New techs/chemical energy carriers by @euronion

Snakefile

@@ -13,7 +13,8 @@ rule compile_cost_assumptions:
         dea_storage = "inputs/technology_data_catalogue_for_energy_storage.xlsx",
         dea_generation = "inputs/technology_data_for_el_and_dh_-_0009.xlsx",
         dea_heating = "inputs/technologydatafor_heating_installations_marts_2018.xlsx",
-        dea_industrial = "inputs/technology_data_for_industrial_process_heat_0002.xlsx"
+        dea_industrial = "inputs/technology_data_for_industrial_process_heat_0002.xlsx",
+        manual_input = "inputs/manual_input.csv"
     output:
         expand("outputs/costs_{year}.csv", year = config["years"])
     threads: 1

config.yaml

@@ -22,3 +22,6 @@ h2_from_budischak : false
 # remove grid connection costs from DEA for offwind because they are calculated
 # seperately in pypsa-eur
 offwind_no_gridcosts : true
+
+desalination:
+    salinity: 35 # in PSU (Practical Salinity Unit) = kg/m^3

scripts/compile_cost_assumptions.py

@@ -46,6 +46,9 @@
                 "co2" :'Entwicklung der spezifischen Kohlendioxid-Emissionen des deutschen Strommix in den Jahren 1990 - 2018',
                 # gas pipeline costs
                 "ISE": "WEGE ZU EINEM KLIMANEUTRALEN ENERGIESYSEM, Anhang zur Studie, Fraunhofer-Institut für Solare Energiesysteme ISE, Freiburg",
+                # Water desalination costs
+                "Caldera2016": "Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004)",
+                "Caldera2017": "Caldera et al 2017: Learning Curve for Seawater Reverse Osmosis Desalination Plants: Capital Cost Trend of the Past, Present, and Future (https://doi.org/10.1002/2017WR021402)",
                 # home battery storage and inverter investment costs
                 "EWG": "Global Energy System based on 100% Renewable Energy, Energywatchgroup/LTU University, 2019"
                 }
@@ -77,10 +80,11 @@
                # 'decentral water tank storage': '142 Small scale hot water tank',
                'fuel cell': '12 LT-PEMFC CHP',
                'hydrogen storage underground': '151c Hydrogen Storage - Caverns',
-               'hydrogen storage tank': '151a Hydrogen Storage - Tanks',
+               'hydrogen storage tank incl. compressor': '151a Hydrogen Storage - Tanks',
                'micro CHP': '219 LT-PEMFC mCHP - natural gas',
                'biogas upgrading': '82 Biogas, upgrading',
                'battery': '180 Lithium Ion Battery',
+               'industrial heat pump medium temperature':'302.a High temp. hp Up to 125 C',
                'electrolysis': '86 AEC 100MW', #'88 Alkaline Electrolyser',
                'direct air capture' : '403.a Direct air capture',
                'biomass CHP capture' : '401.a Post comb - small CHP',
@@ -121,14 +125,15 @@
                     'central water tank storage': 'J:K',
                     'fuel cell': 'I:J',
                     'hydrogen storage underground': 'J:K',
-                    'hydrogen storage tank': 'J:K',
+                    'hydrogen storage tank incl. compressor': 'J:K',
                     'micro CHP': 'I:J',
                     'biogas upgrading': 'I:J',
                     'electrolysis': 'I:J',
                     'battery': 'L,N',
                     'direct air capture': 'I:J',
                     'cement capture': 'I:J',
                     'biomass CHP capture': 'I:J',
+                    'industrial heat pump medium temperature':'H:I',
 }
 
 
@@ -178,6 +183,8 @@ def get_data_DEA(tech, data_in, expectation=None):
         usecols = f"B:J,{uncrtnty_lookup[tech]}"
     elif tech in ['direct air capture', 'cement capture', 'biomass CHP capture']:
         usecols = f"A:F,{uncrtnty_lookup[tech]}"
+    elif tech in ['industrial heat pump medium temperature']:
+        usecols = f"A:E,{uncrtnty_lookup[tech]}"
     else:
         usecols = f"B:G,{uncrtnty_lookup[tech]}"
 
