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morePVs.py
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morePVs.py
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# morePVs Copyright (C) 2018 Mike B Roberts
# multi-occupancy residential electricity with PV and storage model
#
# This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public
# License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later
# version. # This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the
# implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
# details. You should have received a copy of the GNU General Public License along with this program. If not,
# see <http://www.gnu.org/licenses/>.
# Contact: [email protected]
# IMPORT Modules
import numpy as np
import sys
import logging
import sys
import os
import pdb, traceback
import pandas as pd
import en_utilities as um
import concurrent.futures
import datetime as dt
import pathlib
#from en import morePVs_output as opm
# Classes
class Timeseries():
"""DateTimeIndex & related parameters used throughout."""
def __init__(self,
load,
dst_lookup,
dst_region
):
self.timeseries = load.index
self.num_steps = len(self.timeseries)
self.interval = \
pd.to_timedelta(
pd.tseries.frequencies.to_offset(
pd.infer_freq(self.timeseries)
)).total_seconds()
self.num_days = int(self.num_steps * self.interval / (24*60*60))
# Set up weekdays and weekends
self.days = {
'day': self.timeseries[self.timeseries.weekday.isin([0, 1, 2, 3, 4])],
'end': self.timeseries[self.timeseries.weekday.isin([5, 6])],
'both': self.timeseries}
self.step_ts = pd.Series(self.timeseries)
# Set up summer and winter periods for daylight savings:
# NB This is negative because it is applied to tariff period start and end times,
# rather than to timestamp steps
# https://www.xkcd.com/1883/
self.dst_reverse_shift = pd.DateOffset(hours=-1)
self.seasonal_time = {'winter': self.timeseries[0:0],
'summer': self.timeseries[0:0]}
start_label = dst_region + '_start'
end_label = dst_region + '_end'
for year in self.timeseries.year.drop_duplicates().tolist():
dst_start = pd.Timestamp(dst_lookup.loc[year, start_label])
dst_end = pd.Timestamp(dst_lookup.loc[year, end_label])
tsy = self.timeseries[self.timeseries.year == year]
if dst_start < dst_end:
self.seasonal_time['winter'] = \
self.seasonal_time['winter'].join(tsy[(tsy >= pd.Timestamp('1/01/'+str(year) + ' 00:00:00'))
& (tsy < dst_start)], 'outer').join(
tsy[(tsy >= dst_end)
& (tsy < pd.Timestamp('31/12/'+str(year) + ' 23:59:59'))], 'outer')
self.seasonal_time['summer'] = \
self.seasonal_time['summer'].join(tsy[(tsy >= dst_start)
& (tsy < dst_end)], 'outer')
else:
self.seasonal_time['summer'] = \
self.seasonal_time['summer'].join(tsy[(tsy >= pd.Timestamp('1/01/'+str(year) + ' 00:00:00'))
& (tsy < dst_end)], 'outer').join(
tsy[(tsy >= dst_start)
& (tsy < pd.Timestamp('31/12/'+str(year) + ' 23:59:59'))], 'outer')
self.seasonal_time['winter'] = \
self.seasonal_time['winter'].join(tsy[(tsy >= dst_end)
& (tsy < dst_start)], 'outer')
pass
def steps_today(self, this_step):
"""Returns list of earlier timesteps with same day as today"""
today = self.step_ts[this_step].date()
steps_today = self.step_ts.loc[self.step_ts.dt.date == today].index.tolist()
steps_so_far_today = [s for s in steps_today if s <= this_step]
return steps_so_far_today
class TariffData():
"""Reference resource with time-specific price data for multiple tariffs"""
def __init__(
self,
tariff_lookup_path,
output_path,
parameter_list):
"""Initialise tariff look-up table."""
self.saved_tariff_path = os.path.join(output_path, 'saved_tariffs')
os.makedirs(self.saved_tariff_path, exist_ok=True)
# read csv of tariff parameters
self.lookup = pd.read_csv(tariff_lookup_path, index_col=[0])
self.all_tariffs = [t for t in self.lookup.index if t in parameter_list] # list of all tariff ids
# set up dfs for static import and export tariffs
self.static_imports = pd.DataFrame(index=ts.timeseries)
self.static_exports = pd.DataFrame(index=ts.timeseries)
self.static_solar_imports = pd.DataFrame(index=ts.timeseries)
self.tou_rate_list = {'name_1': ['rate_1', 'start_1', 'end_1', 'week_1'],
'name_2': ['rate_2', 'start_2', 'end_2', 'week_2'],
'name_3': ['rate_3', 'start_3', 'end_3', 'week_3'],
'name_4': ['rate_4', 'start_4', 'end_4', 'week_4'],
'name_5': ['rate_5', 'start_5', 'end_5', 'week_5'],
'name_6': ['rate_6', 'start_6', 'end_6', 'week_6'],
'name_7': ['rate_7', 'start_7', 'end_7', 'week_7'],
'name_8': ['rate_8', 'start_8', 'end_8', 'week_8'],
}
def generateStaticTariffs(self):
""" Creates time-based rates for all load-independent tariffs."""
