Fix prepare_sector_network to reproduce old sector prenetworks

from pypsa-eur sector branch

README.md

@@ -0,0 +1,29 @@
+# PyPSA-Eur-Sec: A Sector-Coupled Open Optimisation Model of the European Energy System
+
+PyPSA-Eur-Sec builds on the electricity generation and transmission
+model [PyPSA-Eur](https://github.com/PyPSA/pypsa-eur) to add demand
+and supply for the following sectors: transport, space and water
+heating, biomass, industry and industrial feedstocks. This completes
+the energy system and includes all greenhouse gas emitters except
+waste management, agriculture, forestry and land use.
+
+PyPSA-Eur-Sec includes PyPSA-Eur as a
+[snakemake](https://snakemake.readthedocs.io/en/stable/index.html)
+[subworkflow](https://snakemake.readthedocs.io/en/stable/snakefiles/modularization.html#snakefiles-sub-workflows). PyPSA-Eur-Sec
+uses PyPSA-Eur to build the clustered transmission model along with
+wind, solar PV and hydroelectricity potentials and time series. Then
+PyPSA-Eur-Sec adds other conventional generators, storage units and
+the additional sectors.
+
+Currently the scripts to solve and process the resulting PyPSA models
+are also included in PyPSA-Eur-Sec, although they could in future be
+better integrated with the corresponding scripts in PyPSA-Eur.
+
+# Installation
+
+First install [PyPSA-Eur](https://github.com/PyPSA/pypsa-eur) and all
+its dependencies.
+
+## Data requirements
+
+JRC-IDEES, JRC biomass potentials, .... EEA emissions.

Snakefile

@@ -6,7 +6,8 @@ wildcard_constraints:
     simpl="[a-zA-Z0-9]*",
     clusters="[0-9]+m?",
     sectors="[+a-zA-Z0-9]+",
-    opts="[-+a-zA-Z0-9]*"
+    opts="[-+a-zA-Z0-9]*",
+    sector_opts="[-+a-zA-Z0-9]*"
 
 
 
@@ -23,7 +24,7 @@ rule test_script:
 
 rule prepare_sector_networks:
     input:
-        expand(config['results_dir'] + config['run'] + "/prenetworks/elec_s{simpl}_{clusters}_lv{lv}_{opts}.nc",
+        expand(config['results_dir'] + config['run'] + "/prenetworks/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}.nc",
                  **config['scenario'])
 
 
@@ -141,13 +142,16 @@ rule build_industrial_demand:
 
 
 
-rule prepare_network:
+rule prepare_sector_network:
     input:
-        network=config['results_dir']  +  config['run'] + '/prenetworks/{network}_s{simpl}_{clusters}.nc',
+        network=pypsaeur('networks/{network}_s{simpl}_{clusters}_lv{lv}_{opts}.nc'),
         energy_totals_name='data/energy_totals.csv',
         co2_totals_name='data/co2_totals.csv',
         transport_name='data/transport_data.csv',
         biomass_potentials='data/biomass_potentials.csv',
+        timezone_mappings='data/timezone_mappings.csv',
+        heat_profile="data/heat_load_profile_DK_AdamJensen.csv",
+        costs="data/costs.csv",
         clustered_pop_layout="resources/pop_layout_{network}_s{simpl}_{clusters}.csv",
         industrial_demand="resources/industrial_demand_{network}_s{simpl}_{clusters}.csv",
         heat_demand_urban="resources/heat_demand_urban_{network}_s{simpl}_{clusters}.nc",
@@ -168,8 +172,8 @@ rule prepare_network:
         solar_thermal_total="resources/solar_thermal_total_{network}_s{simpl}_{clusters}.nc",
         solar_thermal_urban="resources/solar_thermal_urban_{network}_s{simpl}_{clusters}.nc",
         solar_thermal_rural="resources/solar_thermal_rural_{network}_s{simpl}_{clusters}.nc"
-    output: config['results_dir']  +  config['run'] + '/prenetworks/{network}_s{simpl}_{clusters}_lv{lv}_{opts}.nc'
+    output: config['results_dir']  +  config['run'] + '/prenetworks/{network}_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}.nc'
     threads: 1
     resources: mem=1000
-    benchmark: "benchmarks/prepare_network/{network}_s{simpl}_{clusters}_lv{lv}_{opts}"
+    benchmark: "benchmarks/prepare_network/{network}_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}"
-    script: "scripts/prepare_network.py"
+    script: "scripts/prepare_sector_network.py"

