Source code for energy_demand.geography.spatial_diffusion

"""This file contains all calculations related
to spatial explicit calculations of technology/innovation
penetration."""
import logging
from collections import defaultdict
import numpy as np

[docs]def spatial_diffusion_values( regions, real_values, speed_con_max, low_congruence_crit, p_outlier ): """Generate spatial diffusion values from real data Arguments --------- regions : dict Regions p_outlier : float (percentage) Percentage of outliers which are capped at both ends of the value spectrum of the real data Returns ------- diffusion_values : dict Spatial diffusion values based on speed assumptions Example ------- This function calculates the values which already incorporate different speeds in diffusion. For example based on real values (e.g. population density) congruence values are calculated. Then, the congruence values are linked to diffusion speed differentes. """ diffusion_values = {} # Diffusion speed assumptions speed_con_min = 1 # Speed at val_con == 0 speed_con_max = speed_con_max # Speed at con_val == 1 if speed_con_max == 1: # No regional difference for region in regions: diffusion_values[region] = 1 #100% congruence else: # ---------------- # plot real values to check for outliers # ---------------- # Select number of outliers to remove lower and higher extremes nr_of_outliers = int(100 / len(regions) * p_outlier) sorted_vals = list(real_values.values()) sorted_vals.sort() # Get value of largest outlier treshold_upper_real_value = sorted_vals[-nr_of_outliers] treshold_lower_real_value = sorted_vals[nr_of_outliers] for reg, val in real_values.items(): if val > treshold_upper_real_value: real_values[reg] = treshold_upper_real_value if val < treshold_lower_real_value: real_values[reg] = treshold_lower_real_value # --------------------------------- # Congruence calculations # ---------------------------------- # Max congruence value con_max = max(real_values.values()) for region in regions: # Multiply speed of diffusion of concept with concept congruence value try: real_value = real_values[region] except KeyError: real_value = np.average(real_values.values()) logging.warning("Set average real data for region %s", region) # Calculate congruence value congruence_value = real_value / con_max # If the assignement is thoe other way round (lowest value has highest congruence value) if low_congruence_crit: congruence_value = 1 - congruence_value else: pass # Calculate diffusion value lower_concept_val = (1 - congruence_value) * speed_con_min higher_concept_val = congruence_value * speed_con_max diffusion_values[region] = lower_concept_val + higher_concept_val return diffusion_values
[docs]def calc_diffusion_f(regions, f_reg, spatial_diff_values, fuels): """From spatial diffusion values calculate diffusion factor for every region (which needs to sum up to one across all regions) and end use. With help of these calculation diffusion factors, a spatial explicit diffusion of innovations can be implemented. Arguments ---------- regions : dict Regions f_reg : dict Regional not weighted diffusion factors spatial_diff_values : dict Spatial diffusion index values fuels : array Fuels per enduse or fuel per sector and enduse Example ------- If the national assumption of a technology diffusion of 50% is defined (e.g. 50% of service are heat pumps), this percentage can be changed per region, i.e. in some regions with higher diffusion factors, a larger percentage adopt the technology on the expense of other regions, where a lower percentage adopt this technology. In sum however, for all regions, the total service still sums up to 50%. Note ----- The total sum can be higher than 1 in case of high values. Therefore the factors need to be capped. TODO MORE INFO """ # Calculate fraction of energy demand of every region of total demand reg_enduse_p = defaultdict(dict) fuels_enduse = {} for fuel_submodel in fuels: # ----------------------------------- # Sum fuel across sectors # ----------------------------------- fuel_submodel_new = defaultdict(dict) for region, entries in fuel_submodel.items(): enduses = entries.keys() try: for enduse in entries: for sector in entries[enduse]: fuel_submodel_new[region][enduse] = 0 for enduse in entries: for sector in entries[enduse]: fuel_submodel_new[region][enduse] += np.sum(entries[enduse][sector]) fuel_submodel = fuel_submodel_new except IndexError: enduses = entries.keys() break # -------------------- # Calculate fraction of fuel for each region # -------------------- for enduse in enduses: fuels_enduse[enduse] = 0 # Total uk fuel of enduse tot_enduse_uk = 0 for region in regions: tot_enduse_uk += np.sum(fuel_submodel[region][enduse]) # Calculate regional % of enduse for region in regions: reg_enduse_p[enduse][region] = np.sum(fuel_submodel[region][enduse]) / tot_enduse_uk fuels_enduse[enduse] += np.sum(fuel_submodel[region][enduse]) # ---------- # Norm spatial factor (f_reg_norm) with population (does not sum upt to 1.p Eq. 7 Appendix) # ---------- f_reg_norm = {} for enduse, regions_fuel_p in reg_enduse_p.items(): # Sum across all regs (factor * fuel_p) sum_p_f_all_regs = 0 for region in regions: sum_p_f_all_regs += f_reg[region] * regions_fuel_p[region] f_reg_norm[enduse] = {} for region, fuel_p in regions_fuel_p.items(): f_reg_norm[enduse][region] = f_reg[region] / sum_p_f_all_regs # ---------- # Norm which sums up to 1 (f_reg_norm_abs) (e.g. distriubte 200 units across space) # ---------- f_reg_norm_abs = {} for enduse, regions_fuel_p in reg_enduse_p.items(): f_reg_norm_abs[enduse] = {} for region, fuel_p in regions_fuel_p.items(): f_reg_norm_abs[enduse][region] = fuel_p * spatial_diff_values[region] #----------- # Normalize f_reg_norm_abs #----------- for enduse in f_reg_norm_abs: sum_enduse = sum(f_reg_norm_abs[enduse].values()) for region in f_reg_norm_abs[enduse]: f_reg_norm_abs[enduse][region] = f_reg_norm_abs[enduse][region] / sum_enduse # Testing for enduse in f_reg_norm_abs: np.testing.assert_almost_equal( sum(f_reg_norm_abs[enduse].values()), 1, decimal=2) return f_reg_norm_abs, f_reg_norm
[docs]def calc_regional_services( enduse, uk_techs_service_p, regions, spatial_factors, fuel_disaggregated, techs_affected_spatial_f, capping_val=1 ): """Calculate regional specific end year service shares of technologies (rs_reg_enduse_tech_p_ey) Arguments --------- uk_techs_service_p : dict Service shares per technology for future year regions : dict Regions spatial_factors : dict Spatial factor per enduse and region fuel_disaggregated : dict Fuels per region techs_affected_spatial_f : list List with technologies where spatial diffusion is affected capping_val : float Maximum service share (1.0). This is needed in case of spatial explicit diffusion modelling where the diffusion speed is very large and thus would lead to areas with largher shares than 1 Returns ------- rs_reg_enduse_tech_p_ey : dict Regional specific model end year service shares of techs Modelling steps ----- A.) Calculation national end use service to reduce (e.g. 50% heat pumps for all regions) (uk_tech_service_ey_p) B.) Distribute this service according to spatial index for techs where the spatial explicit diffusion applies (techs_affected_spatial_f). Otherwise disaggregated according to fuel C.) Convert regional service reduction to ey % in region """ reg_enduse_tech_p_ey = defaultdict(dict) # ------------------------------------ # Calculate national total enduse fuel and service # ------------------------------------ uk_enduse_fuel = 0 for region in regions: reg_enduse_tech_p_ey[region] = {} uk_enduse_fuel += np.sum(fuel_disaggregated[region][enduse]) # ---- # Service of enduse for all regions # ---- for region in regions: # Disaggregation factor f_fuel_disagg = np.sum(fuel_disaggregated[region][enduse]) / uk_enduse_fuel # Calculate fraction of regional service for tech, uk_tech_service_ey_p in uk_techs_service_p.items(): global_tech_service_ey_p = uk_tech_service_ey_p # --------------------------------------------- # B.) Calculate regional service for technology # --------------------------------------------- if tech in techs_affected_spatial_f: # Use spatial factors reg_service_tech = global_tech_service_ey_p * spatial_factors[enduse][region] else: # If not specified, use fuel disaggregation for enduse factor reg_service_tech = global_tech_service_ey_p #* f_fuel_disagg reg_enduse_tech_p_ey[region][tech] = reg_service_tech # --------------------------------------------- # C.) Calculate regional fraction # --------------------------------------------- for tech, service_tech in reg_enduse_tech_p_ey[region].items(): # ---------------------------------- # Capping value in case larger than 1.0 # ---------------------------------- service_share = service_tech if service_share > capping_val: reg_enduse_tech_p_ey[region][tech] = capping_val logging.info("Maximum value is capped: {} {} {}".format( region, service_share, tech)) else: reg_enduse_tech_p_ey[region][tech] = service_share return dict(reg_enduse_tech_p_ey)
