def get_date_strings(days_to_plot, daystep):
"""Calculate date and position for range
input of yeardays
"""
major_ticks_days = []
major_ticks_labels = []
cnt = 0
cnt_daystep = 1
for day in days_to_plot:
str_date = str(date_prop.yearday_to_date(2015, day))
str_date_short = str_date[5:]
yearhour = cnt * 24
if daystep == cnt_daystep:
# Label
major_ticks_labels.append(str_date_short)
# Position
major_ticks_days.append(yearhour)
cnt_daystep = 1
cnt += 1
else:
cnt_daystep += 1
cnt += 1
return major_ticks_days, major_ticks_labels
def test_yearday_to_date():
"""Testing
"""
in_year = 2015
in_month = 6
in_day = 13
in_yearday = 163
expected = date(2015, in_month, in_day)
# call function
out_value = date_prop.yearday_to_date(in_year, in_yearday)
assert out_value == expected
def get_date_strings(days_to_plot, daystep):
"""Calculate date and position for range input of yeardays
Arguments
---------
days_to_plot : list
List with yeardays to plot
daystep : int
Intervall of days to assign label
Return
-------
ticks_position : list
Hourly ticks position
major_ticks_labels : list
Ticks labels
"""
ticks_position, ticks_labels = [], []
cnt = 0
cnt_daystep = 1
for day in days_to_plot:
str_date = str(date_prop.yearday_to_date(2015, day))
str_date_short = str_date[5:]
# Because 0 posit in hour list is 01:00, substract minus one hour
yearhour = (cnt * 24) - 1
if daystep == cnt_daystep:
ticks_labels.append(str_date_short)
ticks_position.append(yearhour)
cnt_daystep = 1
cnt += 1
else:
cnt_daystep += 1
cnt += 1
return ticks_position, ticks_labels
def plot_radar_plots_average_peak_day(scenario_data,
fueltype_to_model,
fueltypes,
year_to_plot,
fig_name,
base_yr=2015):
"""Compare averaged dh profile overall regions for peak day
for future year and base year
MAYBE: SO FAR ONLY FOR ONE SCENARIO
"""
fueltype_int = fueltypes[fueltype_to_model]
# ----------------
# Create base year peak load profile
# Aggregate load profiles of all regions
# -----------------
peak_winter_individ_radars_to_plot_dh = []
trough_summer_individ_radars_to_plot_dh = []
load_factor_fueltype_y_cy = []
results_txt = []
for scenario_cnt, scenario in enumerate(scenario_data):
print("-------Scenario: '{}' '{}'".format(scenario, fueltype_to_model))
# ------------------------
# Future year load profile
# ------------------------
all_regs_fueltypes_yh_by = np.sum(
scenario_data[scenario]['ed_fueltype_regs_yh'][base_yr], axis=1)
all_regs_fueltypes_yh_cy = np.sum(
scenario_data[scenario]['ed_fueltype_regs_yh'][year_to_plot],
axis=1)
# ---------------------------
# Calculate load factors
# ---------------------------
peak_day_nr_by, by_max_h = enduse_func.get_peak_day_single_fueltype(
all_regs_fueltypes_yh_by[fueltype_int])
test_peak_day_by = enduse_func.get_peak_day_all_fueltypes(
all_regs_fueltypes_yh_by)
peak_day_nr_cy, cy_max_h = enduse_func.get_peak_day_single_fueltype(
all_regs_fueltypes_yh_cy[fueltype_int])
test_peak_day_cy = enduse_func.get_peak_day_all_fueltypes(
all_regs_fueltypes_yh_cy)
#peak_day_nr_cy = 32
_ = all_regs_fueltypes_yh_cy[fueltype_int].reshape(365, 24)
print(_[peak_day_nr_cy])
print(np.max(_[peak_day_nr_cy]))
print(cy_max_h)
print("-----{} {}".format(scenario, peak_day_nr_cy))
print(_[peak_day_nr_cy])
print("EGON")
# Minimum trough day
trough_day_nr_by, by_max_h_trough = enduse_func.get_trough_day_single_fueltype(
all_regs_fueltypes_yh_by[fueltype_int])
trough_day_nr_cy, cy_max_h_trough = enduse_func.get_trough_day_single_fueltype(
all_regs_fueltypes_yh_cy[fueltype_int])
scen_load_factor_fueltype_y_by = load_factors.calc_lf_y(
all_regs_fueltypes_yh_by)
load_factor_fueltype_y_by = round(
scen_load_factor_fueltype_y_by[fueltype_int], fueltype_int)
scen_load_factor_fueltype_y_cy = load_factors.calc_lf_y(
all_regs_fueltypes_yh_cy)
load_factor_fueltype_y_cy.append(
round(scen_load_factor_fueltype_y_cy[fueltype_int], fueltype_int))
# Add info on peak days #by2: {}
results_txt.append(
"p_day: {} by1: {} cy1: {}: cy2: {} trough_by: {} trough_cy: {}".
