Features/#261 emob tests (#289)

Co-authored-by: Kilian Helfenbein <Kilian.Helfenbein@rl-institut.de>
Co-authored-by: birgits <birgit.schachler@rl-institut.de>

.gitignore

@@ -20,17 +20,6 @@ doc/_html
 
 # exclude egg-info
 eDisGo.egg-info/
-/examples/Exemplary_PyPSA_bus_results.csv
-/examples/Exemplary_PyPSA_line_results.csv
-
-# exclude directories
-/examples/edisgo2pypsa_export/*
-/examples/data/
-/examples/ding0_example_grid/
-
-# exclude check scripts
-/examples/compare_graphs.py
-/examples/compare_pypsa_network.py
 
 # exclude .json files in opf
 /edisgo/opf/edisgo_scenario_data/*.json

doc/equipment.rst

@@ -11,7 +11,7 @@ The following tables hold all data of cables, lines and transformers used.
    :file: ../edisgo/equipment/equipment-parameters_LV_cables.csv
    :delim: ,
    :header-rows: 2
-   :widths: 3, 1, 1, 1, 1
+   :widths: 3, 1, 1, 1, 1, 1
    :stub-columns: 0
 
 .. _mv_cables_table:

doc/features_in_detail.rst

@@ -261,6 +261,63 @@ It is also possible to curtail specific generators internally, though a user fri
     where :math:`c_{target,t}` is the given curtailment target (power) for timestep :math:`t` to be allocated
     to the generators.
 
+.. _electromobility-integration-label:
+
+Electromobility integration
+----------------------------
+
+The import and integration of electromobility data is implemented in :py:func:`~edisgo.io.electromobility_import`.
+
+Allocation of charging demand
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The allocation of charging processes to charging stations is implemented in
+:py:func:`~edisgo.io.electromobility_import.distribute_charging_demand`.
+After electromobility data is loaded, the charging demand from SimBEV is allocated to potential charging parks
+from TracBEV. The allocation of the charging processes to the charging infrastructure is carried out with the
+help of the weighting factor of the potential charging parks determined by TracBEV. This involves a random and
+weighted selection of one charging park per charging process. In the case of private charging infrastructure, a
+separate charging point is set up for each electric vehicle (EV). All charging processes of the respective EV and charging use case
+are assigned to this charging point. The allocation of private charging processes to charging stations is
+implemented in :py:func:`~edisgo.io.electromobility_import.distribute_private_charging_demand`.
+
+For public charging infrastructure, the allocation is made explicitly per charging process. For each charging
+process it is determined whether a suitable charging point is already available. For this purpose it is checked
+whether the charging point is occupied by another EV in the corresponding period and whether it can provide the
+corresponding charging capacity. If no suitable charging point is available, a charging point is determined
+randomly and weighted in the same way as for private charging. The allocation of public charging processes to
+charging stations is implemented in :py:func:`~edisgo.io.electromobility_import.distribute_public_charging_demand`.
+
+.. _charging_strategies-label:
+
+Charging strategies
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+eDisGo right now provides three charging strategy methodologies called 'dumb', 'reduced' and 'residual', that
+are implemented in :py:mod:`~edisgo.flex_opt.charging_strategies`.
+The aim of the charging strategies 'reduced' and 'residual' is to generate the most grid-friendly charging behavior possible without
+restricting the convenience for end users. Therefore, the boundary condition of all charging strategies is that
+the charging requirement of each charging process must be fully covered. This means that charging processes can
+only be flexibilised if the EV can be fully charged while it is stationary. Furthermore, only private
+charging processes can be used as a flexibility, since the fulfillment of the service is the priority for public
+charging processes.
+
+'dumb'
+""""""""""""""""""
+In this charging strategy the cars are charged directly after arrival with the maximum possible charging capacity.
+
+'reduced'
+""""""""""""""""""
+This is a preventive charging strategy. The cars are charged directly after arrival with the minimum possible
+charging power. The minimum possible charging power is determined by the parking time and the parameter
+minimum_charging_capacity_factor.
+
+'residual'
+""""""""""""""""""
+This is an active charging strategy. The cars are charged when the residual load in the MV grid is lowest
+(high generation and low consumption). Charging processes with a low flexibility are given priority.
+
+
 .. _storage-integration-label:
 
 Storage integration

doc/quickstart.rst

@@ -6,7 +6,7 @@ Getting started
 Installation using Linux
 -------------------------
 
-.. warning:: Make sure to use python 3.7 or higher!
+.. warning:: Make sure to use python 3.8 or higher!
 