config.yaml

@@ -1,3 +1,4 @@
+logging_level: INFO
 
 results_dir: 'results/'
 summary_dir: results
@@ -6,9 +7,10 @@ run: '190416-modular-test'
 scenario:
   sectors: [E] # ,E+EV,E+BEV,E+BEV+V2G] # [ E+EV, E+BEV, E+BEV+V2G ]
   simpl: ['']
-  lv: [1.0]#[1.0, 1.125, 1.25, 1.5, 2.0, opt]# or opt
+  lv: [1.0,1.25]#[1.0, 1.125, 1.25, 1.5, 2.0, opt]# or opt
   clusters: [128] #[90, 128, 181] #[45, 64, 90, 128, 181, 256] #, 362] # (2**np.r_[5.5:9:.5]).astype(int) minimum is 37
-  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]
+  opts: ['']  #for pypsa-eur
+  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]
   # Co2L will give default (5%); Co2L0p25 will give 25% CO2 emissions; Co2Lm0p05 will give 5% negative emissions
 
 
@@ -90,3 +92,66 @@ renewable:
 biomass:
   year: 2030
   scenario: "Med"
+
+
+sector:
+  'central' : True
+  'central_fraction' : 0.6
+  'dsm_restriction_value' : 0.75  #Set to 0 for no restriction on BEV DSM
+  'dsm_restriction_time' : 7  #Time at which SOC of BEV has to be dsm_restriction_value
+  'transport_heating_deadband_upper' : 20.
+  'transport_heating_deadband_lower' : 15.
+  'ICE_lower_degree_factor' : 0.375    #in per cent increase in fuel consumption per degree above deadband
+  'ICE_upper_degree_factor' : 1.6
+  'EV_lower_degree_factor' : 0.98
+  'EV_upper_degree_factor' : 0.63
+  'district_heating_loss' : 0.1
+  'bev' : True #turns on EV battery
+  'bev_availability' : 0.5  #How many cars do smart charging
+  'v2g' : True #allows feed-in to grid from EV battery
+  'transport_fuel_cell_share' : 0.   #0 means all EVs, 1 means all FCs
+  'time_dep_hp_cop' : True
+  'retrofitting' : False
+  'retroI-fraction' : 0.25
+  'retroI-fraction' : 0.55
+  'retrofitting-cost_factor' : 1.0
+  'tes' : True
+  'tes_tau' : 3.
+  'boilers' : True
+  'chp' : True
+  'chp_parameters':
+    'eta_elec' : 0.468 #electrical efficiency with no heat output
+    'c_v' : 0.15 #loss of fuel for each addition of heat
+    'c_m' : 0.75 #backpressure ratio
+    'p_nom_ratio' : 1. #ratio of max heat output to max electrical output
+  'solar_thermal' : True
+  'solar_cf_correction': 0.788457  # =  >>> 1/1.2683
+  'marginal_cost_storage' : 0. #1e-4
+  'methanation' : True
+  'helmeth' : True
+  'dac' : True
+  'ccs_fraction' : 0.9
+
+
+
+costs:
+  year: 2030
+
+  # From a Lion Hirth paper, also reflects average of Noothout et al 2016
+  discountrate: 0.07
+  # [EUR/USD] ECB: https://www.ecb.europa.eu/stats/exchange/eurofxref/html/eurofxref-graph-usd.en.html # noqa: E501
+  USD2013_to_EUR2013: 0.7532
+
+  # Marginal and capital costs can be overwritten
+  # capital_cost:
+  #   Wind: Bla
+  marginal_cost: #
+    solar: 0.01
+    onwind: 0.015
+    offwind: 0.015
+    hydro: 0.
+    H2: 0.
+    battery: 0.
+
+  emission_prices: # only used with the option Ep (emission prices)
+    co2: 0.

scripts/prepare_sector_network.py

@@ -10,17 +10,16 @@ import scipy as sp
 import xarray as xr
 import re, os
 
-from six import iteritems
+from six import iteritems, string_types
-import geopandas as gpd
 
 import pypsa
 
+import yaml
+
 import pytz
 
 from vresutils.costdata import annuity
 
-from add_electricity import load_costs, update_transmission_costs
-
 
 #First tell PyPSA that links can have multiple outputs by
 #overriding the component_attrs. This can be done for
@@ -59,7 +58,8 @@ def add_co2_tracking(n):
            bus="co2 atmosphere")
 
