updated technologies

nismod · Oct 4, 2018 · 8fa6fca · 8fa6fca
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diff --git a/energy_demand/config_data/05-technologies/efficiency_generation_spreadsheet.xlsx b/energy_demand/config_data/05-technologies/efficiency_generation_spreadsheet.xlsx
diff --git a/energy_demand/config_data/05-technologies/technology_definition.csv b/energy_demand/config_data/05-technologies/technology_definition.csv
@@ -1,59 +1,59 @@
-technology,fueltype,efficiency in base year,efficiency in future year,year when efficiency is fully realised,diffusion method (sigmoid or linear),market_entry,technology type,maximum theoretical service share of technology,description,TTTT
-halogen,electricity,0.04,0.07,2050,linear,2010,lighting,1,,lighting
-standard_lighting_bulb,electricity,0.03,0.04,2050,linear,2010,lighting,1,,lighting
-fluorescent_strip_lighting,electricity,0.14,0.21,2050,linear,2010,lighting,1,,lighting
-energy_saving_lighting_bulb,electricity,0.22,0.3,2050,linear,2010,lighting,1,,lighting
-LED,electricity,0.22,0.44,2050,linear,2010,lighting,1,,lighting
-washing_machine,electricity,0.61,0.61,2050,linear,2010,wet,1,NOTE: Check: 1.5,wet
-tumble_dryer,electricity,0.77,7.7,2050,linear,2010,wet,1,,wet
-dishwasher,electricity,0.69,2.3,2050,linear,2010,wet,1,,wet
-washer_dryer,electricity,0.61,3.07,2050,linear,2010,wet,1,,wet
-hob_gas,gas,0.4,0.5,2050,linear,2010,cooking,1,,cooking
-hob_electricity,electricity,0.6,0.7,2050,linear,2010,cooking,1,,cooking
-hob_induction_electricity,electricity,0.75,0.9,2050,linear,2010,cooking,1,,cooking
-hob_hydrogen,hydrogen,0.4,0.5,2050,linear,2010,cooking,1,,cooking
-hob_biomass,biomass,0.4,0.5,2050,linear,2010,cooking,1,,cooking
-chest_freezer,electricity,0.54,2.71,2050,linear,2010,cold,1,,cold
-fridge_freezer,electricity,0.48,2.71,2050,linear,2010,cold,1,NOTE: Check: 1.5,cold
-refrigerator,electricity,0.48,2.71,2050,linear,2010,cold,1,NOTE: Check: 1.5,cold
-upright_freezer,electricity,0.5,2.71,2050,linear,2010,cold,1,NOTE: Check: 1.5,cold
-boiler_solid_fuel,solid_fuel,0.6,0.7,2050,linear,2010,heating_const,1,,heating_const
-boiler_gas,gas,0.65,0.94,2050,linear,2010,heating_const,0.89,,heating_const
-boiler_electricity,electricity,0.99,0.99,2050,linear,2010,heating_const,1,,heating_const
-boiler_oil,oil,0.7,0.94,2050,linear,2010,heating_const,1,,heating_const
-boiler_biomass,biomass,0.6,0.95,2050,linear,2010,heating_const,1,,heating_const
-boiler_hydrogen,hydrogen,0.85,0.95,2050,linear,2010,heating_const,0.89,,heating_const
-boiler_condensing_gas,gas,0.85,0.95,2050,linear,2010,heating_const,1,,heating_const
-boiler_condensing_oil,oil,0.85,0.95,2050,linear,2010,heating_const,1,,heating_const
-stirling_micro_CHP_gas,gas,0.8,0.95,2050,linear,2010,tech_CHP,0.89,overall efficiency,tech_CHP
-fuel_cell_hydrogen,hydrogen,0.85,0.95,2050,linear,2010,tech_CHP,0.89,,tech_CHP
-storage_heater_electricity,electricity,0.99,0.99,2050,linear,2010,storage_heating_electricity,1,,storage_heating_electricity
-secondary_heater_electricity,electricity,0.99,0.99,2050,linear,2010,secondary_heating_electricity,1,,secondary_heating_electricity
-heat_pump_ASHP_electricity,electricity,3.6,4,2050,linear,2010,heating_non_const,1,,heating_non_const
-heat_pump_GSHP_electricity,electricity,4.9,5.4,2050,linear,2010,heating_non_const,1,,heating_non_const
-heat_pump_ASHP_hydrogen,hydrogen,3.6,4,2050,linear,2010,heating_non_const,0.89,,heating_non_const
-heat_pump_GSHP_hydrogen,hydrogen,4.9,5.4,2050,linear,2010,heating_non_const,0.89,,heating_non_const
-central_air_conditioner_electricity,electricity,0.5,1,2050,linear,2010,cooling_const,1,,cooling_const
-decentral_air_conditioner_electricity,electricity,0.5,1,2050,linear,2010,cooling_const,1,,cooling_const
-central_air_conditioner_oil,oil,0.5,0.5,2050,linear,2010,cooling_const,1,,cooling_const