for tid in self.all_tariffs:
# apply discounts to all tariffs:
# -------------------------------
# excluding FiTs and solar tariffs
if not np.isnan(self.lookup.loc[tid, 'discount']):
discount = self.lookup.loc[tid, 'discount']
rates = [c for c in self.lookup.columns if 'rate' in c and not 'fit' in c ]
named_rates = [c for c in rates if
not np.isnan(self.lookup.loc[tid, c]) and
c.replace('rate', 'name') in self.lookup.columns]
solar_rates = [c for c in named_rates if
'solar' in self.lookup.loc[tid, c.replace('rate', 'name')]
or 'Solar' in self.lookup.loc[tid, c.replace('rate', 'name')]]
discounted_rates = [c for c in rates if c not in solar_rates]
self.lookup.loc[tid, discounted_rates] = self.lookup.loc[tid, discounted_rates] * (100 - discount) / 100
# Allocate Flat rate and Zero Tariffs
# -----------------------------------:
self.static_imports[tid] = 0 # for zero rate tariff and as initialisation
self.static_solar_imports[tid] = 0 # for zero rate tariff and as initialisation
if 'Flat' in self.lookup.loc[tid, 'tariff_type']:
self.static_imports[tid] = self.lookup.loc[tid, 'flat_rate']
# Allocate TOU tariffs:
# --------------------
# including residual (non-solar) rates for Solar_Block_TOU
elif 'TOU' in self.lookup.loc[tid, 'tariff_type'] \
or 'Solar_Block' in self.lookup.loc[tid, 'tariff_type']\
or 'Solar_Inst' in self.lookup.loc[tid, 'tariff_type']:
# calculate timeseries TOU tariff based on up to 8 periods (n=1 to 8)
# volumetric tariff is rate_n, between times start_n and end_n
# week_n is 'day' for week, 'end' for weekend, 'both' for both
# NB times stored in csv in form 'h:mm'. Midnight saved as 23:59
for name, parameter in self.tou_rate_list.items():
if not pd.isnull(self.lookup.loc[tid, parameter[1]]): # parameter[1] is rate_
winter_days_affected = ts.days[self.lookup.loc[tid, parameter[3]]].join(ts.seasonal_time['winter'],'inner')
summer_days_affected = ts.days[self.lookup.loc[tid, parameter[3]]].join(ts.seasonal_time['summer'],'inner')
if pd.Timestamp(self.lookup.loc[tid, parameter[1]]).time() > pd.Timestamp(self.lookup.loc[tid, parameter[2]]).time():
# winter tariff period crosses midnight:
winter_period = \
(winter_days_affected[
(winter_days_affected.time >=pd.Timestamp(
self.lookup.loc[tid, parameter[1]]).time()) # [1] is start_)
& (winter_days_affected.time <= pd.Timestamp('23:59').time())]).append(
winter_days_affected[
(winter_days_affected.time>=pd.Timestamp('0:00').time())
& (winter_days_affected.time < pd.Timestamp(
self.lookup.loc[tid, parameter[2]]).time())]) # [2] is end_
else:
# tariff period doesn't cross midnight:
winter_period = \
winter_days_affected[
(winter_days_affected.time >= pd.Timestamp(
self.lookup.loc[tid, parameter[1]]).time()) # start_)
& (winter_days_affected.time < pd.Timestamp(
self.lookup.loc[tid, parameter[2]]).time())] # end_
if (pd.Timestamp(self.lookup.loc[tid, parameter[1]])+ ts.dst_reverse_shift).time() > (pd.Timestamp(
self.lookup.loc[tid, parameter[2]]) + ts.dst_reverse_shift).time():
# summer tariff period crosses midnight:
summer_period = \
(summer_days_affected[
(summer_days_affected.time >= (pd.Timestamp(
self.lookup.loc[tid, parameter[1]]) + ts.dst_reverse_shift).time()) # [1] is start_)
& (summer_days_affected.time <= pd.Timestamp('23:59').time())]).append(
summer_days_affected[
(summer_days_affected.time >= pd.Timestamp('0:00').time())
& (summer_days_affected.time < (pd.Timestamp(
self.lookup.loc[tid, parameter[2]]) + ts.dst_reverse_shift).time())]) # [2] is end_
else:
summer_period = \
summer_days_affected[
(summer_days_affected.time >= (pd.Timestamp(
self.lookup.loc[tid, parameter[1]]) + ts.dst_reverse_shift).time()) # start_)
& (summer_days_affected.time < (pd.Timestamp(
self.lookup.loc[tid, parameter[2]]) + ts.dst_reverse_shift).time())] # end_
period = winter_period.join(summer_period,'outer').sort_values()
if not any(s in self.lookup.loc[tid, name] for s in ['solar', 'Solar']):
# Store solar rate and period separately
# For non-solar periods and rates only:
self.static_imports.loc[period, tid] = self.lookup.loc[tid, parameter[0]] # rate_
else: # Solar (local) periods and rates only:
self.static_solar_imports.loc[period, tid] = self.lookup.loc[tid, parameter[0]] # rate_
pass
# todo: create timeseries for TOU FiT Tariffs in the same way
# (currently only zero or flat rate FiTs)
if self.lookup.loc[tid, 'fit_type'] == 'Zero_Rate':
self.static_exports[tid] = 0
elif self.lookup.loc[tid, 'fit_type'] == 'Flat_Rate':
self.static_exports[tid] = self.lookup['fit_flat_rate'].fillna(0).loc[tid]
# Save tariffs as csvs
import_name = os.path.join(self.saved_tariff_path, 'static_import_tariffs.csv')
solar_name = os.path.join(self.saved_tariff_path, 'static_solar_import_tariffs.csv')
export_name = os.path.join(self.saved_tariff_path, 'static_export_tariffs.csv')
um.df_to_csv(self.static_imports, import_name)
um.df_to_csv(self.static_solar_imports, solar_name)
um.df_to_csv(self.static_exports, export_name)
class Tariff():
def __init__(self,
tariff_id,
scenario):
"""Create time-based rates for single specific tariff."""