     #this tracks CO2 stored, e.g. underground
-    n.add("Bus","co2 stored")
+    n.add("Bus","co2 stored",
+          carrier="co2 stored")
 
     #NB: can also be negative
     #cost of 10 euro/tCO2 for whatever stays
@@ -170,6 +170,16 @@ def average_every_nhours(n, offset):
     logger.info('Resampling the network to {}'.format(offset))
     m = n.copy(with_time=False)
 
+    #fix copying of network attributes
+    #copied from pypsa/io.py, should be in pypsa/components.py#Network.copy()
+    allowed_types = (float,int,bool) + string_types + tuple(np.typeDict.values())
+    attrs = dict((attr, getattr(n, attr))
+                 for attr in dir(n)
+                 if (not attr.startswith("__") and
+                     isinstance(getattr(n,attr), allowed_types)))
+    for k,v in iteritems(attrs):
+        setattr(m,k,v)
+
     snapshot_weightings = n.snapshot_weightings.resample(offset).sum()
     m.set_snapshots(snapshot_weightings.index)
     m.snapshot_weightings = snapshot_weightings
@@ -188,9 +198,6 @@ def average_every_nhours(n, offset):
     return m
 
 
-
-timezone_mappings = pd.read_csv("data/timezone_mappings.csv",index_col=0,squeeze=True,header=None)
-
 def generate_periodic_profiles(dt_index=pd.date_range("2011-01-01 00:00","2011-12-31 23:00",freq="H",tz="UTC"),
                                nodes=[],
                                weekly_profile=range(24*7)):
@@ -256,7 +263,7 @@ def prepare_data(network):
     heat_demand_df = xr.open_dataarray(snakemake.input.heat_demand_total).T.to_pandas().reindex(index=network.snapshots, method="ffill")
 
 
-    intraday_profiles = pd.read_csv("data/heating/heat_load_profile_DK_AdamJensen.csv",index_col=0)
+    intraday_profiles = pd.read_csv(snakemake.input.heat_profile,index_col=0)
 
     intraday_year_profiles = generate_periodic_profiles(heat_demand_df.index.tz_localize("UTC"),
                                                         nodes=heat_demand_df.columns,
@@ -388,7 +395,7 @@ def prepare_data(network):
 def prepare_costs():
 
     #set all asset costs and other parameters
-    costs = pd.read_csv("data/costs.csv",index_col=list(range(3))).sort_index()
+    costs = pd.read_csv(snakemake.input.costs,index_col=list(range(3))).sort_index()
 
     #correct units to MW and EUR
     costs.loc[costs.unit.str.contains("/kW"),"value"]*=1e3
@@ -1165,15 +1172,20 @@ if __name__ == "__main__":
         from vresutils.snakemake import MockSnakemake
         snakemake = MockSnakemake(
             wildcards=dict(network='elec', simpl='', clusters='37', lv='2', opts='Co2L-3H'),
-            input=['networks/{network}_s{simpl}_{clusters}.nc'],
+            input=dict(network='../pypsa-eur/networks/{network}_s{simpl}_{clusters}.nc', timezone_mappings='data/timezone_mappings.csv'),
             output=['networks/{network}_s{simpl}_{clusters}_lv{lv}_{opts}.nc']
         )
+        with open('config.yaml') as f:
+            snakemake.config = yaml.load(f)
+
 
     logging.basicConfig(level=snakemake.config['logging_level'])
 
+    timezone_mappings = pd.read_csv(snakemake.input.timezone_mappings,index_col=0,squeeze=True,header=None)
+
     options = snakemake.config["sector"]
 
-    opts = snakemake.wildcards.opts.split('-')
+    opts = snakemake.wildcards.sector_opts.split('-')
 
     n = pypsa.Network(snakemake.input.network,
                       override_component_attrs=override_component_attrs)
@@ -1209,8 +1221,6 @@ if __name__ == "__main__":
     if "I" in opts:
         add_industry(n)
 
-    set_line_s_max_pu(n)
-
     for o in opts:
         m = re.match(r'^\d+h$', o, re.IGNORECASE)
         if m is not None:
@@ -1239,7 +1249,4 @@ if __name__ == "__main__":
             limit = float(limit.replace("p",".").replace("m","-"))
             restrict_technology_potential(n,"onwind",limit)
 
-
-    set_line_volume_limit(n, snakemake.wildcards.lv)
-
     n.export_to_netcdf(snakemake.output[0])