@@ -203,7 +210,7 @@ def get_data_DEA(tech, data_in, expectation=None):
     uncertainty_columns = ["2050-optimist", "2050-pessimist"]
     if uncrtnty_lookup[tech]:
         # hydrogen storage sheets have reverse order of lower/upper estimates
-        if tech in ["hydrogen storage tank", "hydrogen storage cavern"]:
+        if tech in ["hydrogen storage tank incl. compressor", "hydrogen storage cavern"]:
             uncertainty_columns.reverse()
         excel.rename(columns={excel.columns[-2]: uncertainty_columns[0],
                                 excel.columns[-1]: uncertainty_columns[1]
@@ -268,6 +275,10 @@ def get_data_DEA(tech, data_in, expectation=None):
     if (tech == "offwind") and snakemake.config['offwind_no_gridcosts']:
         df.loc['Nominal investment (MEUR/MW)'] -= excel.loc[' - of which grid connection']
 
+    # Exlucde indirect costs for centralised system with additional piping.
+    if tech.startswith('industrial heat pump'):
+        df = df.drop('Indirect investments cost (MEUR per MW)')
+
     df_final = pd.DataFrame(index=df.index, columns=years)
 
     # [RTD-interpolation-example]
@@ -282,6 +293,54 @@ def get_data_DEA(tech, data_in, expectation=None):
 
     return df_final
 
+def add_desalinsation_data(costs):
+    """
+    add technology data for sea water desalination (SWRO) and water storage.
+    """
+
+    # Interpolate cost based on historic costs/cost projection to fitting year
+    cs = [2070,1917,1603,1282,1025] # in USD/(m^3/d)
+    ys = [2015,2022,2030,2040,2050]
+    c = np.interp(year, ys, cs)
+    c *= 24                         # in USD/(m^3/h)
+    c /= 1.17                       # in EUR/(m^3/h)
+
+    tech = "seawater desalination"
+    costs.loc[(tech, 'investment'), 'value'] = c
+    costs.loc[(tech, 'investment'), 'unit'] = "EUR/(m^3-H2O/h)"
+    costs.loc[(tech, 'investment'), 'source'] = source_dict['Caldera2017'] + ", Table 4."
+
+    costs.loc[(tech, 'FOM'), 'value'] = 4.
+    costs.loc[(tech, 'FOM'), 'unit'] = "%/year"
+    costs.loc[(tech, 'FOM'), 'source'] = source_dict['Caldera2016'] + ", Table 1."
+
+    costs.loc[(tech, 'lifetime'), 'value'] = 30
+    costs.loc[(tech, 'lifetime'), 'unit'] = "years"
+    costs.loc[(tech, 'lifetime'), 'source'] = source_dict['Caldera2016'] + ", Table 1."
+
+    salinity = snakemake.config['desalination']['salinity']
+    costs.loc[(tech, 'electricity-input'), 'value'] = (0.0003*salinity**2+0.0018*salinity+2.6043)
+    costs.loc[(tech, 'electricity-input'), 'unit'] = "kWh/m^3-H2O"
+    costs.loc[(tech, 'electricity-input'), 'source'] = source_dict['Caldera2016'] + ", Fig. 4."
+
+    tech = "clean water tank storage"
+    costs.loc[(tech, 'investment'), 'value'] = 65
+    costs.loc[(tech, 'investment'), 'unit'] = "EUR/m^3-H2O"
+    costs.loc[(tech, 'investment'), 'source'] = source_dict['Caldera2016'] + ", Table 1."
+
+    costs.loc[(tech, 'FOM'), 'value'] = 2
+    costs.loc[(tech, 'FOM'), 'unit'] = "%/year"
+    costs.loc[(tech, 'FOM'), 'source'] = source_dict['Caldera2016'] + ", Table 1."
+
+    costs.loc[(tech, 'lifetime'), 'value'] = 30
+    costs.loc[(tech, 'lifetime'), 'unit'] = "years"
+    costs.loc[(tech, 'lifetime'), 'source'] = source_dict['Caldera2016'] + ", Table 1."
+
+    costs = adjust_for_inflation(costs, ['seawater desalination'], 2015)
+    costs = adjust_for_inflation(costs, ['clean water tank storage'], 2013)
+
+    return costs
+
 def add_conventional_data(costs):
     """"
     add technology data for conventional carriers from Lazards, DIW and BP
@@ -473,14 +532,30 @@ def get_data_from_DEA(data_in, expectation=None):
     """
     d_by_tech = {}
 
