Merge a93aa46 into 715b070

examples/get_costs_example/get_costs.py

@@ -0,0 +1,223 @@
+# -*- coding: utf-8 -*-
+r"""
+General description
+-------------------
+Example that shows how to use of the processing methods get_set_costs_from_lpfile, time_dependent_values_as_dataframe and time_independent_values_as_dataframe.
+
+The methods are combined to get the actual caused costs per timestep and the caused scalar costs e.g. invest-costs
+
+The energysystem is copied from the exampel generic_invest_limit
+
+There are two supply chains. The energy systems looks like that:
+
+.. code-block:: text
+
+                  bus_a_0          bus_a_1
+                   |                 |
+    source_a_0 --->|---> trafo_a --->|--->demand_a
+                                     |
+                       source_a_1--->|
+                                     |
+
+                  bus_b_0          bus_b_1
+                   |                 |
+    source_b_0 --->|---> trafo_b --->|--->demand_b
+                                     |
+                       source_b_1--->|
+                                     |
+
+Everything is identical - the costs for the sources, the demand, the efficiency
+of the Converter. And both Converter have an investment at the output.
+The source '\*_1' is in both cases very expensive, so that
+a investment is probably done in the converter.
+Now, both investments share a third resource, which is called "space" in this
+example. (This could be anything, and you could use as many additional
+resources as you want.) And this resource is limited. In this case, every
+converter capacity unit, which might be installed, needs 2 space for
+'trafo a', and 1 space per installed capacity for 'trafo b'.
+And the total space is limited to 24.
+See what happens, have fun ;)
+
+Code
+----
+Download source code: :download:`example_generic_invest.py </../examples/generic_invest_limit/example_generic_invest.py>`
+
+.. dropdown:: Click to display code
+
+    .. literalinclude:: /../examples/generic_invest_limit/example_generic_invest.py
+        :language: python
+        :lines: 62-
+
+Installation requirements
+-------------------------
+This example requires oemof.solph (v0.5.x), install by:
+
+.. code:: bash
+
+    pip install oemof.solph[examples]
+
+License
+-------
+Johannes Röder <johannes.roeder@uni-bremen.de>
+
+`MIT license <https://github.com/oemof/oemof-solph/blob/dev/LICENSE>`_
+"""
+
+import logging
+import os
+
+from oemof import solph
+from oemof.solph import _experimental_processing 
+
+
+def main():
+    data = [0, 15, 30, 35, 20, 25, 27, 10, 5, 2, 15, 40, 20, 0, 0]
+
+    # create an energy system
+    idx = solph.create_time_index(2020, number=len(data))
+    es = solph.EnergySystem(timeindex=idx, infer_last_interval=False)
+
+    # Parameter: costs for the sources
+    c_0 = 10
+    c_1 = 100
+
+    epc_invest = 500
+
+    # commodity a
+    bus_a_0 = solph.Bus(label="bus_a_0")
+    bus_a_1 = solph.Bus(label="bus_a_1")
+    es.add(bus_a_0, bus_a_1)
+
+    es.add(
+        solph.components.Source(
+            label="source_a_0",
+            outputs={bus_a_0: solph.Flow(variable_costs=c_0)},
+        )
+    )
+
+    es.add(
+        solph.components.Source(
+            label="source_a_1",
+            outputs={bus_a_1: solph.Flow(variable_costs=c_1)},
+        )
+    )
+
+    es.add(
+        solph.components.Sink(
+            label="demand_a",
+            inputs={bus_a_1: solph.Flow(fix=data, nominal_value=1)},
+        )
+    )
+
+    # commodity b
+    bus_b_0 = solph.Bus(label="bus_b_0")
+    bus_b_1 = solph.Bus(label="bus_b_1")
+    es.add(bus_b_0, bus_b_1)
+    es.add(
+        solph.components.Source(
+            label="source_b_0",
+            outputs={bus_b_0: solph.Flow(variable_costs=data)},
+        )
+    )
+
+    es.add(
+        solph.components.Source(
+            label="source_b_1",
+            outputs={bus_b_1: solph.Flow(variable_costs=c_1)},
+        )
+    )
+
+    es.add(
+        solph.components.Sink(
+            label="demand_b",
+            inputs={bus_b_1: solph.Flow(fix=data, nominal_value=1)},
+        )
+    )
+
+    # Converter a
+    es.add(
+        solph.components.Converter(
+            label="trafo_a",
+            inputs={bus_a_0: solph.Flow()},
+            outputs={
+                bus_a_1: solph.Flow(
+                    nominal_value=solph.Investment(
+                        ep_costs=epc_invest, custom_attributes={"space": 2}
+                    )
+                )
+            },
+            conversion_factors={bus_a_1: 0.8},
+        )
+    )
+
+    # Converter b
+    es.add(
+        solph.components.Converter(
+            label="trafo_b",
+            inputs={bus_b_0: solph.Flow()},
+            outputs={
+                bus_b_1: solph.Flow(
+                    nominal_value=solph.Investment(
+                        ep_costs=epc_invest, custom_attributes={"space": 1}
+                    )
+                )
+            },
+            conversion_factors={bus_a_1: 0.8},
+        )
+    )
+
+    # create an optimization problem and solve it
+    om = solph.Model(es)
+
+    # add constraint for generic investment limit
+    om = solph.constraints.additional_investment_flow_limit(
+        om, "space", limit=24
+    )
+
+    # export lp file
+    filename = os.path.join(
+        solph.helpers.extend_basic_path("lp_files"), "GenericInvest.lp"
+    )
+    logging.info("Store lp-file in {0}.".format(filename))
+    om.write(filename, io_options={"symbolic_solver_labels": True})
+
+    # solve model
+    om.solve(solver="cbc", solve_kwargs={"tee": True})
+
+    # to get the set costs use the method get_set_costs_from_lpfile
+
+    set_tdc, set_tic = _experimental_processing.get_set_costs_from_lpfile(filename, om)
+
+    # create result object. The last timestep has to be removed
+    results = solph.processing.results(om, remove_last_time_point=True)
+
+    # now get the timedependent  optimized values as dataframe
+
+    dataframe_tdv = _experimental_processing.time_dependent_values_as_dataframe(
+        results
+    )
+
+    # now get the timeindependent  optimized values as dataframe
+    dataframe_tiv = _experimental_processing.time_independent_values_as_dataframe(
+        results
+    )
+
+    #  filter values with costs
+
+    td_intersect = set_tdc.columns.intersection(dataframe_tdv.columns)
+    ti_intersect = set_tic.columns.intersection(dataframe_tiv.columns)
+
+    # calculate costs
+    time_dependent_costs = dataframe_tdv[td_intersect] * set_tdc[td_intersect]
+
+    time_independent_costs = (
+        dataframe_tiv[ti_intersect] * set_tic[ti_intersect]
+    )
+
+    print(time_dependent_costs)
+
+    print(time_independent_costs)
+
+
+if __name__ == "__main__":
+    main()