Merge pull request #17 from NREL/feature/green_steel_ammonia

Update feature/green_steel_ammonia branch

LCA_single_scenario.py

@@ -0,0 +1,112 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Dec  2 12:09:20 2022
+
+@author: ereznic2
+"""
+import pandas as pd
+
+dircambium = 'Examples/H2_Analysis/Cambium_data/StdScen21_MidCase95by2035_hourly_' 
+
+# grid_connection_scenario = 'hybrid-grid'
+# atb_year = 2020
+# site_name = 'TX'
+# turbine_model = '6MW'
+# electrolysis_scale = 'Centralized'
+# policy_option = 'no policy'
+# grid_price_scenario = 'retail-flat'
+# electrolyzer_energy_kWh_per_kg = 55
+
+def hydrogen_LCA_singlescenario(grid_connection_scenario,atb_year,site_name,turbine_model,electrolysis_scale,policy_option,grid_price_scenario,electrolyzer_energy_kWh_per_kg,hydrogen_hourly_results_RODeO):
+
+    #==============================================================================
+    # DATA
+    #==============================================================================        
+    # Conversions
+    g_to_kg_conv  = 0.001  # Conversion from grams to kilograms
+    kg_to_MT_conv = 0.001 # Converion from kg to metric tonnes
+    MT_to_kg_conv = 1000 # Conversion from metric tonne to kilogram
+    kWh_to_MWh_conv = 0.001 # Conversion from kWh to MWh
+
+    #------------------------------------------------------------------------------
+    # Renewable infrastructure embedded emission intensities
+    #------------------------------------------------------------------------------
+    system_life        = 30
+    ely_stack_capex_EI = 0.019 # PEM electrolyzer CAPEX emissions (kg CO2e/kg H2)
+    wind_capex_EI      = 10    # Electricity generation from wind, nominal value taken (g CO2e/kWh)
+    solar_pv_capex_EI  = 37    # Electricity generation from solar pv, nominal value taken (g CO2e/kWh)
+
+
+    #------------------------------------------------------------------------------
+    # Hydrogen production via water electrolysis
+    #------------------------------------------------------------------------------
+
+    grid_trans_losses   = 0.05 # Grid losses of 5% are assumed (-)
+    fuel_to_grid_curr   = 48   # Fuel mix emission intensity for current power grid (g CO2e/kWh)
+    fuel_to_grid_futu   = 14   # Fuel mix emission intensity for future power grid (g CO2e/kWh)
+
+    if atb_year == 2020:
+        cambium_year = 2025
+    elif atb_year == 2025:
+        cambium_year = 2030
+    elif atb_year == 2030:
+        cambium_year ==2035
+    elif atb_year == 2035:
+        cambium_year = 2040
+
+    # Read in Cambium data  
+    cambiumdata_filepath = dircambium + site_name + '_'+str(cambium_year) + '.csv'
+    cambium_data = pd.read_csv(cambiumdata_filepath,index_col = None,header = 4,usecols = ['lrmer_co2_c','lrmer_ch4_c','lrmer_n2o_c','lrmer_co2_p','lrmer_ch4_p','lrmer_n2o_p','lrmer_co2e_c','lrmer_co2e_p','lrmer_co2e'])
+
+    cambium_data = cambium_data.reset_index().rename(columns = {'index':'Interval','lrmer_co2_c':'LRMER CO2 combustion (kg-CO2/MWh)','lrmer_ch4_c':'LRMER CH4 combustion (g-CH4/MWh)','lrmer_n2o_c':'LRMER N2O combustion (g-N2O/MWh)',\
+                                                  'lrmer_co2_p':'LRMER CO2 production (kg-CO2/MWh)','lrmer_ch4_p':'LRMER CH4 production (g-CH4/MWh)','lrmer_n2o_p':'LRMER N2O production (g-N2O/MWh)','lrmer_co2e_c':'LRMER CO2 equiv. combustion (kg-CO2e/MWh)',\
+                                                  'lrmer_co2e_p':'LRMER CO2 equiv. production (kg-CO2e/MWh)','lrmer_co2e':'LRMER CO2 equiv. total (kg-CO2e/MWh)'})
+