if tariff_id not in scenario.tariff_lookup.index:
msg = '******Exception: Tariff '+ tariff_id+' is not in tariff_lookup.csv'
exit(msg)
# ------------------------------
# Export Tariff and Fixed Charge
# ------------------------------
self.export_tariff = (scenario.static_exports[tariff_id]).values # NB assumes FiTs are fixed
self.fixed_charge = scenario.tariff_lookup.loc[tariff_id, 'daily_fixed_rate']
# Add in Metering Service Charge for network and combined tariffs:
self.tariff_type = scenario.tariff_lookup.loc[tariff_id, 'tariff_type']
self.fixed_charge += \
scenario.tariff_lookup['metering_sc_non_cap'].fillna(0).loc[tariff_id]
# scenario.tariff_lookup['metering_sc_cap'].fillna(0).loc[tariff_id]
# NB Capital component of MSC does not apply as meter capital costs included in en_capex
# Dynamic (Block) Tariff
# ----------------------
if tariff_id in scenario.dynamic_list:
self.is_dynamic = True
self.block_rate_1 = scenario.tariff_lookup.loc[tariff_id, 'block_rate_1']
self.block_rate_2 = scenario.tariff_lookup.loc[tariff_id, 'block_rate_2']
self.block_rate_3 = scenario.tariff_lookup.loc[tariff_id, 'block_rate_3']
self.high_1 = scenario.tariff_lookup.loc[tariff_id, 'high_1']
self.high_2 = scenario.tariff_lookup.loc[tariff_id, 'high_2']
if self.high_1>0 and not self.block_rate_2>0 :
sys.exit('missing block tariff data')
if self.high_2>0 and not self.block_rate_3>0 :
sys.exit('missing block tariff data')
if self.tariff_type == 'Block_Quarterly':
self.block_billing_start = 0 # timestep to start cumulative energy calc
self.steps_in_block = 4380 # quarterly half-hour steps
else:
self.is_dynamic = False
# -------------
# Demand Tariff
# -------------
if tariff_id in scenario.demand_list:
self.is_demand = True
self.demand_type = scenario.tariff_lookup.loc[tariff_id, 'demand_type']
# Demand period is weekday or weekend between demand_start and demand_end
# with dst applied to start and end times during summer
# Assume that demand_end > demand_start
# (ie period does not cross midnight but can be 00:00 to 23:59)
winter_days_affected = ts.days[scenario.tariff_lookup.loc[tariff_id, 'demand_week']].join(ts.seasonal_time['winter'], 'inner')
summer_days_affected = ts.days[scenario.tariff_lookup.loc[tariff_id, 'demand_week']].join(ts.seasonal_time['summer'], 'inner')
if pd.Timestamp(scenario.tariff_lookup.loc[tariff_id, 'demand_start']).time() > \
pd.Timestamp(study.tariff_data.lookup.loc[tariff_id, 'demand_end']).time():
# winter period crosses midnight
winter_period = \
winter_days_affected[
(winter_days_affected.time >= pd.Timestamp(
scenario.tariff_lookup.loc[tariff_id, 'demand_start']).time())
& (winter_days_affected.time < pd.Timestamp('23:59').time())].append(
winter_days_affected[
(winter_days_affected.time >= pd.Timestamp('0:00').time())
& (winter_days_affected.time < pd.Timestamp(
study.tariff_data.lookup.loc[tariff_id, 'demand_end']).time())])
else:
winter_period = \
winter_days_affected[
(winter_days_affected.time >= pd.Timestamp(
scenario.tariff_lookup.loc[tariff_id, 'demand_start']).time())
& (winter_days_affected.time < pd.Timestamp(
study.tariff_data.lookup.loc[tariff_id, 'demand_end']).time())]
if (pd.Timestamp(scenario.tariff_lookup.loc[tariff_id, 'demand_start'])+ ts.dst_reverse_shift).time() > \
(pd.Timestamp(study.tariff_data.lookup.loc[tariff_id, 'demand_end'])+ ts.dst_reverse_shift).time():
# summer period crosses midnight
summer_period = \
summer_days_affected[
(summer_days_affected.time >= (pd.Timestamp(
scenario.tariff_lookup.loc[tariff_id, 'demand_start']) + ts.dst_reverse_shift).time())
& (summer_days_affected.time < pd.Timestamp('23:59').time())].append(
summer_days_affected[
(summer_days_affected.time >= pd.Timestamp('0:00').time())
& (summer_days_affected.time < (pd.Timestamp(
study.tariff_data.lookup.loc[tariff_id, 'demand_end']) + ts.dst_reverse_shift).time())])
else:
summer_period = \
summer_days_affected[
(summer_days_affected.time >= (pd.Timestamp(
scenario.tariff_lookup.loc[tariff_id, 'demand_start']) + ts.dst_reverse_shift).time())
& (summer_days_affected.time < (pd.Timestamp(
study.tariff_data.lookup.loc[tariff_id, 'demand_end']) + ts.dst_reverse_shift).time())]
self.demand_period = winter_period.join(summer_period, 'outer').sort_values()
s = pd.Series(0, index=ts.timeseries)
s[self.demand_period] = 1
self.demand_period_array = np.array(s)
self.assumed_pf = 1.0 ## For kVA demand charges, What is good assumption for this????
self.demand_tariff = scenario.tariff_lookup.loc[tariff_id, 'demand_tariff']
else:
self.is_demand = False
# ------------------------------------------------------
# Solar tariff periods and rates (block or instantaneous)
# ------------------------------------------------------
if tariff_id in scenario.solar_inst_list:
self.is_solar_inst = True
else:
self.is_solar_inst = False
# # Get solar tariff data:
# SOLAR BLOCK TARIFF IMPLEMENTATION INCORRECT but code below also used for solar instantaneous
# # NB solar block tariff period is NOT adjusted for DST
if tariff_id in scenario.solar_list:
for name, parameter in study.tariff_data.tou_rate_list.items():
if not pd.isnull(study.tariff_data.lookup.loc[tariff_id, name]):
if any(s in study.tariff_data.lookup.loc[tariff_id, name] for s in ['solar','Solar']):
self.solar_rate_name = study.tariff_data.lookup.loc[tariff_id, name]
winter_days_affected = ts.days[scenario.tariff_lookup.loc[tariff_id, parameter[3]]].join( # [3] is week_
ts.seasonal_time['winter'], 'inner')
summer_days_affected = ts.days[scenario.tariff_lookup.loc[tariff_id, parameter[3]]].join( # [3] is week_
ts.seasonal_time['summer'], 'inner')
if pd.Timestamp(scenario.tariff_lookup.loc[tariff_id, parameter[1]]).time() > \
