floating offshore wind turbines

Snakefile

@@ -188,7 +188,7 @@ rule build_renewable_profiles:
         corine="data/bundle/corine/g250_clc06_V18_5.tif",
         natura="resources/natura.tiff",
         gebco=lambda w: ("data/bundle/GEBCO_2014_2D.nc"
-                         if "max_depth" in config["renewable"][w.technology].keys()
+                         if any(key in ["max_depth", "min_depth"] for key in config["renewable"][w.technology].keys())
                          else []),
         country_shapes='resources/country_shapes.geojson',
         offshore_shapes='resources/offshore_shapes.geojson',
@@ -226,6 +226,7 @@ rule add_electricity:
         geth_hydro_capacities='data/geth2015_hydro_capacities.csv',
         load='resources/load.csv',
         nuts3_shapes='resources/nuts3_shapes.geojson',
+        gebco='data/bundle/GEBCO_2014_2D.nc',
         **{f"profile_{tech}": f"resources/profile_{tech}.nc"
            for tech in config['renewable']}
     output: "networks/elec.nc"

config.default.yaml

@@ -36,6 +36,7 @@ enable:
   build_natura_raster: false
   retrieve_natura_raster: true
   custom_busmap: false
+  split_offshore_regions: True #splits big offshore regions into smaller regions
 
 electricity:
   voltages: [220., 300., 380.]
@@ -110,19 +111,37 @@ renewable:
     cutout: europe-2013-era5
     resource:
       method: wind
-      turbine: NREL_ReferenceTurbine_5MW_offshore
-    capacity_per_sqkm: 2
+      turbine: NREL_ReferenceTurbine_2020ATB_12MW_offshore
+    capacity_per_sqkm: 2.5
     correction_factor: 0.8855
     # proxy for wake losses
     # from 10.1016/j.energy.2018.08.153
     # until done more rigorously in #153
     corine: [44, 255]
     natura: true
-    max_depth: 50
+    max_depth: 60
     max_shore_distance: 30000
     potential: simple # or conservative
     clip_p_max_pu: 1.e-2
+    calculate_topology_cost: true
   offwind-dc:
+    cutout: europe-2013-era5
+    resource:
+      method: wind
+      turbine: NREL_ReferenceTurbine_2020ATB_12MW_offshore
+    capacity_per_sqkm: 3.5 # from DEA
+    correction_factor: 0.8855
+    # proxy for wake losses
+    # from 10.1016/j.energy.2018.08.153
+    # until done more rigorously in #153
+    corine: [44, 255]
+    natura: true
+    max_depth: 60
+    min_shore_distance: 30000
+    potential: simple # or conservative
+    clip_p_max_pu: 1.e-2
+    calculate_topology_cost: true
+  offwind-float:
     cutout: europe-2013-era5
     resource:
       method: wind
@@ -135,8 +154,7 @@ renewable:
     # until done more rigorously in #153
     corine: [44, 255]
     natura: true
-    max_depth: 50
-    min_shore_distance: 30000
+    min_depth: 60
     potential: simple # or conservative
     clip_p_max_pu: 1.e-2
   solar:
@@ -203,7 +221,8 @@ costs:
   marginal_cost: # EUR/MWh
     solar: 0.01
     onwind: 0.015
-    offwind: 0.015
+    offwind-ac: 0.015
+    offwind-dc: 0.015
     hydro: 0.
     H2: 0.
     electrolysis: 0.

