"""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