[docs]def calc_spatially_diffusion_factors( regions, fuel_disagg, real_values, low_congruence_crit, speed_con_max, p_outlier ): """ Calculate spatial diffusion values Arguments --------- regions : dict Regions fuel_disagg : dict Disaggregated fuel per region real_values : dict Real values p_outlier : float Percentage of min and max outliers are flattened Returns ------- f_reg_norm_abs : dict Diffusion values with normed population. If no value is larger than 1, the total sum of all shares calculated for every region is identical to the defined scenario variable. spatial_diff_values : dict Spatial diffusion values (not normed, only considering differences in speed and congruence values) Explanation ============ (I) Load diffusion values (II) Calculate diffusion factors (III) Calculate sigmoid diffusion values for technology specific enduse service shares for every region """ # ----- # I. Diffusion diffusion values # ----- spatial_diff_values = spatial_diffusion_values( regions=regions, real_values=real_values, speed_con_max=speed_con_max, low_congruence_crit=low_congruence_crit, p_outlier=p_outlier) # ----- # II. Calculation of diffusion factors (Not weighted with demand) # ----- # Not weighted with demand max_value_diffusion = max(list(spatial_diff_values.values())) f_reg = {} for region in regions: f_reg[region] = spatial_diff_values[region] / max_value_diffusion # Weighted with demand f_reg_norm_abs, f_reg_norm = calc_diffusion_f( regions, f_reg, spatial_diff_values, [fuel_disagg['residential'], fuel_disagg['service'], fuel_disagg['industry']]) return f_reg, f_reg_norm, f_reg_norm_abs
'''def spatially_differentiated_modelling( regions, fuel_disagg, rs_share_s_tech_ey_p, ss_share_s_tech_ey_p, is_share_s_tech_ey_p, techs_affected_spatial_f, spatial_diffusion_factor, spatial_explicit_diffusion=False ): """ Regional diffusion shares of technologies is calculated based on calcualted spatial diffusion factors Arguments --------- regions : dict Regions fuel_disagg : dict Fuel per region rs_share_s_tech_ey_p : dict Global technology service shares ss_share_s_tech_ey_p : dict Global technology service shares is_share_s_tech_ey_p : dict Global technology service shares techs_affected_spatial_f : list Technologies which are affected by spatially heterogeneous diffusion spatial_diffusion_factor : dict Spatial diffusion factor Returns -------- XX_reg_share_s_tech_ey_p : Technology specific service shares for every region (residential) considering differences in diffusion speed. If the calculate regional shares are larger than 1.0, the diffusion is set to the maximum criteria (`cap_max`). This means that if some regions reach the maximum defined value, thes cannot futher increase their share. This means that other regions diffuse slower and do not reach such high leves (and because the faster regions cannot over-compensate, the total sum is not identical). Calculate sigmoid diffusion values for technology specific enduse service shares for every region """ # Residential spatial explicit modelling rs_reg_share_s_tech_ey_p = {} for enduse, uk_techs_service_p in rs_share_s_tech_ey_p.items(): rs_reg_share_s_tech_ey_p[enduse] = calc_regional_services( enduse, uk_techs_service_p, regions, spatial_diffusion_factor, fuel_disagg['residential'], techs_affected_spatial_f) ss_reg_share_s_tech_ey_p = {} for sector, uk_techs_service_enduses_p in ss_share_s_tech_ey_p.items(): ss_reg_share_s_tech_ey_p[sector] = {} for enduse, uk_techs_service_p in