 Install latest eDisGo version through pip. Therefore, we highly recommend using
 a virtual environment and its pip.
@@ -15,25 +15,13 @@ a virtual environment and its pip.
 
     python -m pip install edisgo
 
-The above will install all packages for the basic usage of eDisGo. To install
-additional packages e.g. needed to create plots with background maps or to run
-the jupyter notebook examples, we provide installation with extra packages:
-
-.. code-block:: bash
-
-    python -m pip install edisgo[geoplot]  # for plotting with background maps
-    python -m pip install edisgo[examples]  # to run examples
-    python -m pip install edisgo[dev]  # developer packages
-    python -m pip install edisgo[full]  # combines all of the extras above
-
-
 You may also consider installing a developer version as detailed in
 :ref:`dev-notes`.
 
 Installation using Windows
 --------------------------
 
-.. warning:: Make sure to use python 3.7 or higher!
+.. warning:: Make sure to use python 3.8 or higher!
 
 For Windows users we recommend using Anaconda and to install the geo stack
 using the conda-forge channel prior to installing eDisGo. You may use the provided
@@ -50,8 +38,6 @@ Activate the newly created environment with:
 
     conda activate eDisGo_env
 
-This will install eDisGo with all it's dependencies ([full] flag).
-
 Installation using MacOS
 --------------------------
 

doc/usage_details.rst

@@ -266,7 +266,8 @@ The charging strategies can be invoked as follows:
 
     edisgo.apply_charging_strategy()
 
-See :attr:`~.edisgo.EDisGo.apply_charging_strategy` for more information.
+See function docstring of :attr:`~.edisgo.EDisGo.apply_charging_strategy` or
+documentation section :ref:`charging_strategies-label` for more information.
 
 Reactive power time series
 ^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -349,7 +350,86 @@ Further information on the grid reinforcement methodology can be found in sectio
 Electromobility
 -----------------
 
-.. todo:: Add
+Electromobility data including charging processes as well as information on potential charging sites and
+integrated charging parks are stored in the
+:class:`~.network.electromobility.Electromobility` object.
+
+You can access these data as follows:
+
+.. code-block:: python
+
+    # Access DataFrame with all SimBEV charging processes
+    edisgo.electromobility.charging_processes_df
+
+    # Access GeoDataFrame with all TracBEV potential charging parks
+    edisgo.electromobility.potential_charging_parks_gdf
+
+    # Access DataFrame with all charging parks that got integrated
+    edisgo.electromobility.integrated_charging_parks_df
+
+The integrated charging points are also stored in the :class:`~.network.topology.Topology`
+object and can be accessed as follows:
+
+.. code-block:: python
+
+    # Access DataFrame with all integrated charging points.
+    edisgo.topology.charging_points_df
+
+
+So far, adding electromobility data to an eDisGo object requires electromobility
+data from `SimBEV <https://github.com/rl-institut/simbev>`_ (required version:
+`3083c5a <https://github.com/rl-institut/simbev/commit/
+86076c936940365587c9fba98a5b774e13083c5a>`_)
+and `TracBEV <https://github.com/rl-institut/tracbev>`_ (required version:
+`14d864c <https://github.com/rl-institut/tracbev/commit/
+03e335655770a377166c05293a966052314d864c>`_) to be stored in the directories
+specified through the parameters simbev_directory and tracbev_directory.
+SimBEV provides data on standing times, charging demand, etc. per vehicle,
+whereas TracBEV provides potential charging point locations.
+
+.. todo:: Add information on how to retrieve SimBEV and TracBEV data
+
+Here is a small example on how to import electromobility data and apply a
+charging strategy. A more extensive example can be found in
+the example jupyter notebook
+`electromobility_example <https://github.com/openego/eDisGo/blob/dev/examples/electromobility_example.ipynb>`_.
+
+.. code-block:: python
+
+    import pandas as pd
+    from edisgo import EDisGo
+
+    # Set up the EDisGo object
+    timeindex = pd.date_range("1/1/2011", periods=24*7, freq="H")
+    edisgo = EDisGo(
+        ding0_grid=dingo_grid_path,
+        timeindex=timeindex
+    )
+    edisgo.set_time_series_active_power_predefined(
+        fluctuating_generators_ts="oedb",
+        dispatchable_generators_ts=pd.DataFrame(
+            data=1, columns=["other"], index=timeindex),
+        conventional_loads_ts="demandlib",
+    )
+
+    edisgo.set_time_series_reactive_power_control()
+
+    # Resample edisgo timeseries to 15-minute resolution to match with SimBEV and
+    # TracBEV data
+    edisgo.resample_timeseries()
+
+    # Import electromobility data
+    edisgo.import_electromobility(
+        simbev_directory=simbev_path,
+        tracbev_directory=tracbev_path,
+    )
+
+    # Apply charging strategy
+    edisgo.apply_charging_strategy(strategy="dumb")
+
+Further information on the electromobility integration methodology and the charging
+strategies can be found in section :ref:`electromobility-integration-label`.
+
 
