PK ! X . electricitycostcalculator_gabetest/__init__.py__author__ = 'Olivier Van Cutsem'
from electricitycostcalculator_testgabe import cost_calculator, oadr_signal, openei_tariff
__all__ = ["cost_calculator", "oadr_signal", "openei_tariff"]
PK ! rI 2 electricitycostcalculator_gabetest/__init__.py.bak__author__ = 'Olivier Van Cutsem'
from electricitycostcalculator import cost_calculator, oadr_signal, openei_tariff
__all__ = ["cost_calculator", "oadr_signal", "openei_tariff"]
PK ! 4 = electricitycostcalculator_gabetest/cost_calculator/.gitignore*.pyc
PK ! Lz=! ! > electricitycostcalculator_gabetest/cost_calculator/__init__.py__author__ = 'Olivier Van Cutsem'PK ! Lz=! ! B electricitycostcalculator_gabetest/cost_calculator/__init__.py.bak__author__ = 'Olivier Van Cutsem'PK ! ;h?M ?M E electricitycostcalculator_gabetest/cost_calculator/cost_calculator.py__author__ = 'Olivier Van Cutsem'
from .rate_structure import *
from .tariff_structure import TariffType
from dateutil.relativedelta import relativedelta
import pandas as pd
import pytz
class CostCalculator(object):
"""
This class is used to manipulate the building electricity cost:
- Bill calculation given a Smart Meter energy timeseries
- Electricity price timeseries between two dates
- Cost coefficients over a given period, for a linear optimization problem
- Metrics related to the tariff maximum demand
The main component of this class is called the "tariff_structure".
It is a dictionnary that lists electricity cost information for each type (fix, energy or demand).
The list for each type of billing stores "blocks" of data, that collect data about the tariffication of the electricity for a specific PERIOD of time.
Any time a new tariffication (e.g. a PDP event, or a new base tariff) must be added for a new time period, one just need to add the "block" of data in the corresponding list.
"""
# This default structure lists the tariffs type for most of the utilities in US
DEFAULT_TARIFF_MAP = {str(TariffType.FIX_CUSTOM_CHARGE.value): ChargeType.FIXED,
str(TariffType.ENERGY_CUSTOM_CHARGE.value): ChargeType.ENERGY,
str(TariffType.DEMAND_CUSTOM_CHARGE_SEASON.value): ChargeType.DEMAND,
str(TariffType.DEMAND_CUSTOM_CHARGE_TOU.value): ChargeType.DEMAND,
str(TariffType.PDP_ENERGY_CHARGE.value): ChargeType.ENERGY,
str(TariffType.PDP_ENERGY_CREDIT.value): ChargeType.ENERGY,
str(TariffType.PDP_DEMAND_CREDIT.value): ChargeType.DEMAND,
}
def __init__(self, type_tariffs_map=None):
"""
Initialize the class instance
:param type_tariffs_map: [optional] a dictionary that map the main type of tariffs used to describe the whole
billing logic to their type. DEFAULT_TARIFF_TYPE_LIST is used if type_tariffs_list is not specified.
Note: the method 'add_tariff' is used to build the core "tariff_structure" object structure.
"""
# This is the main structure, listing all the "tariff blocks" making up the whole tariff logic
self.__tariffstructures = {}
if type_tariffs_map is None: # The "basic" tariff types as the default ones
self.type_tariffs_map = self.DEFAULT_TARIFF_MAP
else:
self.type_tariffs_map = type_tariffs_map
# Initialize the list of "tariff blocks"
for label, type_tariff in list(self.type_tariffs_map.items()):
self.__tariffstructures[label] = self.generate_type_tariff(type_tariff)
# Useful data about the tariff
self.tariff_min_kw = 0 # The minimum peak demand to stay in this tariff
self.tariff_max_kw = float('inf') # The maximum peak demand to stay in this tariff
self.tariff_min_kwh = 0 # The minimum energy demand to stay in this tariff
self.tariff_max_kwh = float('inf') # The maximum energy demand to stay in this tariff
# --- Useful methods
def compute_bill(self, df, column_data=None, monthly_detailed=False):
"""
#TODO: create a class for the bill !
Return the bill corresponding to the electricity data in a data frame:
{
"label1": cost_detail_1
"label2": cost_detail_2
...
}
where:
- keys label_i corresponds to a type of tariff in the Enum TariffType and the values
- values cost_detail_i has one of the following form:
- if ENERGY or FIX tariff: cost_detail_i = (metric, cost) where metric is either the total energy or the period
- if DEMAND: cost_detail_i is dict where the keys are the price per kW and the values are tuples: (period-mask, max-power-value, max-power-date)
if monthly_detailed is set to True, the bill is detailed for each month:
{
"YY-MM":
{
"label1": (int or float, float) or a dict, -> the metric associated to the label1 and the corresponding cost (in $) in the month
"label2": (int or float, float) or a dict, -> the metric associated to the label2 and the corresponding cost (in $) in the month
...
}
}
:param df: a pandas dataframe containing energy consumption (in Wh) in the column 'column_data'.
If column data is None, it is assumed that only 1 column makes up the df
:param column_data: [optional] the label of the column containing the energy consumption values
:param monthly_detailed: [optional] if False, it is assumed that the df contains values for ONE billing period.
if True, the bill is detailed for each month of the calendar. Set to False by default.
:return: a dictionary representing the bill as described above
"""
ret = {}
# Initialize the returned structure
t_s = df.index[0]
t_i = datetime(year=t_s.year, month=t_s.month, day=1, tzinfo=t_s.tzinfo)
while t_i <= df.index[-1]:
ret[t_i.strftime("%Y-%m")] = {}
for k in list(self.__tariffstructures.keys()):
if self.type_tariffs_map[k] == ChargeType.DEMAND:
ret[t_i.strftime("%Y-%m")][k] = {} # a dict of price -> (max, cost)
else:
ret[t_i.strftime("%Y-%m")][k] = (0, 0) # a tuple
t_i += relativedelta(months=+1)
# Compute the bill for each of the tariff type, for each month
for label, tariff_data in list(self.__tariffstructures.items()):
l_blocks = self.get_tariff_struct(label, (df.index[0], df.index[-1])) # get all the tariff blocks for this period and this tariff type
for tariff_block in l_blocks:
tariff_cost_list = tariff_block.compute_bill(df, column_data) # this returns a dict of time-period pointing to tuple that contains both the metric of the bill and the cost
for time_label, bill_data in list(tariff_cost_list.items()):
self.update_bill_structure(ret[time_label], label, bill_data)
if monthly_detailed is False: # Aggregate all the months
return self.aggregate_monthly_bill(ret)
else:
return ret
def get_electricity_price(self, range_date, timestep):
"""
This function creates the electricity price signal for the specified time frame 'range_date', sampled at 'timestep'
period. It returns a pandas dataframes where the columns point to each type of tariff, specified in the argument
'type_tariffs_map' of the constructor.
:param range_date: a tuple (t_start, t_end) of type 'datetime', representing the period
:param timestep: an element of TariffElemPeriod enumeration (1h, 30min or 15min), representing the sampling
period
:return: a tuple (pd_prices, map_prices) containing:
- pd_prices: a pandas dataframe whose index is a datetime index and containing as many cols as there are
type_tariffs_map elements, i.e. the same keys as in __tariffstructures
- map_prices: a mapping between the cols label and the type of tariff (fix, energy or demand), being of type 'ChargeType'
"""
# Prepare the Pandas dataframe
(start_date_price, end_date_price) = range_date
date_list = pd.date_range(start=start_date_price, end=end_date_price, freq=str(timestep.value))
# Populate the dataframe for each label, for each period
ret_df = None
for label_tariff in list(self.__tariffstructures.keys()):
if self.type_tariffs_map[label_tariff] == ChargeType.FIXED: # fixed charges not in the elec price signal
continue
df_for_label = self.get_price_in_range(label_tariff, range_date, timestep)
if ret_df is None:
ret_df = df_for_label
else:
ret_df = pd.concat([ret_df, df_for_label], axis=1)
return ret_df, self.type_tariffs_map
def get_price_in_range(self, label_tariff, date_range, timestep):
"""
Generate a dataframe of the price of
remark: doesn't work with timestep > 1h ..
"""
# Prepare the Pandas dataframe
(start_date_price, end_date_price) = date_range
date_range = pd.date_range(start=start_date_price, end=end_date_price, freq=str(timestep.value))
ret_df = pd.DataFrame(index=date_range, columns=[label_tariff])
# # Select the corresponding blocks for each day and generate the time dataframe
for idx_day, df_day in ret_df.groupby(ret_df.index.date):
date_range_period = pd.date_range(start=df_day.index[0], periods=2, freq=str(timestep.value))
tariff_block = self.get_tariff_struct(label_tariff, (date_range_period[0], date_range_period[1]))
if len(tariff_block) > 0:
daily_rate = tariff_block[0].rate_schedule.get_daily_rate(df_day.index[0])
rate_df = tariff_block[0].get_daily_price_dataframe(daily_rate, df_day)
ret_df.loc[df_day.index[:], label_tariff] = rate_df['price'].values
return ret_df
def print_aggregated_bill(self, bill_struct, verbose=True):
"""
This method helps manipulating the bill returned by compute_bill().
It takes the bill as an argument and return a tuple (t, tt, ttt):
- t is the total cost
- tt is the total cost per type of tariff (energy, fix, demand)
- ttt is the cost for each tariff label
:param bill_struct: the dictionary returned by compute_bill()
:param verbose: [optional, default is True] print details
:return:
"""
monthly_detailed = False
# If the first keys of the dict point to smth that is not the tariff type, this is a monthly bill
first_keys_bill_struct = list(bill_struct.keys())
if first_keys_bill_struct[0] not in list(self.__tariffstructures.keys()):
monthly_detailed = True
if monthly_detailed is True: # This supposes the bill is calculated per natural month of the calendar
# Aggregation of all the months
acc_tot = 0.0
acc_per_chargetype = {ChargeType.FIXED: 0.0, ChargeType.ENERGY: 0.0, ChargeType.DEMAND: 0.0}
acc_per_label = {}
for k in list(self.type_tariffs_map.keys()):
acc_per_label[k] = 0.0
for m_key, bill_per_label in list(bill_struct.items()):
for lab_tariff, data in list(bill_per_label.items()):
if self.type_tariffs_map[lab_tariff] is not ChargeType.DEMAND:
cost_per_tariff = data[1]
else:
cost_per_tariff = 0.0
for p, data_demand in list(data.items()):
cost_per_tariff += p * data_demand['max-demand']
acc_tot += cost_per_tariff # second item in data is in dollar
acc_per_chargetype[self.type_tariffs_map[lab_tariff]] += cost_per_tariff
acc_per_label[lab_tariff] += cost_per_tariff
else:
# The bill is already aggregated for all the months
acc_tot = 0.0
acc_per_chargetype = {ChargeType.FIXED: 0.0, ChargeType.ENERGY: 0.0, ChargeType.DEMAND: 0.0}
for lab_tariff, data in list(bill_struct.items()):
if self.type_tariffs_map[lab_tariff] is not ChargeType.DEMAND:
cost_per_tariff = data[1]
else:
cost_per_tariff = 0.0
for p, data_demand in list(data.items()):
cost_per_tariff += p * data_demand['max-demand']
acc_tot += cost_per_tariff # second item in data is in dollar
acc_per_chargetype[self.type_tariffs_map[lab_tariff]] += cost_per_tariff
acc_per_label = bill_struct
if verbose:
# Total
print(("\n| Aggregated bill: {0} ($)".format(acc_tot)))
# Per type
print("\n| Total bill per type of charge:")
for t_key, v in list(acc_per_chargetype.items()):
print((" - Charge type '{0}': {1} ($)".format(str(t_key.value), v)))
# Per label
print("\n| Total bill per type or tariff:")
for l_key, v in list(acc_per_label.items()):
# TODO: print nicely the details ...
print((" - Type '{0}': {1} ($)".format(str(l_key), v)))
return acc_tot, acc_per_chargetype, acc_per_label
# --- Construction and internal methods
def add_tariff(self, tariff_obj, tariff_label, tariff_type=None):
"""
Add a tariff block structure that fell into the category "type_rate"
:param tariff_obj: a TariffBase (or children) object
:param tariff_label: the label of the tariff, in the keys given to the constructor
:param tariff_type: the type of tariff, an enum of ChargeType
:return: /
"""
# The tariff type (fix, demand or energy) is not specified: get it from the default structure
if tariff_type is None:
tariff_type = tariff_label
if tariff_label in list(self.DEFAULT_TARIFF_MAP.keys()):
tariff_type = self.DEFAULT_TARIFF_MAP[tariff_label]
else:
print(("[in add_tariff] Couldn't add the tariff object:" \
"The tariff_type is missing and couldn't be retrieved from the label '{0}'".format(tariff_label))) # debug
return
# The label tariff is a new one:
if tariff_label not in list(self.__tariffstructures.keys()):
self.__tariffstructures[tariff_label] = self.generate_type_tariff(tariff_type)
self.__tariffstructures[tariff_label]['list_blocks'].append(tariff_obj)
def get_tariff_struct(self, label_tariff, dates=None):
"""
Get the list of "tariff blocks" that influence the bill for the type of tariff "type_rate".
If "dates" is specified, only the blocks that are effective for that period are returned
:param label_tariff: a string pointing to the type of tariff
:param dates:[optional] a tuple of type datetime defining the period of selection
:return: a list of TariffBase (or children) describing the tariffs
"""
list_struct = self.__tariffstructures[label_tariff]['list_blocks']
if dates is None:
return list_struct
else:
(start_sel, end_sel) = dates
if start_sel.tzinfo is None and len(list_struct) > 0:
first_block = list_struct[0]
start_sel = start_sel.replace(tzinfo=pytz.timezone('UTC'))
if end_sel.tzinfo is None and len(list_struct) > 0:
first_block = list_struct[0]
end_sel = end_sel.replace(tzinfo=pytz.timezone('UTC'))
return [obj for obj in list_struct if ((obj.startdate <= start_sel <= obj.enddate) or (start_sel <= obj.startdate <= end_sel))]
def update_bill_structure(self, intermediate_monthly_bill, label_tariff, new_data):
"""
This method update the current monthly bill with new data for the same month:
- In case of "demand charge per (k)W", apply MAX
- In case of "energy charge per (k)Wh or fixed cost per month", apply SUM
:param intermediate_monthly_bill: the dict structure as return by the compute_bill() method, for a specific month key
:param label_tariff: a string indicating the tariff. Must be a key of self.__tariffstructures
:param new_data: a tuple (metric, cost) where:
- metric is either a float or an int, referring to the metric that influences the cost
- cost is a float, referring to the cost in $
:return:
"""
type_of_tariff = self.__tariffstructures[label_tariff]['type']
if type_of_tariff == ChargeType.DEMAND: # Demand: apply MAX
for p in list(new_data.keys()): # For each price -> dict (mask, max-p, date-max-p)
this_mask = new_data[p]['mask'] # get the new data mask
existing_mask_price = [k for k, v in list(intermediate_monthly_bill[label_tariff].items()) if v['mask'] == this_mask]
if len(existing_mask_price) > 0: # this mask has already been seen: APPLY MAX
existing_mask_price = existing_mask_price[0]
if new_data[p]['max-demand'] > intermediate_monthly_bill[label_tariff][existing_mask_price]['max-demand']:
#print("Demand rate update: for mask {0}, {0} is greater than {2}".format(this_mask, new_data[p]['max-demand'], intermediate_monthly_bill[label_tariff][existing_mask_price]['max-demand'])) # debug
del intermediate_monthly_bill[label_tariff][existing_mask_price]
intermediate_monthly_bill[label_tariff][p] = new_data[p]
else: # This is the first time this mask has been seen: store it
intermediate_monthly_bill[label_tariff][p] = new_data[p]
else: # energy or fixed cost: apply SUM
intermediate_monthly_bill[label_tariff] = (intermediate_monthly_bill[label_tariff][0] + new_data[0],
intermediate_monthly_bill[label_tariff][1] + new_data[1])
def aggregate_monthly_bill(self, monthly_bill):
"""
:param monthly_bill:
:return: /
"""
data_merge = None
for m, data_per_label in list(monthly_bill.items()):
if data_merge is None:
data_merge = data_per_label
else:
for label_tariff, data_tariff in list(data_per_label.items()):
if self.type_tariffs_map[label_tariff] == ChargeType.DEMAND: # take max
for p, data in list(data_tariff.items()): # For each price -> dict (mask, max, date)
this_mask = data['mask'] # get the new data mask
existing_mask_price = [k for k, v in list(data_merge[label_tariff].items()) if v['mask'] == this_mask]
if len(existing_mask_price) > 0: # this mask has already been seen: APPLY MAX
existing_mask_price = existing_mask_price[0]
if data['max-demand'] > data_merge[label_tariff][existing_mask_price]['max-demand']:
#print("Demand rate update: for mask {1}, {0} is greater than {2}".format(this_mask, data, data_merge[label_tariff][p])) # debug
del data_merge[label_tariff][existing_mask_price]
data_merge[label_tariff][p] = data
else: # This is the first time this price has been seen: store it
data_merge[label_tariff][p] = data
else: # sum
data_merge[label_tariff] = (data_merge[label_tariff][0] + data_tariff[0],
data_merge[label_tariff][1] + data_tariff[1])
return data_merge
@staticmethod
def generate_type_tariff(type_tariff):
return {'type': type_tariff,
'list_blocks': []}
PK ! +xGL L I electricitycostcalculator_gabetest/cost_calculator/cost_calculator.py.bak__author__ = 'Olivier Van Cutsem'
from .rate_structure import *
from .tariff_structure import TariffType
from dateutil.relativedelta import relativedelta
import pandas as pd
import pytz
class CostCalculator(object):
"""
This class is used to manipulate the building electricity cost:
- Bill calculation given a Smart Meter energy timeseries
- Electricity price timeseries between two dates
- Cost coefficients over a given period, for a linear optimization problem
- Metrics related to the tariff maximum demand
The main component of this class is called the "tariff_structure".
It is a dictionnary that lists electricity cost information for each type (fix, energy or demand).
The list for each type of billing stores "blocks" of data, that collect data about the tariffication of the electricity for a specific PERIOD of time.
Any time a new tariffication (e.g. a PDP event, or a new base tariff) must be added for a new time period, one just need to add the "block" of data in the corresponding list.
"""
# This default structure lists the tariffs type for most of the utilities in US
DEFAULT_TARIFF_MAP = {str(TariffType.FIX_CUSTOM_CHARGE.value): ChargeType.FIXED,
str(TariffType.ENERGY_CUSTOM_CHARGE.value): ChargeType.ENERGY,
str(TariffType.DEMAND_CUSTOM_CHARGE_SEASON.value): ChargeType.DEMAND,
str(TariffType.DEMAND_CUSTOM_CHARGE_TOU.value): ChargeType.DEMAND,
str(TariffType.PDP_ENERGY_CHARGE.value): ChargeType.ENERGY,
str(TariffType.PDP_ENERGY_CREDIT.value): ChargeType.ENERGY,
str(TariffType.PDP_DEMAND_CREDIT.value): ChargeType.DEMAND,
}
def __init__(self, type_tariffs_map=None):
"""
Initialize the class instance
:param type_tariffs_map: [optional] a dictionary that map the main type of tariffs used to describe the whole
billing logic to their type. DEFAULT_TARIFF_TYPE_LIST is used if type_tariffs_list is not specified.
Note: the method 'add_tariff' is used to build the core "tariff_structure" object structure.
"""
# This is the main structure, listing all the "tariff blocks" making up the whole tariff logic
self.__tariffstructures = {}
if type_tariffs_map is None: # The "basic" tariff types as the default ones
self.type_tariffs_map = self.DEFAULT_TARIFF_MAP
else:
self.type_tariffs_map = type_tariffs_map
# Initialize the list of "tariff blocks"
for label, type_tariff in self.type_tariffs_map.items():
self.__tariffstructures[label] = self.generate_type_tariff(type_tariff)
# Useful data about the tariff
self.tariff_min_kw = 0 # The minimum peak demand to stay in this tariff
self.tariff_max_kw = float('inf') # The maximum peak demand to stay in this tariff
self.tariff_min_kwh = 0 # The minimum energy demand to stay in this tariff
self.tariff_max_kwh = float('inf') # The maximum energy demand to stay in this tariff
# --- Useful methods
def compute_bill(self, df, column_data=None, monthly_detailed=False):
"""
#TODO: create a class for the bill !
Return the bill corresponding to the electricity data in a data frame:
{
"label1": cost_detail_1
"label2": cost_detail_2
...
}
where:
- keys label_i corresponds to a type of tariff in the Enum TariffType and the values
- values cost_detail_i has one of the following form:
- if ENERGY or FIX tariff: cost_detail_i = (metric, cost) where metric is either the total energy or the period
- if DEMAND: cost_detail_i is dict where the keys are the price per kW and the values are tuples: (period-mask, max-power-value, max-power-date)
if monthly_detailed is set to True, the bill is detailed for each month:
{
"YY-MM":
{
"label1": (int or float, float) or a dict, -> the metric associated to the label1 and the corresponding cost (in $) in the month
"label2": (int or float, float) or a dict, -> the metric associated to the label2 and the corresponding cost (in $) in the month
...
}
}
:param df: a pandas dataframe containing energy consumption (in Wh) in the column 'column_data'.
If column data is None, it is assumed that only 1 column makes up the df
:param column_data: [optional] the label of the column containing the energy consumption values
:param monthly_detailed: [optional] if False, it is assumed that the df contains values for ONE billing period.
if True, the bill is detailed for each month of the calendar. Set to False by default.