format(
peak_day_nr_cy,
date_prop.yearday_to_date(2015, peak_day_nr_by),
date_prop.yearday_to_date(2015, peak_day_nr_cy),
#date_prop.yearday_to_date(2015, test_peak_day_by),
date_prop.yearday_to_date(2015, test_peak_day_cy),
date_prop.yearday_to_date(2015, trough_day_nr_by),
date_prop.yearday_to_date(2015, trough_day_nr_cy)))
# ------------------------
# Restult calculations
# ------------------------
# Calculate share or space and water heating of total electrictiy demand in 2050
enduses_to_agg = [
'rs_space_heating', 'rs_water_heating', 'ss_space_heating',
'ss_water_heating', 'is_space_heating'
]
aggregated_enduse_fueltype_cy = np.zeros((8760))
aggregated_enduse_fueltype_by = np.zeros((8760))
for enduse in scenario_data[scenario]['results_enduse_every_year'][
year_to_plot].keys():
if enduse in enduses_to_agg:
aggregated_enduse_fueltype_by += scenario_data[scenario][
'results_enduse_every_year'][2015][enduse][fueltype_int]
aggregated_enduse_fueltype_cy += scenario_data[scenario][
'results_enduse_every_year'][year_to_plot][enduse][
fueltype_int]
aggregated_enduse_fueltype_by_8760 = aggregated_enduse_fueltype_by.reshape(
365, 24)
aggregated_enduse_fueltype_cy_8760 = aggregated_enduse_fueltype_cy.reshape(
365, 24)
# Total demand of selected enduses
selected_enduses_peak_by = round(
np.max(aggregated_enduse_fueltype_by_8760[peak_day_nr_by]), 1)
selected_enduses_peak_cy = round(
np.max(aggregated_enduse_fueltype_cy_8760[peak_day_nr_cy]), 1)
selected_enduses_min_by = round(
np.max(aggregated_enduse_fueltype_by_8760[trough_day_nr_by]), 1)
selected_enduses_min_cy = round(
np.max(aggregated_enduse_fueltype_cy_8760[trough_day_nr_cy]), 1)
# Calculate change in peak
all_regs_fueltypes_yh_by = all_regs_fueltypes_yh_by.reshape(
all_regs_fueltypes_yh_by.shape[0], 365, 24)
all_regs_fueltypes_yh_cy = all_regs_fueltypes_yh_cy.reshape(
all_regs_fueltypes_yh_cy.shape[0], 365, 24)
diff_max_h = round(((100 / by_max_h) * cy_max_h) - 100, 1)
txt = "peak_h_by: {} peak_h_cy: {} winter_heating_by: {} winter_heating_cy: {} trough_by: {} trough_cy: {} summer_heating_by: {} summer_heating_cy: {} {}".format(
round(by_max_h, 1), round(cy_max_h, 1), selected_enduses_peak_by,
selected_enduses_peak_cy, round(by_max_h_trough, 1),
round(cy_max_h_trough, 1), selected_enduses_min_by,
selected_enduses_min_cy, scenario)
results_txt.append(txt)
results_txt.append("----------")
#results_txt.append("trough:day: " + str(all_regs_fueltypes_yh_by[fueltype_int][trough_day_nr_by]))
print("Calculation of diff in peak: {} {} {} {}".format(
scenario, round(diff_max_h, 1), round(by_max_h, 1),
round(cy_max_h, 1)))
# ----------------------------------
# Plot dh for peak day for base year
# ----------------------------------
if scenario_cnt == 0:
peak_winter_individ_radars_to_plot_dh.append(
list(all_regs_fueltypes_yh_by[fueltype_int][peak_day_nr_by]))
trough_summer_individ_radars_to_plot_dh.append(
list(all_regs_fueltypes_yh_by[fueltype_int][trough_day_nr_cy]))
else:
pass
# Add current year maximum winter peak day
peak_winter_individ_radars_to_plot_dh.append(
list(all_regs_fueltypes_yh_cy[fueltype_int][peak_day_nr_cy]))
# Add current year minimum summer trough day
trough_summer_individ_radars_to_plot_dh.append(
list(all_regs_fueltypes_yh_cy[fueltype_int][trough_day_nr_cy]))
# --------------------------
# Save model results to txt
# --------------------------
name_spider_plot_peak = os.path.join(
fig_name, "spider_scenarios_{}.pdf".format(fueltype_to_model))
write_data.write_list_to_txt(
os.path.join(fig_name, name_spider_plot_peak[:-3] + "txt"),
results_txt)
name_spider_plot_trough = os.path.join(
fig_name,
"spider_scenarios_min_summer{}.pdf".format(fueltype_to_model))
write_data.write_list_to_txt(
os.path.join(fig_name, name_spider_plot_trough[:-3] + "txt"),
results_txt)
# --------------------------
# Plot figure
# --------------------------
# PEAK WINTER DAY PLOTS
plot_radar_plot_multiple_lines(peak_winter_individ_radars_to_plot_dh,
name_spider_plot_peak,
plot_steps=50,
scenario_names=list(scenario_data.keys()),
plotshow=False,
lf_y_by=[],
lf_y_cy=[],
list_diff_max_h=results_txt)
# TROUGH Summer DAY PLOTS
plot_radar_plot_multiple_lines(trough_summer_individ_radars_to_plot_dh,
name_spider_plot_trough,
plot_steps=50,
scenario_names=list(scenario_data.keys()),
plotshow=False,
lf_y_by=[],
lf_y_cy=[],
list_diff_max_h=results_txt)
def __init__(self,
lookup_enduses=None,
lookup_sector_enduses=None,
base_yr=None,
weather_by=None,
simulation_end_yr=None,
curr_yr=None,
sim_yrs=None,
paths=None,
enduses=None,
sectors=None,
reg_nrs=None):
"""Constructor
"""
self.lookup_enduses = lookup_enduses
self.lookup_sector_enduses = lookup_sector_enduses
self.submodels_names = lookup_tables.basic_lookups()['submodels_names']
self.nr_of_submodels = len(self.submodels_names)
self.fueltypes = lookup_tables.basic_lookups()['fueltypes']
self.fueltypes_nr = lookup_tables.basic_lookups()['fueltypes_nr']
self.base_yr = base_yr
self.weather_by = weather_by
self.reg_nrs = reg_nrs
self.simulation_end_yr = simulation_end_yr
self.curr_yr = curr_yr
self.sim_yrs = sim_yrs
# ============================================================
# Spatially modelled variables
#
# If spatial explicit diffusion is modelled, all parameters
# or technologies having a spatial explicit diffusion need
# to be defined.
# ============================================================
self.spatial_explicit_diffusion = 0 #0: False, 1: True
# Define all variables which are affected by regional diffusion
self.spatially_modelled_vars = [] # ['smart_meter_p']
# Define technologies which are affected by spatial explicit diffusion
self.techs_affected_spatial_f = ['heat_pumps_electricity']
