def get_demand(year, espini):
if espini:
demand_filename = '/home/malcolm/uclan/data/electricity/espeni.csv'
demand = readers.read_espeni(demand_filename, year)
electric = demand / 1000000.0
else:
# TODO - could we include the actual Scottish demand here?
scotland_factor = 1.1
# read historical electricity demand for reference year
if year == '2009':
demand_filename = '/home/malcolm/uclan/data/electricity/demanddata_2009.csv'
else:
demand_filename = '/home/malcolm/uclan/data/ElectricityDemandData_' + year + '.csv'
demand_ref = readers.read_electric_hourly(demand_filename)
# Convert to TWh
electric = demand_ref[
'ENGLAND_WALES_DEMAND'] * scotland_factor / 1000000.0
return electric
plt.ylabel('Solar PV')
plt.show()
# years = ['2018']
years = ['2015', '2016', '2017', '2018', '2019']
# read historic demand and generation for England and Wales
demand_years = {}
wind_years = {}
pv_years = {}
max_demand = 0.0
max_wind = 0.0
max_pv = 0.0
for year in years:
electric_filename = '/home/malcolm/uclan/data/ElectricityDemandData_' + year + '.csv'
electric_data = readers.read_electric_hourly(electric_filename)
# convert from hourly to daily
electric_data = electric_data.resample('D').sum()
# don't scale by 1.1 for including Scotland because only got generation for
# England and Wales
demand = electric_data['ENGLAND_WALES_DEMAND'] * 1.1
if max_demand < demand.sum():
max_demand = demand.sum()
demand_years[year] = demand
# extract wind
wind = electric_data['EMBEDDED_WIND_GENERATION']
if max_wind < wind.sum():
max_wind = wind.sum()
wind_years[year] = wind
# extract solar
pv = electric_data['EMBEDDED_SOLAR_GENERATION']
# main program
# demand_filename = '/home/malcolm/uclan/tools/python/output/2018/heatCopRef2018weather2018HDD15.5.csv'
# demand_filename = '/home/malcolm/uclan/tools/python/output/2018/heatCopRef2018weather2018Ruhnau.csv'
# demand_filename = '/home/malcolm/uclan/tools/python/output/2018/heatCopRef2018weather2018Watson.csv'
# demand_filename = '/home/malcolm/uclan/tools/python/output/2018/Ref2018Weather2018Sbdew.csv'
# demand_filename = '/home/malcolm/uclan/tools/python/output/2018/Ref2018Weather2018Sbdew.csv'
demand_filename = '/home/malcolm/uclan/tools/python/scripts/heat/output/2018/Ref2018Weather2018Rbdew.csv'
supply_filename = '/home/malcolm/uclan/data/ElectricityDemandData_2018.csv'
# read the electricity demand
demand = readers.read_demand(demand_filename)
print('DEMAND')
print(demand.index)
print(demand)
electric = readers.read_electric_hourly(supply_filename)
# factor the generation to match the demand
# (imagine we added just enough solar and wind annually and then we can
# look at net to see how much storage we might need )
supply = electric['EMBEDDED_WIND_GENERATION'] + electric[
'EMBEDDED_SOLAR_GENERATION']
# print('Annual supply before up sample {}'.format(supply.sum()) )
# supply = supply.resample('60min').pad()
# supply = upsample_df(supply.resample,'60min')
# print('Annual supply after up sample {}'.format(supply.sum()) )
# supply = supply.resample('60min')
print('SUPPLY')
print(supply.index)
print(supply)
supply = supply * (demand.sum() / supply.sum())
d2018['percent_other'] = 0.14 # fes 2019 Net Zero 2050
d2018['boiler_efficiency'] = 0.85 # hydrogen boiler efficiency
d2018['ev_annual_energy'] = 93.38 # annual ev energy TWh fes Net Zero 2050
d2018['electricity_residential'] = 109 # fes 2019 Net Zero 2050
d2018['electricity_industry'] = 175 # fes 2019 Net Zero 2050
d2018['hybrid_threshold'] = 5.0 # temperature below which hybrid heat pumps
# switch completely to hydrogen to balance
# the grid
model['2018'] = d2018
scotland_factor = 1.1 # ( Fragaki et. al )
# read historical electricity demand for reference year
demand_filename = '/home/malcolm/uclan/data/ElectricityDemandData_' + reference_year + '.csv'
demand_ref = readers.read_electric_hourly(demand_filename)
electric_ref = demand_ref['ENGLAND_WALES_DEMAND'] * scotland_factor
# read reference year electric heat series based on purely resistive heating
# so that it can be removed from the reference year series.
demand_filename = '/home/malcolm/uclan/tools/python/scripts/heat/output/{0:}/GBRef{0:}Weather{0:}I-Sbdew_resistive.csv'.format(
reference_year)
ref_resistive_heat = readers.read_demand(demand_filename)
# read weather year electric heat for ref year 2050
demand_filename = '/home/malcolm/uclan/tools/python/scripts/heat/output/{0:}/GBRef2050Weather{0:}I-Sbdew.csv'.format(
weather_year)
demand = readers.read_copheat(demand_filename,
['electricity', 'heat', 'temperature'])
from datetime import datetime
import pytz
import matplotlib.pyplot as plt
import statsmodels.api as sm
# custom code
import stats
import readers
from misc import upsample_df
# main program
# supply_filename = '/home/malcolm/uclan/data/ElectricityDemandData_2018.csv'
demand_filename = '/home/malcolm/uclan/data/ElectricityDemandData_'
# read in the demand
electric15 = readers.read_electric_hourly(demand_filename + '2015.csv')
demand15 = electric15['ENGLAND_WALES_DEMAND']
electric16 = readers.read_electric_hourly(demand_filename + '2016.csv')
demand16 = electric16['ENGLAND_WALES_DEMAND']
electric17 = readers.read_electric_hourly(demand_filename + '2017.csv')
demand17 = electric17['ENGLAND_WALES_DEMAND']
electric18 = readers.read_electric_hourly(demand_filename + '2018.csv')
demand18 = electric18['ENGLAND_WALES_DEMAND']
electric19 = readers.read_electric_hourly(demand_filename + '2019.csv')
demand19 = electric19['ENGLAND_WALES_DEMAND']
#print(demand19.nsmallest())
# replace zeros with NaN
#demand19 = demand19.replace(0, float("NaN"))
# replace missing values (NaN) by interpolation
#demand19 = demand19.interpolate()
import statsmodels.api as sm # custom code import stats import readers from misc import upsample_df # main program # Reference Year = 2018 # Weather Year = 2017 scotland_factor = 1.1 # ( Fragaki et. al ) # read 2017 historical electricity demand demand_filename = '/home/malcolm/uclan/data/ElectricityDemandData_2017.csv' demand17 = readers.read_electric_hourly(demand_filename) electric17 = demand17['ENGLAND_WALES_DEMAND'] * scotland_factor # read 2018 historical electricity demand demand_filename = '/home/malcolm/uclan/data/ElectricityDemandData_2018.csv' demand18 = readers.read_electric_hourly(demand_filename) electric18 = demand18['ENGLAND_WALES_DEMAND'] * scotland_factor # read 2018 electric heat series based on purely resistive heating so that # it can be removed from the 2018 series. (ref year method) demand_filename = '/home/malcolm/uclan/tools/python/scripts/heat/output/2018/GBRef2018Weather2018I-Sbdew_resistive.csv' resistive_heat = readers.read_demand(demand_filename) # read 2017 electric heat for ref year 2018