:return: a dictionary representing the bill as described above
"""
ret = {}
# Initialize the returned structure
t_s = df.index[0]
t_i = datetime(year=t_s.year, month=t_s.month, day=1, tzinfo=t_s.tzinfo)
while t_i <= df.index[-1]:
ret[t_i.strftime("%Y-%m")] = {}
for k in self.__tariffstructures.keys():
if self.type_tariffs_map[k] == ChargeType.DEMAND:
ret[t_i.strftime("%Y-%m")][k] = {} # a dict of price -> (max, cost)
else:
ret[t_i.strftime("%Y-%m")][k] = (0, 0) # a tuple
t_i += relativedelta(months=+1)
# Compute the bill for each of the tariff type, for each month
for label, tariff_data in self.__tariffstructures.items():
l_blocks = self.get_tariff_struct(label, (df.index[0], df.index[-1])) # get all the tariff blocks for this period and this tariff type
for tariff_block in l_blocks:
tariff_cost_list = tariff_block.compute_bill(df, column_data) # this returns a dict of time-period pointing to tuple that contains both the metric of the bill and the cost
for time_label, bill_data in tariff_cost_list.items():
self.update_bill_structure(ret[time_label], label, bill_data)
if monthly_detailed is False: # Aggregate all the months
return self.aggregate_monthly_bill(ret)
else:
return ret
def get_electricity_price(self, range_date, timestep):
"""
This function creates the electricity price signal for the specified time frame 'range_date', sampled at 'timestep'
period. It returns a pandas dataframes where the columns point to each type of tariff, specified in the argument
'type_tariffs_map' of the constructor.
:param range_date: a tuple (t_start, t_end) of type 'datetime', representing the period
:param timestep: an element of TariffElemPeriod enumeration (1h, 30min or 15min), representing the sampling
period
:return: a tuple (pd_prices, map_prices) containing:
- pd_prices: a pandas dataframe whose index is a datetime index and containing as many cols as there are
type_tariffs_map elements, i.e. the same keys as in __tariffstructures
- map_prices: a mapping between the cols label and the type of tariff (fix, energy or demand), being of type 'ChargeType'
"""
# Prepare the Pandas dataframe
(start_date_price, end_date_price) = range_date
date_list = pd.date_range(start=start_date_price, end=end_date_price, freq=str(timestep.value))
# Populate the dataframe for each label, for each period
ret_df = None
for label_tariff in self.__tariffstructures.keys():
if self.type_tariffs_map[label_tariff] == ChargeType.FIXED: # fixed charges not in the elec price signal
continue
df_for_label = self.get_price_in_range(label_tariff, range_date, timestep)
if ret_df is None:
ret_df = df_for_label
else:
ret_df = pd.concat([ret_df, df_for_label], axis=1)
return ret_df, self.type_tariffs_map
def get_price_in_range(self, label_tariff, date_range, timestep):
"""
Generate a dataframe of the price of
remark: doesn't work with timestep > 1h ..
"""
# Prepare the Pandas dataframe
(start_date_price, end_date_price) = date_range
date_range = pd.date_range(start=start_date_price, end=end_date_price, freq=str(timestep.value))
ret_df = pd.DataFrame(index=date_range, columns=[label_tariff])
# # Select the corresponding blocks for each day and generate the time dataframe
for idx_day, df_day in ret_df.groupby(ret_df.index.date):
date_range_period = pd.date_range(start=df_day.index[0], periods=2, freq=str(timestep.value))
tariff_block = self.get_tariff_struct(label_tariff, (date_range_period[0], date_range_period[1]))
if len(tariff_block) > 0:
daily_rate = tariff_block[0].rate_schedule.get_daily_rate(df_day.index[0])
rate_df = tariff_block[0].get_daily_price_dataframe(daily_rate, df_day)
ret_df.loc[df_day.index[:], label_tariff] = rate_df['price'].values
return ret_df
def print_aggregated_bill(self, bill_struct, verbose=True):
"""
This method helps manipulating the bill returned by compute_bill().
It takes the bill as an argument and return a tuple (t, tt, ttt):
- t is the total cost
- tt is the total cost per type of tariff (energy, fix, demand)
- ttt is the cost for each tariff label
:param bill_struct: the dictionary returned by compute_bill()
:param verbose: [optional, default is True] print details
:return:
"""
monthly_detailed = False
# If the first keys of the dict point to smth that is not the tariff type, this is a monthly bill
first_keys_bill_struct = list(bill_struct.keys())
if first_keys_bill_struct[0] not in self.__tariffstructures.keys():
monthly_detailed = True
if monthly_detailed is True: # This supposes the bill is calculated per natural month of the calendar
# Aggregation of all the months
acc_tot = 0.0
acc_per_chargetype = {ChargeType.FIXED: 0.0, ChargeType.ENERGY: 0.0, ChargeType.DEMAND: 0.0}
acc_per_label = {}
for k in self.type_tariffs_map.keys():
acc_per_label[k] = 0.0
for m_key, bill_per_label in bill_struct.items():
for lab_tariff, data in bill_per_label.items():
if self.type_tariffs_map[lab_tariff] is not ChargeType.DEMAND:
cost_per_tariff = data[1]
else:
cost_per_tariff = 0.0
for p, data_demand in data.items():
cost_per_tariff += p * data_demand['max-demand']
acc_tot += cost_per_tariff # second item in data is in dollar
acc_per_chargetype[self.type_tariffs_map[lab_tariff]] += cost_per_tariff
acc_per_label[lab_tariff] += cost_per_tariff
else:
# The bill is already aggregated for all the months
acc_tot = 0.0
acc_per_chargetype = {ChargeType.FIXED: 0.0, ChargeType.ENERGY: 0.0, ChargeType.DEMAND: 0.0}
for lab_tariff, data in bill_struct.items():
if self.type_tariffs_map[lab_tariff] is not ChargeType.DEMAND:
cost_per_tariff = data[1]
else:
cost_per_tariff = 0.0
for p, data_demand in data.items():
cost_per_tariff += p * data_demand['max-demand']
acc_tot += cost_per_tariff # second item in data is in dollar
acc_per_chargetype[self.type_tariffs_map[lab_tariff]] += cost_per_tariff
acc_per_label = bill_struct
if verbose:
# Total
print("\n| Aggregated bill: {0} ($)".format(acc_tot))
# Per type
print("\n| Total bill per type of charge:")
for t_key, v in acc_per_chargetype.items():
print(" - Charge type '{0}': {1} ($)".format(str(t_key.value), v))
# Per label
print("\n| Total bill per type or tariff:")
for l_key, v in acc_per_label.items():
# TODO: print nicely the details ...
print(" - Type '{0}': {1} ($)".format(str(l_key), v))
return acc_tot, acc_per_chargetype, acc_per_label
# --- Construction and internal methods
def add_tariff(self, tariff_obj, tariff_label, tariff_type=None):
"""
Add a tariff block structure that fell into the category "type_rate"
:param tariff_obj: a TariffBase (or children) object
:param tariff_label: the label of the tariff, in the keys given to the constructor
:param tariff_type: the type of tariff, an enum of ChargeType
:return: /
"""
# The tariff type (fix, demand or energy) is not specified: get it from the default structure
if tariff_type is None:
tariff_type = tariff_label
if tariff_label in self.DEFAULT_TARIFF_MAP.keys():
tariff_type = self.DEFAULT_TARIFF_MAP[tariff_label]
else:
print("[in add_tariff] Couldn't add the tariff object:" \
"The tariff_type is missing and couldn't be retrieved from the label '{0}'".format(tariff_label)) # debug
return
# The label tariff is a new one:
if tariff_label not in self.__tariffstructures.keys():
self.__tariffstructures[tariff_label] = self.generate_type_tariff(tariff_type)
self.__tariffstructures[tariff_label]['list_blocks'].append(tariff_obj)
def get_tariff_struct(self, label_tariff, dates=None):
"""
Get the list of "tariff blocks" that influence the bill for the type of tariff "type_rate".
If "dates" is specified, only the blocks that are effective for that period are returned
:param label_tariff: a string pointing to the type of tariff
:param dates:[optional] a tuple of type datetime defining the period of selection
:return: a list of TariffBase (or children) describing the tariffs
"""
list_struct = self.__tariffstructures[label_tariff]['list_blocks']
if dates is None:
return list_struct
else:
(start_sel, end_sel) = dates
if start_sel.tzinfo is None and len(list_struct) > 0:
first_block = list_struct[0]
start_sel = start_sel.replace(tzinfo=pytz.timezone('UTC'))
if end_sel.tzinfo is None and len(list_struct) > 0:
first_block = list_struct[0]
end_sel = end_sel.replace(tzinfo=pytz.timezone('UTC'))
return [obj for obj in list_struct if ((obj.startdate <= start_sel <= obj.enddate) or (start_sel <= obj.startdate <= end_sel))]
def update_bill_structure(self, intermediate_monthly_bill, label_tariff, new_data):
"""
This method update the current monthly bill with new data for the same month:
- In case of "demand charge per (k)W", apply MAX
- In case of "energy charge per (k)Wh or fixed cost per month", apply SUM
:param intermediate_monthly_bill: the dict structure as return by the compute_bill() method, for a specific month key
:param label_tariff: a string indicating the tariff. Must be a key of self.__tariffstructures
:param new_data: a tuple (metric, cost) where:
- metric is either a float or an int, referring to the metric that influences the cost
- cost is a float, referring to the cost in $
:return:
"""
type_of_tariff = self.__tariffstructures[label_tariff]['type']
if type_of_tariff == ChargeType.DEMAND: # Demand: apply MAX
for p in new_data.keys(): # For each price -> dict (mask, max-p, date-max-p)
this_mask = new_data[p]['mask'] # get the new data mask
existing_mask_price = [k for k, v in intermediate_monthly_bill[label_tariff].items() if v['mask'] == this_mask]
if len(existing_mask_price) > 0: # this mask has already been seen: APPLY MAX
existing_mask_price = existing_mask_price[0]
if new_data[p]['max-demand'] > intermediate_monthly_bill[label_tariff][existing_mask_price]['max-demand']:
#print("Demand rate update: for mask {0}, {0} is greater than {2}".format(this_mask, new_data[p]['max-demand'], intermediate_monthly_bill[label_tariff][existing_mask_price]['max-demand'])) # debug
del intermediate_monthly_bill[label_tariff][existing_mask_price]
intermediate_monthly_bill[label_tariff][p] = new_data[p]
else: # This is the first time this mask has been seen: store it
intermediate_monthly_bill[label_tariff][p] = new_data[p]
else: # energy or fixed cost: apply SUM
intermediate_monthly_bill[label_tariff] = (intermediate_monthly_bill[label_tariff][0] + new_data[0],
intermediate_monthly_bill[label_tariff][1] + new_data[1])
def aggregate_monthly_bill(self, monthly_bill):
"""
:param monthly_bill:
:return: /
"""
data_merge = None
for m, data_per_label in monthly_bill.items():
if data_merge is None:
data_merge = data_per_label
else:
for label_tariff, data_tariff in data_per_label.items():
if self.type_tariffs_map[label_tariff] == ChargeType.DEMAND: # take max
for p, data in data_tariff.items(): # For each price -> dict (mask, max, date)
this_mask = data['mask'] # get the new data mask
existing_mask_price = [k for k, v in data_merge[label_tariff].items() if v['mask'] == this_mask]
if len(existing_mask_price) > 0: # this mask has already been seen: APPLY MAX
existing_mask_price = existing_mask_price[0]
if data['max-demand'] > data_merge[label_tariff][existing_mask_price]['max-demand']:
#print("Demand rate update: for mask {1}, {0} is greater than {2}".format(this_mask, data, data_merge[label_tariff][p])) # debug
del data_merge[label_tariff][existing_mask_price]
data_merge[label_tariff][p] = data
else: # This is the first time this price has been seen: store it
data_merge[label_tariff][p] = data
else: # sum
data_merge[label_tariff] = (data_merge[label_tariff][0] + data_tariff[0],
data_merge[label_tariff][1] + data_tariff[1])
return data_merge
@staticmethod
def generate_type_tariff(type_tariff):
return {'type': type_tariff,
'list_blocks': []}
PK ! 9K K D electricitycostcalculator_gabetest/cost_calculator/rate_structure.py__author__ = 'Olivier Van Cutsem'
from enum import Enum
from datetime import datetime
import holidays
# --------------- Schedule structures --------------- #
class ChargeType(Enum):
FIXED = 'fix',
DEMAND = 'demand',
ENERGY = 'energy',
class TouRateSchedule:
"""
This structure stores the Time-Of-Use rates related to power or energy.
It is made of a set of methods that manipulate a dict formatted as follow:
{
"monthly_label1":
{
"months_list": [m1, m2, ...],
"daily_rates":
{
"daily_label1:
{
"days_list": [d1, d2, ...],
"rates": list OR float
},
...
}
},
...
}
Remark: a month spans from 1 (january) to 12 (december) ; a day spans from 0 (sunday) to 6 (saturday)
"""
# TODO: use BlockRate instead of assuming it's a float !
# Keys used internally
MONTHLIST_KEY = 'months_list'
DAILY_RATE_KEY = 'daily_rates'
DAYSLIST_KEY = 'days_list'
RATES_KEY = 'rates'
def __init__(self, rates_schedule):
"""
Constructor
:param rates_schedule: a dict formatted as explain in the class description
"""
# TODO: assert the format is correct
self.__rates = rates_schedule
def get_from_timestamp(self, date):
"""
Return the rate corresponding to a given timestamp
:param date: a float, the timestamp
:return: a float, the rate corresponding to the timestamp. None if there is no associated rate
"""
# Get (m, d, h, m) from date
if type(date) is float or type(date) is int:
date_struct = datetime.fromtimestamp(date)
else:
date_struct = date
m_date = date_struct.month
d_date = date_struct.weekday()
h_date = date_struct.hour
min_date = date_struct.minute
rates = self.get_rate(m_date, d_date)
return self.get_rate_in_day(rates, (h_date, min_date))
def get_daily_rate(self, date):
"""
Return the daily rates, as a vector sampled at a given period
:param date: a float, the timestamp
:return: a list of float
"""
if type(date) is float or type(date) is int:
date_struct = datetime.fromtimestamp(date)
else:
date_struct = date
m_date = date_struct.month
d_date = self.get_day_in_the_week(date_struct)
rate_struct = self.get_rate(m_date, d_date)
if type(rate_struct) is not list: # hourly flat rate
return [rate_struct]
else:
return rate_struct
# --- private
@staticmethod
def get_day_in_the_week(date_sel):
"""
TODO write description
:param date_sel:
:return:
"""
if date_sel in holidays.US(state='CA', years=date_sel.year):
return 0 # Hardcoded: holidays are like Sundays ...
else:
return date_sel.weekday()
def get_rate_in_day(self, rate_struct, time_select):
"""
Return the rate in 'rate_struct' corresponding to instant "time_select"
:param rate_struct: either a float or an int, representing the rate(s) of the day
:param time_select: a tuple (h, m) representing the hour and minute to select
:return: a float, the rate at the selected time
"""
(h, m) = time_select
if type(rate_struct) is not list:
return rate_struct
else:
idx = (h + m/60.0 ) * len(rate_struct) / 24.0
return rate_struct[int(idx)]
def get_rate(self, m_date, d_date):
"""
TODO write description
:param self:
:param m_date:
:param d_date:
:return:
"""
for m_lab, m_data in list(self.__rates.items()):
if m_date in m_data[self.MONTHLIST_KEY]:
for d_lab, d_data in list(m_data[self.DAILY_RATE_KEY].items()):
if d_date in d_data[self.DAYSLIST_KEY]:
return d_data[self.RATES_KEY]
@property
def periods_in_day(self):
"""
TODO write description
:return:
"""
# take a random day and check the vector length
random_day = datetime(2000, 1, 1, hour=0, minute=0, second=0) # the year doesn't matter
vector_data = self.get_daily_rate(random_day)
return len(vector_data)
@property
def main_structure(self):
"""
The raw tariff rates
"""
return self.__rates
class BlockRate:
"""
This class stores and manipulates the rate of energy that vary as a function of the total consumption energy
"""
def __init__(self, cost_base, block_rate=None):
self.__rates = [cost_base]
self.__thresholds = [0]
if block_rate is not None:
(costs, thres) = block_rate
self.__rates += costs
self.__thresholds += thres
self.__thresholds.append(float('inf'))
def get_rate(self, acc=None):
"""
:param acc:
:return:
"""
if acc is None:
return self.__rates[0]
else:
return [self.__rates[i] for i in range(len(self.__rates)) if self.__thresholds[i] <= acc < self.__thresholds[i+1]][0]
PK ! |U? ? H electricitycostcalculator_gabetest/cost_calculator/rate_structure.py.bak__author__ = 'Olivier Van Cutsem'
from enum import Enum
from datetime import datetime
import holidays
# --------------- Schedule structures --------------- #
class ChargeType(Enum):
FIXED = 'fix',
DEMAND = 'demand',
ENERGY = 'energy',
class TouRateSchedule:
"""
This structure stores the Time-Of-Use rates related to power or energy.
It is made of a set of methods that manipulate a dict formatted as follow:
{
"monthly_label1":
{
"months_list": [m1, m2, ...],
"daily_rates":
{
"daily_label1:
{
"days_list": [d1, d2, ...],
"rates": list OR float
},
...
}
},
...
}
Remark: a month spans from 1 (january) to 12 (december) ; a day spans from 0 (sunday) to 6 (saturday)
"""
# TODO: use BlockRate instead of assuming it's a float !
# Keys used internally
MONTHLIST_KEY = 'months_list'
DAILY_RATE_KEY = 'daily_rates'
DAYSLIST_KEY = 'days_list'
RATES_KEY = 'rates'
def __init__(self, rates_schedule):
"""
Constructor
:param rates_schedule: a dict formatted as explain in the class description
"""
# TODO: assert the format is correct
self.__rates = rates_schedule
def get_from_timestamp(self, date):
"""
Return the rate corresponding to a given timestamp
:param date: a float, the timestamp
:return: a float, the rate corresponding to the timestamp. None if there is no associated rate
"""
# Get (m, d, h, m) from date
if type(date) is float or type(date) is int:
date_struct = datetime.fromtimestamp(date)
else:
date_struct = date
m_date = date_struct.month
d_date = date_struct.weekday()
h_date = date_struct.hour
min_date = date_struct.minute
rates = self.get_rate(m_date, d_date)
return self.get_rate_in_day(rates, (h_date, min_date))
def get_daily_rate(self, date):
"""
Return the daily rates, as a vector sampled at a given period
:param date: a float, the timestamp
:return: a list of float
"""
if type(date) is float or type(date) is int:
date_struct = datetime.fromtimestamp(date)
else:
date_struct = date
m_date = date_struct.month
d_date = self.get_day_in_the_week(date_struct)
rate_struct = self.get_rate(m_date, d_date)
if type(rate_struct) is not list: # hourly flat rate
return [rate_struct]
else:
return rate_struct
# --- private
@staticmethod
def get_day_in_the_week(date_sel):
"""
TODO write description
:param date_sel:
:return:
"""
if date_sel in holidays.US(state='CA', years=date_sel.year):
return 0 # Hardcoded: holidays are like Sundays ...
else:
return date_sel.weekday()
def get_rate_in_day(self, rate_struct, time_select):
"""
Return the rate in 'rate_struct' corresponding to instant "time_select"
:param rate_struct: either a float or an int, representing the rate(s) of the day
:param time_select: a tuple (h, m) representing the hour and minute to select
:return: a float, the rate at the selected time
"""
(h, m) = time_select
if type(rate_struct) is not list:
return rate_struct
else:
idx = (h + m/60.0 ) * len(rate_struct) / 24.0
return rate_struct[int(idx)]
def get_rate(self, m_date, d_date):
"""
TODO write description
:param self:
:param m_date:
:param d_date:
:return:
"""
for m_lab, m_data in self.__rates.items():
if m_date in m_data[self.MONTHLIST_KEY]:
for d_lab, d_data in m_data[self.DAILY_RATE_KEY].items():
if d_date in d_data[self.DAYSLIST_KEY]:
return d_data[self.RATES_KEY]
@property
def periods_in_day(self):
"""
TODO write description
:return:
"""
# take a random day and check the vector length
random_day = datetime(2000, 1, 1, hour=0, minute=0, second=0) # the year doesn't matter
vector_data = self.get_daily_rate(random_day)
return len(vector_data)
@property
def main_structure(self):
"""
The raw tariff rates
"""
return self.__rates
class BlockRate:
"""
This class stores and manipulates the rate of energy that vary as a function of the total consumption energy
"""
def __init__(self, cost_base, block_rate=None):
self.__rates = [cost_base]
self.__thresholds = [0]
if block_rate is not None:
(costs, thres) = block_rate
self.__rates += costs
self.__thresholds += thres
self.__thresholds.append(float('inf'))
def get_rate(self, acc=None):
"""
:param acc:
:return:
"""
if acc is None:
return self.__rates[0]
else:
return [self.__rates[i] for i in range(len(self.__rates)) if self.__thresholds[i] <= acc < self.__thresholds[i+1]][0]
PK ! Ν": ": F electricitycostcalculator_gabetest/cost_calculator/tariff_structure.py__author__ = 'Olivier Van Cutsem'
from abc import abstractmethod
from enum import Enum
from datetime import datetime
import calendar
import pandas as pd
# --------------- TARIFF structures --------------- #
class TariffType(Enum):
FIX_CUSTOM_CHARGE = 'customer_fix_charge'
ENERGY_CUSTOM_CHARGE = 'customer_energy_charge'
DEMAND_CUSTOM_CHARGE_SEASON = 'customer_demand_charge_season'
DEMAND_CUSTOM_CHARGE_TOU = 'customer_demand_charge_tou'
PDP_ENERGY_CHARGE = 'pdp_event_energy_charge'
PDP_ENERGY_CREDIT = 'pdp_non_event_energy_credit'
PDP_DEMAND_CREDIT = 'pdp_non_event_demand_credit'
class TariffElemPeriod(Enum):
MONTHLY = 'M'
DAILY = 'D'
HOURLY = '1h'
HALFLY = '30min'
QUARTERLY = '15min'
class TariffElemMetricUnit(Enum):
EN_WH = 1
DEMAND_W = 1
EN_KWH = 1000.0
DEMAND_KW = 1000.0
class TariffElemCostUnit(Enum):
CENT = 0.01
DOLLAR = 1
class TariffBase(object):
"""
This abstract class represent the base of any tariffication structure.