+    cambium_data['Interval']=cambium_data['Interval']+1
+    cambium_data = cambium_data.set_index('Interval')   
+
+    # Read in rodeo data
+    rodeo_data = hydrogen_hourly_results_RODeO[['Interval','Input Power (MW)','Non-Ren Import (MW)','Renewable Input (MW)','Curtailment (MW)','Product Sold (units of product)']]
+    rodeo_data = rodeo_data.rename(columns = {'Input Power (MW)':'Electrolyzer Power (MW)','Non-Ren Import (MW)':'Grid Import (MW)','Renewable Input (MW)':'Renewable Input (MW)', 'Curtailment (MW)':'Curtailment (MW)','Product Sold (units of product)':'Hydrogen production (kg-H2)'})
+    # Combine RODeO and Cambium data into one dataframe
+    combined_data = rodeo_data.merge(cambium_data, on = 'Interval',how = 'outer')   
+
+    # Calculate hourly grid emissions factors of interest. If we want to use different GWPs, we can do that here. The Grid Import is an hourly data i.e., in MWh
+    combined_data['Total grid emissions (kg-CO2e)'] = combined_data['Grid Import (MW)']*combined_data['LRMER CO2 equiv. total (kg-CO2e/MWh)']
+    combined_data['Scope 2 (combustion) grid emissions (kg-CO2e)'] = combined_data['Grid Import (MW)']*combined_data['LRMER CO2 equiv. combustion (kg-CO2e/MWh)']
+    combined_data['Scope 3 (production) grid emissions (kg-CO2e)'] = combined_data['Grid Import (MW)']*combined_data['LRMER CO2 equiv. production (kg-CO2e/MWh)']
+
+    # Sum total emissions
+    scope2_grid_emissions_sum = combined_data['Scope 2 (combustion) grid emissions (kg-CO2e)'].sum()*system_life*kg_to_MT_conv
+    scope3_grid_emissions_sum = combined_data['Scope 3 (production) grid emissions (kg-CO2e)'].sum()*system_life*kg_to_MT_conv
+    h2prod_sum = combined_data['Hydrogen production (kg-H2)'].sum()*system_life*kg_to_MT_conv
+    h2prod_grid_frac = combined_data['Grid Import (MW)'].sum() / combined_data['Electrolyzer Power (MW)'].sum()
+
+    if grid_connection_scenario == 'hybrid-grid' :
+        # Calculate grid-connected electrolysis emissions/ future cases should reflect targeted electrolyzer electricity usage
+        electrolysis_Scope3_EI =  h2prod_grid_frac*scope3_grid_emissions_sum/h2prod_sum + wind_capex_EI * electrolyzer_energy_kWh_per_kg * g_to_kg_conv + ely_stack_capex_EI # kg CO2e/kg H2
+        electrolysis_Scope2_EI =  h2prod_grid_frac*scope2_grid_emissions_sum/h2prod_sum 
+        electrolysis_Scope1_EI = 0
+        electrolysis_total_EI  = electrolysis_Scope1_EI + electrolysis_Scope2_EI + electrolysis_Scope3_EI 
+        electrolysis_total_EI_policy_grid = electrolysis_total_EI - wind_capex_EI * electrolyzer_energy_kWh_per_kg * g_to_kg_conv - ely_stack_capex_EI
+        electrolysis_total_EI_policy_offgrid = 0
+    elif grid_connection_scenario == 'grid-only':
+        # Calculate grid-connected electrolysis emissions
+        electrolysis_Scope3_EI = scope3_grid_emissions_sum/h2prod_sum  + ely_stack_capex_EI # kg CO2e/kg H2
+        electrolysis_Scope2_EI = scope2_grid_emissions_sum/h2prod_sum 
+        electrolysis_Scope1_EI = 0
+        electrolysis_total_EI = electrolysis_Scope1_EI + electrolysis_Scope2_EI + electrolysis_Scope3_EI
+        electrolysis_total_EI_policy_grid = electrolysis_total_EI - ely_stack_capex_EI
+        electrolysis_total_EI_policy_offgrid = 0
+    elif grid_connection_scenario == 'off-grid':    
+        # Calculate renewable only electrolysis emissions        
+        electrolysis_Scope3_EI = wind_capex_EI * electrolyzer_energy_kWh_per_kg * g_to_kg_conv + ely_stack_capex_EI # kg CO2e/kg H2
+        electrolysis_Scope2_EI = 0