data/costs.csv

@@ -1,7 +1,9 @@
 technology,year,parameter,value,unit,source
 solar-rooftop,2030,discount rate,0.04,per unit,standard for decentral
 onwind,2030,lifetime,30,years,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
-offwind,2030,lifetime,30,years,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
+offwind-ac,2030,lifetime,30,years,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
+offwind-dc,2030,lifetime,30,years,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
+offwind-float,2030,lifetime,20,years,C. Maienza 2020 A life cycle cost model for floating offshore wind farms
 solar,2030,lifetime,25,years,IEA2010
 solar-rooftop,2030,lifetime,25,years,IEA2010
 solar-utility,2030,lifetime,25,years,IEA2010
@@ -17,13 +19,18 @@ geothermal,2030,lifetime,40,years,IEA2010
 biomass,2030,lifetime,30,years,ECF2010 in DIW DataDoc http://hdl.handle.net/10419/80348
 oil,2030,lifetime,30,years,ECF2010 in DIW DataDoc http://hdl.handle.net/10419/80348
 onwind,2030,investment,1040,EUR/kWel,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
-offwind,2030,investment,1640,EUR/kWel,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
+offwind-ac,2030,investment,1640,EUR/kWel,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
 offwind-ac-station,2030,investment,250,EUR/kWel,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
 offwind-ac-connection-submarine,2030,investment,2685,EUR/MW/km,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
 offwind-ac-connection-underground,2030,investment,1342,EUR/MW/km,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
+offwind-dc,2030,investment,1640,EUR/kWel,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
 offwind-dc-station,2030,investment,400,EUR/kWel,Haertel 2017; assuming one onshore and one offshore node + 13% learning reduction
 offwind-dc-connection-submarine,2030,investment,2000,EUR/MW/km,DTU report based on Fig 34 of https://ec.europa.eu/energy/sites/ener/files/documents/2014_nsog_report.pdf
 offwind-dc-connection-underground,2030,investment,1000,EUR/MW/km,Haertel 2017; average + 13% learning reduction
+offwind-float,2030,investment,2973,EUR/kWel,"C. Maienza 2020 A life cycle cost model for floating offshore wind farms;""Spar Buyo without cost for cabelin year not specified"""
+offwind-float-station,2030,investment,400,EUR/kWel,Haertel 2017; assuming one onshore and one offshore node + 13% learning reduction
+offwind-float-connection-submarine,2030,investment,2000,EUR/MW/km,DTU report based on Fig 34 of https://ec.europa.eu/energy/sites/ener/files/documents/2014_nsog_report.pdf
+offwind-float-connection-underground,2030,investment,1000,EUR/MW/km,"Haertel 2017; average + 13% learning reduction"""
 solar,2030,investment,600,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
 biomass,2030,investment,2209,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
 geothermal,2030,investment,3392,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
@@ -39,7 +46,9 @@ nuclear,2030,investment,6000,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80
 CCGT,2030,investment,800,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
 oil,2030,investment,400,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
 onwind,2030,FOM,2.450549,%/year,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
-offwind,2030,FOM,2.304878,%/year,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
+offwind-ac,2030,FOM,2.304878,%/year,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
+offwind-dc,2030,FOM,2.304878,%/year,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
+offwind-float,2030,FOM,1.31,%/year,C. Maienza 2020 A life cycle cost model for floating offshore wind farms ;Assumend lifetime 20a
 solar,2030,FOM,4.166667,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
 solar-rooftop,2030,FOM,2,%/year,ETIP PV
 solar-utility,2030,FOM,3,%/year,ETIP PV
@@ -54,7 +63,9 @@ ror,2030,FOM,2,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
 CCGT,2030,FOM,2.5,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
 OCGT,2030,FOM,3.75,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
 onwind,2030,VOM,2.3,EUR/MWhel,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
-offwind,2030,VOM,2.7,EUR/MWhel,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
+offwind-ac,2030,VOM,2.7,EUR/MWhel,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
+offwind-dc,2030,VOM,2.7,EUR/MWhel,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
+offwind-float,2030,VOM,0,EUR/MWhel,no information; included in FOM
 solar,2030,VOM,0.01,EUR/MWhel,RES costs made up to fix curtailment order
 coal,2030,VOM,6,EUR/MWhel,DIW DataDoc http://hdl.handle.net/10419/80348 PC (Advanced/SuperC)
 lignite,2030,VOM,7,EUR/MWhel,DIW DataDoc http://hdl.handle.net/10419/80348
@@ -192,4 +203,4 @@ HVDC submarine,2030,lifetime,40,years,Hagspiel
 HVDC submarine,2030,FOM,2,%/year,Hagspiel
 HVDC inverter pair,2030,investment,150000,EUR/MW,Hagspiel
 HVDC inverter pair,2030,lifetime,40,years,Hagspiel
-HVDC inverter pair,2030,FOM,2,%/year,Hagspiel
+HVDC inverter pair,2030,FOM,2,%/year,Hagspiel

scripts/_helpers.py

@@ -3,6 +3,7 @@
 # SPDX-License-Identifier: MIT
 
 import pandas as pd
+import numpy as np
 from pathlib import Path
 
 
@@ -199,6 +200,46 @@ def aggregate_costs(n, flatten=False, opts=None, existing_only=False):
 
