',
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transport_name='data/transport_data.csv',
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biomass_potentials='data/biomass_potentials.csv',
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timezone_mappings='data/timezone_mappings.csv',
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heat_profile="data/heat_load_profile_DK_AdamJensen.csv",
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costs="data/costs.csv",
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clustered_pop_layout="resources/pop_layout_{network}_s{simpl}_{clusters}.csv",
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industrial_demand="resources/industrial_demand_{network}_s{simpl}_{clusters}.csv",
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heat_demand_urban="resources/heat_demand_urban_{network}_s{simpl}_{clusters}.nc",
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@ -168,8 +172,8 @@ rule prepare_network:
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solar_thermal_total="resources/solar_thermal_total_{network}_s{simpl}_{clusters}.nc",
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solar_thermal_urban="resources/solar_thermal_urban_{network}_s{simpl}_{clusters}.nc",
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solar_thermal_rural="resources/solar_thermal_rural_{network}_s{simpl}_{clusters}.nc"
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output: config['results_dir'] + config['run'] + '/prenetworks/{network}_s{simpl}_{clusters}_lv{lv}_{opts}.nc'
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output: config['results_dir'] + config['run'] + '/prenetworks/{network}_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}.nc'
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threads: 1
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resources: mem=1000
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benchmark: "benchmarks/prepare_network/{network}_s{simpl}_{clusters}_lv{lv}_{opts}"
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script: "scripts/prepare_network.py"
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benchmark: "benchmarks/prepare_network/{network}_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}"
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script: "scripts/prepare_sector_network.py"
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69
config.yaml
69
config.yaml
@ -1,3 +1,4 @@
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logging_level: INFO
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results_dir: 'results/'
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summary_dir: results
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@ -6,9 +7,10 @@ run: '190416-modular-test'
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scenario:
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sectors: [E] # ,E+EV,E+BEV,E+BEV+V2G] # [ E+EV, E+BEV, E+BEV+V2G ]
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simpl: ['']
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lv: [1.0]#[1.0, 1.125, 1.25, 1.5, 2.0, opt]# or opt
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lv: [1.0,1.25]#[1.0, 1.125, 1.25, 1.5, 2.0, opt]# or opt
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clusters: [128] #[90, 128, 181] #[45, 64, 90, 128, 181, 256] #, 362] # (2**np.r_[5.5:9:.5]).astype(int) minimum is 37
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opts: [Co2L0-3H-T-H-B-I,Co2L0-3H-T-H-B-I-onwind0,Co2L0-3H-T-H-B-I-onwind0p1]#,Co2L0p05-3H-T-H-B-I,Co2L0p10-3H-T-H-B-I,Co2L0p20-3H-T-H-B-I,Co2L0p30-3H-T-H-B-I,Co2L0p50-3H-T-H-B-I]#[Co2L-3H-T-H,Co2L0p10-3H-T-H,Co2L0-3H-T-H,Co2L0p20-3H-T-H] #Co2L-3H-T-H,Co2L0p10-3H-T-H,Co2L0p20-3H-T-HCo2L-3H-T-H,Co2L0p10-3H-T-H,Co2L0p30-3H-T-H,Co2L0p50-3H-T-H] #Co2L-3H,Co2L-3H-T,, LC-FL, LC-T, Ep-T, Co2L-T]
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opts: [''] #for pypsa-eur
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sector_opts: [Co2L0-3H-T-H-B-I,Co2L0-3H-T-H-B-I-onwind0,Co2L0-3H-T-H-B-I-onwind0p1,Co2L0p1-3H-T-H-B-I,Co2L0p1-3H-T-H-B-I-onwind0,Co2L0p1-3H-T-H-B-I-onwind0p1]#,Co2L0p05-3H-T-H-B-I,Co2L0p10-3H-T-H-B-I,Co2L0p20-3H-T-H-B-I,Co2L0p30-3H-T-H-B-I,Co2L0p50-3H-T-H-B-I]#[Co2L-3H-T-H,Co2L0p10-3H-T-H,Co2L0-3H-T-H,Co2L0p20-3H-T-H] #Co2L-3H-T-H,Co2L0p10-3H-T-H,Co2L0p20-3H-T-HCo2L-3H-T-H,Co2L0p10-3H-T-H,Co2L0p30-3H-T-H,Co2L0p50-3H-T-H] #Co2L-3H,Co2L-3H-T,, LC-FL, LC-T, Ep-T, Co2L-T]
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# Co2L will give default (5%); Co2L0p25 will give 25% CO2 emissions; Co2Lm0p05 will give 5% negative emissions
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@ -90,3 +92,66 @@ renewable:
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biomass:
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year: 2030
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scenario: "Med"
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sector:
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'central' : True
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'central_fraction' : 0.6
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'dsm_restriction_value' : 0.75 #Set to 0 for no restriction on BEV DSM
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'dsm_restriction_time' : 7 #Time at which SOC of BEV has to be dsm_restriction_value
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'transport_heating_deadband_upper' : 20.
