Merge pull request #332 from openego/features/resample-edisgo-simbev

Automatically resample time series when applying charging strategies

edisgo/edisgo.py

@@ -1601,6 +1601,15 @@ def apply_charging_strategy(self, strategy="dumb", **kwargs):
             capacity of 22 kW and a minimum_charging_capacity_factor of 0.1 this would
             result in a minimum charging power of 2.2 kW. Default: 0.1.
 
+        Notes
+        ------
+        If the frequency of time series data in :class:`~.network.timeseries.TimeSeries`
+        (checked using :attr:`~.network.timeseries.TimeSeries.timeindex`) differs from
+        the frequency of SimBEV data, then the time series in
+        :class:`~.network.timeseries.TimeSeries` is first automatically resampled to
+        match the SimBEV data frequency and after determining the charging demand time
+        series resampled back to the original frequency.
+
         """
         charging_strategy(self, strategy=strategy, **kwargs)
 
@@ -2256,7 +2265,9 @@ def check_integrity(self):
 
             logging.info("Integrity check finished. Please pay attention to warnings.")
 
-    def resample_timeseries(self, method: str = "ffill", freq: str = "15min"):
+    def resample_timeseries(
+        self, method: str = "ffill", freq: str | pd.Timedelta = "15min"
+    ):
         """
         Resamples all generator, load and storage time series to a desired resolution.
 

edisgo/flex_opt/charging_strategies.py

@@ -1,8 +1,16 @@
+from __future__ import annotations
+
 import logging
 
+from numbers import Number
+from typing import TYPE_CHECKING
+
 import numpy as np
 import pandas as pd
 
+if TYPE_CHECKING:
+    from edisgo import EDisGo
+
 RELEVANT_CHARGING_STRATEGIES_COLUMNS = {
     "dumb": [
         "park_start_timesteps",
@@ -40,10 +48,10 @@
 #  wrong results if the timeindex of the edisgo object is not continuously
 #  (e.g. 2 weeks of the year)
 def charging_strategy(
-    edisgo_obj,
-    strategy="dumb",
-    timestamp_share_threshold=0.2,
-    minimum_charging_capacity_factor=0.1,
+    edisgo_obj: EDisGo,
+    strategy: str = "dumb",
+    timestamp_share_threshold: Number = 0.2,
+    minimum_charging_capacity_factor: Number = 0.1,
 ):
     """
     Applies charging strategy to set EV charging time series at charging parks.
@@ -107,12 +115,20 @@ def charging_strategy(
     )
     simbev_timedelta = timeindex[1] - timeindex[0]
 
-    assert edisgo_timedelta == simbev_timedelta, (
-        "The step size of the time series of the edisgo object differs from the"
-        f"simbev step size. The edisgo time delta is {edisgo_timedelta}, while"
-        f" the simbev time delta is {simbev_timedelta}. Make sure to use a "
-        f"matching step size."
-    )
+    resample = edisgo_timedelta != simbev_timedelta
+
+    if resample:
+        logger.warning(
+            f"The frequency of the time series data of the edisgo object differs from "
+            f"the simbev time series frequency. The edisgo frequency is "
+            f"{edisgo_timedelta}, while the simbev frequency is {simbev_timedelta}. "
+            f"The edisgo time series data "
+            f"will be resampled accordingly before applying the charging strategy. "
+            f"After applying the charging strategy all time series will be resampled "
+            f"to the original frequency of the edisgo time series data."
+        )
+
+        edisgo_obj.resample_timeseries(freq=simbev_timedelta)
 
     if strategy == "dumb":
         # "dumb" charging
@@ -292,6 +308,9 @@ def charging_strategy(
     else:
         raise ValueError(f"Strategy {strategy} has not yet been implemented.")
 