pd.Timestamp(scenario.tariff_lookup.loc[tariff_id, parameter[2]]).time():
# winter tariff period crosses midnight:
winter_period = \
winter_days_affected[
(winter_days_affected.time >= pd.Timestamp(
scenario.tariff_lookup.loc[tariff_id, parameter[1]]).time()) # [1] is start
& (winter_days_affected.time < pd.Timestamp('23:59').time())].append(
winter_days_affected[
(winter_days_affected.time >= pd.Timestamp('0:00').time())
& (winter_days_affected.time < pd.Timestamp(
scenario.tariff_lookup.loc[tariff_id, parameter[2]]).time())]) # [2] is end_
else:
# winter tariff period doesn't cross midnight:
winter_period = \
winter_days_affected[
(winter_days_affected.time >= pd.Timestamp(
scenario.tariff_lookup.loc[tariff_id, parameter[1]]).time()) # [1] is start
& (winter_days_affected.time < pd.Timestamp(
scenario.tariff_lookup.loc[tariff_id, parameter[2]]).time())] # [2] is end_
if (pd.Timestamp(scenario.tariff_lookup.loc[tariff_id, parameter[1]]) ).time() > \
(pd.Timestamp(scenario.tariff_lookup.loc[tariff_id, parameter[2]]) ).time():
# summer tariff period crosses midnight:
summer_period = \
summer_days_affected[
(summer_days_affected.time >= (pd.Timestamp(
scenario.tariff_lookup.loc[
tariff_id, parameter[1]])).time()) # [1] is start
& (summer_days_affected.time < pd.Timestamp('23:59').time())].append( # [2] is end_
summer_days_affected[
(summer_days_affected.time >= pd.Timestamp('0:00').time()) # [1] is start
& (summer_days_affected.time < (pd.Timestamp(
scenario.tariff_lookup.loc[
tariff_id, parameter[2]]) ).time())]) # [2] is end_
else:
# summer tariff period doesn't cross midnight:
summer_period = \
summer_days_affected[
(summer_days_affected.time >= (pd.Timestamp(
scenario.tariff_lookup.loc[tariff_id, parameter[1]]) ).time()) # [1] is start
& (summer_days_affected.time < (pd.Timestamp(
scenario.tariff_lookup.loc[tariff_id, parameter[2]])).time())] # [2] is end_
# solar_period, solar_rate and solar_cp_allocation are for solar block tariffs:
# ie fixed quotas with dynamic load-dependent calculation
self.solar_period = winter_period.join(summer_period, 'outer').sort_values()
self.solar_rate = scenario.tariff_lookup.loc[tariff_id, parameter[0]] # rate_
self.solar_cp_allocation = scenario.tariff_lookup['solar_cp_allocation'].fillna(0).loc[tariff_id] # % of total solar generation allocated to cp
# Solar import tariff is static TOU tariff for instantaneous solar quota
self.solar_import_tariff = (scenario.static_solar_imports[tariff_id]).values
pass
else:
self.solar_import_tariff = np.zeros(ts.num_steps)
self.solar_rate_name = ''
# -----------------------------
# All volumetric import tariffs
# -----------------------------
# initialise to zero if dynamically calculated, e.g block tariff,
# otherwise copy from scenario
if tariff_id not in scenario.dynamic_list:
self.import_tariff = (scenario.static_imports[tariff_id]).values
else:
self.import_tariff = np.zeros(ts.num_steps)
class Battery():
# adapted from original script by Luke Marshall
def __init__(self,
scenario,
battery_id,
battery_strategy,
battery_capacity):
self.battery_id = battery_id
self.scenario = scenario
if not battery_id in study.battery_lookup.index:
logging.info("battery-id %s is not in battery_lookup.csv :", battery_id)
sys.exit("battery-id %s is not in battery_lookup.csv :", battery_id)
else:
# Load battery parameters from battery_lookup
# -------------------------------------------
self.capacity_kWh = study.battery_lookup.loc[battery_id, 'capacity_kWh']
self.max_charge_kW = study.battery_lookup.loc[battery_id, 'max_charge_kW']
self.efficiency_cycle = study.battery_lookup.loc[battery_id, 'efficiency_cycle']
if self.efficiency_cycle > 1.0:
logging.info('***************Exception!!! Battery Efficiency must be < 1.0*******')
print('***************Exception!!! Battery Efficiency must be < 1.0*******')
sys.exit("Battery Efficiency > 1")
self.maxDOD = study.battery_lookup.loc[battery_id, 'maxDOD']
self.maxSOC = study.battery_lookup.loc[battery_id, 'maxSOC']
if self.maxDOD + self.maxSOC <= 1.0:
logging.info('***************Exception!!! Battery maxSOC + maxDOD >= 1.0 *******')
print('***************Exception!!! Battery maxSOC + maxDOD <= 1.0*******')
sys.exit("Battery maxDOD + maxSOC <= 1")
self.battery_cost = study.battery_lookup.loc[battery_id, 'battery_cost']
self.battery_inv_cost = study.battery_lookup.loc[battery_id, 'battery_inv_cost']
if np.isnan(study.battery_lookup.loc[battery_id, 'life_bat_inv']):
self.life_bat_inv = scenario.a_term
else:
self.life_bat_inv = study.battery_lookup.loc[battery_id, 'life_bat_inv']
self.battery_life_years = study.battery_lookup.loc[battery_id,'battery_life_years']
self.max_cycles = study.battery_lookup.loc[battery_id, 'max_cycles']
# Scalable Battery
# ----------------
# details in `battery_lookup` are for 1kWh and this is scaled by capacity in `study_parameters`
if any(word in self.battery_id for word in ['scale', 'scalable']):
self.capacity_kWh = self.capacity_kWh * battery_capacity
self.max_charge_kW = self.max_charge_kW * battery_capacity
# Use default values if missing:
# ------------------------------
if pd.isnull(self.max_charge_kW):
self.max_charge_kW = self.capacity_kWh * 0.5
if pd.isnull(self.maxDOD):
self.maxDOD = 0.8
if pd.isnull(self.maxSOC):
self.maxSOC = 1.0
if pd.isnull(self.efficiency_cycle):
self.efficiency_cycle = 0.95
if pd.isnull(self.max_cycles):
self.max_cycles = 2000
if pd.isnull(self.battery_cost):
self.battery_cost =0.0
if pd.isnull(self.battery_inv_cost):
self.battery_inv_cost =0.0
# Define battery charging and discharging strategy
# ------------------------------------------------
# strategy that prioritises using PV to charg over onsite load:
if 'prioritise_battery' in study.battery_strategies.columns:
self.prioritise_battery = study.battery_strategies.fillna(False).loc[battery_strategy, 'prioritise_battery']
else:
self.prioritise_battery = False
# Strategy with different summer / winter charge and discharge periods (DST):
if 'seasonal_strategy' not in study.battery_strategies.columns:
seasonal_strategy = False
else:
seasonal_strategy = study.battery_strategies.fillna(False).loc[battery_strategy, 'seasonal_strategy']
# peak_demand strategy only discharges when net export >= peak_demand_percentage of annual peak load
if 'peak_demand_percentage' not in study.battery_strategies.columns:
self.peak_demand_percentage = 0
else:
self.peak_demand_percentage = study.battery_strategies.fillna(0).loc[battery_strategy, 'peak_demand_percentage']
# Set up restricted discharge period(s) and additional charge period(s)
# ---------------------------------------------------------------------
discharge_start1 = study.battery_strategies.loc[battery_strategy, 'discharge_start1']
discharge_end1 = study.battery_strategies.loc[battery_strategy, 'discharge_end1']
discharge_day1 = study.battery_strategies.loc[battery_strategy, 'discharge_day1']
discharge_start2 = study.battery_strategies.loc[battery_strategy, 'discharge_start2']
discharge_end2 = study.battery_strategies.loc[battery_strategy, 'discharge_end2']
discharge_day2 = study.battery_strategies.loc[battery_strategy, 'discharge_day2']
charge_start1 = study.battery_strategies.loc[battery_strategy, 'charge_start1']
charge_end1 = study.battery_strategies.loc[battery_strategy, 'charge_end1']
charge_day1 = study.battery_strategies.loc[battery_strategy, 'charge_day1']
charge_start2 = study.battery_strategies.loc[battery_strategy, 'charge_start2']
charge_end2 = study.battery_strategies.loc[battery_strategy, 'charge_end2']
charge_day2 = study.battery_strategies.loc[battery_strategy, 'charge_day2']
# Calculate discharge and grid-charge period(s):
# ----------------------------------------------
# If battery strategy is seasonal, add an hour to summer charge and discharge periods
if seasonal_strategy:
# If battery strategy is seasonal, add an hour to summer charge and discharge periods
# discharge_1
if pd.isnull(discharge_start1):
summer_period = pd.DatetimeIndex([])
winter_period = pd.DatetimeIndex([])
elif pd.Timestamp(discharge_start1) > pd.Timestamp(discharge_end1):
# winter period crosses midnight
winter_days_affected = ts.days[discharge_day1].join(
ts.seasonal_time['winter'], 'inner')
winter_period = \
winter_days_affected[
(winter_days_affected.time >= pd.Timestamp(discharge_start1).time()) & (
winter_days_affected.time <= pd.Timestamp('23:59').time())].append(
winter_days_affected[(winter_days_affected.time >= pd.Timestamp('0:00').time()) & (
winter_days_affected.time < pd.Timestamp(discharge_end1).time())]).sort_values()
# summer period crosses midnight
summer_days_affected = ts.days[discharge_day1].join(
ts.seasonal_time['summer'], 'inner')
summer_period = \
summer_days_affected[
(summer_days_affected.time >= (pd.Timestamp(discharge_start1) + ts.dst_reverse_shift).time()) & (
summer_days_affected.time <= pd.Timestamp('23:59').time())].append(
summer_days_affected[(summer_days_affected.time >= pd.Timestamp('0:00').time()) & (
summer_days_affected.time < (pd.Timestamp(discharge_end1) + ts.dst_reverse_shift).time())]).sort_values()
else:
# winter_period doesn't cross midnight
winter_days_affected = ts.days[discharge_day1].join(
ts.seasonal_time['winter'], 'inner')
winter_period = \
winter_days_affected[(winter_days_affected.time >= pd.Timestamp(discharge_start1).time())
& (winter_days_affected.time < pd.Timestamp(discharge_end1).time())]
# summer_period doesn't cross midnight
summer_days_affected = ts.days[discharge_day1].join(
ts.seasonal_time['summer'], 'inner')
summer_period = \
summer_days_affected[(summer_days_affected.time >= (pd.Timestamp(discharge_start1) + ts.dst_reverse_shift).time())
& (summer_days_affected.time < (pd.Timestamp(discharge_end1) + ts.dst_reverse_shift).time())]
discharge_period1 = winter_period.join(summer_period, 'outer').sort_values()
# discharge_2
if pd.isnull(discharge_start2):
summer_period = pd.DatetimeIndex([])
winter_period = pd.DatetimeIndex([])
elif pd.Timestamp(discharge_start2) > pd.Timestamp(discharge_end2):
# winter period crosses midnight
winter_days_affected = ts.days[discharge_day2].join(
ts.seasonal_time['winter'], 'inner')
winter_period = \
winter_days_affected[
(winter_days_affected.time >= pd.Timestamp(discharge_start2).time()) & (
winter_days_affected.time <= pd.Timestamp('23:59').time())].append(
winter_days_affected[(winter_days_affected.time >= pd.Timestamp('0:00').time()) & (
winter_days_affected.time < pd.Timestamp(discharge_end2).time())]).sort_values()
# summer period crosses midnight
summer_days_affected = ts.days[discharge_day2].join(
ts.seasonal_time['summer'], 'inner')
summer_period = \
summer_days_affected[
(summer_days_affected.time >= (pd.Timestamp(discharge_start2) + ts.dst_reverse_shift).time()) & (
summer_days_affected.time <= pd.Timestamp('23:59').time())].append(
summer_days_affected[(summer_days_affected.time >= pd.Timestamp('0:00').time()) & (
summer_days_affected.time < (pd.Timestamp(discharge_end2) + ts.dst_reverse_shift).time())]).sort_values()
else:
# winter_period doesn't cross midnight
winter_days_affected = ts.days[discharge_day2].join(
ts.seasonal_time['winter'], 'inner')
winter_period = \
winter_days_affected[(winter_days_affected.time >= pd.Timestamp(discharge_start2).time())
& (winter_days_affected.time < pd.Timestamp(discharge_end2).time())]