-decentral_air_conditioner_oil,oil,0.5,0.5,2050,linear,2010,cooling_const,1,,cooling_const
-central_air_conditioner_gas,gas,0.5,0.5,2050,linear,2010,cooling_const,0.89,,cooling_const
-decentral_air_conditioner_gas,gas,0.5,0.5,2050,linear,2010,cooling_const,0.89,,cooling_const
-district_heating_CHP_gas,gas,0.45,0.45,2050,linear,2010,tech_CHP,0.2,thermal efficiency,tech_CHP
-district_heating_electricity,electricity,1,1,2050,linear,2010,tech_district_heating,0.2,,tech_district_heating
-district_heating_biomass,biomass,0.6,0.6,2050,linear,2010,tech_district_heating,0.2,,tech_district_heating
-district_heating_fuel_cell,hydrogen,0.85,0.85,2050,linear,2010,tech_district_heating,0.2,,tech_district_heating
-basic_oxygen_furnace,solid_fuel,1,1,2050,linear,2010,steel_production,1,,steel_production
-electric_arc_furnace,electricity,6.1,6.1,2050,linear,2010,steel_production,1,assumed no technology imporvement,steel_production
-SNG_furnace,gas,1,1,2050,linear,2020,steel_production,1,assumed no technology imporvement,steel_production
-biomass_furnace,biomass,1,1,2050,linear,2020,steel_production,1,assumed no technology imporvement,steel_production
-dry_kiln_coal,solid_fuel,1.8,1.8,2050,linear,2010,cement_production,1,assumed no technology imporvement,cement_production
-dry_kiln_oil,oil,1.8,1.8,2050,linear,2010,cement_production,1,assumed no technology imporvement,cement_production
-dry_kiln_gas,gas,1.8,1.8,2050,linear,2010,cement_production,1,assumed no technology imporvement,cement_production
-dry_kiln_electricity,electricity,1.8,1.8,2050,linear,2010,cement_production,1,assumed no technology imporvement,cement_production
-dry_kiln_biomass,biomass,1.8,1.8,2050,linear,2010,cement_production,1,assumed no technology imporvement,cement_production
-dry_kiln_hydrogen,hydrogen,1.8,1.8,2050,linear,2010,cement_production,1,assumed no technology imporvement,cement_production
-wet_kiln_coal,solid_fuel,1,1,2050,linear,2010,cement_production,1,assumed no technology imporvement,cement_production
-wet_kiln_oil,oil,1,1,2050,linear,2010,cement_production,1,assumed no technology imporvement,cement_production
-wet_kiln_gas,gas,1,1,2050,linear,2010,cement_production,1,assumed no technology imporvement,cement_production
-wet_kiln_electricity,electricity,1,1,2050,linear,2010,cement_production,1,assumed no technology imporvement,cement_production
+technology,fueltype,efficiency in base year,efficiency in future year,year when efficiency is fully realised,diffusion method (sigmoid or linear),market_entry,technology type,maximum theoretical service share of technology,description
+standard_lighting_bulb,electricity,0.029,0.034,2050,linear,2000,lighting,1,
+halogen,electricity,0.037,0.073,2050,linear,2000,lighting,1,
+energy_saving_lighting_bulb,electricity,0.220,0.293,2050,linear,2000,lighting,1,
+fluorescent_strip_lighting,electricity,0.146,0.220,2050,linear,2000,lighting,1,
+LED,electricity,0.220,0.439,2050,linear,2000,lighting,1,
+washing_machine,electricity,1.000,1.334,2050,linear,2000,wet,1,
+tumble_dryer,electricity,1.000,3.076,2050,linear,2000,wet,1,
+dishwasher,electricity,1.000,1.367,2050,linear,2000,wet,1,
+washer_dryer,electricity,1.000,1.334,2050,linear,2000,wet,1,
+hob_gas,gas,0.400,0.500,2050,linear,2000,cooking,1,
+hob_electricity,electricity,0.600,0.700,2050,linear,2000,cooking,1,
+hob_induction_electricity,electricity,0.750,0.900,2050,linear,2000,cooking,1,
+hob_hydrogen,hydrogen,0.400,0.500,2050,linear,2000,cooking,1,
+hob_biomass,biomass,0.400,0.500,2050,linear,2000,cooking,1,
+chest_freezer,electricity,1.000,2.709,2050,linear,2000,cold,1,
+fridge_freezer,electricity,1.000,2.405,2050,linear,2000,cold,1,
+refrigerator,electricity,1.000,2.408,2050,linear,2000,cold,1,