-    for tech in sheet_names.keys():
-        print(tech + ' in PyPSA corresponds to ' + sheet_names[tech] +
-              ' in DEA database.')
+    for tech, dea_tech in sheet_names.items():
+        print(f'{tech} in PyPSA corresponds to {dea_tech} in DEA database.')
         df = get_data_DEA(tech, data_in, expectation).fillna(0)
         d_by_tech[tech] = df
 
     return d_by_tech
 
+def adjust_for_inflation(costs, techs, ref_year):
+    """
+    adjust the investment costs for the specified techs for inflation.
+
+    techs: str or list
+        One or more techs in costs index for which the inflation adjustment is done.
+    ref_year: int
+        Reference year for which the costs are provided and based on which the inflation adjustment is done.
+    costs: pd.Dataframe
+        Dataframe containing the costs data with multiindex on technology and one index key 'investment'.
+    """
+
+    inflation = (1 + snakemake.config['rate_inflation'])**(ref_year - snakemake.config['eur_year'])
+    costs.loc[(techs, 'investment'), 'value'] /= inflation
+
+    return costs
+
 
 def clean_up_units(tech_data):
     """
@@ -651,12 +726,12 @@ def set_round_trip_efficiency(tech_data):
     """
 
     # hydrogen storage
-    to_drop = [("hydrogen storage tank", ' - Charge efficiency')]
-    to_drop.append(("hydrogen storage tank", ' - Discharge efficiency'))
+    to_drop = [("hydrogen storage tank incl. compressor", ' - Charge efficiency')]
+    to_drop.append(("hydrogen storage tank incl. compressor", ' - Discharge efficiency'))
     to_drop.append(("hydrogen storage underground", ' - Charge efficiency'))
     to_drop.append(("hydrogen storage underground", ' - Discharge efficiency'))
     tech_data.loc[("hydrogen storage underground", "Round trip efficiency"), years] *= 100
-    tech_data.loc[("hydrogen storage tank", "Round trip efficiency"), years] *= 100
+    tech_data.loc[("hydrogen storage tank incl. compressor", "Round trip efficiency"), years] *= 100
 
 
 
@@ -983,6 +1058,36 @@ def rename_pypsa_old(costs_pypsa):
 
     return costs_pypsa
 
+def add_manual_input(data):
+
+    df = pd.read_csv(snakemake.input['manual_input'], quotechar='"',sep=',', keep_default_na=False)
+
+    # Inflation adjustment for investment and VOM
+    mask = df[df['parameter'].isin(['investment','VOM'])].index
+    df.loc[mask, 'value'] /= (1+snakemake.config['rate_inflation'])**(df.loc[mask, 'currency_year']-snakemake.config['eur_year'])
+
+    l = []
+    for tech in df['technology'].unique():
+        c0 = df[df['technology'] == tech]
+        for param in c0['parameter'].unique():
+
+            c = df.query('technology == @tech and parameter == @param')
+
+            s = pd.Series(index=snakemake.config['years'],
+                      data=np.interp(snakemake.config['years'], c['year'], c['value']),
+                      name=param)
+            s['parameter'] = param
+            s['technology'] = tech
+            for col in ['unit','source','further_description']:
+                s[col] = "; and\n".join(c[col].unique().astype(str))
+
+            l.append(s)
+
+    new_df = pd.DataFrame(l).set_index(['technology','parameter'])
+    data = data.combine_first(new_df)
+
+    return data
+
 
 def rename_ISE(costs_ISE):
     """
@@ -1156,6 +1261,7 @@ def get_factor(df, cols, utility_col):
 # add costs for gas pipelines
 data = pd.concat([data, costs_ISE.loc[["Gasnetz"]]], sort=True)
 
+data = add_manual_input(data)
 # add costs for home batteries
 data = add_home_battery_costs(data)
 # %% (3) ------ add additional sources and save cost as csv ------------------
@@ -1173,6 +1279,9 @@ def get_factor(df, cols, utility_col):
     # add solar data from other source than DEA
     if any([snakemake.config['solar_utility_from_vartiaien'], snakemake.config['solar_rooftop_from_etip']]):
         costs = add_solar_from_other(costs)
+
+    # add desalination and clean water tank storage
+    costs = add_desalinsation_data(costs)
 
     # add electrolyzer and fuel cell efficiency from other source than DEA
     if snakemake.config['h2_from_budischak']:
@@ -1214,5 +1323,5 @@ def get_factor(df, cols, utility_col):
     costs_tot = unify_diw(costs_tot)
     costs_tot.drop("fixed", level=1, inplace=True)
     costs_tot.sort_index(inplace=True)
-    costs_tot = round(costs_tot, ndigits=2)
+    costs_tot = round(costs_tot, ndigits=6)
     costs_tot.to_csv([v for v in snakemake.output if str(year) in v][0])