# Max penetration speed
self.speed_con_max = 1 #1.5 # 1: uniform distribution >1: regional differences
# ============================================================
# Model calibration factors
# ============================================================
#
# These calibration factors are used to match the modelled
# electrictiy demand better with the validation data.
#
# Weekend effects are used to distribut energy demands
# between working and weekend days. With help of these
# factors, the demand on weekends and holidays can be
# be lowered compared to working days.
# This factor can be applied either directly to an enduse
# or to the hdd or cdd calculations (to correct cooling
# or heating demand)
#
# f_ss_cooling_weekend : float
# Weekend effect for cooling enduses
# f_ss_weekend : float
# WWeekend effect for service submodel enduses
# f_is_weekend : float
# Weekend effect for industry submodel enduses
# f_mixed_floorarea : float
# Share of floor_area which is assigned to either
# residential or non_residential floor area
# ------------------------------------------------------------
self.f_ss_cooling_weekend = 0.45 # Temporal calibration factor
self.f_ss_weekend = 0.8 # Temporal calibration factor
self.f_is_weekend = 0.45 # Temporal calibration factor
# ============================================================
# Modelled day related factors
# ============================================================
# model_yeardays_date : dict
# Contains for the base year for each days
# the information wheter this is a working or holiday
# ------------------------------------------------------------
self.model_yeardays = list(range(365))
# Calculate dates
self.model_yeardays_date = []
for yearday in self.model_yeardays:
self.model_yeardays_date.append(
date_prop.yearday_to_date(base_yr, yearday))
# ============================================================
# Dwelling stock related assumptions
# ============================================================
#
# Assumptions to generate a virtual dwelling stock
#
# assump_diff_floorarea_pp : float
# Change in floor area per person (%, 1=100%)
# assump_diff_floorarea_pp_yr_until_changed : int
# Year until this change in floor area happens
# dwtype_distr_by : dict
# Housing Stock Distribution by Type
# Source: UK Housing Energy Fact File, Table 4c
# dwtype_distr_fy : dict
# welling type distribution end year
# Source: UK Housing Energy Fact File, Table 4c
# dwtype_floorarea_by : dict
# Floor area per dwelling type (Annex Table 3.1)
# Source: UK Housing Energy Fact File, Table 4c
# dwtype_floorarea_fy : dict
# Floor area per dwelling type
# Source: UK Housing Energy Fact File, Table 4c
# dwtype_age_distr : dict
# Floor area per dwelling type
# Source: Housing Energy Fact Sheet)
# yr_until_changed : int
# Year until change is realised
#
# https://www.gov.uk/government/statistics/english-housing-survey-2014-to-2015-housing-stock-report
# ------------------------------------------------------------
yr_until_changed_all_things = 2050
self.dwtype_distr_by = {
'semi_detached': 0.26,
'terraced': 0.283,
'flat': 0.203,
'detached': 0.166,
'bungalow': 0.088
}
self.dwtype_distr_fy = {
'yr_until_changed': yr_until_changed_all_things,
'semi_detached': 0.26,
'terraced': 0.283,
'flat': 0.203,
'detached': 0.166,
'bungalow': 0.088
}
self.dwtype_floorarea_by = {
'semi_detached': 96,
'terraced': 82.5,
'flat': 61,
'detached': 147,
'bungalow': 77
}
self.dwtype_floorarea_fy = {
'yr_until_changed': yr_until_changed_all_things,
'semi_detached': 96,
'terraced': 82.5,
'flat': 61,
'detached': 147,
'bungalow': 77
}
# (Average builing age within age class, fraction)
# The newest category of 2015 is added to implement change in refurbishing rate
# For the base year, this is set to zero (if e.g. with future scenario set to 5%, then
# proportionally to base year distribution number of houses are refurbished)
self.dwtype_age_distr = {
2015: {
'1918': 0.21,
'1941': 0.36,
'1977.5': 0.3,
'1996.5': 0.08,
'2002': 0.05
}
}
# ============================================================
# Scenario drivers
# ============================================================
#
# For every enduse the relevant factors which affect enduse
# consumption can be added in a list.
#
# Note: If e.g. floorarea and population are added, the
# effects will be overestimates (i.e. no multi-
# collinearity are considered).
#
# scenario_drivers : dict
# Scenario drivers per enduse
# ------------------------------------------------------------
self.scenario_drivers = {
# --Residential
'rs_space_heating':
['floorarea',
'hlc'], # Do not use HDD or pop because otherweise double count
'rs_water_heating': ['population'],
'rs_lighting': ['population', 'floorarea'],
'rs_cooking': ['population'],
'rs_cold': ['population'],
'rs_wet': ['population'],
'rs_consumer_electronics': ['population', 'gva'],
'rs_home_computing': ['population'],
# --Service
'ss_space_heating': ['floorarea'],
'ss_water_heating': ['population'],
'ss_lighting': ['floorarea'],
'ss_catering': ['population'],
'ss_ICT_equipment': ['population'],
'ss_cooling_humidification': ['floorarea', 'population'],
'ss_fans': ['floorarea', 'population'],
'ss_small_power': ['population'],
'ss_cooled_storage': ['population'],
'ss_other_gas': ['population'],
'ss_other_electricity': ['population'],
# Industry
'is_high_temp_process': ['gva'],
'is_low_temp_process': ['gva'],
'is_drying_separation': ['gva'],