uk_techs_service_enduses_p.items(): ss_reg_share_s_tech_ey_p[sector][enduse] = calc_regional_services( enduse, uk_techs_service_p, regions, spatial_diffusion_factor, fuel_disagg['ss_fuel_disagg_sum_all_sectors'], techs_affected_spatial_f) is_reg_share_s_tech_ey_p = {} for sector, uk_techs_service_enduses_p in is_share_s_tech_ey_p.items(): is_reg_share_s_tech_ey_p[sector] = {} for enduse, uk_techs_service_p in uk_techs_service_enduses_p.items(): is_reg_share_s_tech_ey_p[sector][enduse] = calc_regional_services( enduse, uk_techs_service_p, regions, spatial_diffusion_factor, fuel_disagg['is_aggr_fuel_sum_all_sectors'], techs_affected_spatial_f) return rs_reg_share_s_tech_ey_p, ss_reg_share_s_tech_ey_p, is_reg_share_s_tech_ey_p'''
[docs]def factor_improvements_single( factor_uk, regions, f_reg, f_reg_norm, f_reg_norm_abs, fuel_regs_enduse ): """Calculate regional specific end year service shares of technologies (rs_reg_enduse_tech_p_ey) Arguments ========= factor_uk : float Improvement of either an enduse or a variable for the whole UK regions : dict Regions f_reg : dict Regional spatial factors not normed with fuel demand f_reg_norm : dict Regional spatial factors normed with fuel demand (sum is not 1) f_reg_norm_abs : dict Regional spatial factors normed with fuel demand and normed that sum is 1 spatial_diff_values : dict Spatial diffusion values fuel_regs_enduse : dict Fuels per region and end use Returns ------- rs_reg_enduse_tech_p_ey : dict Regional specific model end year service shares of techs Modelling steps ----- A.) Calculation national end use service to reduce (e.g. 50% heat pumps for all regions) (uk_tech_service_ey_p) B.) Distribute this service according to spatial index for techs where the spatial explicit diffusion applies (techs_affected_spatial_f). Otherwise disaggregated according to fuel C.) Convert regional service reduction to ey % in region """ reg_enduse_tech_p_ey = {} # Check which factors is to be used # if only distribute: f_reg_norm_abs # if max 1: f_reg_nrm # if not intersted in correct sum: f_reg if fuel_regs_enduse == {}: logging.info("spatial_factor: fuel_regs_enduse") spatial_factor = f_reg else: logging.info("spatial_factor: f_reg_norm_abs") spatial_factor = f_reg_norm_abs # Sum fuel for all regions uk_enduse_fuel = sum(fuel_regs_enduse.values()) test = 0 for region in regions: try: test += (reg_enduse_tech_p_ey[region] * np.sum(fuel_regs_enduse[region])) logging.info( "FUEL FACTOR reg: {} val: {}, fuel: {} fuel: {} ".format( region, round(reg_enduse_tech_p_ey[region], 3), round(uk_enduse_fuel, 3), round(np.sum(fuel_regs_enduse[region]), 3))) except: pass reg_enduse_tech_p_ey[region] = factor_uk * spatial_factor[region] logging.info("spatial single factor reg: {} val: {}".format( region, round(reg_enduse_tech_p_ey[region], 3))) # --------- # PROBLEM THAT MORE THAN 100 percent could be reached if nt normed # --------- reg_enduse_tech_p_ey_capped = {} # Cap regions which have already reached and are larger than 1.0 cap_max_crit = 1.0 #100% demand_lost = 0 for region, region_factor in reg_enduse_tech_p_ey.items(): if region_factor > cap_max_crit: logging.warning("INFO: FOR A REGION THE SHARE OF IMPROVEMENTIS LARGER THAN 1.0.") # Demand which is lost and capped diff_to_cap = region_factor - cap_max_crit demand_lost += diff_to_cap * np.sum(fuel_regs_enduse[region]) reg_enduse_tech_p_ey_capped[region] = cap_max_crit else: reg_enduse_tech_p_ey_capped[region] = region_factor # Replace reg_enduse_tech_p_ey = reg_enduse_tech_p_ey_capped #logging.warning("FAKTOR UK :" + str(factor_uk)) #logging.warning("Lost demand: " + str(demand_lost)) #logging.warning("TESTDUM a " + str(test)) #logging.warning("TESTDUM b " + str(uk_enduse_fuel * factor_uk)) return reg_enduse_tech_p_ey
[docs]def get_enduse_regs( enduse, fuels_disagg): """ Get a specific enduse for all regions Arguments --------- enduse : str Enduse to sum fuels_disagg : list Fuels per disaggregated regions Returns ------- fuels_enduse : dict Fuels of an enduse for all regions {'reg': np.array(enduse_fuel)} """ fuels_enduse = {} for fuel_submodel in fuels_disagg: for reg, enduse_fuels in fuel_submodel.items(): for enduse_to_match, fuels_regs in enduse_fuels.items(): if enduse == enduse_to_match: fuels_enduse[reg] = fuels_regs return fuels_enduse