The main components are the starting and ending date of the structure.
"""
def __init__(self, dates, unit_cost, name=None):
# Starting and ending dates, as timestamps
ts, te = dates
self.__startdate = ts
self.__enddate = te
self.name = name
self.unit_cost = unit_cost
def compute_bill(self, df, data_col=None):
"""
Compute the bill due to the power/energy consumption in df, for each billing period specified in billing_periods
It outputs a dictionary formatted as follow:
{
"bill_period_label1": (float or dict, float), -> the monthly 'metric' and its cost
...
}
:param df: a pandas dataframe containing power consumption timeseries
:param billing_periods: a dictionary mapping the billing periods label to a tuple (t_start, t_end) of datetime,
defining the period related to the billing label
:return: a dictionary formatted as in this method signature
"""
ret = {}
# Select only the data in this tariff window
start_sel = self.startdate
start_sel = start_sel.replace(tzinfo=df.index[0].tzinfo)
end_sel = self.enddate
end_sel = end_sel.replace(tzinfo=df.index[0].tzinfo)
mask = (df.index >= start_sel) & (df.index <= end_sel)
df = df.loc[mask]
# Loop over the months
t_s = df.index[0]
last_day_of_month = calendar.monthrange(t_s.year, t_s.month)[1] # The last day of this month
t_e = datetime(t_s.year, t_s.month, last_day_of_month, hour=23, minute=59, second=59, tzinfo=t_s.tzinfo) # end of the current month
t_e = min(df.index[-1], t_e)
while t_s <= t_e:
mask = (df.index >= t_s) & (df.index <= t_e)
df_month = df.loc[mask]
monthly_bill = self.compute_monthly_bill(df_month, data_col)
ret[t_s.strftime("%Y-%m")] = monthly_bill
# Prepare the next billing month
month = t_e.month + 1
year = t_e.year
if month >= 13:
month = 1
year += 1
t_s = datetime(year, month, 1, hour=0, minute=0, second=0, tzinfo=t_s.tzinfo)
last_day_of_month = calendar.monthrange(year, month)[1]
t_e = datetime(year, month, last_day_of_month, hour=23, minute=59, second=59, tzinfo=t_s.tzinfo)
t_e = min(df.index[-1], t_e)
return ret
@abstractmethod
def compute_monthly_bill(self, df, data_col=None):
"""
Compute the monthly bill due to the power/energy consumption in df
:param df: a pandas dataframe
:param data_col: the column label containing the data
:return: a tuple (float, float) -> (value, cost), representing the bill and the corresponding metric linked to the cost
"""
pass
@property
def startdate(self):
"""
GETTER of the tariff starting date
:return: a timestamp
"""
return self.__startdate
@property
def enddate(self):
"""
GETTER of the tariff end date
:return: a timestamp
"""
return self.__enddate
@abstractmethod
def period_metric(self):
pass
@abstractmethod
def get_price_from_timestamp(self, timestamp):
pass
# --------------- FIXED TARIFF --------------- #
class FixedTariff(TariffBase):
"""
Represent a tariff fixed over a given period (among TariffPeriod)
"""
def __init__(self, dates, rate_value, unit_cost=TariffElemCostUnit.DOLLAR, bill_period=TariffElemPeriod.MONTHLY, name=None):
"""
Constructor
:param dates: see FixedTariff init
:param bill_period: the period
:param name: see FixedTariff init
"""
super(FixedTariff, self).__init__(dates, unit_cost, name)
self.__rate_period = bill_period
self.__rate_value = rate_value
def compute_monthly_bill(self, df, data_col=None):
"""
Compute the monthly bill due to a fixed periodic cost
:param df: a pandas dataframe
:return: a tuple (float, float), representing the bill and the duration (in months)
"""
first_day = df.index[0].day
last_day = df.index[-1].day
nb_days = last_day - first_day + 1
nb_days_per_month = 365/12
bill = 0
if self.__rate_period == TariffElemPeriod.MONTHLY:
bill = self.__rate_value * nb_days/nb_days_per_month # a fraction of the month
elif self.__rate_period == TariffElemPeriod.DAILY:
bill = self.__rate_value * nb_days # sum of each day
return nb_days, bill
def period_metric(self):
return self.__rate_period
def get_price_from_timestamp(self, timestamp):
return self.__rate_value
# --------------- TOU TARIFFs --------------- #
class TimeOfUseTariff(TariffBase):
"""
This class represents a tariff fixed over a given period (among TariffElemPeriod)
"""
def __init__(self, dates, rate_schedule, unit_metric, unit_cost, name=None):
"""
Constructor
:param dates: see FixedTariff init
:param rate_list:
:param time_schedule: TODO
:param name: see FixedTariff init
"""
super(TimeOfUseTariff, self).__init__(dates, unit_cost, name)
self.__schedule = rate_schedule # A table mapping (month, day) to hourly rate index
self.__unit_metric = unit_metric
@abstractmethod
def compute_monthly_bill(self, df, data_col=None):
"""
idem super
"""
pass
@property
def rate_schedule(self):
return self.__schedule
@property
def unit_metric(self):
return self.__unit_metric
def period_metric(self):
nb_periods_in_day = self.__schedule.periods_in_day
# TODO: replace ifs by map
if nb_periods_in_day == 24:
return TariffElemPeriod.HOURLY
elif nb_periods_in_day == 24 * 2:
return TariffElemPeriod.HALFLY
elif nb_periods_in_day == 24 * 4:
return TariffElemPeriod.QUARTERLY
else:
return TariffElemPeriod.DAILY
def get_price_from_timestamp(self, timestamp):
return self.__schedule.get_from_timestamp(timestamp)
@staticmethod
def get_daily_price_dataframe(daily_rate, df_day):
# Constructing the dataframe for an easier manipulation of time
period_rate = len(daily_rate) / 24.0
# In some cases the day might not be full: missing data or DST
daily_prices = [daily_rate[int((df_day.index[i].hour + df_day.index[i].minute / 60.0) * period_rate)] for i in range(len(df_day.index))]
data = {'date': df_day.index[:], 'price': daily_prices}
df_prices = pd.DataFrame(data=data)
df_prices.set_index('date')
return df_prices
class TouDemandChargeTariff(TimeOfUseTariff):
"""
This class represents a Time Of Use Demand Charge tariff
"""
def __init__(self, dates, time_schedule, unit_metric=TariffElemMetricUnit.DEMAND_KW, unit_cost=TariffElemCostUnit.DOLLAR, name=None):
"""
Constructor
:param dates: see FixedTariff init
:param rate_list: TODO
:param time_schedule: TODO
:param name: see FixedTariff init
"""
super(TouDemandChargeTariff, self).__init__(dates, time_schedule, unit_metric, unit_cost, name)
def compute_monthly_bill(self, df, data_col=None):
"""
Compute the bill due to a TOU tariff
:param df: a pandas dataframe
:return: a tuple (dict, float) -> ({p1: (max_power_p1, time_max_p1), p2: (max_power_p2, time_max_p2), cost)
"""
# Scaling the power unit and cost
metric_unit_mult = float(self.unit_metric.value)
metric_price_mult = float(self.unit_cost.value)
max_per_set = {}
# df is in kWh and demand in kW: convert to Power
timestep_data = self.get_pd_timestep_data(df)
for idx, df_day in df.groupby(df.index.date):
daily_rate = self.rate_schedule.get_daily_rate(df_day.index[0])
set_of_daily_prices = set(daily_rate)
df_prices = self.get_daily_price_dataframe(daily_rate, df_day)
power_coeff = 1
if timestep_data == '15T':
power_coeff = 4
elif timestep_data == '30T':
power_coeff = 2
elif timestep_data == '60T' or timestep_data == 'H':
power_coeff = 1
for day_p in set_of_daily_prices:
# Create the mask in the day for this price
mask_price = df_prices['price'] == day_p
mask_price = mask_price.tolist()
date_max_period = None
if data_col is not None:
df_masked = df_day.loc[mask_price, data_col]
if len(df_masked) > 0:
date_max_period = df_masked.idxmax()
else:
df_masked = df_day.loc[mask_price]
if len(df_masked) > 0:
date_max_period = df_masked.idxmax()
if date_max_period is None:
continue
if data_col is not None:
max_power_period = df_day.loc[date_max_period, data_col] / metric_unit_mult
else:
max_power_period = df_day[date_max_period] / metric_unit_mult
max_power_period *= power_coeff # from kWh to kW
# Search for the same mask and update the value if a new mask
mask_price24h = mask_price
if len(mask_price) != len(daily_rate): # if this price is already in the list, take the corresponding mask ...
price_key = metric_price_mult * day_p
if price_key in list(max_per_set.keys()):
mask_price24h = max_per_set[price_key]['mask']
add_this_demand = True
existing_mask_price = [k for k, v in list(max_per_set.items()) if v['mask'] == mask_price24h]
if len(existing_mask_price) > 0: # Find the identical mask over the day
existing_mask_price = existing_mask_price[0]
if max_power_period > max_per_set[existing_mask_price]['max-demand']: # Check if the corresponding demand is greater
del max_per_set[existing_mask_price] # delete the former value, and add it (after)
else:
add_this_demand = False
# This is the first time this mask is seen OR this new demand is higher than the corresponding former: add it
if add_this_demand:
if type(date_max_period) is not pd.Timestamp:
max_power_date = date_max_period[data_col].to_pydatetime()
else:
max_power_date = date_max_period.to_pydatetime()
# The mask must be 24 hour long:
# it might not be the case for the first and last day, and the DST
price_key = metric_price_mult * day_p
max_per_set[price_key] = {'mask': mask_price24h, 'max-demand': max_power_period, 'max-demand-date': max_power_date}
return max_per_set
def get_pd_timestep_data(self, df):
"""
Return the most likely data frequency
:return:
"""
freq = 1
# Dataframe is complete
if df.index.freq is not None:
return df.index.freq
# Days are missing: loop through the day until getting a frequency
for idx, df_day in df.groupby(df.index.date):
if df_day.index.freq is not None:
return df_day.index.freq
elif len(df_day.index) > 2: # need at least 3 dates
return pd.infer_freq(df_day.index)
return 1
class TouEnergyChargeTariff(TimeOfUseTariff):
"""
This class represents a Time Of Use Energy Charge tariff
"""
def __init__(self, dates, time_schedule, unit_metric=TariffElemMetricUnit.EN_KWH, unit_cost=TariffElemCostUnit.DOLLAR, name=None):
"""
Constructor
:param dates: see FixedTariff init
:param time_schedule: TODO
:param name: see FixedTariff init
"""
super(TouEnergyChargeTariff, self).__init__(dates, time_schedule, unit_metric, unit_cost, name)
def compute_monthly_bill(self, df, data_col=None):
"""
Compute the bill due to a TOU tariff
:param df: a pandas dataframe
:return: a tuple (float, float) -> (cost, tot_energy)
"""
# Iterates over the days
energy = 0.0
cost = 0.0
# TODO: check for blockrate instead of assuming it's a float !
for idx, df_day in df.groupby(df.index.date):
daily_rate = self.rate_schedule.get_daily_rate(df_day.index[0])
df_prices = self.get_daily_price_dataframe(daily_rate, df_day)
# Unit and cost scale
mult_energy_unit = float(self.unit_metric.value)
mult_cost_unit = float(self.unit_cost.value)
# Cumulate the energy over the month
if data_col is not None:
df_values_in_day = df_day.loc[:, data_col]
else:
df_values_in_day = df_day[:]
energy += sum(df_values_in_day) / mult_energy_unit
# Cumulate the bill over the month
cost += sum(mult_cost_unit * df_values_in_day.multiply(df_prices.loc[:, 'price'].tolist())) / mult_energy_unit
return energy, cost
PK ! V: : J electricitycostcalculator_gabetest/cost_calculator/tariff_structure.py.bak__author__ = 'Olivier Van Cutsem'
from abc import abstractmethod
from enum import Enum
from datetime import datetime
import calendar
import pandas as pd
# --------------- TARIFF structures --------------- #
class TariffType(Enum):
FIX_CUSTOM_CHARGE = 'customer_fix_charge'
ENERGY_CUSTOM_CHARGE = 'customer_energy_charge'
DEMAND_CUSTOM_CHARGE_SEASON = 'customer_demand_charge_season'
DEMAND_CUSTOM_CHARGE_TOU = 'customer_demand_charge_tou'
PDP_ENERGY_CHARGE = 'pdp_event_energy_charge'
PDP_ENERGY_CREDIT = 'pdp_non_event_energy_credit'
PDP_DEMAND_CREDIT = 'pdp_non_event_demand_credit'
class TariffElemPeriod(Enum):
MONTHLY = 'M'
DAILY = 'D'
HOURLY = '1h'
HALFLY = '30min'
QUARTERLY = '15min'
class TariffElemMetricUnit(Enum):
EN_WH = 1
DEMAND_W = 1
EN_KWH = 1000.0
DEMAND_KW = 1000.0
class TariffElemCostUnit(Enum):
CENT = 0.01
DOLLAR = 1
class TariffBase(object):
"""
This abstract class represent the base of any tariffication structure.
The main components are the starting and ending date of the structure.
"""
def __init__(self, dates, unit_cost, name=None):
# Starting and ending dates, as timestamps
ts, te = dates
self.__startdate = ts
self.__enddate = te
self.name = name
self.unit_cost = unit_cost
def compute_bill(self, df, data_col=None):
"""
Compute the bill due to the power/energy consumption in df, for each billing period specified in billing_periods
It outputs a dictionary formatted as follow:
{
"bill_period_label1": (float or dict, float), -> the monthly 'metric' and its cost
...
}
:param df: a pandas dataframe containing power consumption timeseries
:param billing_periods: a dictionary mapping the billing periods label to a tuple (t_start, t_end) of datetime,
defining the period related to the billing label
:return: a dictionary formatted as in this method signature
"""
ret = {}
# Select only the data in this tariff window
start_sel = self.startdate
start_sel = start_sel.replace(tzinfo=df.index[0].tzinfo)
end_sel = self.enddate
end_sel = end_sel.replace(tzinfo=df.index[0].tzinfo)
mask = (df.index >= start_sel) & (df.index <= end_sel)
df = df.loc[mask]
# Loop over the months
t_s = df.index[0]
last_day_of_month = calendar.monthrange(t_s.year, t_s.month)[1] # The last day of this month
t_e = datetime(t_s.year, t_s.month, last_day_of_month, hour=23, minute=59, second=59, tzinfo=t_s.tzinfo) # end of the current month
t_e = min(df.index[-1], t_e)
while t_s <= t_e:
mask = (df.index >= t_s) & (df.index <= t_e)
df_month = df.loc[mask]
monthly_bill = self.compute_monthly_bill(df_month, data_col)
ret[t_s.strftime("%Y-%m")] = monthly_bill
# Prepare the next billing month
month = t_e.month + 1
year = t_e.year
if month >= 13:
month = 1
year += 1
t_s = datetime(year, month, 1, hour=0, minute=0, second=0, tzinfo=t_s.tzinfo)
last_day_of_month = calendar.monthrange(year, month)[1]
t_e = datetime(year, month, last_day_of_month, hour=23, minute=59, second=59, tzinfo=t_s.tzinfo)
t_e = min(df.index[-1], t_e)
return ret
@abstractmethod
def compute_monthly_bill(self, df, data_col=None):
"""
Compute the monthly bill due to the power/energy consumption in df
:param df: a pandas dataframe
:param data_col: the column label containing the data
:return: a tuple (float, float) -> (value, cost), representing the bill and the corresponding metric linked to the cost
"""
pass
@property
def startdate(self):
"""
GETTER of the tariff starting date
:return: a timestamp
"""
return self.__startdate
@property
def enddate(self):
"""
GETTER of the tariff end date
:return: a timestamp
"""
return self.__enddate
@abstractmethod
def period_metric(self):
pass
@abstractmethod
def get_price_from_timestamp(self, timestamp):
pass
# --------------- FIXED TARIFF --------------- #
class FixedTariff(TariffBase):
"""
Represent a tariff fixed over a given period (among TariffPeriod)
"""
def __init__(self, dates, rate_value, unit_cost=TariffElemCostUnit.DOLLAR, bill_period=TariffElemPeriod.MONTHLY, name=None):
"""
Constructor
:param dates: see FixedTariff init
:param bill_period: the period
:param name: see FixedTariff init
"""
super(FixedTariff, self).__init__(dates, unit_cost, name)
self.__rate_period = bill_period
self.__rate_value = rate_value
def compute_monthly_bill(self, df, data_col=None):
"""
Compute the monthly bill due to a fixed periodic cost
:param df: a pandas dataframe
:return: a tuple (float, float), representing the bill and the duration (in months)
"""
first_day = df.index[0].day
last_day = df.index[-1].day
nb_days = last_day - first_day + 1
nb_days_per_month = 365/12
bill = 0
if self.__rate_period == TariffElemPeriod.MONTHLY:
bill = self.__rate_value * nb_days/nb_days_per_month # a fraction of the month
elif self.__rate_period == TariffElemPeriod.DAILY:
bill = self.__rate_value * nb_days # sum of each day
return nb_days, bill
def period_metric(self):
return self.__rate_period
def get_price_from_timestamp(self, timestamp):
return self.__rate_value
# --------------- TOU TARIFFs --------------- #
class TimeOfUseTariff(TariffBase):
"""
This class represents a tariff fixed over a given period (among TariffElemPeriod)
"""
def __init__(self, dates, rate_schedule, unit_metric, unit_cost, name=None):
"""
Constructor
:param dates: see FixedTariff init
:param rate_list:
:param time_schedule: TODO
:param name: see FixedTariff init
"""
super(TimeOfUseTariff, self).__init__(dates, unit_cost, name)
self.__schedule = rate_schedule # A table mapping (month, day) to hourly rate index
self.__unit_metric = unit_metric
@abstractmethod
def compute_monthly_bill(self, df, data_col=None):
"""
idem super
"""
pass
@property
def rate_schedule(self):
return self.__schedule
@property
def unit_metric(self):
return self.__unit_metric
def period_metric(self):
nb_periods_in_day = self.__schedule.periods_in_day
# TODO: replace ifs by map
if nb_periods_in_day == 24:
return TariffElemPeriod.HOURLY
elif nb_periods_in_day == 24 * 2:
return TariffElemPeriod.HALFLY
elif nb_periods_in_day == 24 * 4:
return TariffElemPeriod.QUARTERLY
else:
return TariffElemPeriod.DAILY
def get_price_from_timestamp(self, timestamp):
return self.__schedule.get_from_timestamp(timestamp)
@staticmethod
def get_daily_price_dataframe(daily_rate, df_day):
# Constructing the dataframe for an easier manipulation of time
period_rate = len(daily_rate) / 24.0
# In some cases the day might not be full: missing data or DST
daily_prices = [daily_rate[int((df_day.index[i].hour + df_day.index[i].minute / 60.0) * period_rate)] for i in range(len(df_day.index))]
data = {'date': df_day.index[:], 'price': daily_prices}
df_prices = pd.DataFrame(data=data)
df_prices.set_index('date')
return df_prices
class TouDemandChargeTariff(TimeOfUseTariff):
"""
This class represents a Time Of Use Demand Charge tariff
"""
def __init__(self, dates, time_schedule, unit_metric=TariffElemMetricUnit.DEMAND_KW, unit_cost=TariffElemCostUnit.DOLLAR, name=None):
"""
Constructor
:param dates: see FixedTariff init
:param rate_list: TODO
:param time_schedule: TODO
:param name: see FixedTariff init
"""
super(TouDemandChargeTariff, self).__init__(dates, time_schedule, unit_metric, unit_cost, name)
def compute_monthly_bill(self, df, data_col=None):
"""
Compute the bill due to a TOU tariff
:param df: a pandas dataframe
:return: a tuple (dict, float) -> ({p1: (max_power_p1, time_max_p1), p2: (max_power_p2, time_max_p2), cost)
"""
# Scaling the power unit and cost
metric_unit_mult = float(self.unit_metric.value)
metric_price_mult = float(self.unit_cost.value)
max_per_set = {}
# df is in kWh and demand in kW: convert to Power
timestep_data = self.get_pd_timestep_data(df)
for idx, df_day in df.groupby(df.index.date):
daily_rate = self.rate_schedule.get_daily_rate(df_day.index[0])
set_of_daily_prices = set(daily_rate)
df_prices = self.get_daily_price_dataframe(daily_rate, df_day)
power_coeff = 1
if timestep_data == '15T':
power_coeff = 4
elif timestep_data == '30T':
power_coeff = 2
elif timestep_data == '60T' or timestep_data == 'H':
power_coeff = 1
for day_p in set_of_daily_prices:
# Create the mask in the day for this price
mask_price = df_prices['price'] == day_p
mask_price = mask_price.tolist()
date_max_period = None
if data_col is not None:
df_masked = df_day.loc[mask_price, data_col]
if len(df_masked) > 0:
date_max_period = df_masked.idxmax()
else:
df_masked = df_day.loc[mask_price]
if len(df_masked) > 0:
date_max_period = df_masked.idxmax()
if date_max_period is None:
continue
if data_col is not None:
max_power_period = df_day.loc[date_max_period, data_col] / metric_unit_mult
else:
max_power_period = df_day[date_max_period] / metric_unit_mult
max_power_period *= power_coeff # from kWh to kW
# Search for the same mask and update the value if a new mask
mask_price24h = mask_price
if len(mask_price) != len(daily_rate): # if this price is already in the list, take the corresponding mask ...