+upright_freezer,electricity,1.000,2.500,2050,linear,2000,cold,1,
+central_air_conditioner_electricity,electricity,1.000,2.000,2050,linear,2000,cooling_const,1,
+decentral_air_conditioner_electricity,electricity,1.000,2.000,2050,linear,2000,cooling_const,1,
+central_air_conditioner_oil,oil,1.000,2.000,2050,linear,2000,cooling_const,1,
+decentral_air_conditioner_oil,oil,1.000,2.000,2050,linear,2000,cooling_const,1,
+central_air_conditioner_gas,gas,1.000,2.000,2050,linear,2000,cooling_const,0.89,
+decentral_air_conditioner_gas,gas,1.000,2.000,2050,linear,2000,cooling_const,0.89,
+boiler_solid_fuel,solid_fuel,0.600,0.700,2050,linear,2000,heating_const,1,
+boiler_gas,gas,0.650,0.940,2050,linear,2000,heating_const,0.89,
+boiler_electricity,electricity,0.990,0.990,2050,linear,2000,heating_const,1,
+boiler_oil,oil,0.700,0.940,2050,linear,2000,heating_const,1,
+boiler_biomass,biomass,0.600,0.950,2050,linear,2000,heating_const,1,
+boiler_hydrogen,hydrogen,0.850,0.950,2050,linear,2000,heating_const,0.89,
+boiler_condensing_gas,gas,0.850,0.950,2050,linear,2000,heating_const,1,
+boiler_condensing_oil,oil,0.850,0.950,2050,linear,2000,heating_const,1,
+stirling_micro_CHP_gas,gas,0.800,0.950,2050,linear,2000,tech_CHP,0.89,
+fuel_cell_hydrogen,hydrogen,0.850,0.950,2050,linear,2000,tech_CHP,0.89,
+storage_heater_electricity,electricity,0.990,0.990,2050,linear,2000,storage_heating_electricity,1,
+secondary_heater_electricity,electricity,0.990,0.990,2050,linear,2000,secondary_heating_electricity,1,
+heat_pump_ASHP_electricity,electricity,3.600,4.000,2050,linear,2000,heating_non_const,1,
+heat_pump_GSHP_electricity,electricity,4.900,5.400,2050,linear,2000,heating_non_const,1,
+heat_pump_ASHP_hydrogen,hydrogen,3.600,4.000,2050,linear,2000,heating_non_const,0.89,
+heat_pump_GSHP_hydrogen,hydrogen,4.900,5.400,2050,linear,2000,heating_non_const,0.89,
+district_heating_CHP_gas,gas,0.450,0.450,2050,linear,2000,tech_CHP,0.2,thermal efficiency
+district_heating_electricity,electricity,1.000,1.000,2050,linear,2000,tech_district_heating,0.2,
+district_heating_biomass,biomass,0.600,0.600,2050,linear,2000,tech_district_heating,0.2,
+district_heating_fuel_cell,hydrogen,0.850,0.850,2050,linear,2000,tech_district_heating,0.2,
+basic_oxygen_furnace,solid_fuel,1,1,2050,linear,2010,steel_production,1,assumed no technology imporvement
+electric_arc_furnace,electricity,6.1,6.1,2050,linear,2010,steel_production,1,assumed no technology imporvement
+SNG_furnace,gas,1,1,2050,linear,2020,steel_production,1,assumed no technology imporvement
+biomass_furnace,biomass,1,1,2050,linear,2020,steel_production,1,assumed no technology imporvement
+dry_kiln_coal,solid_fuel,1.8,1.8,2050,linear,2010,cement_production,1,assumed no technology imporvement
+dry_kiln_oil,oil,1.8,1.8,2050,linear,2010,cement_production,1,assumed no technology imporvement
+dry_kiln_gas,gas,1.8,1.8,2050,linear,2010,cement_production,1,assumed no technology imporvement
+dry_kiln_electricity,electricity,1.8,1.8,2050,linear,2010,cement_production,1,assumed no technology imporvement
+dry_kiln_biomass,biomass,1.8,1.8,2050,linear,2010,cement_production,1,assumed no technology imporvement
+dry_kiln_hydrogen,hydrogen,1.8,1.8,2050,linear,2010,cement_production,1,assumed no technology imporvement
+wet_kiln_coal,solid_fuel,1,1,2050,linear,2010,cement_production,1,assumed no technology imporvement
+wet_kiln_oil,oil,1,1,2050,linear,2010,cement_production,1,assumed no technology imporvement
+wet_kiln_gas,gas,1,1,2050,linear,2010,cement_production,1,assumed no technology imporvement
+wet_kiln_electricity,electricity,1,1,2050,linear,2010,cement_production,1,assumed no technology imporvement
diff --git a/energy_demand/plotting/_scrap.py b/energy_demand/plotting/_scrap.py
@@ -17,8 +17,14 @@
 from energy_demand.plotting import fig_p2_weather_var
 