'is_motors': ['gva'],
'is_compressed_air': ['gva'],
'is_lighting': ['gva'],
'is_space_heating': ['gva'],
'is_other': ['gva'],
'is_refrigeration': ['gva']
}
# ============================================================
# Cooling related assumptions
# ============================================================
# assump_cooling_floorarea : int
# The percentage of cooled floor space in the base year
#
# Literature
# ----------
# Abela, A. et al. (2016). Study on Energy Use by Air
# Conditioning. Bre, (June), 31. Retrieved from
# https://www.bre.co.uk/filelibrary/pdf/projects/aircon-energy-use/StudyOnEnergyUseByAirConditioningFinalReport.pdf
# ------------------------------------------------------------
# See Abela et al. (2016) & Carbon Trust. (2012). Air conditioning. Maximising comfort, minimising energy consumption
self.cooled_ss_floorarea_by = 0.35
# ============================================================
# Smart meter related base year assumptions
# ============================================================
# smart_meter_p_by : int
# The percentage of households with smart meters in by
# ------------------------------------------------------------
self.smart_meter_assump = {}
# Currently in 2017 8.6 mio smart meter installed of 27.2 mio households --> 31.6%
# https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/671930/Smart_Meters_2017_update.pdf)
# In 2015, 5.8 % percent of all househods had one: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/533060/2016_Q1_Smart_Meters_Report.pdf
self.smart_meter_assump['smart_meter_p_by'] = 0.05
# Long term smart meter induced general savings, purley as
# a result of having a smart meter (e.g. 0.03 --> 3% savings)
# DECC 2015: Smart Metering Early Learning Project: Synthesis report
# https://www.gov.uk/government/publications/smart-metering-early-learning-project-and-small-scale-behaviour-trials
# Reasonable assumption is between 0.03 and 0.01 (DECC 2015)
self.smart_meter_assump['savings_smart_meter'] = {
# Residential
'rs_cold': 0.03,
'rs_cooking': 0.03,
'rs_lighting': 0.03,
'rs_wet': 0.03,
'rs_consumer_electronics': 0.03,
'rs_home_computing': 0.03,
'rs_space_heating': 0.03,
'rs_water_heating': 0.03,
# Service
'ss_space_heating': 0.03,
'ss_water_heating': 0.03,
'ss_cooling_humidification': 0.03,
'ss_fans': 0.03,
'ss_lighting': 0.03,
'ss_catering': 0.03,
'ss_small_power': 0.03,
'ss_ICT_equipment': 0.03,
'ss_cooled_storage': 0.03,
'ss_other_gas': 0.03,
'ss_other_electricity': 0.03,
# Industry submodule
'is_high_temp_process': 0,
'is_low_temp_process': 0,
'is_drying_separation': 0,
'is_motors': 0,
'is_compressed_air': 0,
'is_lighting': 0,
'is_space_heating': 0,
'is_other': 0,
'is_refrigeration': 0
}
# ============================================================
# Base temperature assumptions
# ============================================================
#
# Parameters related to smart metering
#
# rs_t_heating : int
# Residential submodel base temp of heating of base year
# rs_t_cooling_by : int
# Residential submodel base temp of cooling of base year
# ...
#
# Note
# ----
# Because demand for cooling cannot directly be linked to
# calculated cdd, the paramters 'ss_t_base_cooling' is used
# as a calibration factor. By artifiallcy lowering this
# parameter, the energy demand assignement over the days
# in a year is improved.
# ------------------------------------------------------------
t_bases = {
'rs_t_heating': 15.5,
'ss_t_heating': 15.5,
'ss_t_cooling': 5,
'is_t_heating': 15.5
}
self.t_bases = DummyClass(t_bases)
# ============================================================
# Enduses lists affed by hdd/cdd
# ============================================================
#
# These lists show for which enduses temperature related
# calculations are performed.
#
# enduse_space_heating : list
# All enduses for which hdd are used for yd calculations
# ss_enduse_space_cooling : list
# All service submodel enduses for which cdd are used for
# yd calculations
# ------------------------------------------------------------
self.enduse_space_heating = [
'rs_space_heating', 'ss_space_heating', 'is_space_heating'
]
self.ss_enduse_space_cooling = ['ss_cooling_humidification']
# ============================================================
# Industry related
#
# High temperature processing (high_temp_ process) dominates
# energy consumption in the iron and steel
#
# ---- Steel production - Enduse: is_high_temp_process, Sector: basic_metals
# With industry service switch, the future shares of 'is_temp_high_process'
# in sector 'basic_metals' can be set for 'basic_oxygen_furnace',
# 'electric_arc_furnace', and 'SNG_furnace' can be specified
#
# ---- Cement production - Enduse: is_high_temp_process, Sector: non_metallic_mineral_products
# Dry kilns, semidry kilns can be set
# ============================================================
# Share of cold rolling in steel manufacturing
self.p_cold_rolling_steel_by = 0.2 # Estimated based on https://aceroplatea.es/docs/EuropeanSteelFigures_2015.pdf
self.eff_cold_rolling_process = 1.8 # 80% more efficient than hot rolling Fruehan et al. (2002)
self.eff_hot_rolling_process = 1.0 # 100% assumed efficiency
# ============================================================
# Assumption related to heat pump technologies
# ============================================================
#
# Assumptions related to technologies
#
# gshp_fraction : list
# Fraction of installed gshp_fraction heat pumps in base year
# ASHP = 1 - gshp_fraction
# ------------------------------------------------------------
self.gshp_fraction = 0.1