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'transport_heating_deadband_lower' : 15.
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'ICE_lower_degree_factor' : 0.375 #in per cent increase in fuel consumption per degree above deadband
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'ICE_upper_degree_factor' : 1.6
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'EV_lower_degree_factor' : 0.98
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'EV_upper_degree_factor' : 0.63
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'district_heating_loss' : 0.1
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'bev' : True #turns on EV battery
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'bev_availability' : 0.5 #How many cars do smart charging
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'v2g' : True #allows feed-in to grid from EV battery
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'transport_fuel_cell_share' : 0. #0 means all EVs, 1 means all FCs
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'time_dep_hp_cop' : True
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'retrofitting' : False
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'retroI-fraction' : 0.25
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'retroI-fraction' : 0.55
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'retrofitting-cost_factor' : 1.0
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'tes' : True
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'tes_tau' : 3.
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'boilers' : True
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'chp' : True
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'chp_parameters':
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'eta_elec' : 0.468 #electrical efficiency with no heat output
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'c_v' : 0.15 #loss of fuel for each addition of heat
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'c_m' : 0.75 #backpressure ratio
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'p_nom_ratio' : 1. #ratio of max heat output to max electrical output
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'solar_thermal' : True
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'solar_cf_correction': 0.788457 # = >>> 1/1.2683
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'marginal_cost_storage' : 0. #1e-4
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'methanation' : True
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'helmeth' : True
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'dac' : True
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'ccs_fraction' : 0.9
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costs:
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year: 2030
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# From a Lion Hirth paper, also reflects average of Noothout et al 2016
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discountrate: 0.07
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# [EUR/USD] ECB: https://www.ecb.europa.eu/stats/exchange/eurofxref/html/eurofxref-graph-usd.en.html # noqa: E501
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USD2013_to_EUR2013: 0.7532
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# Marginal and capital costs can be overwritten
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# capital_cost:
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# Wind: Bla
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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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hydro: 0.
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H2: 0.
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battery: 0.
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emission_prices: # only used with the option Ep (emission prices)
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co2: 0.
