Merge branch 'dev' of github.com:openego/eDisGo into dev

doc/api/edisgo.flex_opt.rst

@@ -33,6 +33,14 @@ edisgo.flex\_opt.exceptions module
    :undoc-members:
    :show-inheritance:
 
+edisgo.flex\_opt.heat_pump_operation module
+---------------------------------------------
+
+.. automodule:: edisgo.flex_opt.heat_pump_operation
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 edisgo.flex\_opt.q\_control module
 -----------------------------------
 

doc/api/edisgo.network.rst

@@ -25,6 +25,14 @@ edisgo.network.grids module
    :undoc-members:
    :show-inheritance:
 
+edisgo.network.heat module
+---------------------------
+
+.. automodule:: edisgo.network.heat
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 edisgo.network.results module
 -----------------------------
 

doc/dev_notes.rst

@@ -47,7 +47,7 @@ Activate the newly created environment and install the pre-commit hooks with:
     conda activate eDisGo_env_dev
     pre-commit install  # install pre-commit hooks
 
-This will install eDisGo with all it's dependencies.
+This will install eDisGo with all its dependencies.
 
 Installation using MacOS
 ~~~~~~~~~~~~~~~~~~~~~~~~~

doc/usage_details.rst

@@ -25,6 +25,9 @@ The class :class:`~.network.electromobility.Electromobility` holds data on charg
 processes (how long cars are parking at a charging station, how much they need to charge,
 etc.) necessary to apply different charging strategies, as well as information on
 potential charging sites and integrated charging parks.
+The class :class:`~.network.heat.HeatPump` holds data on
+heat pump COP, heat demand to be served by the heat pumps and thermal storage units, which is
+necessary to determine flexibility potential of heat pumps.
 Results data holding results e.g. from the power flow analysis and grid
 expansion is stored in the :class:`~.network.results.Results` class.
 Configuration data from the config files (see :ref:`default_configs`) is stored
@@ -44,6 +47,9 @@ code examples `edisgo` constitues an :class:`~.EDisGo` object.
     # Access Electromobility data container object
     edisgo.electromobility
 
+    # Access HeatPump data container object
+    edisgo.heat_pump
+
     # Access Results data container object
     edisgo.results
 
@@ -251,7 +257,8 @@ by using the function :attr:`~.edisgo.EDisGo.set_timeindex`.
 Optimised
 ..........
 
-Use this mode to optimise flexibilities, e.g. charging of electric vehicles.
+Use this mode to optimise flexibilities, e.g. charging of electric vehicles or
+dispatch of heat pumps with thermal storage units.
 
 .. todo:: Add more details once the optimisation is merged.
 
@@ -269,6 +276,18 @@ The charging strategies can be invoked as follows:
 See function docstring of :attr:`~.edisgo.EDisGo.apply_charging_strategy` or
 documentation section :ref:`charging_strategies-label` for more information.
 
+Further, there is currently one operating strategy for heat pumps implemented where
+the heat demand is directly served by the heat pump without buffering heat using a
+thermal storage.
+The operating strategy can be invoked as follows:
+
+.. code-block:: python
+
+    edisgo.apply_heat_pump_operating_strategy()
+
+See function docstring of :attr:`~.edisgo.EDisGo.apply_heat_pump_operating_strategy`
+for more information.
+
 Reactive power time series
 ^^^^^^^^^^^^^^^^^^^^^^^^^^
 
@@ -430,6 +449,70 @@ the example jupyter notebook
 Further information on the electromobility integration methodology and the charging
 strategies can be found in section :ref:`electromobility-integration-label`.
 
+Heat pumps
+-----------------
+
+Heat pump data including the heat pump's time variant COP, heat demand to be served
+as well as thermal storage capacities are stored in the
+:class:`~.network.heat.HeatPump` object.
+
+You can access these data as follows:
+
+.. code-block:: python
+
+    # Access DataFrame with COP time series
+    edisgo.heat_pump.cop_df
+
+    # Access DataFrame with heat demand time series
+    edisgo.heat_pump.heat_demand_df
+
+    # Access DataFrame with information on thermal storage capacities
+    edisgo.heat_pump.thermal_storage_units_df
+
+The heat pumps themselves are also stored in the :class:`~.network.topology.Topology`
+object and can be accessed as follows:
+
+.. code-block:: python
+
+    # Access DataFrame with all integrated heat pumps
+    edisgo.topology.loads_df[edisgo.topology.loads_df.type == "heat_pump"]
+
+Here is a small example on how to integrate a heat pump and apply an
+operating strategy.
+
+.. code-block:: python
+
+    import pandas as pd
+    from edisgo import EDisGo
+
+    # Set up the EDisGo object
+    timeindex = pd.date_range("1/1/2011", periods=4, freq="H")
+    edisgo = EDisGo(
+        ding0_grid=dingo_grid_path,
+        timeindex=timeindex
+    )
+
+    # Set up dummy heat pump data
+    bus = edisgo.topology.loads_df[
+        edisgo.topology.loads_df.sector == "residential"].bus[0]
+    heat_pump_params = {"bus": bus, "p_set": 0.015, "type": "heat_pump"}
+
+    cop = pd.Series([1.0, 2.0, 1.5, 3.4], index=timeindex)
+    heat_demand = pd.Series([0.01, 0.03, 0.015, 0.0], index=timeindex)
+
+    # Add heat pump to grid topology
+    hp_name = edisgo.add_component("load", **heat_pump_params)
+
+    # Add heat pump COP and heat demand to be served
+    edisgo.heat_pump.set_cop(edisgo, cop.to_frame(name=hp_name))
+    edisgo.heat_pump.set_heat_demand(edisgo, heat_demand.to_frame(name=hp_name))
+
+    # Apply operating strategy - this sets the heat pump's dispatch time series
+    # in timeseries.loads_active_power
+    edisgo.apply_heat_pump_operating_strategy()
+
+    hp_dispatch = edisgo.timeseries.loads_active_power.loc[:, hp_name]
+    hp_dispatch.plot()
 
 Battery storage systems
 ------------------------