# Load defined technologies
self.technologies, self.tech_list = read_data.read_technologies(
paths['path_technologies'])
self.installed_heat_pump_by = tech_related.generate_ashp_gshp_split(
self.gshp_fraction)
# Add heat pumps to technologies
self.technologies, self.tech_list[
'heating_non_const'], self.heat_pumps = tech_related.generate_heat_pump_from_split(
self.technologies, self.installed_heat_pump_by, self.fueltypes)
# ============================================================
# Fuel Stock Definition
# Provide for every fueltype of an enduse the share of fuel
# which is used by technologies in the base year
# ============================================================$
fuel_tech_p_by = fuel_shares.assign_by_fuel_tech_p(
enduses, sectors, self.fueltypes, self.fueltypes_nr)
# ========================================
# Get technologies of an enduse and sector
# ========================================
self.specified_tech_enduse_by = helpers.get_def_techs(fuel_tech_p_by)
_specified_tech_enduse_by = helpers.add_undef_techs(
self.heat_pumps, self.specified_tech_enduse_by,
self.enduse_space_heating)
self.specified_tech_enduse_by = _specified_tech_enduse_by
# ========================================
# General other info
# ========================================
self.seasons = date_prop.get_season(year_to_model=base_yr)
self.model_yeardays_daytype, self.yeardays_month, self.yeardays_month_days = date_prop.get_yeardays_daytype(
year_to_model=base_yr)
# ========================================
# Helper functions
# ========================================
self.fuel_tech_p_by, self.specified_tech_enduse_by, self.technologies = tech_related.insert_placholder_techs(
self.technologies, fuel_tech_p_by, self.specified_tech_enduse_by)
# ========================================
# Calculations with assumptions
# ========================================
self.cdd_weekend_cfactors = hdd_cdd.calc_weekend_corr_f(
self.model_yeardays_daytype, self.f_ss_cooling_weekend)
self.ss_weekend_f = hdd_cdd.calc_weekend_corr_f(
self.model_yeardays_daytype, self.f_ss_weekend)
self.is_weekend_f = hdd_cdd.calc_weekend_corr_f(
self.model_yeardays_daytype, self.f_is_weekend)
# ========================================
# Testing
# ========================================
testing_functions.testing_fuel_tech_shares(self.fuel_tech_p_by)
testing_functions.testing_tech_defined(self.technologies,
self.specified_tech_enduse_by)
def compare_peak(name_fig, path_result, real_elec_2015_peak, modelled_peak_dh,
peak_day):
"""Compare peak electricity day with calculated peak energy demand
Arguments
---------
name_fig : str
Name of figure
local_paths : dict
Paths
real_elec_2015_peak : array
Real data of peak day
modelled_peak_dh : array
Modelled peak day
"""
logging.debug("...compare elec peak results")
real_elec_peak = np.copy(real_elec_2015_peak)
# -------------------------------
# Compare values
# -------------------------------
fig = plt.figure(figsize=basic_plot_functions.cm2inch(8, 8))
# smooth line
x_smoothed, y_modelled_peak_dh_smoothed = basic_plot_functions.smooth_data(
range(24), modelled_peak_dh, num=500)
plt.plot(x_smoothed,
y_modelled_peak_dh_smoothed,
color='blue',
linestyle='--',
linewidth=0.5,
label='model')
x_smoothed, real_elec_peak_smoothed = basic_plot_functions.smooth_data(
range(24), real_elec_peak, num=500)
plt.plot(x_smoothed,
real_elec_peak_smoothed,
color='black',
linestyle='-',
linewidth=0.5,
label='validation')
#raise Exception
# Calculate hourly differences in %
diff_p_h = np.round((100 / real_elec_peak) * modelled_peak_dh, 1)
# Calculate maximum difference
max_h_real = np.max(real_elec_peak)
max_h_modelled = np.max(modelled_peak_dh)
max_h_diff = round((100 / max_h_real) * max_h_modelled, 2)
max_h_diff_gwh = round((abs(100 - max_h_diff) / 100) * max_h_real, 2)
# Y-axis ticks
plt.xlim(0, 23)
plt.yticks(range(0, 60, 10))
# because position 0 in list is 01:00, the labelling starts with 1
plt.xticks([0, 5, 11, 17, 23], [1, 6, 12, 18, 24]) #ticks, labels
plt.legend(frameon=False)
# Labelling
date_yearday = date_prop.yearday_to_date(2015, peak_day)
plt.title("peak comparison {}".format(date_yearday))
plt.xlabel("h (max {} ({} GWH)".format(max_h_diff, max_h_diff_gwh))
plt.ylabel("uk electrictiy use [GW]")
plt.text(
5, #position
5, #position
diff_p_h,
fontdict={
'family': 'arial',
'color': 'black',
'weight': 'normal',
'size': 4
})
# Tight layout
plt.tight_layout()
plt.margins(x=0)
# Save fig
plt.savefig(os.path.join(path_result, name_fig))
plt.close()
def __init__(self,
base_yr=None,
curr_yr=None,
simulated_yrs=None,
paths=None,
enduses=None,
sectors=None,
fueltypes=None,
fueltypes_nr=None):
yr_until_changed_all_things = 2050
# Simulation parameters
self.base_yr = base_yr
self.curr_yr = curr_yr
self.simulated_yrs = simulated_yrs
# ============================================================
# Spatially modelled variables
# ============================================================
# Define all variables which are affected by regional diffusion
self.spatially_modelled_vars = ['smart_meter_improvement_p']
# Define technologies which are affected by spatial explicit diffusion
self.techs_affected_spatial_f = ['heat_pumps_electricity']