 uk_gdf = pd.DataFrame(np.array(
-    [[-5, 1], [-3, 10], [0, 100], [3, 100], [5, 100]]),
-                      columns=['diff_av_max', 'b'])
+    [
+        [-5, 1],
+        [-3, 10],
+        [0, 100],
+        [3, 100],
+        [5, 100]]),
+
+    columns=['diff_av_max', 'b'])
 
 uk_gdf = fig_p2_weather_var.user_defined_bin_classification(
     uk_gdf,

diff --git a/energy_demand/plotting/fig_p2_annual_hours_sorted.py b/energy_demand/plotting/fig_p2_annual_hours_sorted.py
@@ -0,0 +1,132 @@
+"""Fig 2 figure
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+#from scipy.stats import mstats
+import pandas as pd
+import geopandas as gpd
+from scipy import stats
+from shapely.geometry import Point
+import matplotlib.pyplot as plt
+from collections import defaultdict
+from matplotlib.colors import Normalize
+
+from energy_demand.plotting import result_mapping
+from energy_demand.technologies import tech_related
+from energy_demand.plotting import basic_plot_functions
+
+def run(
+        data_input,
+        regions,
+        simulation_yrs_to_plot,
+        fueltype_str,
+        path_shapefile,
+        fig_name
+    ):
+    """
+    """
+    fueltype_int = tech_related.get_fueltype_int(fueltype_str)
+
+    # Figure related
+    fig = plt.figure() #(figsize = cm2inch(10,10))
+    ax = fig.add_subplot(111)
+    period_h = range(8760)
+
+    for sim_yr_to_plot in simulation_yrs_to_plot:
+
+        # ---Collect data for every weather year
+        weather_yrs_data_regional = defaultdict(dict)
+        weather_yrs_data_total = []
+        for weather_yr, data_weather_yr in data_input.items():
+
+            # Weather year specific data for every region
+            regions_fuel = data_weather_yr[sim_yr_to_plot][fueltype_int]
+
+            # Total fuel
+            national_fuel = np.sum(regions_fuel, axis=0)
+
+            for region_nr, region_name in enumerate(regions):
+                try:
+                    weather_yrs_data_regional[region_name].append(regions_fuel[region_nr])
+                except (KeyError, AttributeError):
+                    weather_yrs_data_regional[region_name] = [regions_fuel[region_nr]]
+            try:
+                weather_yrs_data_total.append(national_fuel)
+            except:
+                weather_yrs_data_total = [national_fuel]
+
+        # ---Collect data for every weather year
+
+        # Convert regional data to dataframe
+        national_fuel_array = np.array(weather_yrs_data_total)
+
+        df = pd.DataFrame(
+            national_fuel_array,
+            columns=range(8760))
+
+        # Calculate regional statistics
+        mean_data = df.mean(axis=0)
+
+        mean_data = mean_data.sort_values(ascending=False)
+
+        # Reorder df according to ordering of mean values
+        ordering_of_hours_index = list(mean_data.index)
+
+        df = df[ordering_of_hours_index]
+
+        #std_dev = df.std(axis=0) #standard deviation across every hour
+        # Calculate quantiles