price_key = metric_price_mult * day_p
if price_key in max_per_set.keys():
mask_price24h = max_per_set[price_key]['mask']
add_this_demand = True
existing_mask_price = [k for k, v in max_per_set.items() if v['mask'] == mask_price24h]
if len(existing_mask_price) > 0: # Find the identical mask over the day
existing_mask_price = existing_mask_price[0]
if max_power_period > max_per_set[existing_mask_price]['max-demand']: # Check if the corresponding demand is greater
del max_per_set[existing_mask_price] # delete the former value, and add it (after)
else:
add_this_demand = False
# This is the first time this mask is seen OR this new demand is higher than the corresponding former: add it
if add_this_demand:
if type(date_max_period) is not pd.Timestamp:
max_power_date = date_max_period[data_col].to_pydatetime()
else:
max_power_date = date_max_period.to_pydatetime()
# The mask must be 24 hour long:
# it might not be the case for the first and last day, and the DST
price_key = metric_price_mult * day_p
max_per_set[price_key] = {'mask': mask_price24h, 'max-demand': max_power_period, 'max-demand-date': max_power_date}
return max_per_set
def get_pd_timestep_data(self, df):
"""
Return the most likely data frequency
:return:
"""
freq = 1
# Dataframe is complete
if df.index.freq is not None:
return df.index.freq
# Days are missing: loop through the day until getting a frequency
for idx, df_day in df.groupby(df.index.date):
if df_day.index.freq is not None:
return df_day.index.freq
elif len(df_day.index) > 2: # need at least 3 dates
return pd.infer_freq(df_day.index)
return 1
class TouEnergyChargeTariff(TimeOfUseTariff):
"""
This class represents a Time Of Use Energy Charge tariff
"""
def __init__(self, dates, time_schedule, unit_metric=TariffElemMetricUnit.EN_KWH, unit_cost=TariffElemCostUnit.DOLLAR, name=None):
"""
Constructor
:param dates: see FixedTariff init
:param time_schedule: TODO
:param name: see FixedTariff init
"""
super(TouEnergyChargeTariff, self).__init__(dates, time_schedule, unit_metric, unit_cost, name)
def compute_monthly_bill(self, df, data_col=None):
"""
Compute the bill due to a TOU tariff
:param df: a pandas dataframe
:return: a tuple (float, float) -> (cost, tot_energy)
"""
# Iterates over the days
energy = 0.0
cost = 0.0
# TODO: check for blockrate instead of assuming it's a float !
for idx, df_day in df.groupby(df.index.date):
daily_rate = self.rate_schedule.get_daily_rate(df_day.index[0])
df_prices = self.get_daily_price_dataframe(daily_rate, df_day)
# Unit and cost scale
mult_energy_unit = float(self.unit_metric.value)
mult_cost_unit = float(self.unit_cost.value)
# Cumulate the energy over the month
if data_col is not None:
df_values_in_day = df_day.loc[:, data_col]
else:
df_values_in_day = df_day[:]
energy += sum(df_values_in_day) / mult_energy_unit
# Cumulate the bill over the month
cost += sum(mult_cost_unit * df_values_in_day.multiply(df_prices.loc[:, 'price'].tolist())) / mult_energy_unit
return energy, cost
PK ! #3 3 9 electricitycostcalculator_gabetest/oadr_signal/.gitignore*.pyc
events.csv
*.xml
signals/*.xml
settings.json
PK ! s
= electricitycostcalculator_gabetest/oadr_signal/DR_template.pydef getSignalString(requestId, vtnId, eventId, modificationNumber, createdDateTime, eventStatus, vtnComment,
startTime, duration, signals, group, groupId=None, resourceId=None):
xml = '\n'
xml = xml + '\n'
xml = xml + '\t\n'
xml = xml + '\t\t\n'
xml = xml + '\t\t\t%s\n' % requestId
xml = xml + '\t\t\t%s\n' % vtnId
xml = xml + '\t\t\t\n'
xml = xml + '\t\t\t\t\n'
xml = xml + '\t\t\t\t\t\n'
xml = xml + '\t\t\t\t\t\t%s\n' % eventId
xml = xml + '\t\t\t\t\t\t%d\n' % modificationNumber
xml = xml + '\t\t\t\t\t\t\n'
xml = xml + '\t\t\t\t\t\t%s\n' % createdDateTime
xml = xml + '\t\t\t\t\t\t%s\n' % eventStatus
xml = xml + '\t\t\t\t\t\t%s\n' % vtnComment
xml = xml + '\t\t\t\t\t\n'
xml = xml + '\t\t\t\t\t\n'
xml = xml + '\t\t\t\t\t\t\n'
xml = xml + '\t\t\t\t\t\t\t\n'
xml = xml + '\t\t\t\t\t\t\t\t%s\n' % startTime
xml = xml + '\t\t\t\t\t\t\t\n'
xml = xml + '\t\t\t\t\t\t\t\n'
xml = xml + '\t\t\t\t\t\t\t\t%s\n' % duration
xml = xml + '\t\t\t\t\t\t\t\n'
xml = xml + '\t\t\t\t\t\t\n'
xml = xml + '\t\t\t\t\t\n'
xml = xml + '\t\t\t\t\t\n'
for signal in signals:
tagName = 'currencyPerKWh'
# default signalType = 'energy'
if signal['signalType'] == 'demand':
tagName = 'currencyPerKW'
itemUnits = signal['itemUnits']
scaleCode = signal['scaleCode']
eventHours = signal['eventHours']
prices = signal['prices']
signalName = signal['signalName']
signalId = signal['signalId']
currentPrice = signal['currentPrice']
xml = xml + '\t\t\t\t\t\t\n'
xml = xml + '\t\t\t\t\t\t\t\n' % tagName
xml = xml + '\t\t\t\t\t\t\t\t%s\n' % tagName
xml = xml + '\t\t\t\t\t\t\t\t%s\n' % itemUnits
xml = xml + '\t\t\t\t\t\t\t\t%s\n' % scaleCode
xml = xml + '\t\t\t\t\t\t\t\n' % tagName
xml = xml + '\t\t\t\t\t\t\t\n'
for i in range(len(eventHours)):
xml = xml + '\t\t\t\t\t\t\t\t\n'
xml = xml + '\t\t\t\t\t\t\t\t\t\n'
xml = xml + '\t\t\t\t\t\t\t\t\t\t%s\n' % (eventHours[i])
xml = xml + '\t\t\t\t\t\t\t\t\t\n'
xml = xml + '\t\t\t\t\t\t\t\t\t\n'
xml = xml + '\t\t\t\t\t\t\t\t\t\tPT60M\n'
xml = xml + '\t\t\t\t\t\t\t\t\t\n'
xml = xml + '\t\t\t\t\t\t\t\t\t\n'
xml = xml + '\t\t\t\t\t\t\t\t\t\t%d\n' % (i)
xml = xml + '\t\t\t\t\t\t\t\t\t\n'
xml = xml + '\t\t\t\t\t\t\t\t\t\n'
xml = xml + '\t\t\t\t\t\t\t\t\t\t\n'
xml = xml + '\t\t\t\t\t\t\t\t\t\t\t%.2f\n' % (float(prices[i]))
xml = xml + '\t\t\t\t\t\t\t\t\t\t\n'
xml = xml + '\t\t\t\t\t\t\t\t\t\n'
xml = xml + '\t\t\t\t\t\t\t\t\n'
xml = xml + '\t\t\t\t\t\t\t\n'
xml = xml + '\t\t\t\t\t\t\t%s\n' % signalName
xml = xml + '\t\t\t\t\t\t\tprice\n'
xml = xml + '\t\t\t\t\t\t\t%s\n' % signalId
xml = xml + '\t\t\t\t\t\t\t\n'
xml = xml + '\t\t\t\t\t\t\t\t\n'
xml = xml + '\t\t\t\t\t\t\t\t\t%.2f\n' % currentPrice
xml = xml + '\t\t\t\t\t\t\t\t\n'
xml = xml + '\t\t\t\t\t\t\t\n'
xml = xml + '\t\t\t\t\t\t\n'
xml = xml + '\t\t\t\t\t\n'
xml = xml + '\t\t\t\t\t\n'
if group:
xml = xml + '\t\t\t\t\t\t%s\n' % groupId
else:
xml = xml + '\t\t\t\t\t\t%s\n' % resourceId
xml = xml + '\t\t\t\t\t\n'
xml = xml + '\t\t\t\t\n'
xml = xml + '\t\t\t\n'
xml = xml + '\t\t\n'
xml = xml + '\t\n'
xml = xml + '\n'
return (xml)PK ! ! A electricitycostcalculator_gabetest/oadr_signal/DR_template.py.bakdef getSignalString(requestId, vtnId, eventId, modificationNumber, createdDateTime, eventStatus, vtnComment,
startTime, duration, signals, group, groupId=None, resourceId=None):
xml = u'\n'
xml = xml + u'\n'
xml = xml + u'\t\n'
xml = xml + u'\t\t\n'
xml = xml + u'\t\t\t%s\n' % requestId
xml = xml + u'\t\t\t%s\n' % vtnId
xml = xml + u'\t\t\t\n'
xml = xml + u'\t\t\t\t\n'
xml = xml + u'\t\t\t\t\t\n'
xml = xml + u'\t\t\t\t\t\t%s\n' % eventId
xml = xml + u'\t\t\t\t\t\t%d\n' % modificationNumber
xml = xml + u'\t\t\t\t\t\t\n'
xml = xml + u'\t\t\t\t\t\t%s\n' % createdDateTime
xml = xml + u'\t\t\t\t\t\t%s\n' % eventStatus
xml = xml + u'\t\t\t\t\t\t%s\n' % vtnComment
xml = xml + u'\t\t\t\t\t\n'
xml = xml + u'\t\t\t\t\t\n'
xml = xml + u'\t\t\t\t\t\t\n'
xml = xml + u'\t\t\t\t\t\t\t\n'
xml = xml + u'\t\t\t\t\t\t\t\t%s\n' % startTime
xml = xml + u'\t\t\t\t\t\t\t\n'
xml = xml + u'\t\t\t\t\t\t\t\n'
xml = xml + u'\t\t\t\t\t\t\t\t%s\n' % duration
xml = xml + u'\t\t\t\t\t\t\t\n'
xml = xml + u'\t\t\t\t\t\t\n'
xml = xml + u'\t\t\t\t\t\n'
xml = xml + u'\t\t\t\t\t\n'
for signal in signals:
tagName = 'currencyPerKWh'
# default signalType = 'energy'
if signal['signalType'] == 'demand':
tagName = 'currencyPerKW'
itemUnits = signal['itemUnits']
scaleCode = signal['scaleCode']
eventHours = signal['eventHours']
prices = signal['prices']
signalName = signal['signalName']
signalId = signal['signalId']
currentPrice = signal['currentPrice']
xml = xml + u'\t\t\t\t\t\t\n'
xml = xml + u'\t\t\t\t\t\t\t\n' % tagName
xml = xml + u'\t\t\t\t\t\t\t\t%s\n' % tagName
xml = xml + u'\t\t\t\t\t\t\t\t%s\n' % itemUnits
xml = xml + u'\t\t\t\t\t\t\t\t%s\n' % scaleCode
xml = xml + u'\t\t\t\t\t\t\t\n' % tagName
xml = xml + u'\t\t\t\t\t\t\t\n'
for i in range(len(eventHours)):
xml = xml + u'\t\t\t\t\t\t\t\t\n'
xml = xml + u'\t\t\t\t\t\t\t\t\t\n'
xml = xml + u'\t\t\t\t\t\t\t\t\t\t%s\n' % (eventHours[i])
xml = xml + u'\t\t\t\t\t\t\t\t\t\n'
xml = xml + u'\t\t\t\t\t\t\t\t\t\n'
xml = xml + u'\t\t\t\t\t\t\t\t\t\tPT60M\n'
xml = xml + u'\t\t\t\t\t\t\t\t\t\n'
xml = xml + u'\t\t\t\t\t\t\t\t\t\n'
xml = xml + u'\t\t\t\t\t\t\t\t\t\t%d\n' % (i)
xml = xml + u'\t\t\t\t\t\t\t\t\t\n'
xml = xml + u'\t\t\t\t\t\t\t\t\t\n'
xml = xml + u'\t\t\t\t\t\t\t\t\t\t\n'
xml = xml + u'\t\t\t\t\t\t\t\t\t\t\t%.2f\n' % (float(prices[i]))
xml = xml + u'\t\t\t\t\t\t\t\t\t\t\n'
xml = xml + u'\t\t\t\t\t\t\t\t\t\n'
xml = xml + u'\t\t\t\t\t\t\t\t\n'
xml = xml + u'\t\t\t\t\t\t\t\n'
xml = xml + u'\t\t\t\t\t\t\t%s\n' % signalName
xml = xml + u'\t\t\t\t\t\t\tprice\n'
xml = xml + u'\t\t\t\t\t\t\t%s\n' % signalId
xml = xml + u'\t\t\t\t\t\t\t\n'
xml = xml + u'\t\t\t\t\t\t\t\t\n'
xml = xml + u'\t\t\t\t\t\t\t\t\t%.2f\n' % currentPrice
xml = xml + u'\t\t\t\t\t\t\t\t\n'
xml = xml + u'\t\t\t\t\t\t\t\n'
xml = xml + u'\t\t\t\t\t\t\n'
xml = xml + u'\t\t\t\t\t\n'
xml = xml + u'\t\t\t\t\t\n'
if group:
xml = xml + u'\t\t\t\t\t\t%s\n' % groupId
else:
xml = xml + u'\t\t\t\t\t\t%s\n' % resourceId
xml = xml + u'\t\t\t\t\t\n'
xml = xml + u'\t\t\t\t\n'
xml = xml + u'\t\t\t\n'
xml = xml + u'\t\t\n'
xml = xml + u'\t\n'
xml = xml + u'\n'
return (xml)PK ! -b& & : electricitycostcalculator_gabetest/oadr_signal/__init__.py__author__ = 'Anand Krishnan Prakash'
PK ! .$x > electricitycostcalculator_gabetest/oadr_signal/__init__.py.bak__author__ = 'Anand Krishnan Prakash'
import os, sys
sys.path.append(os.path.abspath("../cost_calculator/"))
sys.path.append(os.path.abspath("../openei_tariff/"))PK ! `Y5 5 = electricitycostcalculator_gabetest/oadr_signal/getDRSignal.pyfrom .DR_template import getSignalString
import os.path
import os, sys
from .getSCEEvents import *
from .getPelicanDREvents import *
from .tariff_maps import tariff_maps
from cost_calculator.cost_calculator import *
from openei_tariff.openei_tariff_analyzer import *
from .utils import *
from xbos import get_client
def pollEvents(pollSceApi, sceConfig, pollPelicans, pelicanConfig, mdalClient=None):
eventStartTimes = []
if pollSceApi:
sceStartTimes = pollSCEEvents(sceConfig = sceConfig)
eventStartTimes = eventStartTimes + sceStartTimes
if pollPelicans:
pelicanStartTimes = pollPelicanEvents(pelicanConfig=pelicanConfig, client=mdalClient)
eventStartTimes = eventStartTimes + pelicanStartTimes
return eventStartTimes
def checkAndAddNormalDays(eventStartTimes):
today_day = dtime.datetime.now().date()
tomorrow_day = today_day + dtime.timedelta(days=1)
pgeEventDayPresent = False
sceEventDayPresent = False
for event in eventStartTimes:
for eventName in event:
event_day = convertEpochToUTC(event[eventName]['event_day']).date()
if event_day == tomorrow_day:
if eventName == 'PGE_EVENT_SCHEDULED':
pgeEventDayPresent = True
if eventName == 'SCE_EVENT_SCHEDULED' or eventName == 'CPP_COMMERCIAL_SCHEDULED':
sceEventDayPresent = True
if pgeEventDayPresent == False:
pge_normal_day = {
'PGE_NORMAL_DAY': {
'date': float(tomorrow_day.strftime("%s"))
}
}
eventStartTimes.append(pge_normal_day)
if sceEventDayPresent == False:
sce_normal_day = {
'SCE_NORMAL_DAY': {
'date': float(tomorrow_day.strftime("%s"))
}
}
eventStartTimes.append(sce_normal_day)
return eventStartTimes
def getHourlyDayPrices(startDateTime, tariff_name='PGEA10', verbose=False, isItEventDay=True):
eventStartDate = startDateTime.date()
tariff_openei_data = tariff_maps[tariff_name]
bill_calc = CostCalculator()
if isItEventDay:
pdp_events = list(populate_pdp_events_from_json(openei_tarif_obj=tariff_openei_data, pdp_event_filenames='PDP_events.json'))
utilityId = int(tariff_openei_data.req_param['eia'])
# TODO: make more generic
st = eventStartDate.strftime("%Y-%m-%dT00:00:00-08:00")
et = eventStartDate.strftime("%Y-%m-%dT23:59:59-08:00")
if not tariff_openei_data.checkIfPDPDayPresent(utilityId=utilityId, st=st, et=et):
pdp_events.append({'utility_id': utilityId, 'start_date': st, 'end_date': et})
update_pdp_json(openei_tarif_obj=tariff_openei_data, pdp_dict=pdp_events, pdp_event_filenames='PDP_events.json')
if tariff_openei_data.read_from_json() == 0: # Reading revised JSON blocks containing the utility rates
if verbose:
print("Tariff read from JSON successful")
else:
print("An error occurred when reading the JSON file" ) # <------------------- handle error
return
# TODO: compare start dates of events
tariff_struct_from_openei_data(tariff_openei_data, bill_calc, pdp_event_filenames='PDP_events.json')
pd_prices, map_prices = bill_calc.get_electricity_price(timestep=TariffElemPeriod.HOURLY,
range_date=(dtime.datetime(eventStartDate.year,
eventStartDate.month,
eventStartDate.day, 0, 0, 0).replace(tzinfo=pytz.timezone('US/Pacific')),
dtime.datetime(eventStartDate.year,
eventStartDate.month,
eventStartDate.day, 23, 59,
59).replace(tzinfo=pytz.timezone('US/Pacific'))))
pd_prices = pd_prices.fillna(0)
energyPrices = pd_prices.customer_energy_charge.values + pd_prices.pdp_non_event_energy_credit.values + pd_prices.pdp_event_energy_charge.values
demandPrices = pd_prices.customer_demand_charge_season.values + pd_prices.pdp_non_event_demand_credit.values + pd_prices.customer_demand_charge_tou.values
return {'energyPrices': energyPrices, 'demandPrices': demandPrices}
def createPriceSignal(eventHours, prices, signalId, itemUnits='USD', scaleCode='none',
isEnergySignal=True, signalName='ENERGY_PRICE', currentPrice=0):
signal = {}
if isEnergySignal:
signal['signalType'] = 'energy'
else:
signal['signalType'] = 'demand'
signal['itemUnits'] = itemUnits
signal['scaleCode'] = scaleCode
signal['eventHours'] = eventHours
signal['prices'] = prices
signal['signalName'] = signalName
signal['signalId'] = signalId
signal['currentPrice'] = currentPrice
return signal
def generateDRSignal(startTime, requestId, eventId, modificationNumber, eventStatus, drEventFilename,
signals, group=True, vtnId='LBL-python-VTN', vtnComment='Module', duration='PT1440M',
groupId='PGEA10', resourceId='csu-dominguez'):
createdDateTime = convertFromDatetimeToString(dtime.datetime.utcnow())
xml = getSignalString(requestId=requestId,
vtnId=vtnId,
eventId=eventId,
modificationNumber=modificationNumber,
createdDateTime=createdDateTime,
eventStatus=eventStatus,
vtnComment=vtnComment,
startTime=startTime,
duration=duration,
signals=signals,
group=group,
groupId=groupId,
resourceId=resourceId)
if not os.path.isdir(OADR_PATH+'signals'):
os.makedirs(OADR_PATH+'signals')
with open(OADR_PATH+'signals/'+drEventFilename, "w") as f:
f.write(xml)
return drEventFilename, eventId, modificationNumber, startTime
def getMdalClient(pelicanConfig):
client = None
if "xbosEntityPath" in list(pelicanConfig.keys()):
entityPath = pelicanConfig["xbosEntityPath"]
c = get_client(entity=entityPath)
client = mdal.MDALClient("xbos/mdal", client=c)
else:
c = get_client()
client = mdal.MDALClient("xbos/mdal", client=c)
return client
if __name__ == '__main__':
with open(OADR_PATH+'settings.json') as configFile:
config = json.load(configFile)
params = config['params']
includeDemandPricesFlag = params['includeCurrencyPerKWFlag']
pollPelicansFlag = params['pollPelicansFlag']
pollSCEApiFlag = params['pollSCEApiFlag']
sendToRecipientFlag = params['sendToRecipientFlag']
recipientURL = params['signal_recipient_url']
storeEventsFilename = params['eventsHistoryFilename']
sceTariffs = params['sceTariffs']
pgeTariffs = params['pgeTariffs']
sceConfig = None
if pollSCEApiFlag:
if 'sce' in list(config.keys()):
sceConfig = config['sce']
else:
print("cannot find sce configuration in settings.json")
exit(1)
pelicanConfig = None
if pollPelicansFlag:
if 'pelican' in list(config.keys()):
pelicanConfig = config['pelican']
mdalClient = getMdalClient(pelicanConfig=pelicanConfig)
else:
print("cannot find pelican configuration in settings.json")
exit(1)
events = getEventsHistory(eventsFilename=storeEventsFilename)
eventStartTimes = pollEvents(pollSceApi=pollSCEApiFlag, sceConfig=sceConfig,
pollPelicans=pollPelicansFlag, pelicanConfig=pelicanConfig, mdalClient=mdalClient)
checkAndAddNormalDays(eventStartTimes)
for eventInfoDict in eventStartTimes:
eventName = list(eventInfoDict.keys())[0]
isItAnEventDay = True
if eventName.endswith('_SCHEDULED'):
if eventName.startswith('PGE_EVENT'):
tariffs = pgeTariffs
else:
tariffs = sceTariffs
event_st_epoch = eventInfoDict[eventName]["start_time"]
event_et_epoch = eventInfoDict[eventName]["end_time"]
event_day_st_epoch = eventInfoDict[eventName]["event_day"]
st = convertEpochToUTC(event_day_st_epoch)
elif eventName.endswith('_NORMAL_DAY'):
isItAnEventDay = False
if eventName.startswith('PGE'):
tariffs = pgeTariffs
else:
tariffs = sceTariffs
normal_day_st_epoch = eventInfoDict[eventName]["date"]
st = convertEpochToUTC(normal_day_st_epoch)
startTime = convertFromDatetimeToString(st)
# TODO: more flexible
eventHours = getEventHours(startTime=st, num=24)
for tariff in tariffs:
prices = getHourlyDayPrices(startDateTime=st, tariff_name=tariff, isItEventDay=isItAnEventDay)
if not includeDemandPricesFlag:
prices = {'energyPrices': prices['energyPrices']}
signals = []
for priceSignal in list(prices.keys()):
# print("LOG--------- price key: ",priceSignal, eventName)
signalId = generateAlphanumericId()
signal = {}
if priceSignal == 'demandPrices':
signal = createPriceSignal(eventHours=eventHours, prices=prices['demandPrices'],
isEnergySignal=False, signalId=signalId,
signalName='DEMAND_PRICE', currentPrice=0);
else:
signal = createPriceSignal(eventHours=eventHours, prices=prices['energyPrices'],
isEnergySignal=True, signalId=signalId,
signalName='ENERGY_PRICE', currentPrice=0);
signals.append(signal)
eventId = generateAlphanumericId()
modificationNumber = 0
eventStatus = 'far'
requestId = generateAlphanumericId()
if isItAnEventDay:
eventExists = checkIfEventExists(events=events, eventName=eventName, startDate=event_st_epoch, tariff = tariff,
status=eventStatus)
if eventExists['prevEventExists']:
# TODO: include prices in log file
prevPrices = getHourlyDayPrices(startDateTime=st, tariff_name=tariff, isItEventDay=isItAnEventDay)
if not includeDemandPricesFlag:
prevPrices = {'energyPrices': prevPrices['energyPrices']}
if arePricesDifferent(prices1=prices, prices2=prevPrices):
# print("LOG------ diff prices", eventName)
modificationNumber = eventExists['prevModNumber'] + 1
eventId = eventExists['prevEventId']
else:
# print("LOG------ same prices", eventName)
continue
# print("LOG------ prev event does not exist", eventName)
# filenames of all the created files
drSignalFilenames = ""
# one file for demand prices, one file for energy prices
for signal in signals:
drSignalFilename = '%s_%s_%s_%d_%s.xml' % (eventName, tariff, eventId, modificationNumber, signal['signalType'])
filename, eventId, modificationNumber, startTime = generateDRSignal(
startTime=startTime,
requestId=requestId,
eventId=eventId,
modificationNumber=modificationNumber,
eventStatus=eventStatus,
drEventFilename=drSignalFilename,
group=True,
groupId=tariff,
signals=[signal]
)
print(("Event created: %s" % drSignalFilename))
if sendToRecipientFlag:
rsp = sendSignalToServer(url=recipientURL, filename=drSignalFilename)
if drSignalFilenames == "":
drSignalFilenames = drSignalFilename
else:
drSignalFilenames = drSignalFilenames + '_' + drSignalFilename
newIdx = 0
if events.empty:
newIdx = 0
else:
newIdx = events.tail(1).index.values[0] + 1
if isItAnEventDay:
appendToHistory(idx=newIdx,
eventId=eventId,
eventName=eventName,
modNumber=modificationNumber,
startDate=event_st_epoch,
status=eventStatus,
drSignalFilename=drSignalFilenames,
eventsFilename=storeEventsFilename,
tariff=tariff)
events = pandas.read_csv(OADR_PATH+storeEventsFilename, index_col=0)
# elif eventName.endswith('_ACTIVE'):
# handle active events
# eventStatus = 'active'
# time.sleep(10)
PK ! K2/S5 5 A electricitycostcalculator_gabetest/oadr_signal/getDRSignal.py.bakfrom DR_template import getSignalString
import os.path
import os, sys
from getSCEEvents import *
from getPelicanDREvents import *
from tariff_maps import tariff_maps
from cost_calculator.cost_calculator import *
from openei_tariff.openei_tariff_analyzer import *
from utils import *
from xbos import get_client
def pollEvents(pollSceApi, sceConfig, pollPelicans, pelicanConfig, mdalClient=None):
eventStartTimes = []
if pollSceApi:
sceStartTimes = pollSCEEvents(sceConfig = sceConfig)
eventStartTimes = eventStartTimes + sceStartTimes
if pollPelicans:
pelicanStartTimes = pollPelicanEvents(pelicanConfig=pelicanConfig, client=mdalClient)
eventStartTimes = eventStartTimes + pelicanStartTimes
return eventStartTimes
def checkAndAddNormalDays(eventStartTimes):
today_day = dtime.datetime.now().date()
tomorrow_day = today_day + dtime.timedelta(days=1)
pgeEventDayPresent = False
sceEventDayPresent = False
for event in eventStartTimes:
for eventName in event:
event_day = convertEpochToUTC(event[eventName]['event_day']).date()
if event_day == tomorrow_day:
if eventName == 'PGE_EVENT_SCHEDULED':
pgeEventDayPresent = True
if eventName == 'SCE_EVENT_SCHEDULED' or eventName == 'CPP_COMMERCIAL_SCHEDULED':
sceEventDayPresent = True
if pgeEventDayPresent == False:
pge_normal_day = {
'PGE_NORMAL_DAY': {
'date': float(tomorrow_day.strftime("%s"))
}
}
eventStartTimes.append(pge_normal_day)
if sceEventDayPresent == False:
sce_normal_day = {
'SCE_NORMAL_DAY': {
'date': float(tomorrow_day.strftime("%s"))
}
}
eventStartTimes.append(sce_normal_day)
return eventStartTimes
def getHourlyDayPrices(startDateTime, tariff_name='PGEA10', verbose=False, isItEventDay=True):
eventStartDate = startDateTime.date()
tariff_openei_data = tariff_maps[tariff_name]
bill_calc = CostCalculator()
if isItEventDay:
pdp_events = list(populate_pdp_events_from_json(openei_tarif_obj=tariff_openei_data, pdp_event_filenames='PDP_events.json'))
utilityId = int(tariff_openei_data.req_param['eia'])
# TODO: make more generic
st = eventStartDate.strftime("%Y-%m-%dT00:00:00-08:00")
et = eventStartDate.strftime("%Y-%m-%dT23:59:59-08:00")
if not tariff_openei_data.checkIfPDPDayPresent(utilityId=utilityId, st=st, et=et):
pdp_events.append({'utility_id': utilityId, 'start_date': st, 'end_date': et})
update_pdp_json(openei_tarif_obj=tariff_openei_data, pdp_dict=pdp_events, pdp_event_filenames='PDP_events.json')
if tariff_openei_data.read_from_json() == 0: # Reading revised JSON blocks containing the utility rates
if verbose:
print("Tariff read from JSON successful")
else:
print("An error occurred when reading the JSON file" ) # <------------------- handle error
return
# TODO: compare start dates of events
tariff_struct_from_openei_data(tariff_openei_data, bill_calc, pdp_event_filenames='PDP_events.json')
pd_prices, map_prices = bill_calc.get_electricity_price(timestep=TariffElemPeriod.HOURLY,
range_date=(dtime.datetime(eventStartDate.year,
eventStartDate.month,
eventStartDate.day, 0, 0, 0).replace(tzinfo=pytz.timezone('US/Pacific')),
dtime.datetime(eventStartDate.year,
eventStartDate.month,
eventStartDate.day, 23, 59,
59).replace(tzinfo=pytz.timezone('US/Pacific'))))
pd_prices = pd_prices.fillna(0)
energyPrices = pd_prices.customer_energy_charge.values + pd_prices.pdp_non_event_energy_credit.values + pd_prices.pdp_event_energy_charge.values
demandPrices = pd_prices.customer_demand_charge_season.values + pd_prices.pdp_non_event_demand_credit.values + pd_prices.customer_demand_charge_tou.values
return {'energyPrices': energyPrices, 'demandPrices': demandPrices}
def createPriceSignal(eventHours, prices, signalId, itemUnits='USD', scaleCode='none',
isEnergySignal=True, signalName='ENERGY_PRICE', currentPrice=0):
signal = {}
if isEnergySignal:
signal['signalType'] = 'energy'
else:
signal['signalType'] = 'demand'
signal['itemUnits'] = itemUnits
signal['scaleCode'] = scaleCode
signal['eventHours'] = eventHours
signal['prices'] = prices
signal['signalName'] = signalName
signal['signalId'] = signalId
signal['currentPrice'] = currentPrice
return signal
def generateDRSignal(startTime, requestId, eventId, modificationNumber, eventStatus, drEventFilename,
signals, group=True, vtnId='LBL-python-VTN', vtnComment='Module', duration='PT1440M',
groupId='PGEA10', resourceId='csu-dominguez'):
createdDateTime = convertFromDatetimeToString(dtime.datetime.utcnow())
xml = getSignalString(requestId=requestId,
vtnId=vtnId,
eventId=eventId,
modificationNumber=modificationNumber,
createdDateTime=createdDateTime,
eventStatus=eventStatus,
vtnComment=vtnComment,
startTime=startTime,
duration=duration,
signals=signals,
group=group,
groupId=groupId,
resourceId=resourceId)
if not os.path.isdir(OADR_PATH+'signals'):
os.makedirs(OADR_PATH+'signals')
with open(OADR_PATH+'signals/'+drEventFilename, "w") as f:
f.write(xml)
return drEventFilename, eventId, modificationNumber, startTime
def getMdalClient(pelicanConfig):
client = None
if "xbosEntityPath" in pelicanConfig.keys():
entityPath = pelicanConfig["xbosEntityPath"]
c = get_client(entity=entityPath)
client = mdal.MDALClient("xbos/mdal", client=c)
else:
c = get_client()
client = mdal.MDALClient("xbos/mdal", client=c)
return client
if __name__ == '__main__':
with open(OADR_PATH+'settings.json') as configFile:
config = json.load(configFile)
params = config['params']
includeDemandPricesFlag = params['includeCurrencyPerKWFlag']
pollPelicansFlag = params['pollPelicansFlag']
pollSCEApiFlag = params['pollSCEApiFlag']
sendToRecipientFlag = params['sendToRecipientFlag']
recipientURL = params['signal_recipient_url']
storeEventsFilename = params['eventsHistoryFilename']
sceTariffs = params['sceTariffs']
pgeTariffs = params['pgeTariffs']
sceConfig = None
if pollSCEApiFlag:
if 'sce' in config.keys():
sceConfig = config['sce']
else:
print("cannot find sce configuration in settings.json")
exit(1)
pelicanConfig = None
if pollPelicansFlag:
if 'pelican' in config.keys():
pelicanConfig = config['pelican']
mdalClient = getMdalClient(pelicanConfig=pelicanConfig)
else:
print("cannot find pelican configuration in settings.json")
exit(1)
events = getEventsHistory(eventsFilename=storeEventsFilename)
eventStartTimes = pollEvents(pollSceApi=pollSCEApiFlag, sceConfig=sceConfig,
pollPelicans=pollPelicansFlag, pelicanConfig=pelicanConfig, mdalClient=mdalClient)
checkAndAddNormalDays(eventStartTimes)
for eventInfoDict in eventStartTimes:
eventName = eventInfoDict.keys()[0]
isItAnEventDay = True
if eventName.endswith('_SCHEDULED'):
if eventName.startswith('PGE_EVENT'):
tariffs = pgeTariffs
else:
tariffs = sceTariffs
event_st_epoch = eventInfoDict[eventName]["start_time"]
event_et_epoch = eventInfoDict[eventName]["end_time"]
event_day_st_epoch = eventInfoDict[eventName]["event_day"]
st = convertEpochToUTC(event_day_st_epoch)
elif eventName.endswith('_NORMAL_DAY'):
isItAnEventDay = False
if eventName.startswith('PGE'):
tariffs = pgeTariffs
else:
tariffs = sceTariffs
normal_day_st_epoch = eventInfoDict[eventName]["date"]
st = convertEpochToUTC(normal_day_st_epoch)
startTime = convertFromDatetimeToString(st)
# TODO: more flexible
eventHours = getEventHours(startTime=st, num=24)
for tariff in tariffs:
prices = getHourlyDayPrices(startDateTime=st, tariff_name=tariff, isItEventDay=isItAnEventDay)
if not includeDemandPricesFlag:
prices = {'energyPrices': prices['energyPrices']}
signals = []
for priceSignal in prices.keys():
# print("LOG--------- price key: ",priceSignal, eventName)
signalId = generateAlphanumericId()
signal = {}
if priceSignal == 'demandPrices':
signal = createPriceSignal(eventHours=eventHours, prices=prices['demandPrices'],
isEnergySignal=False, signalId=signalId,
signalName='DEMAND_PRICE', currentPrice=0);
else:
signal = createPriceSignal(eventHours=eventHours, prices=prices['energyPrices'],
isEnergySignal=True, signalId=signalId,
signalName='ENERGY_PRICE', currentPrice=0);
signals.append(signal)
eventId = generateAlphanumericId()
modificationNumber = 0
eventStatus = 'far'
requestId = generateAlphanumericId()
if isItAnEventDay:
eventExists = checkIfEventExists(events=events, eventName=eventName, startDate=event_st_epoch, tariff = tariff,
status=eventStatus)
if eventExists['prevEventExists']:
# TODO: include prices in log file
prevPrices = getHourlyDayPrices(startDateTime=st, tariff_name=tariff, isItEventDay=isItAnEventDay)
if not includeDemandPricesFlag:
prevPrices = {'energyPrices': prevPrices['energyPrices']}
if arePricesDifferent(prices1=prices, prices2=prevPrices):
# print("LOG------ diff prices", eventName)
modificationNumber = eventExists['prevModNumber'] + 1
eventId = eventExists['prevEventId']
else:
# print("LOG------ same prices", eventName)
continue
# print("LOG------ prev event does not exist", eventName)
# filenames of all the created files
drSignalFilenames = ""
# one file for demand prices, one file for energy prices
for signal in signals:
drSignalFilename = '%s_%s_%s_%d_%s.xml' % (eventName, tariff, eventId, modificationNumber, signal['signalType'])
filename, eventId, modificationNumber, startTime = generateDRSignal(
startTime=startTime,
requestId=requestId,
eventId=eventId,
modificationNumber=modificationNumber,
eventStatus=eventStatus,
drEventFilename=drSignalFilename,
group=True,
groupId=tariff,
signals=[signal]
)
print("Event created: %s" % drSignalFilename)
if sendToRecipientFlag:
rsp = sendSignalToServer(url=recipientURL, filename=drSignalFilename)
if drSignalFilenames == "":
drSignalFilenames = drSignalFilename
else:
drSignalFilenames = drSignalFilenames + '_' + drSignalFilename
newIdx = 0
if events.empty:
newIdx = 0
else:
newIdx = events.tail(1).index.values[0] + 1
if isItAnEventDay:
appendToHistory(idx=newIdx,
eventId=eventId,
eventName=eventName,
modNumber=modificationNumber,
startDate=event_st_epoch,
status=eventStatus,
drSignalFilename=drSignalFilenames,
eventsFilename=storeEventsFilename,
tariff=tariff)
events = pandas.read_csv(OADR_PATH+storeEventsFilename, index_col=0)
# elif eventName.endswith('_ACTIVE'):
# handle active events
# eventStatus = 'active'
# time.sleep(10)
PK ! Z)8G G D electricitycostcalculator_gabetest/oadr_signal/getPelicanDREvents.pyimport datetime
from xbos.services import mdal
def get_uuid_data(UUIDs, freq, names, st, et, client, mdal_functions=mdal.MEAN, aligned=True):
query1 = {
"Composition": UUIDs,
"Selectors": mdal_functions,
"Time": {
"T0": st,
"T1": et,
"WindowSize": freq,
"Aligned": aligned,
},
}
resp1 = client.do_query(query1, timeout=300)
test1 = resp1["df"]
test1.columns = names
return test1
'''
return [
{"PGE_EVENT_SCHEDULED": {
"start_time": 1530939600,
"end_time": 1530954000,
"event_day": 1530889200
}
},
{"SCE_EVENT_SCHEDULED": {
"start_time": 1530939600,
"end_time": 1530954000,
"event_day": 1530889200
}
}]
'''
def pollPelicanEvents(pelicanConfig, client):
uuid_tariff_map = pelicanConfig['pelican_uuid_tariff_map']
checkHoursBefore = pelicanConfig['checkHoursBefore']
events = []
for tariffName in list(uuid_tariff_map.keys()):
sensors = list(uuid_tariff_map[tariffName].values())
names = [col for col in list(uuid_tariff_map[tariffName].keys())]
mdal_functions = [mdal.MAX for i in range(len(names))]
freq = '30min'
dt_now = datetime.datetime.now()
st = (dt_now - datetime.timedelta(hours=checkHoursBefore)).strftime("%Y-%m-%d %H:%M:%S PDT")
et = dt_now.strftime("%Y-%m-%d %H:%M:%S PDT")
df = get_uuid_data(UUIDs=sensors, freq=freq, client=client, names=names, mdal_functions=mdal_functions,
st=st, et=et)
df = df.fillna(0)
signals = df.fillna(0).loc[(df.start != 0)]
event = {}
if signals.start.count() > 0:
# ASSUMPTION: event starts from 2PM - 6PM
# converting nanoseconds to seconds (all in UTC)
epoch = signals.sort_index(ascending=False).tail(1).start.values[0]/1000000000.0
eventStartTime = datetime.datetime.utcfromtimestamp(epoch)
eventEndTime = eventStartTime + datetime.timedelta(hours=4)
eventDayStartTime = eventStartTime - datetime.timedelta(hours=14)
# TODO: remove the -07:00 time difference (strftime converts to local time)
event[tariffName+'_EVENT_SCHEDULED'] = {
'event_day': float(eventDayStartTime.strftime("%s"))-7*3600,
'start_time': float(eventStartTime.strftime("%s"))-7*3600,
'end_time': float(eventEndTime.strftime("%s"))-7*3600
}
events.append(event)
return events
# return [{
# 'PGE_EVENT_SCHEDULED': {
# "start_time": 1536181200,
# "end_time": 1536195600,
# "event_day": 1536130800
# }
# }]PK ! _5 5 H electricitycostcalculator_gabetest/oadr_signal/getPelicanDREvents.py.bakimport datetime
from xbos.services import mdal
def get_uuid_data(UUIDs, freq, names, st, et, client, mdal_functions=mdal.MEAN, aligned=True):
query1 = {
"Composition": UUIDs,
"Selectors": mdal_functions,
"Time": {
"T0": st,
"T1": et,
"WindowSize": freq,
"Aligned": aligned,
},
}
resp1 = client.do_query(query1, timeout=300)
test1 = resp1["df"]
test1.columns = names
return test1
'''
return [
{"PGE_EVENT_SCHEDULED": {
"start_time": 1530939600,
"end_time": 1530954000,
"event_day": 1530889200
}
},
{"SCE_EVENT_SCHEDULED": {
"start_time": 1530939600,
"end_time": 1530954000,
"event_day": 1530889200
}
}]
'''
def pollPelicanEvents(pelicanConfig, client):
uuid_tariff_map = pelicanConfig['pelican_uuid_tariff_map']
checkHoursBefore = pelicanConfig['checkHoursBefore']
events = []
for tariffName in uuid_tariff_map.keys():
sensors = uuid_tariff_map[tariffName].values()
names = [col for col in uuid_tariff_map[tariffName].keys()]
mdal_functions = [mdal.MAX for i in range(len(names))]
freq = '30min'
dt_now = datetime.datetime.now()
st = (dt_now - datetime.timedelta(hours=checkHoursBefore)).strftime("%Y-%m-%d %H:%M:%S PDT")
et = dt_now.strftime("%Y-%m-%d %H:%M:%S PDT")
df = get_uuid_data(UUIDs=sensors, freq=freq, client=client, names=names, mdal_functions=mdal_functions,
st=st, et=et)
df = df.fillna(0)
signals = df.fillna(0).loc[(df.start != 0)]
event = {}
if signals.start.count() > 0:
# ASSUMPTION: event starts from 2PM - 6PM
# converting nanoseconds to seconds (all in UTC)
epoch = signals.sort_index(ascending=False).tail(1).start.values[0]/1000000000.0
eventStartTime = datetime.datetime.utcfromtimestamp(epoch)
eventEndTime = eventStartTime + datetime.timedelta(hours=4)
eventDayStartTime = eventStartTime - datetime.timedelta(hours=14)
# TODO: remove the -07:00 time difference (strftime converts to local time)
event[tariffName+'_EVENT_SCHEDULED'] = {
'event_day': float(eventDayStartTime.strftime("%s"))-7*3600,
'start_time': float(eventStartTime.strftime("%s"))-7*3600,
'end_time': float(eventEndTime.strftime("%s"))-7*3600
}
events.append(event)
return events
# return [{
# 'PGE_EVENT_SCHEDULED': {
# "start_time": 1536181200,
# "end_time": 1536195600,
# "event_day": 1536130800
# }
# }]PK ! 9BK > electricitycostcalculator_gabetest/oadr_signal/getSCEEvents.pyimport pandas as pd
# import urllib2
import datetime
from lxml import html
import requests
import pytz
'''
return [
{"CPP_COMMERCIAL_SCHEDULED": {
"start_time": 1530939600,
"end_time": 1530954000,
"event_day": 1530889200
}
},
{"CPP_COMMERCIAL_SCHEDULED": {
"start_time": 1530939600,
"end_time": 1530954000,
"event_day": 1530889200
}
}]
'''
def pollSCEEvents(sceConfig):
url = sceConfig['url']
eventTypes = sceConfig['eventTypesToListenFor'] # CPP
page = requests.get(url)
tree = html.fromstring(page.content.replace("\r", "").replace("\t", ""))
events = []
for eventType in eventTypes:
for element in tree.iter():
if str(element.text).find(eventType) != -1 and str(element.tag) == 'td':
eventName = eventType+'_COMMERCIAL_SCHEDULED'
detail_list = [element.text]
for cpp_detail in element.itersiblings():
detail_list.append(cpp_detail.text)
i = 0
while i < len(detail_list):
if i % 5 == 0:
st_date = detail_list[i + 1]
et_date = detail_list[i + 2]
st_time = detail_list[i + 3]
et_time = detail_list[i + 4]
event_day = _local_to_utc_epoch(
datetime.datetime.strptime("%s %s" % (st_date, "00:00"), "%m/%d/%Y %H:%M"))
st = _local_to_utc_epoch(
datetime.datetime.strptime("%s %s" % (st_date, st_time), "%m/%d/%Y %I:%M %p"))
et = _local_to_utc_epoch(
datetime.datetime.strptime("%s %s" % (et_date, et_time), "%m/%d/%Y %I:%M %p"))
event = {eventName: {
'event_day': float(event_day),
'start_time': float(st),
'end_time': float(et),
}}
events.append(event)
i += 5
return events
# return [{
# 'CPP_COMMERCIAL_SCHEDULED': {
# "start_time": 1536181200,
# "end_time": 1536195600,
# "event_day": 1536130800
# }
# }]
def _local_to_utc_epoch(timestamp, local_zone="America/Los_Angeles"):
timestamp_new = pd.to_datetime(timestamp, infer_datetime_format=True, errors='coerce')
timestamp_new = timestamp_new.tz_localize(local_zone)
timestamp_new = timestamp_new.strftime('%s')
return timestamp_new
PK ! 9BK B electricitycostcalculator_gabetest/oadr_signal/getSCEEvents.py.bakimport pandas as pd
# import urllib2
import datetime
from lxml import html
import requests
import pytz
'''
return [
{"CPP_COMMERCIAL_SCHEDULED": {
"start_time": 1530939600,
"end_time": 1530954000,
"event_day": 1530889200
}
},
{"CPP_COMMERCIAL_SCHEDULED": {
"start_time": 1530939600,
"end_time": 1530954000,
"event_day": 1530889200
}
}]
'''
def pollSCEEvents(sceConfig):
url = sceConfig['url']
eventTypes = sceConfig['eventTypesToListenFor'] # CPP
page = requests.get(url)
tree = html.fromstring(page.content.replace("\r", "").replace("\t", ""))
events = []
for eventType in eventTypes:
for element in tree.iter():
if str(element.text).find(eventType) != -1 and str(element.tag) == 'td':
eventName = eventType+'_COMMERCIAL_SCHEDULED'
detail_list = [element.text]
for cpp_detail in element.itersiblings():
detail_list.append(cpp_detail.text)
i = 0
while i < len(detail_list):
if i % 5 == 0:
st_date = detail_list[i + 1]
et_date = detail_list[i + 2]
st_time = detail_list[i + 3]
et_time = detail_list[i + 4]
event_day = _local_to_utc_epoch(
datetime.datetime.strptime("%s %s" % (st_date, "00:00"), "%m/%d/%Y %H:%M"))
st = _local_to_utc_epoch(
datetime.datetime.strptime("%s %s" % (st_date, st_time), "%m/%d/%Y %I:%M %p"))
et = _local_to_utc_epoch(
datetime.datetime.strptime("%s %s" % (et_date, et_time), "%m/%d/%Y %I:%M %p"))
event = {eventName: {
'event_day': float(event_day),
'start_time': float(st),
'end_time': float(et),
}}
events.append(event)
i += 5
return events
# return [{
# 'CPP_COMMERCIAL_SCHEDULED': {
# "start_time": 1536181200,
# "end_time": 1536195600,
# "event_day": 1536130800
# }
# }]
def _local_to_utc_epoch(timestamp, local_zone="America/Los_Angeles"):
timestamp_new = pd.to_datetime(timestamp, infer_datetime_format=True, errors='coerce')
timestamp_new = timestamp_new.tz_localize(local_zone)
timestamp_new = timestamp_new.strftime('%s')
return timestamp_new
PK ! ε E electricitycostcalculator_gabetest/oadr_signal/settings_template.json{
"sce":{
"url": ,
"eventsToListenFor": [],
"eventsHistoryFilename":
},
"pge":{}
}PK ! ( = electricitycostcalculator_gabetest/oadr_signal/tariff_maps.pyimport os, sys
from electricitycostcalculator_testgabe.openei_tariff.openei_tariff_analyzer import OpenEI_tariff
tariff_maps= {