# ============================================================
# Model calibration factors
# ============================================================
#
# These calibration factors are used to match the modelled
# electrictiy demand better with the validation data.
#
# Weekend effects are used to distribut energy demands
# between working and weekend days. With help of these
# factors, the demand on weekends and holidays can be
# be lowered compared to working days.
# This factor can be applied either directly to an enduse
# or to the hdd or cdd calculations (to correct cooling
# or heating demand)
#
# f_ss_cooling_weekend : float
# Weekend effect for cooling enduses
# f_ss_weekend : float
# WWeekend effect for service submodel enduses
# f_is_weekend : float
# Weekend effect for industry submodel enduses
# f_mixed_floorarea : float
# Share of floor_area which is assigned to either
# residential or non_residential floor area
# ------------------------------------------------------------
# Temporal calibration factors
self.f_ss_cooling_weekend = 0.45 # 0.55
self.f_ss_weekend = 0.75 # 0.7
self.f_is_weekend = 0.45 # 0.4
# Spatial calibration factor
self.f_mixed_floorarea = 0.5 # 0.5
# ============================================================
# Modelled day related factors
# ============================================================
#
# model_yeardays_nrs : int
# Number of modelled yeardays (default=365)
# model_yearhours_nrs : int
# Number of modelled yearhours (default=8760)
# model_yeardays_date : dict
# Contains for the base year for each days
# the information wheter this is a working or holiday
# ------------------------------------------------------------
self.model_yeardays = list(range(365))
# Calculate dates
self.model_yeardays_date = []
for yearday in self.model_yeardays:
self.model_yeardays_date.append(
date_prop.yearday_to_date(base_yr, yearday))
self.model_yeardays_nrs = len(self.model_yeardays)
self.model_yearhours_nrs = len(self.model_yeardays) * 24
# ============================================================
# Dwelling stock related assumptions
# ============================================================
#
# Assumptions to generate a virtual dwelling stock
#
# assump_diff_floorarea_pp : float
# Change in floor area per person (%, 1=100%)
# assump_diff_floorarea_pp_yr_until_changed : int
# Year until this change in floor area happens
# dwtype_distr_by : dict
# Housing Stock Distribution by Type
# Source: UK Housing Energy Fact File, Table 4c
# dwtype_distr_fy : dict
# welling type distribution end year
# Source: UK Housing Energy Fact File, Table 4c
# dwtype_floorarea_by : dict
# Floor area per dwelling type (Annex Table 3.1)
# Source: UK Housing Energy Fact File, Table 4c
# dwtype_floorarea_fy : dict
# Floor area per dwelling type
# Source: UK Housing Energy Fact File, Table 4c
# dwtype_age_distr : dict
# Floor area per dwelling type
# Source: Housing Energy Fact Sheet)
# yr_until_changed : int
# Year until change is realised
#
# https://www.gov.uk/government/statistics/english-housing-survey-2014-to-2015-housing-stock-report
# ------------------------------------------------------------
self.assump_diff_floorarea_pp = 1.0
self.assump_diff_floorarea_pp_yr_until_changed = yr_until_changed_all_things
self.dwtype_distr_by = {
'semi_detached': 0.26,
'terraced': 0.283,
'flat': 0.203,
'detached': 0.166,
'bungalow': 0.088
}
self.dwtype_distr_fy = {
'yr_until_changed': yr_until_changed_all_things,
'semi_detached': 0.26,
'terraced': 0.283,
'flat': 0.203,
'detached': 0.166,
'bungalow': 0.088
}
self.dwtype_floorarea_by = {
'semi_detached': 96,
'terraced': 82.5,
'flat': 61,
'detached': 147,
'bungalow': 77
}
self.dwtype_floorarea_fy = {
'yr_until_changed': yr_until_changed_all_things,
'semi_detached': 96,
'terraced': 82.5,
'flat': 61,
'detached': 147,
'bungalow': 77
}
# (Average builing age within age class, fraction)
# The newest category of 2015 is added to implement change in refurbishing rate
# For the base year, this is set to zero (if e.g. with future scenario set to 5%, then
# proportionally to base year distribution number of houses are refurbished)
self.dwtype_age_distr = {
2015: {
'1918': 0.21,
'1941': 0.36,
'1977.5': 0.3,
'1996.5': 0.08,
'2002': 0.05
}
}
# ============================================================
# Scenario drivers
# ============================================================
#
# For every enduse the relevant factors which affect enduse
# consumption can be added in a list.
#
# Note: If e.g. floorarea and population are added, the
# effects will be overestimates (i.e. no multi-
# collinearity are considered).
#
# scenario_drivers : dict
# Scenario drivers per enduse
# ------------------------------------------------------------
self.scenario_drivers = {}
# --Residential SubModel
self.scenario_drivers['rs_submodule'] = {
'rs_space_heating':
['floorarea',
'hlc'], # Do not use HDD or pop because otherweise double count
'rs_water_heating': ['population'],
'rs_lighting': ['population', 'floorarea'],
'rs_cooking': ['population'],
'rs_cold': ['population'],
'rs_wet': ['population'],
'rs_consumer_electronics':
['population'
], #GVA TODO. As soon as GVA is avaiable, drive it with GVA
'rs_home_computing': ['population']
} #GVA
# --Service Submodel (Table 5.5a)
self.scenario_drivers['ss_submodule'] = {
'ss_space_heating': ['floorarea'],
'ss_water_heating': ['population'],
'ss_lighting': ['floorarea'],
'ss_catering': ['population'],
'ss_ICT_equipment': ['population'],
'ss_cooling_humidification': ['floorarea', 'population'],
'ss_fans': ['floorarea', 'population'],
'ss_small_power': ['population'],
'ss_cooled_storage': ['population'],
'ss_other_gas': ['population'],
'ss_other_electricity': ['population']
}
# --Industry Submodel
self.scenario_drivers['is_submodule'] = {
'is_high_temp_process': ['gva'],
'is_low_temp_process': ['gva'],
'is_drying_separation': ['gva'],
'is_motors': ['gva'],
'is_compressed_air': ['gva'],
'is_lighting': ['gva'],
'is_space_heating': ['gva'],
'is_other': ['gva'],
'is_refrigeration': ['gva']