+        electrolysis_Scope1_EI = 0
+        electrolysis_total_EI = electrolysis_Scope1_EI + electrolysis_Scope2_EI + electrolysis_Scope3_EI
+        electrolysis_total_EI_policy_offgrid = electrolysis_total_EI - wind_capex_EI * electrolyzer_energy_kWh_per_kg * g_to_kg_conv - ely_stack_capex_EI
+        electrolysis_total_EI_policy_grid = 0
+
+    return(electrolysis_total_EI_policy_grid,electrolysis_total_EI_policy_offgrid,h2prod_grid_frac)    
+
+

SMR_steel_ammonia_run_scenarios.py

@@ -0,0 +1,129 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Nov 30 13:29:30 2022
+
+@author: mkoleva
+"""
+
+# Specify file path to PyFAST
+import sys
+import os
+import glob
+sys.path.insert(1,'../PyFAST/')
+import pandas as pd
+sys.path.append('../PyFAST/')
+import run_pyfast_for_hydrogen_SMR
+sys.path.append('')
+import warnings
+warnings.filterwarnings("ignore")
+import hopp_tools_steel
+import hopp_tools
+
+parent_path = os.path.abspath('')
+dir1 = os.getcwd()
+dirin_el_prices = 'examples/H2_Analysis/'
+el_prices_files = glob.glob(os.path.join(dir1 + dirin_el_prices, 'annual_average_retail_prices.csv'))
+renewable_cost_path = ('examples/H2_Analysis/green_steel_site_renewable_costs_ATB.xlsx')
+fin_sum_dir = parent_path + '/examples/H2_Analysis/RODeO_financial_summary_results/'
+
+lcoh_check_all = []
+lcoh = []
+years_all = []
+sites_all = []
+policy_all = []
+NG_price_all = []
+scenario = dict()
+scenario['Wind PTC'] = 0.0
+SMR_LCOH_dic = {'Year':[], 'Location':[], 'Policy': [], 'NG price case': [], 'LCOH':[], 'LCOA':[], 'LCOS':[]}
+
+atb_years = [2020] #[2020,2025,2030,2035]
+
+site_selection = [
+                'Site 1',
+                #'Site 2',
+                #'Site 3',
+                #'Site 4',
+                ] 
+
+policy_cases = ['no policy','max'] 
+#['no policy', 'base', 'max']
+
+''' SMR doesn't get any of the policy options below:
+'''
+policy_option = {
+    'no policy': {'Wind ITC': 0, 'Wind PTC': 0, "H2 PTC": 0, 'Storage ITC': 0},
+    #'base': {'Wind ITC': 0, 'Wind PTC': 0.006, "H2 PTC": 0.6, 'Storage ITC': 6},
+    #'max': {'Wind ITC': 0, 'Wind PTC': 0.029, "H2 PTC": 3.0, 'Storage ITC': 50},
+    # 'max on grid hybrid': {'Wind ITC': 0, 'Wind PTC': 0.006, "H2 PTC": 0.60, 'Storage ITC': 6},
+    # 'max on grid hybrid': {'Wind ITC': 0, 'Wind PTC': 0.029, "H2 PTC": 0.60, 'Storage ITC': 50},
+    # 'option 3': {'Wind ITC': 6, 'Wind PTC': 0, "H2 PTC": 0.6},
+    # 'option 4': {'Wind ITC': 30, 'Wind PTC': 0, "H2 PTC": 3},
+    # 'option 5': {'Wind ITC': 50, 'Wind PTC': 0, "H2 PTC": 3},
+}
+
+NG_price_case = ['default']
+CCS_options = ['wCCS','woCCS']
+
+o2_heat_integration = 1
+
+# Site specific turbine information
+xl = pd.ExcelFile(renewable_cost_path)
+
+scenario_df = xl.parse()
+scenario_df.set_index(["Parameter"], inplace = True)
+
+for atb_year in atb_years:
+    for site_location in site_selection:
+        for policy_case in policy_cases:
+            for CCS_option in CCS_options:
+                site_df = scenario_df[site_location]                
+                site_name = site_df['State']                
+                hydrogen_annual_production, hydrogen_storage_duration_hr, lcoh, lcoh_breakdown, lcoe, plant_life, natural_gas_cost,\
+                price_breakdown_storage,price_breakdown_compression,\
+                price_breakdown_SMR_plant,\
+                price_breakdown_SMR_FOM, price_breakdown_SMR_VOM,\
+                price_breakdown_taxes,\
+                price_breakdown_water_charges,\
+                remaining_financial = \
