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elec_slp.py
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elec_slp.py
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# -*- coding: utf-8 -*-
"""
Implementation of the standard load profiles
SPDX-FileCopyrightText: Birgit Schachler
SPDX-FileCopyrightText: Uwe Krien <krien@uni-bremen.de>
SPDX-FileCopyrightText: jnnr
SPDX-FileCopyrightText: Stephen Bosch
SPDX-FileCopyrightText: Patrik Schönfeldt
SPDX-License-Identifier: MIT
"""
import calendar
import datetime
import os
import warnings
import pandas as pd
from demandlib.tools import add_weekdays2df
class ElecSlp:
"""Generate electrical standardized load profiles based on the BDEW method.
Parameters
----------
year : integer
Year of the demand series.
Optional Parameters
-------------------
seasons : dictionary
Describing the time ranges for summer, winter and transition periods.
The seasons dictionary will update the existing one, so only changed
keys have to be defined. Make sure not to create time gaps. The
"h0_dyn" will not work with changed seasons, so you have to use your
own smoothing curve to create a "h0_dyn" profile.
holidays : dictionary or list
The keys of the dictionary or the items of the list should be datetime
objects of the days that are holidays.
"""
def __init__(self, year, seasons=None, holidays=None):
if calendar.isleap(year):
hoy = 8784
else:
hoy = 8760
self._datapath = os.path.join(os.path.dirname(__file__), "bdew_data")
self._date_time_index = pd.date_range(
datetime.datetime(year, 1, 1, 0), periods=hoy * 4, freq="15Min"
)
self._seasons = {
"summer1": [5, 15, 9, 14], # summer: 15.05. to 14.09
"transition1": [3, 21, 5, 14], # transition1 :21.03. to 14.05
"transition2": [9, 15, 10, 31], # transition2 :15.09. to 31.10
"winter1": [1, 1, 3, 20], # winter1: 01.01. to 20.03
"winter2": [11, 1, 12, 31], # winter2: 01.11. to 31.12
}
if seasons is not None:
self._seasons.update(seasons)
self._year = year
# Create the default profiles
self.slp_frame = self.all_load_profiles(
self._date_time_index, holidays=holidays
)
# Add the dynamic H0 profile
self.create_dynamic_h0_profile()
@property
def date_time_index(self):
return self._date_time_index
def all_load_profiles(self, time_df, holidays=None):
slp_types = [
"h0",
"g0",
"g1",
"g2",
"g3",
"g4",
"g5",
"g6",
"l0",
"l1",
"l2",
]
new_df = self.create_bdew_load_profiles(
time_df, slp_types, holidays=holidays
)
return new_df
def create_bdew_load_profiles(self, dt_index, slp_types, holidays=None):
"""
Calculates the hourly electricity load profile in MWh/h of a region.
"""
# define file path of slp csv data
file_path = os.path.join(self._datapath, "selp_series.csv")
# Read standard load profile series from csv file
selp_series = pd.read_csv(file_path)
tmp_df = selp_series
# Create an index to merge. The year and month will be ignored only the
# time index is necessary.
index = pd.date_range(
datetime.datetime(2007, 1, 1, 0), periods=2016, freq="15Min"
)
tmp_df.set_index(index, inplace=True)
# Create empty DataFrame to take the results.
new_df = pd.DataFrame(index=dt_index, columns=slp_types).fillna(0)
new_df = add_weekdays2df(
new_df, holidays=holidays, holiday_is_sunday=True
)
new_df["hour"] = dt_index.hour
new_df["minute"] = dt_index.minute
time_df = new_df[["date", "hour", "minute", "weekday"]].copy()
tmp_df[slp_types] = tmp_df[slp_types].astype(float)
# Inner join the slps on the time_df to the slp's for a whole year
tmp_df["hour_of_day"] = tmp_df.index.hour
tmp_df["minute_of_hour"] = tmp_df.index.minute
left_cols = ["hour_of_day", "minute_of_hour", "weekday"]
right_cols = ["hour", "minute", "weekday"]
tmp_df = tmp_df.reset_index(drop=True)
for p in self._seasons.keys():
a = datetime.datetime(
self._year, self._seasons[p][0], self._seasons[p][1], 0, 0
)
b = datetime.datetime(
self._year, self._seasons[p][2], self._seasons[p][3], 23, 59
)
merged_df = pd.DataFrame.merge(
tmp_df[tmp_df["period"] == p[:-1]],
time_df[a:b],
left_on=left_cols,
right_on=right_cols,
how="inner",
).drop(["hour_of_day"], axis=1)
merged_df.index = (
pd.to_datetime(merged_df["date"])
+ pd.to_timedelta(merged_df["hour"], unit="h")
+ pd.to_timedelta(merged_df["minute"], unit="m")
)
merged_df.sort_index(inplace=True)
new_df.update(merged_df)
new_df.drop(
["date", "minute", "hour", "weekday"], axis=1, inplace=True
)
return new_df.div(new_df.sum(axis=0), axis=1)
def create_dynamic_h0_profile(self):
r"""
Use the dynamisation function of the BDEW to smoothen the seasonal
edges. Functions resolution is daily.
.. math::
F_t = -3,92\cdot10^{-10} \cdot t^4 + 3,2\cdot10^{-7}
\cdot t^3– 7,02\cdot10^{-5}\cdot t^2 + 2,1\cdot10^{-3}
\cdot t + 1,24
With `t` the day of the year as a decimal number.