     return costs
 
+def calculate_offwind_cost(WD, MW=12, no=54, D=236, HH=138, SP=343, DT=8):
+    """
+    Helper calculating offshore wind capex considering the average water depth of the region.
+
+    Parameters
+    ----------
+    WD : xarray
+        Average water depth of the different regions
+    MW : float
+        Power of the wind turbine in MW
+    no: int
+        Average number of wind turbines in farm
+    D: int
+        Rotor diameter of wind turbine in meters
+    HH: int
+        Hub height of wind turbine in meters
+    SP: int
+        Specific power of wind turbine in W/m2
+    DT: int
+        Distance between the wind turbine in number of rotor diameters
+
+    Returns
+    -------
+    capex: xarray
+        Capex of the wind turbine in the different regions
+    """
+    RA=(D/2)**2*np.pi
+    IA=DT*D
+    wind_turbine_invest=(-0.6*SP+750+(0.53*HH*RA+5500)/(1000*MW))*1.1
+    wind_turbine_install= 300*MW**(-0.6)
+    foundation_invest=(8*np.abs(WD)+30)*(1+(0.003*(350-np.min([400,SP]))))
+    foundation_install=2.5*np.abs(WD)+600*MW**(-0.6)
+    array_cable=IA*500/MW/1000 
+    turbine_transport=50
+    insurance=100
+    finance_cost=100
+    continences=50
+    capex=np.sum([wind_turbine_invest,wind_turbine_install, foundation_invest, foundation_install, array_cable, turbine_transport, insurance, finance_cost, continences])*1000 # in €/MW
+    return capex
+
 def progress_retrieve(url, file):
     import urllib
     from progressbar import ProgressBar
@@ -240,7 +281,7 @@ def mock_snakemake(rulename, **wildcards):
         if os.path.exists(p):
             snakefile = p
             break
-    workflow = sm.Workflow(snakefile, overwrite_configfiles=[])
+    workflow = sm.Workflow(snakefile, overwrite_configfiles=[], rerun_triggers=[])
     workflow.include(snakefile)
     workflow.global_resources = {}
     rule = workflow.get_rule(rulename)

scripts/add_electricity.py

@@ -263,6 +263,7 @@ def update_transmission_costs(n, costs, length_factor=1.0):
 
 
 def attach_wind_and_solar(n, costs, input_profiles, technologies, line_length_factor=1):
+    from _helpers import calculate_offwind_cost
     # TODO: rename tech -> carrier, technologies -> carriers
 
     for tech in technologies:
@@ -275,29 +276,45 @@ def attach_wind_and_solar(n, costs, input_profiles, technologies, line_length_fa
             suptech = tech.split('-', 2)[0]
             if suptech == 'offwind':
                 underwater_fraction = ds['underwater_fraction'].to_pandas()
-                connection_cost = (line_length_factor *
-                                   ds['average_distance'].to_pandas() *
-                                   (underwater_fraction *
+                cable_cost = (line_length_factor *
+                                ds['average_distance'].to_pandas() *
+                                (underwater_fraction *
                                     costs.at[tech + '-connection-submarine', 'capital_cost'] +
                                     (1. - underwater_fraction) *
                                     costs.at[tech + '-connection-underground', 'capital_cost']))
-                capital_cost = (costs.at['offwind', 'capital_cost'] +
-                                costs.at[tech + '-station', 'capital_cost'] +
-                                connection_cost)
+                grid_connection_cost=costs.at[tech + '-station', 'capital_cost'] + cable_cost
+                calculate_topology_cost=technologies[tech].get("calculate_topology_cost", False)
+                if calculate_topology_cost:
+                    turbine_cost=calculate_offwind_cost(ds["water_depth"].to_pandas())*(calculate_annuity(costs.at[tech, 'lifetime'], costs.at[tech, "discount rate"]) + costs.at[tech, "FOM"]/100.) * Nyears
+                else:
+                    turbine_cost=costs.at[tech, 'capital_cost']
+                capital_cost = (turbine_cost + grid_connection_cost)
+
                 logger.info("Added connection cost of {:0.0f}-{:0.0f} Eur/MW/a to {}"
-                            .format(connection_cost.min(), connection_cost.max(), tech))
+                            .format(cable_cost.min(), cable_cost.max(), tech))
+                n.madd("Generator", ds.indexes['bus'], ' ' + tech,
+                   bus=ds.indexes['bus'].str.split("_").str[0],
+                   carrier=tech,
+                   p_nom_extendable=True,
+                   p_nom_max=ds['p_nom_max'].to_pandas(),
+                   weight=ds['weight'].to_pandas(),
+                   marginal_cost=costs.at[tech, 'marginal_cost'],
+                   capital_cost=capital_cost,
+                   grid_connection_cost=grid_connection_cost,
+                   turbine_cost=turbine_cost,
+                   efficiency=costs.at[tech, 'efficiency'],
+                   p_max_pu=ds['profile'].transpose('time', 'bus').to_pandas())
             else:
                 capital_cost = costs.at[tech, 'capital_cost']
-
-            n.madd("Generator", ds.indexes['bus'], ' ' + tech,
-                   bus=ds.indexes['bus'],
+                n.madd("Generator", ds.indexes['bus'], ' ' + tech,
+                   bus=ds.indexes['bus'].str.split("_").str[0],
                    carrier=tech,
                    p_nom_extendable=True,
                    p_nom_max=ds['p_nom_max'].to_pandas(),
                    weight=ds['weight'].to_pandas(),
-                   marginal_cost=costs.at[suptech, 'marginal_cost'],
+                   marginal_cost=costs.at[tech, 'marginal_cost'],
                    capital_cost=capital_cost,
-                   efficiency=costs.at[suptech, 'efficiency'],
+                   efficiency=costs.at[tech, 'efficiency'],
                    p_max_pu=ds['profile'].transpose('time', 'bus').to_pandas())