# summer_period doesn't cross midnight
summer_days_affected = ts.days[discharge_day2].join(
ts.seasonal_time['summer'], 'inner')
summer_period = \
summer_days_affected[(summer_days_affected.time >= (pd.Timestamp(discharge_start2) + ts.dst_reverse_shift).time())
& (summer_days_affected.time < (pd.Timestamp(discharge_end2) + ts.dst_reverse_shift).time())]
discharge_period2 = winter_period.join(summer_period, 'outer').sort_values()
# charge_1
if pd.isnull(charge_start1):
summer_period = pd.DatetimeIndex([])
winter_period = pd.DatetimeIndex([])
elif pd.Timestamp(charge_start1) > pd.Timestamp(charge_end1):
# winter period crosses midnight
winter_days_affected = ts.days[charge_day1].join(
ts.seasonal_time['winter'], 'inner')
winter_period = \
winter_days_affected[
(winter_days_affected.time >= pd.Timestamp(charge_start1).time()) & (
winter_days_affected.time <= pd.Timestamp('23:59').time())].append(
winter_days_affected[(winter_days_affected.time >= pd.Timestamp('0:00').time()) & (
winter_days_affected.time < pd.Timestamp(charge_end1).time())]).sort_values()
# summer period crosses midnight
summer_days_affected = ts.days[charge_day1].join(
ts.seasonal_time['summer'], 'inner')
summer_period = \
summer_days_affected[
(summer_days_affected.time >= (pd.Timestamp(charge_start1) + ts.dst_reverse_shift).time()) & (
summer_days_affected.time <= pd.Timestamp('23:59').time())].append(
summer_days_affected[(summer_days_affected.time >= pd.Timestamp('0:00').time()) & (
summer_days_affected.time < (pd.Timestamp(charge_end1) + ts.dst_reverse_shift).time())]).sort_values()
else:
# winter_period doesn't cross midnight
winter_days_affected = ts.days[charge_day1].join(
ts.seasonal_time['winter'], 'inner')
winter_period = \
winter_days_affected[(winter_days_affected.time >= pd.Timestamp(charge_start1).time())
& (winter_days_affected.time < pd.Timestamp(charge_end1).time())]
# summer_period doesn't cross midnight
summer_days_affected = ts.days[charge_day1].join(
ts.seasonal_time['summer'], 'inner')
summer_period = \
summer_days_affected[(summer_days_affected.time >= (pd.Timestamp(charge_start1) + ts.dst_reverse_shift).time())
& (summer_days_affected.time < (pd.Timestamp(charge_end1) + ts.dst_reverse_shift).time())]
charge_period1 = winter_period.join(summer_period, 'outer').sort_values()
# charge_2
if pd.isnull(charge_start2):
summer_period = pd.DatetimeIndex([])
winter_period = pd.DatetimeIndex([])
elif pd.Timestamp(charge_start2) > pd.Timestamp(charge_end2):
# winter period crosses midnight
winter_days_affected = ts.days[charge_day2].join(
ts.seasonal_time['winter'], 'inner')
winter_period = \
winter_days_affected[
(winter_days_affected.time >= pd.Timestamp(charge_start2).time()) & (
winter_days_affected.time <= pd.Timestamp('23:59').time())].append(
winter_days_affected[(winter_days_affected.time >= pd.Timestamp('0:00').time()) & (
winter_days_affected.time < pd.Timestamp(charge_end2).time())]).sort_values()
# summer period crosses midnight
summer_days_affected = ts.days[charge_day2].join(
ts.seasonal_time['summer'], 'inner')
summer_period = \
summer_days_affected[
(summer_days_affected.time >= (pd.Timestamp(charge_start2) + ts.dst_reverse_shift).time()) & (
summer_days_affected.time <= pd.Timestamp('23:59').time())].append(
summer_days_affected[(summer_days_affected.time >= pd.Timestamp('0:00').time()) & (
summer_days_affected.time < (pd.Timestamp(charge_end2) + ts.dst_reverse_shift).time())]).sort_values()
else:
# winter_period doesn't cross midnight
winter_days_affected = ts.days[charge_day2].join(
ts.seasonal_time['winter'], 'inner')
winter_period = \
winter_days_affected[(winter_days_affected.time >= pd.Timestamp(charge_start2).time())
& (winter_days_affected.time < pd.Timestamp(charge_end2).time())]
# summer_period doesn't cross midnight
summer_days_affected = ts.days[charge_day2].join(
ts.seasonal_time['summer'], 'inner')
summer_period = \
summer_days_affected[(summer_days_affected.time >= (pd.Timestamp(charge_start2) + ts.dst_reverse_shift).time())
& (summer_days_affected.time < (pd.Timestamp(charge_end2) + ts.dst_reverse_shift).time())]
charge_period2 = winter_period.join(summer_period, 'outer').sort_values()
else:
# If non-seasonal battery , use same periods for whole year:
# discharge_1
if pd.isnull(discharge_start1):
discharge_period1 = pd.DatetimeIndex([])
elif pd.Timestamp(discharge_start1) > pd.Timestamp(discharge_end1):
discharge_period1 = (ts.days[discharge_day1][(ts.days[discharge_day1].time >= pd.Timestamp(discharge_start1).time()) & (
ts.days[discharge_day1].time <= pd.Timestamp('23:59').time())].append(
ts.days[discharge_day1][(ts.days[discharge_day1].time >= pd.Timestamp('0:00').time()) & (
ts.days[discharge_day1].time < pd.Timestamp(discharge_end1).time())])).sort_values()
else:
discharge_period1 = \
ts.days[discharge_day1][(ts.days[discharge_day1].time >= pd.Timestamp(discharge_start1).time())
& (ts.days[discharge_day1].time < pd.Timestamp(discharge_end1).time())]
# discharge_2
if pd.isnull(discharge_start2):
discharge_period2 = pd.DatetimeIndex([])
elif pd.Timestamp(discharge_start2) > pd.Timestamp(discharge_end2):
discharge_period2 = (
ts.days[discharge_day2][(ts.days[discharge_day2].time >= pd.Timestamp(discharge_start2).time()) & (
ts.days[discharge_day2].time <= pd.Timestamp('23:59').time())].append(
ts.days[discharge_day2][(ts.days[discharge_day2].time >= pd.Timestamp('0:00').time()) & (
ts.days[discharge_day2].time < pd.Timestamp(discharge_end2).time())])).sort_values()
else:
discharge_period2 = \
ts.days[discharge_day2][(ts.days[discharge_day2].time >= pd.Timestamp(discharge_start2).time())