'PGEA10': OpenEI_tariff(utility_id='14328', sector='Commercial', tariff_rate_of_interest='A-10', distrib_level_of_interest='Secondary', phasewing=None, tou=True),
'PGEA01': OpenEI_tariff(utility_id='14328', sector='Commercial', tariff_rate_of_interest='A-1 Small General Service', distrib_level_of_interest=None, phasewing='Single', tou=True),
'PGEA06': OpenEI_tariff(utility_id='14328', sector='Commercial', tariff_rate_of_interest='A-6', distrib_level_of_interest=None, phasewing=None, tou=True, option_exclusion=['(X)', '(W)', 'Poly']),
'PGEE19': OpenEI_tariff(utility_id='14328', sector='Commercial', tariff_rate_of_interest='E-19', distrib_level_of_interest='Secondary', phasewing=None, tou=True, option_exclusion=['Option R', 'Voluntary']),
'PGEE20': OpenEI_tariff(utility_id='14328', sector='Commercial', tariff_rate_of_interest='E-20', distrib_level_of_interest='Primary', phasewing=None, tou=True),
'FLAT06': OpenEI_tariff(utility_id='90', sector='Commercial', tariff_rate_of_interest='FLAT-06', phasewing=None, tou=False, distrib_level_of_interest=None),
'SCE08B': OpenEI_tariff(utility_id='17609', sector='Commercial', tariff_rate_of_interest='TOU-8', distrib_level_of_interest=None, phasewing=None, tou=True, option_mandatory=['Option B', 'under 2 kV'], option_exclusion=['Option R']),
"SCETGS3": OpenEI_tariff(utility_id='17609', sector='Commercial', tariff_rate_of_interest='TOU-GS-3', distrib_level_of_interest=None, phasewing=None, tou=True, option_mandatory=['Option CPP', '2kV - 50kV'], option_exclusion=['Option B', 'Option A'])
}
PK ! խA A electricitycostcalculator_gabetest/oadr_signal/tariff_maps.py.bakimport os, sys
sys.path.append(os.path.abspath("../openei_tariff/"))
from openei_tariff_analyzer import OpenEI_tariff
tariff_maps= {
'PGEA10': OpenEI_tariff(utility_id='14328', sector='Commercial', tariff_rate_of_interest='A-10', distrib_level_of_interest='Secondary', phasewing=None, tou=True),
'PGEA01': OpenEI_tariff(utility_id='14328', sector='Commercial', tariff_rate_of_interest='A-1 Small General Service', distrib_level_of_interest=None, phasewing='Single', tou=True),
'PGEA06': OpenEI_tariff(utility_id='14328', sector='Commercial', tariff_rate_of_interest='A-6', distrib_level_of_interest=None, phasewing=None, tou=True, option_exclusion=['(X)', '(W)', 'Poly']),
'PGEE19': OpenEI_tariff(utility_id='14328', sector='Commercial', tariff_rate_of_interest='E-19', distrib_level_of_interest='Secondary', phasewing=None, tou=True, option_exclusion=['Option R', 'Voluntary']),
'PGEE20': OpenEI_tariff(utility_id='14328', sector='Commercial', tariff_rate_of_interest='E-20', distrib_level_of_interest='Primary', phasewing=None, tou=True),
'FLAT06': OpenEI_tariff(utility_id='90', sector='Commercial', tariff_rate_of_interest='FLAT-06', phasewing=None, tou=False, distrib_level_of_interest=None),
'SCE08B': OpenEI_tariff(utility_id='17609', sector='Commercial', tariff_rate_of_interest='TOU-8', distrib_level_of_interest=None, phasewing=None, tou=True, option_mandatory=['Option B', 'under 2 kV'], option_exclusion=['Option R']),
"SCETGS3": OpenEI_tariff(utility_id='17609', sector='Commercial', tariff_rate_of_interest='TOU-GS-3', distrib_level_of_interest=None, phasewing=None, tou=True, option_mandatory=['Option CPP', '2kV - 50kV'], option_exclusion=['Option B', 'Option A'])
}PK ! qr 7 electricitycostcalculator_gabetest/oadr_signal/utils.pyimport random
import string
import datetime as dtime
import pandas
import pytz
import requests
def generateAlphanumericId(length=20, createdRandomIds=[]):
rndm = ''.join(random.choice(string.ascii_lowercase[:6] + string.digits) for _ in range(length))
if rndm in createdRandomIds:
return generateAlphanumericId()
return rndm
def convertFromDatetimeToString(dt):
return dt.strftime('%Y-%m-%dT%H:%M:%S.00Z')
def getEventHours(startTime, num=24):
return [convertFromDatetimeToString(startTime + dtime.timedelta(hours=i)) for i in range(num)]
def convertEpochToUTC(epoch):
return dtime.datetime.utcfromtimestamp(epoch).replace(tzinfo=pytz.utc)
def checkIfEventExists(events, startDate, eventName, tariff, status=None):
# handle different eventStatuses <----------------- do this
if events.loc[(events.eventName == eventName) & (events.eventStartDate == startDate) & (events.tariff == tariff)].eventStartDate.count() > 0:
e = events.loc[(events.eventName == eventName) & (events.eventStartDate == startDate) & (events.tariff == tariff)].tail(1)
prevModNumber = e.modNumber.values[0]
prevEventId = e.eventId.values[0]
return {'prevEventExists': True, 'prevEventId': prevEventId, 'prevModNumber': prevModNumber}
else:
return {'prevEventExists': False}
def getEventsHistory(eventsFilename='events.csv'):
path = OADR_PATH + eventsFilename
if os.path.exists(path) == False:
with open(OADR_PATH+eventsFilename, 'w') as f:
f.write('idx,eventId,eventName,modNumber,eventStartDate,eventStatus,drSignalFilename,tariff\n')
events = pandas.read_csv(OADR_PATH+eventsFilename, index_col=0)
return events
def appendToHistory(idx, eventId, eventName, modNumber, startDate, status, drSignalFilename, tariff, eventsFilename='events.csv'):
with open(OADR_PATH+eventsFilename, 'a') as f:
f.write('%d,%s,%s,%d,%s,%s,%s,%s\n' % (idx, eventId, eventName, modNumber, startDate, status, drSignalFilename, tariff))
def arePricesDifferent(prices1, prices2):
keys1 = list(prices1.keys())
keys2 = list(prices2.keys())
if len(keys1) != len(keys2):
return True
elif len(keys1) == 2:
energyPrices1 = prices1['energyPrices']
energyPrices2 = prices2['energyPrices']
demandPrices1 = prices1['demandPrices']
demandPrices2 = prices2['demandPrices']
return not (((energyPrices1 == energyPrices2).all()) and ((demandPrices1 == demandPrices2).all()))
elif len(keys1) == 1:
if keys1[0] != keys2[0]:
return True
else:
key = keys1[0]
return not ((prices1[key] == prices2[key]).all())
# TODO: change default return
return False
def sendSignalToServer(url, filename):
# do something
xml = open(OADR_PATH+"signals/"+filename, "r").read()
headers = {'Content-Type': 'application/xml'} # set what your server accepts
rsp = requests.post(url, data=xml, headers=headers)
return rsp
PK ! O^( ( ; electricitycostcalculator_gabetest/oadr_signal/utils.py.bakimport random
import string
import datetime as dtime
import os, sys
sys.path.append(os.path.abspath("../")) # to access env.py
from env import *
import pandas
import pytz
import requests
def generateAlphanumericId(length=20, createdRandomIds=[]):
rndm = ''.join(random.choice(string.ascii_lowercase[:6] + string.digits) for _ in range(length))
if rndm in createdRandomIds:
return generateAlphanumericId()
return rndm
def convertFromDatetimeToString(dt):
return dt.strftime('%Y-%m-%dT%H:%M:%S.00Z')
def getEventHours(startTime, num=24):
return [convertFromDatetimeToString(startTime + dtime.timedelta(hours=i)) for i in range(num)]
def convertEpochToUTC(epoch):
return dtime.datetime.utcfromtimestamp(epoch).replace(tzinfo=pytz.utc)
def checkIfEventExists(events, startDate, eventName, tariff, status=None):
# handle different eventStatuses <----------------- do this
if events.loc[(events.eventName == eventName) & (events.eventStartDate == startDate) & (events.tariff == tariff)].eventStartDate.count() > 0:
e = events.loc[(events.eventName == eventName) & (events.eventStartDate == startDate) & (events.tariff == tariff)].tail(1)
prevModNumber = e.modNumber.values[0]
prevEventId = e.eventId.values[0]
return {'prevEventExists': True, 'prevEventId': prevEventId, 'prevModNumber': prevModNumber}
else:
return {'prevEventExists': False}
def getEventsHistory(eventsFilename='events.csv'):
path = OADR_PATH + eventsFilename
if os.path.exists(path) == False:
with open(OADR_PATH+eventsFilename, 'w') as f:
f.write('idx,eventId,eventName,modNumber,eventStartDate,eventStatus,drSignalFilename,tariff\n')
events = pandas.read_csv(OADR_PATH+eventsFilename, index_col=0)
return events
def appendToHistory(idx, eventId, eventName, modNumber, startDate, status, drSignalFilename, tariff, eventsFilename='events.csv'):
with open(OADR_PATH+eventsFilename, 'a') as f:
f.write('%d,%s,%s,%d,%s,%s,%s,%s\n' % (idx, eventId, eventName, modNumber, startDate, status, drSignalFilename, tariff))
def arePricesDifferent(prices1, prices2):
keys1 = prices1.keys()
keys2 = prices2.keys()
if len(keys1) != len(keys2):
return True
elif len(keys1) == 2:
energyPrices1 = prices1['energyPrices']
energyPrices2 = prices2['energyPrices']
demandPrices1 = prices1['demandPrices']
demandPrices2 = prices2['demandPrices']
return not (((energyPrices1 == energyPrices2).all()) and ((demandPrices1 == demandPrices2).all()))
elif len(keys1) == 1:
if keys1[0] != keys2[0]:
return True
else:
key = keys1[0]
return not ((prices1[key] == prices2[key]).all())
# TODO: change default return
return False
def sendSignalToServer(url, filename):
# do something
xml = open(OADR_PATH+"signals/"+filename, "r").read()
headers = {'Content-Type': 'application/xml'} # set what your server accepts
rsp = requests.post(url, data=xml, headers=headers)
return rspPK ! y_ ; electricitycostcalculator_gabetest/openei_tariff/.gitignore*.pyc
PDP_events.jsonPK ! v%ބг г C electricitycostcalculator_gabetest/openei_tariff/E-190-revised.json[
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"voltagecategory": "Secondary"
}
]PK ! @ @ C electricitycostcalculator_gabetest/openei_tariff/PDP_events_ex.json[{"utility_id": 14328, "start_date": "2017-07-08T00:00:00-08:00", "end_date": "2017-07-08T23:00:00-08:00"},{"utility_id": 14328, "start_date": "2017-07-17T00:00:00-08:00", "end_date": "2017-07-17T23:00:00-08:00"},{"utility_id": 14328, "start_date": "2017-07-27T00:00:00-08:00", "end_date": "2017-07-27T23:00:00-08:00"}]
PK ! R6M" " < electricitycostcalculator_gabetest/openei_tariff/__init__.py__author__ = 'Olivier Van Cutsem'
PK ! R6M" " @ electricitycostcalculator_gabetest/openei_tariff/__init__.py.bak__author__ = 'Olivier Van Cutsem'
PK ! ydWU WU J electricitycostcalculator_gabetest/openei_tariff/openei_tariff_analyzer.py__author__ = 'Olivier Van Cutsem'
# Import COST CALCULATOR LIB
from electricitycostcalculator_testgabe.cost_calculator.tariff_structure import *
from electricitycostcalculator_testgabe.cost_calculator.rate_structure import *
import time
from datetime import datetime
import requests
import json
import pytz
import os
from dateutil.parser import parse
# ----------- FUNCTIONS SPECIFIC TO OpenEI REQUESTS -------------- #
THIS_PATH = os.path.dirname(os.path.abspath(__file__)) + '/'
PDP_PATH = os.path.dirname(os.path.abspath(__file__)) + '/'
SUFFIX_REVISED = '_revised' # this is the suffix we added to the json filename after correctly the OpenEI data manually
class OpenEI_tariff(object):
URL_OPENEI = 'https://api.openei.org/utility_rates'
API_KEY = 'BgEcyD9nM0C24J2vL4ezN7ZNAllII0vKA9l7UEBu'
FORMAT = 'json'
VERSION = 'latest'
DIRECTION_SORT = 'asc'
DETAIL = 'full'
LIMIT = '500'
ORDER_BY_SORT = 'startdate'
def __init__(self, utility_id=0, sector='commercial', tariff_rate_of_interest='tou', distrib_level_of_interest='Secondary', phasewing='Single', tou=False, pdp=True, option_mandatory=None, option_exclusion=None):
self.req_param = {}
# Request param
self.req_param['api_key'] = self.API_KEY
self.req_param['eia'] = str(utility_id)
self.req_param['sector'] = sector
self.req_param['format'] = self.FORMAT
self.req_param['version'] = self.VERSION
self.req_param['direction'] = self.DIRECTION_SORT
self.req_param['detail'] = self.DETAIL
self.req_param['limit'] = self.LIMIT
self.req_param['orderby'] = self.ORDER_BY_SORT
# Post-req filter
self.tariff_rate_of_interest = tariff_rate_of_interest
self.distrib_level_of_interest = distrib_level_of_interest
self.phase_wing = phasewing
self.tou = tou
self.option_exclusion = option_exclusion
self.option_mandatory = option_mandatory
self.pdp_participate = pdp
# The raw filtered answer from an API call
self.data_openei = None
self.pdp_events = []
def set_pdp_events(self, pdp_events_dict):
self.pdp_events = pdp_events_dict
def call_api(self, store_as_json=None):
r = requests.get(self.URL_OPENEI, params=self.req_param)
data_openei = r.json()
data_filtered = []
for data_block in data_openei['items']:
print((data_block['name']))
# Check the tariff name, this is stored in the field "name"
if self.tariff_rate_of_interest not in data_block['name'] and self.tariff_rate_of_interest + '-' not in data_block['name']:
continue
print((" - {0}".format(data_block['name'])))
# Check the wiring option
if self.phase_wing is not None:
if 'phasewiring' in list(data_block.keys()):
if not(self.phase_wing in data_block['phasewiring']):
continue
else: # check the title if this field is missing
if self.phase_wing not in data_block['name']:
continue
# Check the grid level option
if self.distrib_level_of_interest is not None:
if self.distrib_level_of_interest not in data_block['name']:
continue
print((" -- {0}".format(data_block['name'])))
# Check the Time of Use option
if (self.tou and 'TOU' not in data_block['name']) or (not self.tou and 'TOU' in data_block['name']):
continue
# Ensure some options on the rate:
if self.option_mandatory is not None:
continue_block = False
for o in self.option_mandatory:
if o not in data_block['name']:
continue_block = True
break
if continue_block:
continue
# Exclude some options on the rate
if self.option_exclusion is not None:
continue_block = False
for o in self.option_exclusion:
if o in data_block['name']:
continue_block = True
break
if continue_block:
continue
#print(" -------> {0}".format(data_block['name']))
# The conditions are fulfilled: add this block
data_filtered.append(data_block)
# Make sure we work with integer timestamps
for rate_data in data_filtered:
# Starting time
if not (type(rate_data['startdate']) is int):
t_s = time.mktime(
datetime.strptime(rate_data['startdate'],
'%Y-%m-%dT%H:%M:%S.000Z').timetuple()) # Always specified
rate_data['startdate'] = t_s
# Ending time
if 'enddate' in list(rate_data.keys()):
if not (type(rate_data['enddate']) is int):
t_e = time.mktime(datetime.strptime(rate_data['enddate'],
'%Y-%m-%dT%H:%M:%S.000Z').timetuple()) # maybe not specified - assumed it's until now
rate_data['enddate'] = t_e
else:
rate_data['enddate'] = time.time()
# Make sure that the dates are consecutive
for i in range(len(data_filtered) - 1):
data_cur = data_filtered[i]
data_next = data_filtered[i + 1]
# Replace END time of the current elem by the START time of the next one if necessary
data_cur['enddate'] = min(data_next['startdate'], data_cur['enddate'])
# Re-encode the date as human
for block in data_filtered:
block['startdate'] = datetime.fromtimestamp(block['startdate'], tz=pytz.timezone("UTC")).strftime('%Y-%m-%dT%H:%M:%S.000Z')
block['enddate'] = datetime.fromtimestamp(block['enddate'], tz=pytz.timezone("UTC")).strftime('%Y-%m-%dT%H:%M:%S.000Z')
# Store internally the filtered result
self.data_openei = data_filtered
# Store the result of this processed API request in a JSON file that has the name built from the tariff info
if store_as_json is not None:
filename = self.json_filename
with open(THIS_PATH+filename+'.json', 'w') as outfile:
json.dump(data_filtered, outfile, indent=2, sort_keys=True)
def read_from_json(self, filename=None):
"""
Read tariff data from a JSON file to build the internal structure. The JSON file
:return:
- 0 if the data has been loaded from the json successfully,
- 1 if the data couldn't be loaded from the json file
- 2 if the file couldn't be read
"""
try:
if filename == None:
filename = THIS_PATH+str(self.json_filename)+str(SUFFIX_REVISED)+'.json'
#print (filename)
with open(filename, 'r') as input_file:
try:
self.data_openei = json.load(input_file)
except ValueError:
print('cant parse json')
return 1
except Exception as e:
print(('cant open file' + str(e)))
return 2
# Encode the start/end dates as integers
for block in self.data_openei:
block['enddate'] = datetime.strptime(block['enddate'], '%Y-%m-%dT%H:%M:%S.000Z').replace(tzinfo=pytz.timezone('UTC'))
block['startdate'] = datetime.strptime(block['startdate'], '%Y-%m-%dT%H:%M:%S.000Z').replace(tzinfo=pytz.timezone('UTC'))
return 0 # everything went well
def checkIfPDPDayPresent(self, utilityId, st, et):
for event in self.pdp_events:
if event['utility_id'] == utilityId and event['start_date'] == st and event['end_date'] == et:
return True
return False
@property
def json_filename(self):
# Conditional field: TOU or nothing
if_tou = ''
if self.tou:
if_tou = '_TOU'
# Wiring
phase_info = ''
if self.phase_wing is not None:
phase_info = '_phase'+self.phase_wing
# Grid level
gridlevel_info = ''
if self.distrib_level_of_interest is not None:
gridlevel_info = '_gridlevel'+self.distrib_level_of_interest
return 'u'+str(self.req_param['eia'])+'_'+self.req_param['sector']+'_'+self.tariff_rate_of_interest+if_tou+phase_info+gridlevel_info
# --- Inject data from OpenEI_tariff object to the Bill Calculator
def tariff_struct_from_openei_data(openei_tarif_obj, bill_calculator, pdp_event_filenames="PDP_events.json"):
"""
Analyze the content of an OpenEI request in order to fill a CostCalculator object
:param openei_tarif_obj: an instance of OpenEI_tariff that already call the API
:param bill_calculator: an (empty) instance of CostCalculator
:return: /
"""
tariff_struct = {}
# Analyse each block
for block_rate in openei_tarif_obj.data_openei:
# Tariff starting and ending dates
if type(block_rate['startdate']) is not datetime:
block_rate['startdate'] = datetime.strptime(block_rate['startdate'], '%Y-%m-%dT%H:%M:%S.000Z').replace(
tzinfo=pytz.timezone('UTC'))
if type(block_rate['enddate']) is not datetime:
block_rate['enddate'] = datetime.strptime(block_rate['enddate'], '%Y-%m-%dT%H:%M:%S.000Z').replace(
tzinfo=pytz.timezone('UTC'))
tariff_dates = (block_rate['startdate'], block_rate['enddate'])
# --- Fix charges
if 'fixedchargefirstmeter' in list(block_rate.keys()):
tariff_fix = block_rate['fixedchargefirstmeter']
period_fix_charge = TariffElemPeriod.MONTHLY
if '/day' in block_rate['fixedchargeunits']:
period_fix_charge = TariffElemPeriod.DAILY
bill_calculator.add_tariff(FixedTariff(tariff_dates, tariff_fix, period_fix_charge), str(TariffType.FIX_CUSTOM_CHARGE.value))
# --- Demand charges: flat
tariff_flatdemand_obj = get_flatdemand_obj_from_openei(block_rate)
if tariff_flatdemand_obj is not None:
bill_calculator.add_tariff(TouDemandChargeTariff(tariff_dates, tariff_flatdemand_obj),
str(TariffType.DEMAND_CUSTOM_CHARGE_SEASON.value))
# --- Energy charges
tariff_energy_obj = get_energyrate_obj_from_openei(block_rate)
if tariff_energy_obj is not None:
bill_calculator.add_tariff(TouEnergyChargeTariff(tariff_dates, tariff_energy_obj), str(TariffType.ENERGY_CUSTOM_CHARGE.value))
# --- Demand charges: tou
tariff_toudemand_obj = get_demandrate_obj_from_openei(block_rate)
if tariff_toudemand_obj is not None:
bill_calculator.add_tariff(TouDemandChargeTariff(tariff_dates, tariff_toudemand_obj), str(TariffType.DEMAND_CUSTOM_CHARGE_TOU.value))
if openei_tarif_obj.pdp_participate:
# --- PDP credits for energy - todo: remove the pdp days
tariff_pdp_credit_energy_obj = get_pdp_credit_energyrate_obj_from_openei(block_rate)
if tariff_pdp_credit_energy_obj is not None:
bill_calculator.add_tariff(TouEnergyChargeTariff(tariff_dates, tariff_pdp_credit_energy_obj),
str(TariffType.PDP_ENERGY_CREDIT.value))
# --- PDP credits for demand
tariff_pdp_credit_demand_obj = get_pdp_credit_demandrate_obj_from_openei(block_rate)
if tariff_pdp_credit_demand_obj is not None:
bill_calculator.add_tariff(TouDemandChargeTariff(tariff_dates, tariff_pdp_credit_demand_obj),
str(TariffType.PDP_DEMAND_CREDIT.value))