+        quantile_95 = 0.95
+        quantile_05 = 0.05
+
+        df_q_95 = df.quantile(quantile_95)
+        df_q_05 = df.quantile(quantile_05)
+
+        #Transpose for plotting purposes
+        df = df.T
+        df_q_95 = df_q_95.T
+        df_q_05 = df_q_05.T
+
+        # ---------------
+        # Smoothing lines
+        # ---------------
+        try:
+            period_h_smoothed, df_q_95_smoothed = basic_plot_functions.smooth_data(period_h, df_q_95, num=40000)
+            period_h_smoothed, df_q_05_smoothed = basic_plot_functions.smooth_data(period_h, df_q_05, num=40000)
+            period_h_smoothed, mean_data_smoothed = basic_plot_functions.smooth_data(period_h, mean_data, num=40000)
+        except:
+            period_h_smoothed = period_h
+            df_q_95_smoothed = df_q_95
+            df_q_05_smoothed = df_q_05
+            mean_data_smoothed = mean_data
+
+        plt.plot(period_h_smoothed, mean_data_smoothed, color='tomato', linestyle='-', linewidth=2, label="average")
+        #plt.plot(period_h_smoothed, df_q_05_smoothed, color='black', linestyle='--', linewidth=0.5, label="0.05")
+        #plt.plot(period_h_smoothed, df_q_95_smoothed, color='black', linestyle='--', linewidth=0.5, label="0.95")
+
+        # -----------------
+        # Uncertainty range
+        # -----------------
+        plt.fill_between(
+            period_h_smoothed, #x
+            df_q_95_smoothed,  #y1
+            df_q_05_smoothed,  #y2
+            alpha=.40,
+            facecolor="grey",
+            label="uncertainty band")
+
+        plt.legend(
+            prop={
+                'family':'arial',
+                'size': 10},
+            loc='best',
+            frameon=False,
+            shadow=True)
+
+        plt.xlabel("Load duration curve")
+        plt.ylabel("energy demand")
+
+        plt.xlim(0, 8760)
+        plt.show()
+
+        print("--")
diff --git a/energy_demand/plotting/fig_p2_weather_var.py b/energy_demand/plotting/fig_p2_weather_var.py
@@ -54,7 +54,9 @@ def run(
         'mean_peak_h',
         'diff_av_max',
         'mean_peak_h_pp',
-        'diff_av_max_pp']
+        'diff_av_max_pp',
+        'std_dev_average_every_h',
+        'std_dev_peak_h_norm_pop']
 
     df_stats = pd.DataFrame(columns=regional_statistics_columns)
 
@@ -68,6 +70,14 @@ def run(
 
         # Calculate regional statistics
         mean = df.mean(axis=0)
+        std_dev = df.std(axis=0) #standard deviation across every hour
+
+        # Get maximum per colum
+        #max_every_h = df.max()
+        #colum_max_h = max_every_h.argmax() #get colum (respesctively hour) of maximum value
+
+        # Average standard deviation across every hour
+        std_dev_average_every_h = np.std(list(std_dev))
 
         max_entry = df.max(axis=0) #maximum entry for every hour
         min_entry = df.min(axis=0) #maximum entry for every hour
@@ -76,6 +86,9 @@ def run(
         hour_nr_max = max_entry.argmax()
         hour_nr_min = min_entry.argmin()
 