}
# ============================================================
# Cooling related assumptions
# ============================================================
#
# Parameters related to cooling enduses are defined here.
#
# assump_cooling_floorarea : int
# The percentage of cooled floor space in the base year
#
# Literature
# ----------
# Abela, A. et al. (2016). Study on Energy Use by Air
# Conditioning. Bre, (June), 31. Retrieved from
# https://www.bre.co.uk/filelibrary/pdf/projects/aircon-energy-use
# /StudyOnEnergyUseByAirConditioningFinalReport.pdf
# ------------------------------------------------------------
# See Abela et al. (2016)
# Carbon Trust. (2012). Air conditioning. Maximising comfort, minimising energy consumption
self.cooled_ss_floorarea_by = 0.35
# ============================================================
# Smart meter related base year assumptions
# ============================================================
#
# Parameters related to smart metering
#
# smart_meter_p_by : int
# The percentage of households with smart meters in by
# smart_meter_diff_params : dict
# Sigmoid diffusion parameter of smater meters
# ------------------------------------------------------------
self.smart_meter_assump = {}
self.smart_meter_assump['smart_meter_p_by'] = 0.1
self.smart_meter_assump['smart_meter_diff_params'] = {
'sig_midpoint': 0,
'sig_steepness': 1
}
# ============================================================
# Base temperature assumptions
# ============================================================
#
# Parameters related to smart metering
#
# rs_t_heating_by : int
# Residential submodel base temp of heating of base year
# rs_t_cooling_by : int
# Residential submodel base temp of cooling of base year
# base_temp_diff_params : dict
# Sigmoid temperature diffusion parameters
# ...
#
# Note
# ----
# Because demand for cooling cannot directly be linked to
# calculated cdd, the paramters 'ss_t_cooling_by' is used
# as a calibration factor. By artifiallcy lowering this
# parameter, the energy demand assignement over the days
# in a year is improved.
# ------------------------------------------------------------
t_bases = {}
t_bases['rs_t_heating_by'] = 15.5 #
#t_bases['rs_t_cooling_by'] = Not implemented
t_bases['ss_t_heating_by'] = 15.5 #
t_bases['ss_t_cooling_by'] = 5 # Orig: 5
t_bases['is_t_heating_by'] = 15.5 #
#self.t_bases['is_t_cooling_by'] = Not implemented
self.t_bases = DummyClass(t_bases)
self.base_temp_diff_params = {
'sig_midpoint': 0,
'sig_steepness': 1,
'yr_until_changed': yr_until_changed_all_things
}
# ============================================================
# Enduses lists affed by hdd/cdd
# ============================================================
#
# These lists show for which enduses temperature related
# calculations are performed.
#
# enduse_space_heating : list
# All enduses for which hdd are used for yd calculations
# enduse_rs_space_cooling : list
# All residential enduses for which cdd are used for
# yd calculations
# ss_enduse_space_cooling : list
# All service submodel enduses for which cdd are used for
# yd calculations
# ------------------------------------------------------------
self.enduse_space_heating = [
'rs_space_heating', 'ss_space_heating', 'is_space_heating'
]
self.enduse_rs_space_cooling = []
self.ss_enduse_space_cooling = ['ss_cooling_humidification']
# ============================================================
# Industry submodel related parameters
# ============================================================
#
# Assumptions related to industrial enduses
#
# Overal changes in industry related enduse can be changed
# in 'enduse_overall_change_enduses'
#
# ------------------------------------------------------------
# --------------------------------------------
# heating
# --------------------------------------------
# --> No scenario drivers but driven by switches
# --------------------------------------------
# lighting
#
# No individual technologies are defined. Only
# overall efficiency increase can be implemented
#--------------------------------------------
# --------------------------------------------
# high_temp_ process
#
# High temperature processing dominates energy consumption in the iron and steel,
# non-ferrous metal, bricks, cement, glass and potteries industries. This includes
# - coke ovens
# - blast furnaces and other furnaces
# - kilns and
# - glass tanks.
# High consumption in Chemicals, Non_metallic mineral products, paper, food_production
# Fuel use ratio - electric arc furnace over blast furnace steel making in cement sector
# BAT - iron & steel - continous/Ingot casting Sectoral share - continuous %
# --------------------------------------------
# Share of cold rolling in steel manufacturing
# *****************
self.p_cold_rolling_steel_by = 0.2 # Estimated based on https://aceroplatea.es/docs/EuropeanSteelFigures_2015.pdf
self.eff_cold_rolling_process = 1.8 # 80% more efficient than hot rolling Fruehan et al. (2002)
self.eff_hot_rolling_process = 1.0 # 100% assumed efficiency
# Steel production - Enduse: is_high_temp_process, Sector: basic_metals
# *****************
# With industry service switch, the future shares
# in 'basic_oxygen_furnace', 'electric_arc_furnace', and 'SNG_furnace'
# can be specified
#scrap-based production: electric arc furnace
#direct reduction process: natrual gas based, electric arc furnace
#BF-BOF (blast furnace - basix oxgen furnace)
# Example service switch:
# is_high_temp_process,SNG_furnace,1.0,2050,basic_metals
# Cement production - Enduse: is_high_temp_process, Sector: non_metallic_mineral_products
# *****************
# technologies: Dry kilns, semidry kilns
# CHEMICALs - Enduse: is_high_temp_process, Sector: CHEMICALS
# *****************
# technologies: Dry & wet kilns
# ----------------
# Efficiency of motors
# ----------------
#is_motors_eff_change = 0
# ----------------
# Efficiency of others
# ----------------
#is_others_eff_change = 0
# ----------------
# Efficiency of others
# ----------------
#is_refrigeration_eff_change = 0
# ----------------
# Efficiency of is_compressed_air
# ----------------
#is_compressed_air_eff_change =
# ----------------
# Efficiency of is_drying_separation
# ----------------
#is_drying_separation_eff_change =
# ----------------
# Efficiency of is_low_temp_process
# ----------------
#is_low_temp_process_eff_change =
# ============================================================
# Assumption related to heat pump technologies
# ============================================================
#
# Assumptions related to technologies
#
# gshp_fraction : list
# Fraction of installed gshp_fraction heat pumps in base year
# ASHP = 1 - gshp_fraction
# ------------------------------------------------------------
self.gshp_fraction = 0.1
self.technologies, self.tech_list = read_data.read_technologies(
paths['path_technologies'], fueltypes)
self.installed_heat_pump_by = tech_related.generate_ashp_gshp_split(
self.gshp_fraction)
# Add heat pumps to technologies
self.technologies, self.tech_list[
'heating_non_const'], self.heat_pumps = tech_related.generate_heat_pump_from_split(
self.technologies, self.installed_heat_pump_by, fueltypes)
# Collect all heating technologies
# TODO: MAYBE ADD IN TECH DOC ANOTHER LIST SPECIFYING ALL HEATING TECHs
self.heating_technologies = get_all_heating_techs(self.tech_list)