 Battery storage systems
 ------------------------

doc/whatsnew/v0-2-0.rst

@@ -9,6 +9,8 @@ Changes
 * added pre-commit hooks (flake8, black, isort, pyupgrade) `#229 <https://github.com/openego/eDisGo/pull/229>`_
 * added issue and pull request templates `#220 <https://github.com/openego/eDisGo/issues/220>`_
 * added Windows installation yml and documentation
+* added automatic testing for Windows `#317 <https://github.com/openego/eDisGo/pull/317>`_
+* dropped support for python 3.7
 * added functionality to set up different loggers with individual logging levels and where to write output `#295 <https://github.com/openego/eDisGo/issues/295>`_
 * added integrity checks of eDisGo object `#231 <https://github.com/openego/eDisGo/issues/231>`_
 * added functionality to save to and load from zip archive `#216 <https://github.com/openego/eDisGo/pull/216>`_

edisgo/edisgo.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import copy
 import logging
 import os
 import pickle
@@ -854,6 +855,7 @@ def reinforce(
         max_while_iterations: int = 20,
         combined_analysis: bool = False,
         mode: str | None = None,
+        without_generator_import: bool = False,
         **kwargs,
     ) -> Results:
         """
@@ -876,44 +878,67 @@ def reinforce(
         """
         if kwargs.get("is_worst_case", self.timeseries.is_worst_case):
 
-            logger.info(
-                "Running reinforcement in worst-case mode by differentiating between mv"
-                " and lv load and feed-in cases."
+            logger.debug(
+                "Running reinforcement in worst-case mode by differentiating between "
+                "MV and LV load and feed-in cases."
             )
 
+            if copy_grid:
+                edisgo_obj = copy.deepcopy(self)
+            else:
+                edisgo_obj = self
+
             timeindex_worst_cases = self.timeseries.timeindex_worst_cases
-            timesteps_pfa = pd.DatetimeIndex(
-                timeindex_worst_cases.loc[
-                    timeindex_worst_cases.index.str.contains("mv")
-                ]
-            )
-            mode = "mv"
 
-            reinforce_grid(
+            if mode != "lv":
+
+                timesteps_pfa = pd.DatetimeIndex(
+                    timeindex_worst_cases.loc[
+                        timeindex_worst_cases.index.str.contains("mv")
+                    ]
+                )
+                reinforce_grid(
+                    edisgo_obj,
+                    max_while_iterations=max_while_iterations,
+                    copy_grid=False,
+                    timesteps_pfa=timesteps_pfa,
+                    combined_analysis=combined_analysis,
+                    mode="mv",
+                    without_generator_import=without_generator_import,
+                )
+
+            if mode != "mv":
+                timesteps_pfa = pd.DatetimeIndex(
+                    timeindex_worst_cases.loc[
+                        timeindex_worst_cases.index.str.contains("lv")
+                    ]
+                )
+                reinforce_mode = mode if mode == "mvlv" else "lv"
+                reinforce_grid(
+                    edisgo_obj,
+                    max_while_iterations=max_while_iterations,
+                    copy_grid=False,
+                    timesteps_pfa=timesteps_pfa,
+                    combined_analysis=combined_analysis,
+                    mode=reinforce_mode,
+                    without_generator_import=without_generator_import,
+                )
+
+            if mode not in ["mv", "lv"]:
+                edisgo_obj.analyze(mode=mode)
+            results = edisgo_obj.results
+
+        else:
+            results = reinforce_grid(
                 self,
                 max_while_iterations=max_while_iterations,
                 copy_grid=copy_grid,
                 timesteps_pfa=timesteps_pfa,
                 combined_analysis=combined_analysis,
                 mode=mode,
+                without_generator_import=without_generator_import,
             )
 
-            timesteps_pfa = pd.DatetimeIndex(
-                timeindex_worst_cases.loc[
-                    timeindex_worst_cases.index.str.contains("lv")
-                ]
-            )
-            mode = "lv"
-
-        results = reinforce_grid(
-            self,
-            max_while_iterations=max_while_iterations,
-            copy_grid=copy_grid,
-            timesteps_pfa=timesteps_pfa,
-            combined_analysis=combined_analysis,
-            mode=mode,
-        )
-
         # add measure to Results object
         if not copy_grid:
             self.results.measures = "grid_expansion"
@@ -1442,7 +1467,7 @@ def import_electromobility(
                 with any charging demand are imported. Any other input will lead
                 to all parking and driving events being imported. Default "frugal".
             charging_processes_dir : str
-                Charging processes sub-directory. Default "simbev_run".
+                Charging processes sub-directory. Default None.
             simbev_config_file : str
                 Name of the simbev config file. Default "metadata_simbev_run.json".