& (ts.days[discharge_day2].time < pd.Timestamp(discharge_end2).time())]
# charge_1
if pd.isnull(charge_start1):
charge_period1 = pd.DatetimeIndex([])
elif pd.Timestamp(charge_start1) > pd.Timestamp(charge_end1):
charge_period1 = (ts.days[charge_day1][(ts.days[charge_day1].time >= pd.Timestamp(charge_start1).time()) & (
ts.days[charge_day1].time <= pd.Timestamp('23:59').time())].append(
ts.days[charge_day1][(ts.days[charge_day1].time >= pd.Timestamp('0:00').time()) & (
ts.days[charge_day1].time < pd.Timestamp(charge_end1).time())])).sort_values()
else:
charge_period1 = \
ts.days[charge_day1][(ts.days[charge_day1].time >= pd.Timestamp(charge_start1).time())
& (ts.days[charge_day1].time < pd.Timestamp(charge_end1).time())]
# charge_2
if pd.isnull(charge_start2):
charge_period2 = pd.DatetimeIndex([])
elif pd.Timestamp(charge_start2) > pd.Timestamp(charge_end2):
charge_period2 = (
ts.days[charge_day2][(ts.days[charge_day2].time >= pd.Timestamp(charge_start2).time()) & (
ts.days[charge_day2].time <= pd.Timestamp('23:59').time())].append(
ts.days[charge_day2][(ts.days[charge_day2].time >= pd.Timestamp('0:00').time()) & (
ts.days[charge_day2].time < pd.Timestamp(charge_end2).time())])).sort_values()
else:
charge_period2 = \
ts.days[charge_day2][(ts.days[charge_day2].time >= pd.Timestamp(charge_start2).time())
& (ts.days[charge_day2].time < pd.Timestamp(charge_end2).time())]
# Combine multiple charge and discharge periods:
# ---------------------------------------------
self.discharge_period = discharge_period1.join(discharge_period2, how='outer')
if len(self.discharge_period) == 0:
self.discharge_period = ts.timeseries # if no discharge period set, discharge any time
self.charge_period = charge_period1.join(charge_period2, how='outer')
# discharge period as array for calculating peak demand
# -----------------------------------------------------
s = pd.Series(0, index=ts.timeseries)
s[self.discharge_period] = 1
self.discharge_period_array = np.array(s)
# Calculate charge and discharge rates
# ------------------------------------
self.charge_rate_kW = self.max_charge_kW
if 'charge_c_rate' in study.battery_strategies.columns:
if not pd.isnull(study.battery_strategies.loc[battery_strategy, 'charge_c_rate']):
self.charge_rate_kW = min(self.max_charge_kW,study.battery_strategies.loc[
battery_strategy, 'charge_c_rate']* self.capacity_kWh)
self.discharge_rate_kW = self.max_charge_kW
if 'discharge_c_rate' in study.battery_strategies.columns:
if not pd.isnull(study.battery_strategies.loc[battery_strategy, 'discharge_c_rate']):
self.discharge_rate_kW = min(self.max_charge_kW, study.battery_strategies.loc[
battery_strategy, 'discharge_c_rate'] * self.capacity_kWh)
# Initialise remaining battery variables
# --------------------------------------
self.initial_SOC = 0.5 # BATTERY STARTS AT 50% SOC
self.charge_level_kWh = self.capacity_kWh * self.initial_SOC
self.number_cycles = 0
self.SOH = 100 # State of health
# Max charge / discharge rate is accepted / delivered energy
self.max_timestep_delivered = self.discharge_rate_kW * ts.interval / 3600
self.max_timestep_accepted = self.charge_rate_kW * ts.interval / 3600
self.cumulative_losses = 0
self.net_discharge = np.zeros(ts.num_steps) # this is +ve for discharge, -ve for charge. Used for SC and SS calcs
# Assume losses are all in charging part of cycle:
# This works if energy capacity is actually "useful discharge capacity"
# see discussion here: https://electronics.stackexchange.com/questions/379778/how-to-estimate-li-ion-battery-soc/379793?noredirect=1#comment921865_379793
self.efficiency_charge = self.efficiency_cycle
self.efficiency_discharge = 1
# Initialise SOC log
# ------------------
self.SOC_log = np.zeros(ts.num_steps)
def reset(self,
annual_load): # annual road as np.array
self.charge_level_kWh = self.capacity_kWh * self.initial_SOC
self.number_cycles = 0
self.SOH = 100
self.SOC_log = np.zeros(ts.num_steps)
self.cumulative_losses = 0
self.net_discharge = np.zeros(ts.num_steps)
annual_peak_load = np.multiply(annual_load, self.discharge_period_array).max()
self.peak_demand_threshold = annual_peak_load * self.peak_demand_percentage / 100
def charge(self, desired_charge):
amount_to_charge = min((self.capacity_kWh * self.maxSOC - self.charge_level_kWh),
self.max_timestep_accepted * self.efficiency_charge,
desired_charge * self.efficiency_charge)
self.charge_level_kWh += amount_to_charge
energy_accepted = amount_to_charge / self.efficiency_charge
if amount_to_charge > 0:
self.number_cycles += 0.5 * amount_to_charge / (self.capacity_kWh * (self.maxSOC - 1 + self.maxDOD))
self.net_discharge_for_ts = - energy_accepted
self.cumulative_losses += energy_accepted * (1 - self.efficiency_charge)
return desired_charge - energy_accepted # returns unstored portion of energy
def discharge(self, desired_discharge):
if self.charge_level_kWh > self.capacity_kWh * (1 - self.maxDOD):
amount_to_discharge = min(desired_discharge / self.efficiency_discharge,
(self.charge_level_kWh - self.capacity_kWh * (1 - self.maxDOD)),
self.max_timestep_delivered / self.efficiency_discharge)
self.charge_level_kWh -= amount_to_discharge
self.number_cycles += 0.5 * amount_to_discharge / (self.capacity_kWh * (self.maxSOC - 1 + self.maxDOD))
else:
amount_to_discharge = 0
energy_delivered = amount_to_discharge * self.efficiency_discharge # Unneccessary step if losses are all in charge cycle
self.cumulative_losses += amount_to_discharge * (1 - self.efficiency_discharge)
self.net_discharge_for_ts = energy_delivered
return energy_delivered # returns delivered energy
def dispatch(self, generation, load, step):
"""Determines charge and discharge of battery at timestep."""
