Remove cellular modelling stub

The approach was experimental in incomplete, also did not allow to have
more then two levels. The same models can be created without code in
solph.

docs/examples/index.rst

@@ -11,7 +11,6 @@ Examples
     time_index.rst
     nametuple.rst
     simple_dispatch.rst
-    cellular.rst
     activity_costs.rst
     min_max_runtimes.rst
     startup_costs.rst

docs/usage.rst

@@ -1282,130 +1282,6 @@ Besides the `invest` variable, new variables are introduced as well. These are:
       monthly periods, but you would need to be very careful in parameterizing your energy system and your model and also,
       this would mean monthly discounting (if applicable) as well as specifying your plants lifetimes in months.
 
-Modelling cellular energy systems and modularizing energy system models
------------------------------------------------------------------------
-
-The cellular approach is a concept proposed by the [VDE-ETG](https://shop.vde.com/en/vde-study-the-cellular-approach). It is
-related to smart-grids and multi-microgrid systems but extends both. The idea is to group the components of an energy system
-into a hierarchically aggregating structure of cells. For example, the sources, sinks, storages and converters of a household
-could be a single cell. Then a group of physically neighboring households could form another cell, consisting of household-cells.
-This behaviour can be scaled up. The real game-changer in the cellular approach is the way the cells are operated, which will
-not be covered here. Here, we focus on the way such cellular energy systems can be modeled.
-
-So far, the implementation in solph is just a neat way to group different parts of a larger energy system into cells. However,
-the implementation can also be regarded as a precursor for further functionality. Decomposition techniques such
-as [Benders](https://en.wikipedia.org/wiki/Benders_decomposition) or
-[Dantzig-Wolfe](https://en.wikipedia.org/wiki/Dantzig%E2%80%93Wolfe_decomposition) could be implemented in solph. These methods
-are dependent on a special constraint matrix structure, which the cellular modelling approach presented here is helping to obtain.
-
-Modelling procedure
-^^^^^^^^^^^^^^^^^^^
-
-Similar to the creation of regular energy systems, the creation of energy cells is the first step in model creation. Essentially,
-each energy cell is just an energy system, therefore we use the class :py:class:`oemof.solph.EnergySystem` to create energy cells.
-
-.. code-block:: python
-
-    from oemof.solph import EnergySystem
-
-    es = EnergySystem(
-        label="es", timeindex=timeindex, infer_last_interval=False
-    )
-    ec_1 = EnergySystem(
-        label="ec_1", timeindex=timeindex, infer_last_interval=False
-    )
-    ec_2 = EnergySystem(
-        label="ec_2", timeindex=timeindex, inver_last_interval=False
-    )
-
-Now we can go on and add components to the energy cells just like we do with regular energy systems.
-
-.. code-block:: python
-
-    from oemof import solph
-
-    bus_el_es = solph.buses.Bus(label="bus_el_es")
-    es.add(bus_el_es)
-
-    bus_el_ec_1 = solph.buses.Bus(label="bus_el_ec_1")
-    sink_el_ec_1 = solph.components.Sink(
-        label="sink_el_ec_1",
-        inputs={bus_el_ec_1: flows.Flow(fix=10, nominal_value=1)},
-    )
-    source_el_ec_1 = solph.components.Source(
-        label="source_el_ec_1",
-        outputs={
-            bus_el_ec_1: flows.Flow(
-                max=30, nominal_value=1, variable_costs=10,
-            ),
-        },
-    )
-    ec_1.add(bus_el_ec_1, sink_el_ec_1, source_el_ec_1)
-
-.. note:: This is just an exemplary piece of code. A (little bit more interesting) working
-    example can be found in the examples.
-
-The next step would be to model the connections between cells. Here, we resort to the class
-:py:class:`oemof.solph.components.Link`. Each connection Link has two inputs (from the
-"parent cell" and the "child cell") and two outputs (to the "parent cell" and the "child
-cell"). A connection between the "parent cell" `es` and the "child cell" `ec_1` could look
-like this:
-
-.. code-block:: python
-
-    connector_el_ec_1 = solph.buses.Bus(
-        label="connector_el_ec_1",
-        inputs={
-            bus_el_es: flows.Flow(),
-            bus_el_ec_1: flows.Flow(),
-        },
-        outputs={
-            bus_el_es: flows.Flow(),
-            bus_el_ec_1: flows.Flow(),
-        },
-        conversion_factors={
-            (bus_el_es, bus_el_ec_1): 0.85,
-            (bus_el_ec_1, bus_el_es): 0.85
-        }
-    )
-    es.add(connector_el_ec_1)
-
-The `conversion_factors` can be used to model transmission losses. Here, a symmetrical
-loss of 15% is assumed.
-All connection Links are added to the upmost energy cell.
-
-.. note:: Note that we do not add the energy cells as components to their parent cells!
-    Instead, the hierarchical structure is flattened and all connections between the cells
-    are created as depicted above.
-
-The last step is to create (and solve) the model. Again, this is fairly similar to the
-regular model creation. However, instead of passing just one instance of
-:py:class:`oemof.solph.EnergySystem`, a list of energy systems is passed.
-
-.. warning::
-    By convention the first element of the list is assumed to be the upmost energy cell.
-    The ordering afterwards does not play a role.
-
-.. note:: The resulting model is monolithic. This means that all components of all energy
-    cells are actually grouped into one pyomo model. It would, therefore, also be possible
-    to model all the components in one :py:class:`oemof.solph.EnergySystem` instance and
-    the results would be identical.
-
-.. code-block:: python
-
-    cmodel = Model(
-        energysystem=[es, ec_1, ec_2, ...]
-    )
-    cmodel.solve()
-
-As pointed out above, the resulting model is monolithic. Nonetheless, this modelling approach
-holds some benefits:
-
-* Better overview through segmentation of the energy system
-* (Facilitated) opportunity to model cellular energy systems where the energy exchanged between cells
-  is of interest
-* Segmentation of the energy system is a necessary precursor for distributed optimization via Dantzig-Wolfe
-
 
 Mixed Integer (Linear) Problems
 -------------------------------

docs/whatsnew/v0-5-3.rst

@@ -25,6 +25,7 @@ Other changes
 #############
 
 * Unified (usage) documentation for `OffsetConverter`
+* Remove approach to model cellular systes (was more confusing than it helped)
 
 Known issues
 ############
@@ -34,3 +35,4 @@ Contributors
 
 * Lennart Schürmann
 * Richard Keil
+* Patrik Schönfeldt