+                run_pyfast_for_hydrogen_SMR.run_pyfast_for_hydrogen_SMR(atb_year,site_name,policy_case,NG_price_case,CCS_option)
+
+                lime_unit_cost = site_df['Lime ($/metric tonne)'] + site_df['Lime Transport ($/metric tonne)']
+                carbon_unit_cost = site_df['Carbon ($/metric tonne)'] + site_df['Carbon Transport ($/metric tonne)']
+                iron_ore_pellets_unit_cost = site_df['Iron Ore Pellets ($/metric tonne)'] + site_df['Iron Ore Pellets Transport ($/metric tonne)']
+                steel_economics_from_pyfast, steel_economics_summary, steel_breakeven_price, steel_annual_production_mtpy,steel_price_breakdown = \
+                                                                                hopp_tools_steel.steel_LCOS_SMR(lcoh,hydrogen_annual_production,
+                                                                                                                        lime_unit_cost,
+                                                                                                                        carbon_unit_cost,
+                                                                                                                        iron_ore_pellets_unit_cost,
+                                                                                                                        lcoe, scenario, natural_gas_cost, o2_heat_integration)
+
+                cooling_water_cost = 0.000113349938601175 # $/Gal
+                iron_based_catalyst_cost = 23.19977341 # $/kg
+                oxygen_cost = 0.0285210891617726       # $/kg 
+                ammonia_economics_from_pyfast, ammonia_economics_summary, ammonia_breakeven_price, ammonia_annual_production_kgpy,ammonia_price_breakdown = \
+                                                                                  hopp_tools_steel.levelized_cost_of_ammonia_SMR(lcoh,hydrogen_annual_production,
+                                                                                                                        cooling_water_cost,
+                                                                                                                        iron_based_catalyst_cost,
+                                                                                                                        oxygen_cost, 
+                                                                                                                        lcoe, scenario)
+
+
+                atb_year, lcoh = hopp_tools.write_outputs_PyFAST_SMR(fin_sum_dir,atb_year,
+                              site_name,
+                              lcoe,
+                              lcoh,
+                              hydrogen_storage_duration_hr,
+                              hydrogen_annual_production,
+                              price_breakdown_storage,price_breakdown_compression,
+                              price_breakdown_SMR_plant,
+                              price_breakdown_SMR_FOM, price_breakdown_SMR_VOM,
+                              price_breakdown_taxes,
+                              price_breakdown_water_charges,
+                              remaining_financial,
+                              steel_annual_production_mtpy,
+                              steel_breakeven_price,
+                              steel_price_breakdown,
+                              ammonia_annual_production_kgpy,
+                              ammonia_breakeven_price,
+                              ammonia_price_breakdown,policy_case,CCS_option,o2_heat_integration) 
+