+    if resample:
+        edisgo_obj.resample_timeseries(freq=edisgo_timedelta)
+
     # set reactive power time series to 0 Mvar
     # fmt: off
     edisgo_obj.timeseries.add_component_time_series(

edisgo/network/timeseries.py

@@ -2135,7 +2135,9 @@ def _check_if_components_exist(
             return set(component_names) - set(comps_not_in_network)
         return component_names
 
-    def resample_timeseries(self, method: str = "ffill", freq: str = "15min"):
+    def resample_timeseries(
+        self, method: str = "ffill", freq: str | pd.Timedelta = "15min"
+    ):
         """
         Resamples all generator, load and storage time series to a desired resolution.
 

setup.py

@@ -64,7 +64,8 @@ def read(fname):
 dev_requirements = [
     "pytest",
     "pytest-notebook",
-    "sphinx_rtd_theme",
+    "sphinx >= 4.3.0, < 5.1.0",
+    "sphinx_rtd_theme >=0.5.2",
     "sphinx-autodoc-typehints",
     "pre-commit",
     "black",

tests/flex_opt/test_charging_strategy.py

@@ -23,17 +23,16 @@ def setup_class(cls):
         timeindex = pd.date_range("1/1/2011", periods=24 * 7, freq="H")
         cls.edisgo_obj.set_timeindex(timeindex)
 
-        cls.edisgo_obj.resample_timeseries()
         cls.edisgo_obj.import_electromobility(cls.simbev_path, cls.tracbev_path)
 
-    def test_charging_strategy(self):
+    def test_charging_strategy(self, caplog):
         charging_demand_lst = []
 
+        ts = self.edisgo_obj.timeseries
+
         for strategy in self.charging_strategies:
             charging_strategy(self.edisgo_obj, strategy=strategy)
 
-            ts = self.edisgo_obj.timeseries
-
             # Check if all charging points have a valid chargingdemand_kWh > 0
             df = ts.charging_points_active_power(self.edisgo_obj).loc[
                 :, (ts.charging_points_active_power(self.edisgo_obj) <= 0).any(axis=0)
@@ -50,7 +49,8 @@ def test_charging_strategy(self):
             self.edisgo_obj, strategy="dumb", timestamp_share_threshold=0.5
         )
 
-        ts = self.edisgo_obj.timeseries
+        # Check if resampling warning is raised
+        assert "The frequency of the time series data of the edisgo object differs" in caplog.text
 
         # Check if all charging points have a valid chargingdemand_kWh > 0
         df = ts.charging_points_active_power(self.edisgo_obj).loc[
@@ -64,8 +64,6 @@ def test_charging_strategy(self):
             self.edisgo_obj, strategy="reduced", minimum_charging_capacity_factor=0.5
         )
 
-        ts = self.edisgo_obj.timeseries
-
         # Check if all charging points have a valid chargingdemand_kWh > 0
         df = ts.charging_points_active_power(self.edisgo_obj).loc[
             :, (ts.charging_points_active_power(self.edisgo_obj) <= 0).any(axis=0)
@@ -83,3 +81,13 @@ def test_charging_strategy(self):
             (_.round(4) == charging_demand_lst[0].round(4)).all()
             for _ in charging_demand_lst
         )
+
+        # ##################### check time index #####################
+        assert ts._loads_active_power.index.freqstr == "H"
+        # change time index to quarter-hourly
+        timeindex = pd.date_range("1/1/2011", periods=24 * 7, freq="0.25H")
+        self.edisgo_obj.set_timeindex(timeindex)
+        charging_strategy(
+            self.edisgo_obj, strategy="dumb"
+        )
+        assert ts._loads_active_power.index.freqstr == "15T"

tests/network/test_timeseries.py

@@ -8,7 +8,11 @@
 import pandas as pd
 import pytest
 
-from pandas.util.testing import assert_frame_equal, assert_series_equal
+from pandas.util.testing import (
+    assert_frame_equal,
+    assert_index_equal,
+    assert_series_equal,
+)
 
 from edisgo import EDisGo
 from edisgo.network import timeseries
@@ -2360,6 +2364,7 @@ def test_resample_timeseries(self):
 
         len_timeindex_orig = len(self.edisgo.timeseries.timeindex)
         mean_value_orig = self.edisgo.timeseries.generators_active_power.mean()
+        index_orig = self.edisgo.timeseries.timeindex.copy()
 
         # test up-sampling
         self.edisgo.timeseries.resample_timeseries()
@@ -2375,6 +2380,9 @@ def test_resample_timeseries(self):
                 atol=1e-5,
             )
         ).all()
+        # check if index is the same after resampled back
+        self.edisgo.timeseries.resample_timeseries(freq="1h")
+        assert_index_equal(self.edisgo.timeseries.timeindex, index_orig)
 
         # same tests for down-sampling
         self.edisgo.timeseries.resample_timeseries(freq="2h")