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@ -10,17 +10,16 @@ import scipy as sp
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import xarray as xr
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import re, os
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from six import iteritems
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import geopandas as gpd
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from six import iteritems, string_types
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import pypsa
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import yaml
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import pytz
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from vresutils.costdata import annuity
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from add_electricity import load_costs, update_transmission_costs
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#First tell PyPSA that links can have multiple outputs by
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#overriding the component_attrs. This can be done for
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@ -59,7 +58,8 @@ def add_co2_tracking(n):
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bus="co2 atmosphere")
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#this tracks CO2 stored, e.g. underground
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n.add("Bus","co2 stored")
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n.add("Bus","co2 stored",
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carrier="co2 stored")
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#NB: can also be negative
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#cost of 10 euro/tCO2 for whatever stays
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@ -170,6 +170,16 @@ def average_every_nhours(n, offset):
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logger.info('Resampling the network to {}'.format(offset))
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m = n.copy(with_time=False)
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#fix copying of network attributes
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#copied from pypsa/io.py, should be in pypsa/components.py#Network.copy()
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allowed_types = (float,int,bool) + string_types + tuple(np.typeDict.values())
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attrs = dict((attr, getattr(n, attr))
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for attr in dir(n)
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if (not attr.startswith("__") and
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isinstance(getattr(n,attr), allowed_types)))
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for k,v in iteritems(attrs):
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setattr(m,k,v)
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snapshot_weightings = n.snapshot_weightings.resample(offset).sum()
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m.set_snapshots(snapshot_weightings.index)
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m.snapshot_weightings = snapshot_weightings
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@ -188,9 +198,6 @@ def average_every_nhours(n, offset):
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return m
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timezone_mappings = pd.read_csv("data/timezone_mappings.csv",index_col=0,squeeze=True,header=None)
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def generate_periodic_profiles(dt_index=pd.date_range("2011-01-01 00:00","2011-12-31 23:00",freq="H",tz="UTC"),
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nodes=[],
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weekly_profile=range(24*7)):
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@ -256,7 +263,7 @@ def prepare_data(network):
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heat_demand_df = xr.open_dataarray(snakemake.input.heat_demand_total).T.to_pandas().reindex(index=network.snapshots, method="ffill")
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intraday_profiles = pd.read_csv("data/heating/heat_load_profile_DK_AdamJensen.csv",index_col=0)
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intraday_profiles = pd.read_csv(snakemake.input.heat_profile,index_col=0)
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intraday_year_profiles = generate_periodic_profiles(heat_demand_df.index.tz_localize("UTC"),
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nodes=heat_demand_df.columns,
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@ -388,7 +395,7 @@ def prepare_data(network):
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def prepare_costs():
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#set all asset costs and other parameters
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costs = pd.read_csv("data/costs.csv",index_col=list(range(3))).sort_index()
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costs = pd.read_csv(snakemake.input.costs,index_col=list(range(3))).sort_index()
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#correct units to MW and EUR
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costs.loc[costs.unit.str.contains("/kW"),"value"]*=1e3
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@ -1165,15 +1172,20 @@ if __name__ == "__main__":
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from vresutils.snakemake import MockSnakemake
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snakemake = MockSnakemake(
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wildcards=dict(network='elec', simpl='', clusters='37', lv='2', opts='Co2L-3H'),
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input=['networks/{network}_s{simpl}_{clusters}.nc'],
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input=dict(network='../pypsa-eur/networks/{network}_s{simpl}_{clusters}.nc', timezone_mappings='data/timezone_mappings.csv'),
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output=['networks/{network}_s{simpl}_{clusters}_lv{lv}_{opts}.nc']
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)
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with open('config.yaml') as f:
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snakemake.config = yaml.load(f)
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logging.basicConfig(level=snakemake.config['logging_level'])
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timezone_mappings = pd.read_csv(snakemake.input.timezone_mappings,index_col=0,squeeze=True,header=None)
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options = snakemake.config["sector"]
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opts = snakemake.wildcards.opts.split('-')
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opts = snakemake.wildcards.sector_opts.split('-')
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n = pypsa.Network(snakemake.input.network,
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override_component_attrs=override_component_attrs)
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@ -1209,8 +1221,6 @@ if __name__ == "__main__":
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if "I" in opts:
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add_industry(n)
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set_line_s_max_pu(n)
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for o in opts:
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m = re.match(r'^\d+h$', o, re.IGNORECASE)
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if m is not None:
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@ -1239,7 +1249,4 @@ if __name__ == "__main__":
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limit = float(limit.replace("p",".").replace("m","-"))
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restrict_technology_potential(n,"onwind",limit)
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set_line_volume_limit(n, snakemake.wildcards.lv)
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n.export_to_netcdf(snakemake.output[0])
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