self.net_discharge_for_ts = 0.0 # reset
# -------------------------------
# Make battery control decisions:
# -------------------------------
if not self.prioritise_battery:
# A) Strategy to maximise SC :
# ---------------------------
# 1) meet onsite load first:
# --------------------------
available_kWh = generation - load
# 2) Use excess PV to charge
# --------------------------
if available_kWh > 0:
available_kWh = \
self.charge(available_kWh)
# 3) Discharge if needed to meet load, within discharge period
# ------------------------------------------------------------
if available_kWh < -self.peak_demand_threshold and ts.timeseries[step] in self.discharge_period:
available_kWh += \
self.discharge(-available_kWh)
# 4) Charge from grid in additional charge period:
# ------------------------------------------------
if available_kWh <= 0 and ts.timeseries[step] in self.charge_period:
available_kWh -= (self.max_timestep_accepted -
self.charge(self.max_timestep_accepted))
else:
# B) Strategy to reduce peak demand (apply PV to charge first)
# -----------------------------------------------------------
# Within discharge period:
if ts.timeseries[step] in self.discharge_period:
# 1) Apply PV to load
# -------------------
available_kWh = generation - load
# 2) Discharge battery to meet residual load
# ------------------------------------------
if available_kWh < -self.peak_demand_threshold:
available_kWh += self.discharge(-available_kWh)
# 3) or use excess PV to charge battery:
# --------------------------------------
elif available_kWh > 0 :
available_kWh = \
self.charge(available_kWh)
else: # outside discharge period
# 1) Use PV to charge battery:
# ----------------------------
if generation > 0:
generation = self.charge(generation)
# 2) use excess PV to meet load
available_kWh = generation - load
# If in grid-charging period, charge from grid
if available_kWh <=0 and ts.timeseries[step] in self.charge_period:
available_kWh -= (self.max_timestep_accepted -
self.charge(self.max_timestep_accepted))
# For monitoring purposes, log battery SOC:
# -----------------------------------------
if self.capacity_kWh > 0:
self.SOC_log[step] = self.charge_level_kWh / self.capacity_kWh * 100
self.SOH = 100 - (self.number_cycles / self.max_cycles) * 100
# For SS and SC calcs, log net discharge:
# ---------------------------------------
self.net_discharge[step] = self.net_discharge_for_ts
return available_kWh
def calcBatCapex(self):
"""Calculates capex for battery"""
# ---------------------------------------
# 1) Use 'battery_capex_per_kWh' and scale
# ---------------------------------------
# If 'battery_capex_per_kWh' is in the parameter file, it overrides capex info in battery_lookup
if self.scenario.battery_capex_per_kWh > 0:
self.battery_cost = self.scenario.battery_capex_per_kWh * self.capacity_kWh
bat_inv_capex = 0
# --------------------------------------------
# 2) Use 'battery_cost' and 'battery_inv_cost'
# --------------------------------------------
else:
# Use capex parameters in battery_lookup.csv :
# Battery capex includes inverter replacement if amortization period > inverter lifetime
if self.life_bat_inv < self.scenario.a_term:
bat_inv_capex = int((float(self.scenario.a_term) / self.life_bat_inv)-0.001) * self.battery_inv_cost
else:
bat_inv_capex = self.battery_inv_cost
# ---------------------------------------------------------------------
# For 1) or 2) replace battery (or combined battery-inverter) as needed:
# ----------------------------------------------------------------------
# Battery capex includes battery replacement if it exceeds max_cycles
# or battery_life_years (whichever is sooner) within amortization period
if np.isnan(self.max_cycles):
self.max_cycles = 100000
if np.isnan(self.battery_life_years):
self.battery_life_years = 1000
if self.battery_life_years == 0:
self.battery_life_years = 1000
if self.number_cycles > 0:
cycle_life = self.max_cycles / self.number_cycles
else:
cycle_life = 1000 # years to reach cycle lifetime
actual_lifetime = np.min([cycle_life, self.battery_life_years])
if float(self.scenario.a_term) > actual_lifetime:
number_batteries = int(float(self.scenario.a_term)/actual_lifetime - 0.01) + 1
bat_capex = number_batteries * self.battery_cost
else:
bat_capex = self.battery_cost
tot_capex = bat_inv_capex + bat_capex
return tot_capex
class Customer():
"""Can be resident, strata body, or ENO representing aggregation of residents."""
def __init__(self,
name, # string
):
self.name = name
self.tariff_data = study.tariff_data
self.en_capex_repayment = 0
self.en_opex = 0
self.bat_capex_repayment = 0
self.exports = np.zeros(ts.num_steps)
self.imports = np.zeros(ts.num_steps)
# self.local_exports = np.zeros(ts.num_steps) # not used, available for local trading
self.solar_allocation = np.zeros(ts.num_steps) # used for allocation of local generation
self.local_consumption = np.zeros(ts.num_steps)
self.flows = np.zeros(ts.num_steps)
self.cashflows = np.zeros(ts.num_steps)
self.import_charge = np.zeros(ts.num_steps)
self.local_solar_bill = 0
self.total_payment =0
def initialiseCustomerLoad(self,
customer_load): # as 1-d np.array
"""Set customer load, energy flows and cashflows to zero."""
self.load = customer_load
self.coincidence = np.zeros(ts.num_steps) # used for calculating self-consumption and self sufficiency
def initialiseCustomerTariff(self,
customer_tariff_id, # string
scenario):
self.tariff_id = customer_tariff_id
self.scenario = scenario
self.tariff = Tariff(tariff_id=self.tariff_id,
scenario=scenario)
def initialiseCustomerPV(self, pv_generation): # 1-D array
self.generation = pv_generation
def calcStaticEnergy(self):
"""Calculate Customer imports and exports for whole time period"""
self.flows = self.generation - self.load
self.exports = self.flows.clip(0)
self.imports = (-1 * self.flows).clip(0)
self.local_consumption = np.minimum(self.generation, self.load)
def calcDynamicEnergy(self, step):
"""Calculate Customer imports and exports for single timestep"""
# Used for scenarios with batteries
# -------------------------------------------------------------------------------
# Calculate energy flow without battery, then modify by calling battery.dispatch:
# -------------------------------------------------------------------------------
self.flows[step] = self.generation[step] - self.load[step]
if self.has_battery:
self.flows[step] = self.battery.dispatch(generation = self.generation[step],
load=self.load[step],
step=step)
else:
self.flows[step] = self.generation[step] - self.load[step]
self.exports[step] = self.flows[step].clip(0)
self.imports[step] = (-1 * self.flows[step]).clip(0)
# Local Consumption is PV self-consumed by customer (which is charged for in btm_p arrangement)
self.local_consumption[step] = np.minimum(self.generation[step], self.load[step])
def calcDemandCharge(self):
if self.tariff.is_demand:
max_demand = np.multiply(self.imports,self.tariff.demand_period_array).max() * 2 # convert kWh to kW