# --- PDP credits for demand
# Other useful information, beside the tariff
# Loop over all the blocks to be sure, maybe such fields are missing in some ..
for block_rate in openei_tarif_obj.data_openei:
if 'peakkwcapacitymax' in list(block_rate.keys()):
bill_calculator.tariff_max_kw = block_rate['peakkwcapacitymax']
if 'peakkwcapacitymin' in list(block_rate.keys()):
bill_calculator.tariff_min_kw = block_rate['peakkwcapacitymin']
if 'peakkwhusagemax' in list(block_rate.keys()):
bill_calculator.tariff_max_kwh = block_rate['peakkwhusagemax']
if 'peakkwhusagemin' in list(block_rate.keys()):
bill_calculator.tariff_min_kwh = block_rate['peakkwhusagemin']
# Analyse PdP events
if openei_tarif_obj.pdp_participate:
pdp_data = []
try:
pdp_data = populate_pdp_events_from_json(openei_tarif_obj, pdp_event_filenames=pdp_event_filenames)
except EnvironmentError:
print("PdP events: can't open file")
pdp_data_filter = [event for event in pdp_data if event['utility_id'] == int(openei_tarif_obj.req_param['eia'])]
for pdp_event in pdp_data_filter:
pdp_dates = parse(pdp_event['start_date']).replace(tzinfo=pytz.timezone('UTC')), parse(pdp_event['end_date']).replace(tzinfo=pytz.timezone('UTC'))
tariff_pdp_obj = get_pdp_energycharge(openei_tarif_obj, pdp_dates[0])
if tariff_pdp_obj is not None:
bill_calculator.add_tariff(TouEnergyChargeTariff(pdp_dates, tariff_pdp_obj),
str(TariffType.PDP_ENERGY_CHARGE.value))
def populate_pdp_events_from_json(openei_tarif_obj, pdp_event_filenames='PDP_events.json'):
empty = []
if not os.path.exists(PDP_PATH+pdp_event_filenames):
with open(PDP_PATH+pdp_event_filenames, 'w') as pdp_file:
json.dump(empty, pdp_file)
with open(PDP_PATH+pdp_event_filenames, 'r') as pdp_file:
try:
pdp_data = json.load(pdp_file)
openei_tarif_obj.set_pdp_events(pdp_data)
return pdp_data
except ValueError:
print("PdP events: can't parse json")
def update_pdp_json(openei_tarif_obj, pdp_dict, pdp_event_filenames='PDP_events.json'):
if cmp(openei_tarif_obj.pdp_events, pdp_dict) != 0:
with open(PDP_PATH + pdp_event_filenames, 'w') as pdp_fp:
json.dump(pdp_dict, pdp_fp)
openei_tarif_obj.set_pdp_events(pdp_dict)
return True
return False
def get_energyrate_obj_from_openei(open_ei_block):
# TODO later: use BlockRate instead of assuming it's a float !
if 'energyratestructure' not in list(open_ei_block.keys()):
return None
en_rate_list = open_ei_block['energyratestructure']
weekdays_schedule = open_ei_block['energyweekdayschedule']
weekends_schedule = open_ei_block['energyweekendschedule']
rate_struct = read_tou_rates(en_rate_list, weekdays_schedule, weekends_schedule)
if rate_struct != {}:
return TouRateSchedule(rate_struct)
else:
return None
def get_flatdemand_obj_from_openei(open_ei_block):
rate_struct = {}
if 'flatdemandstructure' in list(open_ei_block.keys()): # there is a flat demand rate
dem_rate_list = open_ei_block['flatdemandstructure']
dem_time_schedule_month = open_ei_block['flatdemandmonths']
rate_struct = read_flat_rates(dem_rate_list, dem_time_schedule_month)
if rate_struct != {}:
return TouRateSchedule(rate_struct)
else:
return None
def get_demandrate_obj_from_openei(open_ei_block):
if 'demandratestructure' not in list(open_ei_block.keys()):
return None
demand_rate_list = open_ei_block['demandratestructure']
weekdays_schedule = open_ei_block['demandweekdayschedule']
weekends_schedule = open_ei_block['demandweekendschedule']
rate_struct = read_tou_rates(demand_rate_list, weekdays_schedule, weekends_schedule)
if rate_struct != {}:
return TouRateSchedule(rate_struct)
else:
return None
def read_tou_rates(rate_map, weekdays_schedule, weekends_schedule):
ret = {}
for m_i in range(12):
already_added = False
daily_weekdays_rate = [rate_map[x][0]['rate'] for x in weekdays_schedule[m_i]]
daily_weekends_rate = [rate_map[x][0]['rate'] for x in weekends_schedule[m_i]]
# Check if this schedule is already present
for m_group_lab, m_group_data in list(ret.items()):
if daily_weekdays_rate == m_group_data[TouRateSchedule.DAILY_RATE_KEY]['weekdays'][
TouRateSchedule.RATES_KEY] and daily_weekends_rate == \
m_group_data[TouRateSchedule.DAILY_RATE_KEY]['weekends'][TouRateSchedule.RATES_KEY]:
m_group_data[TouRateSchedule.MONTHLIST_KEY].append(m_i + 1)
already_added = True
break
if not already_added:
ret['m_' + str(m_i + 1)] = {TouRateSchedule.MONTHLIST_KEY: [m_i + 1],
TouRateSchedule.DAILY_RATE_KEY: {
'weekdays': {
TouRateSchedule.DAYSLIST_KEY: [0, 1, 2, 3, 4],
TouRateSchedule.RATES_KEY: daily_weekdays_rate
},
'weekends': {
TouRateSchedule.DAYSLIST_KEY: [5, 6],
TouRateSchedule.RATES_KEY: daily_weekends_rate}
}
}
return ret
def read_flat_rates(rate_map, month_schedule):
"""
:param rate_map:
:param month_schedule:
:return:
"""
map_month_label = {1: 'winter', 0: 'summer'}
rate_struct = {}
for rate_idx in range(len(rate_map)):
months_list = [i + 1 for i, j in enumerate(month_schedule) if j == rate_idx]
rate_struct[map_month_label[rate_idx]] = {TouRateSchedule.MONTHLIST_KEY: months_list,
TouRateSchedule.DAILY_RATE_KEY: {
'allweek': {
TouRateSchedule.DAYSLIST_KEY: list(range(7)),
TouRateSchedule.RATES_KEY: 24 * [
rate_map[rate_idx][0]['rate']]
}
}
}
return rate_struct
# -- PDP manipulation
def get_pdp_energycharge(openei_tarif_obj, date_start_event):
"""
:param openei_tarif_obj:
:param daterange:
:return:
"""
# Search the corresponding block in the OpenEI data
block_l = [data for data in openei_tarif_obj.data_openei if data['startdate'] <= date_start_event <= data['enddate']]
if len(block_l) > 0: # no block found ..
block = block_l[0]
if 'pdp_charge_energy' not in block: # this tariff doesn't support PDP
return None
energy_charge = block['pdp_charge_energy']
rate_struct = {}
rate_struct['allmonth'] = {TouRateSchedule.MONTHLIST_KEY: list(range(1,13)),
TouRateSchedule.DAILY_RATE_KEY: {
'allweek': {
TouRateSchedule.DAYSLIST_KEY: list(range(7)),
TouRateSchedule.RATES_KEY: energy_charge
}
}
}
return TouRateSchedule(rate_struct)
else:
return None
def get_pdp_credit_energyrate_obj_from_openei(open_ei_block):
"""
:param block_rate:
:return:
"""
# TODO later: use BlockRate instead of assuming it's a float !
if 'pdp_credit_energyratestructure' not in list(open_ei_block.keys()):
return None
en_rate_list = open_ei_block['pdp_credit_energyratestructure']
weekdays_schedule = open_ei_block['energyweekdayschedule']
weekends_schedule = open_ei_block['energyweekendschedule']
rate_struct = read_tou_rates(en_rate_list, weekdays_schedule, weekends_schedule)
if rate_struct != {}:
return TouRateSchedule(rate_struct)
else:
return None
def get_pdp_credit_demandrate_obj_from_openei(open_ei_block):
"""
:param block_rate:
:return:
"""
if 'pdp_credit_demandratestructure' not in list(open_ei_block.keys()):
return None
pdp_demand_credit_list = open_ei_block['pdp_credit_demandratestructure']
rate_struct = {}
if 'demandweekdayschedule' in list(open_ei_block.keys()): # TOU demand
weekdays_schedule = open_ei_block['demandweekdayschedule']
weekends_schedule = open_ei_block['demandweekendschedule']
rate_struct = read_tou_rates(pdp_demand_credit_list, weekdays_schedule, weekends_schedule)
elif 'flatdemandstructure' in list(open_ei_block.keys()): # flat demand
monthly_schedule = open_ei_block['flatdemandmonths']
rate_struct = read_flat_rates(pdp_demand_credit_list, monthly_schedule)
if rate_struct != {}:
return TouRateSchedule(rate_struct)
else:
return None
PK !
>AU AU N electricitycostcalculator_gabetest/openei_tariff/openei_tariff_analyzer.py.bak__author__ = 'Olivier Van Cutsem'
# Import COST CALCULATOR LIB
from electricitycostcalculator.cost_calculator.tariff_structure import *
from electricitycostcalculator.cost_calculator.rate_structure import *
import time
from datetime import datetime
import requests
import json
import pytz
import os
from dateutil.parser import parse
# ----------- FUNCTIONS SPECIFIC TO OpenEI REQUESTS -------------- #
THIS_PATH = os.path.dirname(os.path.abspath(__file__)) + '/'
PDP_PATH = os.path.dirname(os.path.abspath(__file__)) + '/'
SUFFIX_REVISED = '_revised' # this is the suffix we added to the json filename after correctly the OpenEI data manually
class OpenEI_tariff(object):
URL_OPENEI = 'https://api.openei.org/utility_rates'
API_KEY = 'BgEcyD9nM0C24J2vL4ezN7ZNAllII0vKA9l7UEBu'
FORMAT = 'json'
VERSION = 'latest'
DIRECTION_SORT = 'asc'
DETAIL = 'full'
LIMIT = '500'
ORDER_BY_SORT = 'startdate'
def __init__(self, utility_id=0, sector='commercial', tariff_rate_of_interest='tou', distrib_level_of_interest='Secondary', phasewing='Single', tou=False, pdp=True, option_mandatory=None, option_exclusion=None):
self.req_param = {}
# Request param
self.req_param['api_key'] = self.API_KEY
self.req_param['eia'] = str(utility_id)
self.req_param['sector'] = sector
self.req_param['format'] = self.FORMAT
self.req_param['version'] = self.VERSION
self.req_param['direction'] = self.DIRECTION_SORT
self.req_param['detail'] = self.DETAIL
self.req_param['limit'] = self.LIMIT
self.req_param['orderby'] = self.ORDER_BY_SORT
# Post-req filter
self.tariff_rate_of_interest = tariff_rate_of_interest
self.distrib_level_of_interest = distrib_level_of_interest
self.phase_wing = phasewing
self.tou = tou
self.option_exclusion = option_exclusion
self.option_mandatory = option_mandatory
self.pdp_participate = pdp
# The raw filtered answer from an API call
self.data_openei = None
self.pdp_events = []
def set_pdp_events(self, pdp_events_dict):
self.pdp_events = pdp_events_dict
def call_api(self, store_as_json=None):
r = requests.get(self.URL_OPENEI, params=self.req_param)
data_openei = r.json()
data_filtered = []
for data_block in data_openei['items']:
print(data_block['name'])
# Check the tariff name, this is stored in the field "name"
if self.tariff_rate_of_interest not in data_block['name'] and self.tariff_rate_of_interest + '-' not in data_block['name']:
continue
print((" - {0}".format(data_block['name'])))
# Check the wiring option
if self.phase_wing is not None:
if 'phasewiring' in list(data_block.keys()):
if not(self.phase_wing in data_block['phasewiring']):
continue
else: # check the title if this field is missing
if self.phase_wing not in data_block['name']:
continue
# Check the grid level option
if self.distrib_level_of_interest is not None:
if self.distrib_level_of_interest not in data_block['name']:
continue
print((" -- {0}".format(data_block['name'])))
# Check the Time of Use option
if (self.tou and 'TOU' not in data_block['name']) or (not self.tou and 'TOU' in data_block['name']):
continue
# Ensure some options on the rate:
if self.option_mandatory is not None:
continue_block = False
for o in self.option_mandatory:
if o not in data_block['name']:
continue_block = True
break
if continue_block:
continue
# Exclude some options on the rate
if self.option_exclusion is not None:
continue_block = False
for o in self.option_exclusion:
if o in data_block['name']:
continue_block = True
break
if continue_block:
continue
#print(" -------> {0}".format(data_block['name']))
# The conditions are fulfilled: add this block
data_filtered.append(data_block)
# Make sure we work with integer timestamps
for rate_data in data_filtered:
# Starting time
if not (type(rate_data['startdate']) is int):
t_s = time.mktime(
datetime.strptime(rate_data['startdate'],
'%Y-%m-%dT%H:%M:%S.000Z').timetuple()) # Always specified
rate_data['startdate'] = t_s
# Ending time
if 'enddate' in list(rate_data.keys()):
if not (type(rate_data['enddate']) is int):
t_e = time.mktime(datetime.strptime(rate_data['enddate'],
'%Y-%m-%dT%H:%M:%S.000Z').timetuple()) # maybe not specified - assumed it's until now
rate_data['enddate'] = t_e
else:
rate_data['enddate'] = time.time()
# Make sure that the dates are consecutive
for i in range(len(data_filtered) - 1):
data_cur = data_filtered[i]
data_next = data_filtered[i + 1]
# Replace END time of the current elem by the START time of the next one if necessary
data_cur['enddate'] = min(data_next['startdate'], data_cur['enddate'])
# Re-encode the date as human
for block in data_filtered:
block['startdate'] = datetime.fromtimestamp(block['startdate'], tz=pytz.timezone("UTC")).strftime('%Y-%m-%dT%H:%M:%S.000Z')
block['enddate'] = datetime.fromtimestamp(block['enddate'], tz=pytz.timezone("UTC")).strftime('%Y-%m-%dT%H:%M:%S.000Z')
# Store internally the filtered result
self.data_openei = data_filtered
# Store the result of this processed API request in a JSON file that has the name built from the tariff info
if store_as_json is not None:
filename = self.json_filename
with open(THIS_PATH+filename+'.json', 'w') as outfile:
json.dump(data_filtered, outfile, indent=2, sort_keys=True)
def read_from_json(self, filename=None):
"""
Read tariff data from a JSON file to build the internal structure. The JSON file
:return:
- 0 if the data has been loaded from the json successfully,
- 1 if the data couldn't be loaded from the json file
- 2 if the file couldn't be read
"""
try:
if filename == None:
filename = THIS_PATH+str(self.json_filename)+str(SUFFIX_REVISED)+'.json'
#print (filename)
with open(filename, 'r') as input_file:
try:
self.data_openei = json.load(input_file)
except ValueError:
print('cant parse json')
return 1
except Exception as e:
print('cant open file' + str(e))
return 2
# Encode the start/end dates as integers
for block in self.data_openei:
block['enddate'] = datetime.strptime(block['enddate'], '%Y-%m-%dT%H:%M:%S.000Z').replace(tzinfo=pytz.timezone('UTC'))
block['startdate'] = datetime.strptime(block['startdate'], '%Y-%m-%dT%H:%M:%S.000Z').replace(tzinfo=pytz.timezone('UTC'))
return 0 # everything went well
def checkIfPDPDayPresent(self, utilityId, st, et):
for event in self.pdp_events:
if event['utility_id'] == utilityId and event['start_date'] == st and event['end_date'] == et:
return True
return False
@property
def json_filename(self):
# Conditional field: TOU or nothing
if_tou = ''
if self.tou:
if_tou = '_TOU'
# Wiring
phase_info = ''
if self.phase_wing is not None:
phase_info = '_phase'+self.phase_wing
# Grid level
gridlevel_info = ''
if self.distrib_level_of_interest is not None:
gridlevel_info = '_gridlevel'+self.distrib_level_of_interest
return 'u'+str(self.req_param['eia'])+'_'+self.req_param['sector']+'_'+self.tariff_rate_of_interest+if_tou+phase_info+gridlevel_info
# --- Inject data from OpenEI_tariff object to the Bill Calculator
def tariff_struct_from_openei_data(openei_tarif_obj, bill_calculator, pdp_event_filenames="PDP_events.json"):
"""
Analyze the content of an OpenEI request in order to fill a CostCalculator object
:param openei_tarif_obj: an instance of OpenEI_tariff that already call the API
:param bill_calculator: an (empty) instance of CostCalculator
:return: /
"""
tariff_struct = {}
# Analyse each block
for block_rate in openei_tarif_obj.data_openei:
# Tariff starting and ending dates
if type(block_rate['startdate']) is not datetime:
block_rate['startdate'] = datetime.strptime(block_rate['startdate'], '%Y-%m-%dT%H:%M:%S.000Z').replace(
tzinfo=pytz.timezone('UTC'))
if type(block_rate['enddate']) is not datetime:
block_rate['enddate'] = datetime.strptime(block_rate['enddate'], '%Y-%m-%dT%H:%M:%S.000Z').replace(
tzinfo=pytz.timezone('UTC'))
tariff_dates = (block_rate['startdate'], block_rate['enddate'])
# --- Fix charges
if 'fixedchargefirstmeter' in list(block_rate.keys()):
tariff_fix = block_rate['fixedchargefirstmeter']
period_fix_charge = TariffElemPeriod.MONTHLY
if '/day' in block_rate['fixedchargeunits']:
period_fix_charge = TariffElemPeriod.DAILY
bill_calculator.add_tariff(FixedTariff(tariff_dates, tariff_fix, period_fix_charge), str(TariffType.FIX_CUSTOM_CHARGE.value))
# --- Demand charges: flat
tariff_flatdemand_obj = get_flatdemand_obj_from_openei(block_rate)
if tariff_flatdemand_obj is not None:
bill_calculator.add_tariff(TouDemandChargeTariff(tariff_dates, tariff_flatdemand_obj),
str(TariffType.DEMAND_CUSTOM_CHARGE_SEASON.value))
# --- Energy charges
tariff_energy_obj = get_energyrate_obj_from_openei(block_rate)
if tariff_energy_obj is not None:
bill_calculator.add_tariff(TouEnergyChargeTariff(tariff_dates, tariff_energy_obj), str(TariffType.ENERGY_CUSTOM_CHARGE.value))
# --- Demand charges: tou
tariff_toudemand_obj = get_demandrate_obj_from_openei(block_rate)
if tariff_toudemand_obj is not None:
bill_calculator.add_tariff(TouDemandChargeTariff(tariff_dates, tariff_toudemand_obj), str(TariffType.DEMAND_CUSTOM_CHARGE_TOU.value))
if openei_tarif_obj.pdp_participate:
# --- PDP credits for energy - todo: remove the pdp days
tariff_pdp_credit_energy_obj = get_pdp_credit_energyrate_obj_from_openei(block_rate)
if tariff_pdp_credit_energy_obj is not None:
bill_calculator.add_tariff(TouEnergyChargeTariff(tariff_dates, tariff_pdp_credit_energy_obj),
str(TariffType.PDP_ENERGY_CREDIT.value))
# --- PDP credits for demand
tariff_pdp_credit_demand_obj = get_pdp_credit_demandrate_obj_from_openei(block_rate)
if tariff_pdp_credit_demand_obj is not None:
bill_calculator.add_tariff(TouDemandChargeTariff(tariff_dates, tariff_pdp_credit_demand_obj),
str(TariffType.PDP_DEMAND_CREDIT.value))