+        # standard deviation of peak hour
+        std_dev_peak_h = std_dev[hour_nr_max]
+
         # Difference between average and max
         diff_av_max = max_entry[hour_nr_max] - mean[hour_nr_max]
         mean_peak_h = mean[hour_nr_max]
@@ -91,6 +104,10 @@ def run(
                 break
         pop = population[nr_of_reg]
 
+        # Divide standard deviation of peak hour by population
+        # which gives measure of weather variability in peak hour
+        std_dev_peak_h_norm_pop = std_dev_peak_h / pop
+
         diff_av_max_pp = diff_av_max / pop
         mean_peak_h_pp = mean_peak_h / pop
 
@@ -99,7 +116,9 @@ def run(
             mean_peak_h,
             diff_av_max,
             mean_peak_h_pp,
-            diff_av_max_pp]]
+            diff_av_max_pp,
+            std_dev_average_every_h,
+            std_dev_peak_h_norm_pop]]
 
         line_df = pd.DataFrame(
             line_entry, columns=regional_statistics_columns)
@@ -111,6 +130,7 @@ def run(
     #print("---------")
     print(df_stats['diff_av_max'].max())
     print(df_stats['mean_peak_h'].max())
+    print(df_stats['std_dev_peak_h_norm_pop'].max())
     print("-")
     print(df_stats['diff_av_max_pp'].max())
     print(df_stats['diff_av_max_pp'].min())
@@ -144,6 +164,8 @@ def run(
     # Field to plot
     field_to_plot = "diff_av_max_pp" # Difference between average and peak per person in KWh
     #field_to_plot = "diff_av_max"    # Difference between average and peak
+    field_to_plot = 'std_dev_peak_h_norm_pop'
+
     nr_of_intervals = 6
 
     bin_values = result_mapping.get_reasonable_bin_values_II(

diff --git a/energy_demand/plotting/plots_paper_II.py b/energy_demand/plotting/plots_paper_II.py
@@ -15,6 +15,7 @@
 from energy_demand.validation import elec_national_data
 from energy_demand.technologies import tech_related
 from energy_demand.plotting import fig_p2_weather_var
+from energy_demand.plotting import fig_p2_annual_hours_sorted
 
 def main(
         path_data_ed,
@@ -73,7 +74,7 @@ def main(
 
     population_data = read_data.read_scenaric_population_data(
         os.path.join(path_data_ed, 'model_run_pop'))
-
+    
     # ####################################################################
     # Create plot with regional and non-regional plots for second paper
     # Compare hdd calculations and disaggregation of regional and local
@@ -192,7 +193,7 @@ def main(
 
         # plot over period of time across all weather scenario
         fig_weather_variability_priod.run(
-            data_input=weather_yr_container['tot_fueltype_yh'], #[weather_yr],
+            data_input=weather_yr_container['tot_fueltype_yh'],
             fueltype_str='electricity',
             simulation_yr_to_plot=2015, # Simulation year to plot
             period_h=list(range(200,500)), #period to plot
@@ -212,4 +213,17 @@ def main(
             fueltype_str='electricity',
             path_shapefile=path_shapefile_input,
             fig_name=os.path.join(path_out_plots, "fig_paper_IIb_weather_var_map.pdf"))
-
+
+    # ####################################################################
+    # Create plot with regional and non-regional plots for second paper
+    # Compare hdd calculations and disaggregation of regional and local
+    # ####################################################################
+    if plot_crit_dict['plot_scenarios_sorted']:
+
+        fig_p2_annual_hours_sorted.run(
+            data_input=weather_yr_container['results_enduse_every_year'],
+            regions=data['regions'],
+            simulation_yrs_to_plot=[2015], # Simulation year to plot
+            fueltype_str='electricity',
+            path_shapefile=path_shapefile_input,
+            fig_name=os.path.join(path_out_plots, "fig_paper_IIb_weather_var_map.pdf"))