# ============================================================
# Enduse diffusion paramters
# ============================================================
#
# Assumptions related to general diffusion
#
# This parameters are used to specify e.g. diffusion of
# an enduse which is not specified by technologies
# or the diffusion of a policy of changing a parameter
# over time.
# ------------------------------------------------------------
self.enduse_overall_change = {}
self.enduse_overall_change['other_enduse_mode_info'] = {
'diff_method': 'linear',
'sigmoid': {
'sig_midpoint': 0,
'sig_steepness': 1
}
}
# ============================================================
# Fuel Stock Definition
# Provide for every fueltype of an enduse
# the share of fuel which is used by technologies for thebase year
# ============================================================
self.rs_fuel_tech_p_by, self.ss_fuel_tech_p_by, self.is_fuel_tech_p_by = assumptions_fuel_shares.assign_by_fuel_tech_p(
enduses, sectors, fueltypes, fueltypes_nr)
# ========================================
# Get technologies of an enduse
# ========================================
self.rs_specified_tech_enduse_by = helpers.get_def_techs(
self.rs_fuel_tech_p_by, sector_crit=False)
self.ss_specified_tech_enduse_by = helpers.get_def_techs(
self.ss_fuel_tech_p_by, sector_crit=True)
self.is_specified_tech_enduse_by = helpers.get_def_techs(
self.is_fuel_tech_p_by, sector_crit=True)
rs_specified_tech_enduse_by_new = helpers.add_undef_techs(
self.heat_pumps, self.rs_specified_tech_enduse_by,
'rs_space_heating')
self.rs_specified_tech_enduse_by = rs_specified_tech_enduse_by_new
ss_specified_tech_enduse_by_new = helpers.add_undef_techs(
self.heat_pumps, self.ss_specified_tech_enduse_by,
'ss_space_heating')
self.ss_specified_tech_enduse_by = ss_specified_tech_enduse_by_new
is_specified_tech_enduse_by_new = helpers.add_undef_techs(
self.heat_pumps, self.is_specified_tech_enduse_by,
'is_space_heating')
self.is_specified_tech_enduse_by = is_specified_tech_enduse_by_new
# ============================================================
# Read in switches
# ============================================================
# Read in scenaric fuel switches
self.rs_fuel_switches = read_data.read_fuel_switches(
paths['rs_path_fuel_switches'], enduses, fueltypes,
self.technologies)
self.ss_fuel_switches = read_data.read_fuel_switches(
paths['ss_path_fuel_switches'], enduses, fueltypes,
self.technologies)
self.is_fuel_switches = read_data.read_fuel_switches(
paths['is_path_fuel_switches'], enduses, fueltypes,
self.technologies)
# Read in scenaric service switches
self.rs_service_switches = read_data.service_switch(
paths['rs_path_service_switch'], self.technologies)
self.ss_service_switches = read_data.service_switch(
paths['ss_path_service_switch'], self.technologies)
self.is_service_switches = read_data.service_switch(
paths['is_path_industry_switch'], self.technologies)
# Read in scenaric capacity switches
self.rs_capacity_switches = read_data.read_capacity_switch(
paths['rs_path_capacity_installation'])
self.ss_capacity_switches = read_data.read_capacity_switch(
paths['ss_path_capacity_installation'])
self.is_capacity_switches = read_data.read_capacity_switch(
paths['is_path_capacity_installation'])
# Testing
self.crit_switch_happening = testing_functions.switch_testing(
fuel_switches=[
self.rs_fuel_switches, self.ss_fuel_switches,
self.is_fuel_switches
],
service_switches=[
self.rs_service_switches, self.ss_service_switches,
self.is_service_switches
],
capacity_switches=[
self.rs_capacity_switches, self.ss_capacity_switches,
self.is_capacity_switches
])
# ========================================
# General other assumptions
# ========================================
self.seasons = date_prop.get_season(year_to_model=base_yr)
self.model_yeardays_daytype, self.yeardays_month, self.yeardays_month_days = date_prop.get_model_yeardays_daytype(
year_to_model=base_yr)
# ========================================
# Helper functions
# ========================================
self.rs_fuel_tech_p_by, self.rs_specified_tech_enduse_by, self.technologies = tech_related.insert_placholder_techs(
self.technologies,
self.rs_fuel_tech_p_by,
self.rs_specified_tech_enduse_by,
sector_crit=False)
self.ss_fuel_tech_p_by, self.ss_specified_tech_enduse_by, self.technologies = tech_related.insert_placholder_techs(
self.technologies,
self.ss_fuel_tech_p_by,
self.ss_specified_tech_enduse_by,
sector_crit=True)
self.is_fuel_tech_p_by, self.is_specified_tech_enduse_by, self.technologies = tech_related.insert_placholder_techs(
self.technologies,
self.is_fuel_tech_p_by,
self.is_specified_tech_enduse_by,
sector_crit=True)
# ========================================
# Calculations with assumptions
# ========================================
self.cdd_weekend_cfactors = hdd_cdd.calc_weekend_corr_f(
self.model_yeardays_daytype, self.f_ss_cooling_weekend)
self.ss_weekend_f = hdd_cdd.calc_weekend_corr_f(
self.model_yeardays_daytype, self.f_ss_weekend)
self.is_weekend_f = hdd_cdd.calc_weekend_corr_f(
self.model_yeardays_daytype, self.f_is_weekend)
# ========================================
# Testing
# ========================================
testing_functions.testing_fuel_tech_shares(self.rs_fuel_tech_p_by)
for enduse in self.ss_fuel_tech_p_by:
testing_functions.testing_fuel_tech_shares(
self.ss_fuel_tech_p_by[enduse])
for enduse in self.is_fuel_tech_p_by:
testing_functions.testing_fuel_tech_shares(
self.is_fuel_tech_p_by[enduse])
testing_functions.testing_tech_defined(
self.technologies, self.rs_specified_tech_enduse_by)
testing_functions.testing_tech_defined(
self.technologies, self.ss_specified_tech_enduse_by)
testing_functions.testing_tech_defined(
self.technologies, self.is_specified_tech_enduse_by)