# --- PDP credits for demand
# Other useful information, beside the tariff
# Loop over all the blocks to be sure, maybe such fields are missing in some ..
for block_rate in openei_tarif_obj.data_openei:
if 'peakkwcapacitymax' in list(block_rate.keys()):
bill_calculator.tariff_max_kw = block_rate['peakkwcapacitymax']
if 'peakkwcapacitymin' in list(block_rate.keys()):
bill_calculator.tariff_min_kw = block_rate['peakkwcapacitymin']
if 'peakkwhusagemax' in list(block_rate.keys()):
bill_calculator.tariff_max_kwh = block_rate['peakkwhusagemax']
if 'peakkwhusagemin' in list(block_rate.keys()):
bill_calculator.tariff_min_kwh = block_rate['peakkwhusagemin']
# Analyse PdP events
if openei_tarif_obj.pdp_participate:
pdp_data = []
try:
pdp_data = populate_pdp_events_from_json(openei_tarif_obj, pdp_event_filenames=pdp_event_filenames)
except EnvironmentError:
print("PdP events: can't open file")
pdp_data_filter = [event for event in pdp_data if event['utility_id'] == int(openei_tarif_obj.req_param['eia'])]
for pdp_event in pdp_data_filter:
pdp_dates = parse(pdp_event['start_date']).replace(tzinfo=pytz.timezone('UTC')), parse(pdp_event['end_date']).replace(tzinfo=pytz.timezone('UTC'))
tariff_pdp_obj = get_pdp_energycharge(openei_tarif_obj, pdp_dates[0])
if tariff_pdp_obj is not None:
bill_calculator.add_tariff(TouEnergyChargeTariff(pdp_dates, tariff_pdp_obj),
str(TariffType.PDP_ENERGY_CHARGE.value))
def populate_pdp_events_from_json(openei_tarif_obj, pdp_event_filenames='PDP_events.json'):
empty = []
if not os.path.exists(PDP_PATH+pdp_event_filenames):
with open(PDP_PATH+pdp_event_filenames, 'w') as pdp_file:
json.dump(empty, pdp_file)
with open(PDP_PATH+pdp_event_filenames, 'r') as pdp_file:
try:
pdp_data = json.load(pdp_file)
openei_tarif_obj.set_pdp_events(pdp_data)
return pdp_data
except ValueError:
print("PdP events: can't parse json")
def update_pdp_json(openei_tarif_obj, pdp_dict, pdp_event_filenames='PDP_events.json'):
if cmp(openei_tarif_obj.pdp_events, pdp_dict) != 0:
with open(PDP_PATH + pdp_event_filenames, 'w') as pdp_fp:
json.dump(pdp_dict, pdp_fp)
openei_tarif_obj.set_pdp_events(pdp_dict)
return True
return False
def get_energyrate_obj_from_openei(open_ei_block):
# TODO later: use BlockRate instead of assuming it's a float !
if 'energyratestructure' not in list(open_ei_block.keys()):
return None
en_rate_list = open_ei_block['energyratestructure']
weekdays_schedule = open_ei_block['energyweekdayschedule']
weekends_schedule = open_ei_block['energyweekendschedule']
rate_struct = read_tou_rates(en_rate_list, weekdays_schedule, weekends_schedule)
if rate_struct != {}:
return TouRateSchedule(rate_struct)
else:
return None
def get_flatdemand_obj_from_openei(open_ei_block):
rate_struct = {}
if 'flatdemandstructure' in list(open_ei_block.keys()): # there is a flat demand rate
dem_rate_list = open_ei_block['flatdemandstructure']
dem_time_schedule_month = open_ei_block['flatdemandmonths']
rate_struct = read_flat_rates(dem_rate_list, dem_time_schedule_month)
if rate_struct != {}:
return TouRateSchedule(rate_struct)
else:
return None
def get_demandrate_obj_from_openei(open_ei_block):
if 'demandratestructure' not in list(open_ei_block.keys()):
return None
demand_rate_list = open_ei_block['demandratestructure']
weekdays_schedule = open_ei_block['demandweekdayschedule']
weekends_schedule = open_ei_block['demandweekendschedule']
rate_struct = read_tou_rates(demand_rate_list, weekdays_schedule, weekends_schedule)
if rate_struct != {}:
return TouRateSchedule(rate_struct)
else:
return None
def read_tou_rates(rate_map, weekdays_schedule, weekends_schedule):
ret = {}
for m_i in range(12):
already_added = False
daily_weekdays_rate = [rate_map[x][0]['rate'] for x in weekdays_schedule[m_i]]
daily_weekends_rate = [rate_map[x][0]['rate'] for x in weekends_schedule[m_i]]
# Check if this schedule is already present
for m_group_lab, m_group_data in list(ret.items()):
if daily_weekdays_rate == m_group_data[TouRateSchedule.DAILY_RATE_KEY]['weekdays'][
TouRateSchedule.RATES_KEY] and daily_weekends_rate == \
m_group_data[TouRateSchedule.DAILY_RATE_KEY]['weekends'][TouRateSchedule.RATES_KEY]:
m_group_data[TouRateSchedule.MONTHLIST_KEY].append(m_i + 1)
already_added = True
break
if not already_added:
ret['m_' + str(m_i + 1)] = {TouRateSchedule.MONTHLIST_KEY: [m_i + 1],
TouRateSchedule.DAILY_RATE_KEY: {
'weekdays': {
TouRateSchedule.DAYSLIST_KEY: [0, 1, 2, 3, 4],
TouRateSchedule.RATES_KEY: daily_weekdays_rate
},
'weekends': {
TouRateSchedule.DAYSLIST_KEY: [5, 6],
TouRateSchedule.RATES_KEY: daily_weekends_rate}
}
}
return ret
def read_flat_rates(rate_map, month_schedule):
"""
:param rate_map:
:param month_schedule:
:return:
"""
map_month_label = {1: 'winter', 0: 'summer'}
rate_struct = {}
for rate_idx in range(len(rate_map)):
months_list = [i + 1 for i, j in enumerate(month_schedule) if j == rate_idx]
rate_struct[map_month_label[rate_idx]] = {TouRateSchedule.MONTHLIST_KEY: months_list,
TouRateSchedule.DAILY_RATE_KEY: {
'allweek': {
TouRateSchedule.DAYSLIST_KEY: list(range(7)),
TouRateSchedule.RATES_KEY: 24 * [
rate_map[rate_idx][0]['rate']]
}
}
}
return rate_struct
# -- PDP manipulation
def get_pdp_energycharge(openei_tarif_obj, date_start_event):
"""
:param openei_tarif_obj:
:param daterange:
:return:
"""
# Search the corresponding block in the OpenEI data
block_l = [data for data in openei_tarif_obj.data_openei if data['startdate'] <= date_start_event <= data['enddate']]
if len(block_l) > 0: # no block found ..
block = block_l[0]
if 'pdp_charge_energy' not in block: # this tariff doesn't support PDP
return None
energy_charge = block['pdp_charge_energy']
rate_struct = {}
rate_struct['allmonth'] = {TouRateSchedule.MONTHLIST_KEY: list(range(1,13)),
TouRateSchedule.DAILY_RATE_KEY: {
'allweek': {
TouRateSchedule.DAYSLIST_KEY: list(range(7)),
TouRateSchedule.RATES_KEY: energy_charge
}
}
}
return TouRateSchedule(rate_struct)
else:
return None
def get_pdp_credit_energyrate_obj_from_openei(open_ei_block):
"""
:param block_rate:
:return:
"""
# TODO later: use BlockRate instead of assuming it's a float !
if 'pdp_credit_energyratestructure' not in list(open_ei_block.keys()):
return None
en_rate_list = open_ei_block['pdp_credit_energyratestructure']
weekdays_schedule = open_ei_block['energyweekdayschedule']
weekends_schedule = open_ei_block['energyweekendschedule']
rate_struct = read_tou_rates(en_rate_list, weekdays_schedule, weekends_schedule)
if rate_struct != {}:
return TouRateSchedule(rate_struct)
else:
return None
def get_pdp_credit_demandrate_obj_from_openei(open_ei_block):
"""
:param block_rate:
:return:
"""
if 'pdp_credit_demandratestructure' not in list(open_ei_block.keys()):
return None
pdp_demand_credit_list = open_ei_block['pdp_credit_demandratestructure']
rate_struct = {}
if 'demandweekdayschedule' in list(open_ei_block.keys()): # TOU demand
weekdays_schedule = open_ei_block['demandweekdayschedule']
weekends_schedule = open_ei_block['demandweekendschedule']
rate_struct = read_tou_rates(pdp_demand_credit_list, weekdays_schedule, weekends_schedule)
elif 'flatdemandstructure' in list(open_ei_block.keys()): # flat demand
monthly_schedule = open_ei_block['flatdemandmonths']
rate_struct = read_flat_rates(pdp_demand_credit_list, monthly_schedule)
if rate_struct != {}:
return TouRateSchedule(rate_struct)
else:
return None
PK ! q] q electricitycostcalculator_gabetest/openei_tariff/u14328_Commercial_A-1 Small General Service_TOU_phaseSingle.json[
{
"approved": true,
"basicinformationcomments": "Fixed Monthly Charge is the Total Customer Charge per day, averaged out for a month. ($0.32854 per meter per day * 365 days/ 12 months).",
"country": "USA",
"demandrateunit": "kW",
"demandunits": "kW",
"description": "This schedule is also not available to customers whose meter indicates a maximum demand of 75 kW or greater for three consecutive months.",
"dgrules": "Net Metering",
"eiaid": 14328,
"enddate": "2015-02-28T01:00:00.000Z",
"energycomments": "Winter: Period 1: Off Peak, Period 2: Part Peak\r\nSummer: Period 3: Off Peak, Period 4: Part Peak, Period 5: Peak",
"energyratestructure": [
[
{
"rate": 0.14881,
"unit": "kWh"
}
],
[
{
"rate": 0.16801,
"unit": "kWh"
}
],
[
{
"rate": 0.21195,
"unit": "kWh"
}
],
[
{
"rate": 0.23875,
"unit": "kWh"
}
],
[
{
"rate": 0.24756,
"unit": "kWh"
}
]
],
"energyweekdayschedule": [
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[
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],
"energyweekendschedule": [
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],
"fixedchargefirstmeter": 10,
"fixedchargeunits": "$/month",
"flatdemandunit": "kW",
"label": "55fc81b5682bea28da64e210",
"name": "A-1 Small General Service TOU (Single-Phase)",
"peakkwcapacitymax": 75,
"phasewiring": "Single Phase",
"revisions": [
1409237100,
1409237278,
1409314221,
1427405192,
1431429330
],
"sector": "Commercial",
"source": "http://www.pge.com/tariffs/tm2/pdf/ELEC_SCHEDS_A-1.pdf",
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1483629926,
1513335327
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"sector": "Commercial",
"source": "http://www.pge.com/tariffs/tm2/pdf/ELEC_SCHEDS_E-19.pdf",
"sourceparent": "http://www.pge.com/tariffs/ERS.SHTML",
"startdate": "2018-01-01T00:00:00.000Z",
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},
{
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1],
[3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 5, 5, 5, 5, 5, 4, 4, 3],
[3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 5, 5, 5, 5, 5, 4, 4, 3],
[3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 5, 5, 5, 5, 5, 4, 4, 3],
[3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 5, 5, 5, 5, 5, 4, 4, 3],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1]
],
"description": "Ongoing daily Time-of-Use (TOU) meter charges applicable to customers taking \r\nvoluntary TOU service under this rate schedule will no longer be applied if the customer \r\nhas a SmartMeter\u2122 installed.\r\n\r\nPeak Day Pricing Rates are applicable to customers taking this rate. A maximum of fifteen (15) PDP events and a minimum of nine (9) PDP events may be called in any calendar year.",
"eiaid": 14328,
"enddate": "2018-12-31T23:59:59.000Z",
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[
{
"rate": 0,
"unit": "kWh"
}
],
[
{
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"unit": "kWh"
}
],
[
{
"rate": 0,
"unit": "kWh"
}
],
[
{
"rate": 0,
"unit": "kWh"
}
],
[
{
"rate": 0,
"unit": "kWh"
}
],
[
{
"rate": 0,
"unit": "kWh"
}
]
],
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],
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[
{
"rate": 0.06384,
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}
],
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{
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}
],
[
{
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"unit": "kWh"
}
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{
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}
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[
{
"rate": 0.11729,
"unit": "kWh"
}
],
[
{
"rate": 0.14213,
"unit": "kWh"
}
]
],
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[
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1],
[3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 5, 5, 5, 5, 5, 4, 4, 3],
[3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 5, 5, 5, 5, 5, 4, 4, 3],
[3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 5, 5, 5, 5, 5, 4, 4, 3],
[3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 5, 5, 5, 5, 5, 4, 4, 3],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1]
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[
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1],
[3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 5, 5, 5, 5, 5, 4, 4, 3],
[3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 5, 5, 5, 5, 5, 4, 4, 3],
[3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 5, 5, 5, 5, 5, 4, 4, 3],
[3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 5, 5, 5, 5, 5, 4, 4, 3],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1]
],
"fixedchargefirstmeter": 139,
"fixedchargeunits": "$/month",
"flatdemandmonths": [
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0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"flatdemandstructure": [
[
{
"rate": 19.45
}
]
],
"flatdemandunit": "kW",
"label": "586ec8ea682bea4eea938603",
"name": "E-19 Medium General Demand TOU (Secondary)",
"peakkwcapacitymax": 999,
"peakkwcapacitymin": 500,
"revisions": [
1483629573,
1483629926,
1513335327
],
"sector": "Commercial",
"source": "http://www.pge.com/tariffs/tm2/pdf/ELEC_SCHEDS_E-19.pdf",
"sourceparent": "http://www.pge.com/tariffs/ERS.SHTML",
"startdate": "2018-03-01T00:00:00.000Z",
"supersedes": "574db6805457a30a7e5e629e",
"uri": "https://openei.org/apps/IURDB/rate/view/586ec8ea682bea4eea938603",
"utility": "Pacific Gas & Electric Co",
"voltagecategory": "Secondary"
},
{
"approved": true,
"country": "USA",
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[
{
"rate": 0,
"name": "off_peak_winter"
}
],
[
{
"rate": 1.48,
"name": "mid_peak_winter"
}
],
[
{
"rate": 0,
"name": "off_peak_summer"
}
],
[
{
"rate": 2.85,
"name": "mid_peak_summer"
}
],
[
{
"rate": 18.35,
"name": "on_peak_summer"
}
]
],
"demandrateunit": "kW",
"demandunits": "kW",
"demandweekdayschedule": [
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
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[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 4, 4, 4, 4, 4, 3, 3, 2],
[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 4, 4, 4, 4, 4, 3, 3, 2],
[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 4, 4, 4, 4, 4, 3, 3, 2],
[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 4, 4, 4, 4, 4, 3, 3, 2],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0]
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"demandweekendschedule": [
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 4, 4, 4, 4, 4, 3, 3, 2],
[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 4, 4, 4, 4, 4, 3, 3, 2],
[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 4, 4, 4, 4, 4, 3, 3, 2],
[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 4, 4, 4, 4, 4, 3, 3, 2],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
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],
"description": "Ongoing daily Time-of-Use (TOU) meter charges applicable to customers taking \r\nvoluntary TOU service under this rate schedule will no longer be applied if the customer \r\nhas a SmartMeter\u2122 installed.\r\n\r\nPeak Day Pricing Rates are applicable to customers taking this rate. A maximum of fifteen (15) PDP events and a minimum of nine (9) PDP events may be called in any calendar year.",
"eiaid": 14328,
"enddate": "2020-12-31T23:59:59.000Z",
"energycomments": "Period 1: Off Peak Winter, Period 2: Part Peak Winter, Period 3:Off Peak Summer , Period 4: Part Peak Summer, Period 5: Peak Summer",
"energyratestructure": [
[
{
"rate": 0.100,
"unit": "kWh",
"name": "off_peak_winter"
}
],
[
{
"rate": 0.126,
"unit": "kWh",
"name": "mid_peak_winter"
}
],
[
{
"rate": 0.100,
"unit": "kWh",
"name": "off_peak_summer"
}
],
[
{
"rate": 0.117,
"unit": "kWh",
"name": "mid_peak_summer"
}
],
[
{
"rate": 0.142,
"unit": "kWh",
"name": "on_peak_summer"
}
],
[
{
"rate": 0.064,
"unit": "kWh",
"name": "super_offpeak_spring"
}
]
],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 1, 1, 1, 1, 1, 0, 0, 0],
[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 4, 4, 4, 4, 4, 3, 3, 2],
[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 4, 4, 4, 4, 4, 3, 3, 2],
[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 4, 4, 4, 4, 4, 3, 3, 2],
[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 4, 4, 4, 4, 4, 3, 3, 2],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0]
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"energyweekendschedule": [
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 1, 1, 1, 1, 1, 0, 0, 0],
[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 4, 4, 4, 4, 4, 3, 3, 2],
[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 4, 4, 4, 4, 4, 3, 3, 2],
[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 4, 4, 4, 4, 4, 3, 3, 2],
[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 4, 4, 4, 4, 4, 3, 3, 2],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0]
],
"fixedchargefirstmeter": 139,
"fixedchargeunits": "$/month",
"flatdemandmonths": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"flatdemandstructure": [
[
{
"rate": 19.45
}
]
],
"flatdemandunit": "kW",
"label": "55fc81b5682bea28da64e266",
"name": "E-19 Medium General Demand- TOU (Secondary, Mandatory)",
"peakkwcapacitymax": 999,
"peakkwcapacitymin": 500,
"sector": "Commercial",
"source": "http://www.pge.com/tariffs/tm2/pdf/ELEC_SCHEDS_E-19.pdf",
"sourceparent": "http://www.pge.com/tariffs/ERS.SHTML",
"startdate": "2019-01-01T00:00:00.000Z",
"supersedes": "539f6fcbec4f024411ecd2d1",
"uri": "https://openei.org/apps/IURDB/rate/view/55fc81b5682bea28da64e266",
"utility": "Pacific Gas & Electric Co",
"voltagecategory": "Secondary"
}
]
PK ! @]% % c electricitycostcalculator_gabetest/openei_tariff/u14328_Commercial_E-19_TOU_gridlevelSecondary.json[
{
"approved": true,
"country": "USA",
"demandratestructure": [
[
{
"rate": 0
}
],
[
{
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}
],
[
{
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}
],
[
{
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}
]
],
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"source": "http://www.pge.com/tariffs/tm2/pdf/ELEC_SCHEDS_E-20.pdf",
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"eiaid": 14328,
"enddate": "2016-12-31T00:00:00.000Z",
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"label": "55fc8105682bea28da645268",
"name": "Time-Of-Use - General Service - Large: GS3, Option CPP",
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"phasewiring": "3-Phase",
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1289145152,
1301419862,
1355008249,
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"source": "http://www.sce.com/NR/sc3/tm2/pdf/ce54-12.pdf",
"startdate": "2018-10-01T00:00:00.000Z",
"uri": "https://openei.org/apps/IURDB/rate/view/55fc8105682bea28da645268",
"utility": "Southern California Edison Co",
"voltagemaximum": 2000
},
{
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],
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[
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"description": "Ongoing daily Time-of-Use (TOU) meter charges applicable to customers taking \r\nvoluntary TOU service under this rate schedule will no longer be applied if the customer \r\nhas a SmartMeter\u2122 installed.\r\n\r\nPeak Day Pricing Rates are applicable to customers taking this rate. A maximum of fifteen (15) PDP events and a minimum of nine (9) PDP events may be called in any calendar year.",
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"enddate": "2019-12-31T23:59:59.000Z",
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[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 2, 2, 2],
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