Adjustment of accuracy: from -3,92 to -3.916649251
"""
# Create a Series with the day of the year as decimal number
decimal_day = pd.Series(
[((q + 1) / (24 * 4)) for q in range(len(self.slp_frame))],
index=self.slp_frame.index,
)
# Calculate the smoothing factor of the BDEW dynamic H0 profile
smoothing_factor = (
-3.916649251 * 10 ** -10 * decimal_day ** 4
+ 3.2 * 10 ** -7 * decimal_day ** 3
- 7.02 * 10 ** -5 * decimal_day ** 2
+ 0.0021 * decimal_day
+ 1.24
)
# Multiply the smoothing factor with the default H0 profile
self.slp_frame["h0_dyn"] = self.slp_frame["h0"].mul(
smoothing_factor, axis=0
)
return self.slp_frame["h0_dyn"]
def get_profile(self, ann_el_demand_per_sector):
"""
DEPRECATED: Use :py:meth:`~get_scaled_power_profiles()` instead
Parameters
----------
ann_el_demand_per_sector : dictionary
Key: sector, value: annual value
Returns
-------
pandas.DataFrame : Table with all profiles
"""
msg = (
"This method is deprecated and will be removed in future "
"versions\nUse the method get_scaled_power_profiles() instead."
)
warnings.warn(msg, FutureWarning)
return (
self.slp_frame.multiply(
pd.Series(ann_el_demand_per_sector), axis=1
).dropna(how="all", axis=1)
* 4
)
def get_profiles(self, *args):
"""Get all or the selected profiles. To select profiles you can pass
the name of the types as strings. The profiles are normalised to 1.
Try `print(get_profiles().columns` to get all valid types.
Returns
-------
pandas.DataFrame : Table with all or the selected profiles.
Examples
--------
>>> from demandlib import bdew
>>> e_slp = bdew.ElecSlp(year=2020)
>>> ", ".join(sorted(e_slp.get_profiles().columns))
'g0, g1, g2, g3, g4, g5, g6, h0, h0_dyn, l0, l1, l2'
>>> e_slp.get_profiles("h0", "g0").head()
h0 g0
2020-01-01 00:00:00 0.000017 0.000016
2020-01-01 00:15:00 0.000015 0.000015
2020-01-01 00:30:00 0.000014 0.000015
2020-01-01 00:45:00 0.000012 0.000014
2020-01-01 01:00:00 0.000012 0.000013
>>> e_slp.get_profiles("h0", "g0").sum()
h0 1.0
g0 1.0
dtype: float64
"""
if len(args) == 0:
return self.slp_frame
else:
return self.slp_frame[list(args)]
def get_scaled_profiles(self, ann_el_demand_per_sector):
"""Get profiles scaled by there annual value.
Parameters
----------
ann_el_demand_per_sector : dict
The annual demand in an energy unit for each type.
Returns
-------
pandas.DataFrame : Table with scaled profiles.
Examples
--------
>>> from demandlib import bdew
>>> e_slp = bdew.ElecSlp(year=2020)
>>> e_slp.get_scaled_profiles({"h0": 3000, "g0": 5000}).head()
g0 h0
2020-01-01 00:00:00 0.080084 0.050657
2020-01-01 00:15:00 0.076466 0.045591
2020-01-01 00:30:00 0.072897 0.041125
2020-01-01 00:45:00 0.069671 0.037408
2020-01-01 01:00:00 0.067030 0.034650
>>> e_slp.get_scaled_profiles({"h0": 3000, "g0": 5000}).sum()
g0 5000.0
h0 3000.0
dtype: float64
"""
return self.slp_frame.multiply(
pd.Series(ann_el_demand_per_sector), axis=1
).dropna(how="all", axis=1)
def get_scaled_power_profiles(
self, ann_el_demand_per_sector, conversion_factor=4
):
"""
Get profiles scaled by there annual value. Each value represents
the average power of an interval. Therefore, it is not possible to
sum up the array. A conversion factor is used to calculate power
units from energy units. By default the conversion factor is `4`. As
the interval of each profile is 15 minutes a conversion factor of `4`
will convert energy units like Wh, kWh, MWh etc. to power units like W,
kW, MW etc..
Parameters
----------
ann_el_demand_per_sector : dict
The annual demand in an energy unit for each type.
conversion_factor : float
Factor to convert the energy unit of the annual value to the
power unit of each interval.
Returns
-------
pandas.DataFrame : Table with scaled profiles.
Examples
--------
>>> from demandlib import bdew
>>> e_slp = bdew.ElecSlp(year=2020)
>>> e_slp.get_scaled_power_profiles({"h0": 3000, "g0": 5000}).head()
g0 h0
2020-01-01 00:00:00 0.320338 0.202627
2020-01-01 00:15:00 0.305866 0.182365
2020-01-01 00:30:00 0.291590 0.164500
2020-01-01 00:45:00 0.278682 0.149633
2020-01-01 01:00:00 0.268122 0.138602
>>> cf = 4
>>> spp = e_slp.get_scaled_power_profiles({"h0": 3000, "g0": 5000},
... conversion_factor=cf)
>>> spp.sum()
g0 20000.0
h0 12000.0
dtype: float64
>>> spp.div(cf).sum()
g0 5000.0
h0 3000.0
dtype: float64
"""
return (
self.get_scaled_profiles(ann_el_demand_per_sector)
* conversion_factor
)