Lennart Schürmann <[email protected]> `MIT license <https://github.com/oemof/oemof-solph/blob/dev/LICENSE>`_ """ - from oemof.solph import EnergySystem from oemof.solph import Model from oemof.solph import buses from oemof.solph import components as cmp

\times P_{\mathrm{out},n}(p, t) \quad \forall t \in T, \forall n in {1,2} \\ & """ + CONSTRAINT_GROUP = True def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs)

+--------------------+------------------------+------+--------------------------------------------+ Note that :math:`P_{max}(t) \cdot Y(t)` is merged into one variable, called `status_nominal[n, o, p, t]`. """ # noqa: E501 + CONSTRAINT_GROUP = True def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs)

:class:`~oemof.solph.components.experimental._piecewise_linear_converter.PiecewiseLinearConverter` **The following constraints are created:** """ + CONSTRAINT_GROUP = True def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs)

:math:`\eta_{el,min,woDH}` `Eta_el_min_woDH[n,t]` P el. eff. at min. fuel flow w/o distr. heating :math:`\eta_{el,max,woDH}` `Eta_el_max_woDH[n,t]` P el. eff. at max. fuel flow w/o distr. heating =============================== ======================= ==== ============================================= """ # noqa: E501 + CONSTRAINT_GROUP = True def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs)

for lb in labels: subset = ( df.T.groupby( lambda x1: ( - lambda fr, to, ty: "output" - if (fr == lb and ty == "flow") - else "input" - if (to == lb and ty == "flow") - else "level" - if (fr == lb and ty != "flow") - else None + lambda fr, to, ty: ( + "output" + if (fr == lb and ty == "flow") + else ( + "input" + if (to == lb and ty == "flow") + else ( + "level" + if (fr == lb and ty != "flow") + else None + ) + ) + ) )(*x1) ) .sum() .T )

# Extraction steps that are the same for both model types df = create_dataframe(model) # create a dict of dataframes keyed by oemof tuples df_dict = { - k - if len(k) > 1 - else (k[0], None): v[["timestep", "variable_name", "value"]] + k if len(k) > 1 else (k[0], None): v[ + ["timestep", "variable_name", "value"] + ] for k, v in df.groupby("oemof_tuple") } # Define index if model.es.timeindex is None:

converted into strings that represent the object labels e.g. results[('pp1','bus1')]. """ if keep_none_type: converted = { - tuple([str(e) if e is not None else None for e in k]) - if isinstance(k, tuple) - else str(k) - if k is not None - else None: v + ( + tuple([str(e) if e is not None else None for e in k]) + if isinstance(k, tuple) + else str(k) if k is not None else None + ): v for k, v in result.items() } else: converted = { tuple(map(str, k)) if isinstance(k, tuple) else str(k): v

by the contributors recorded in the version control history of the file, available from its original location oemof/tests/test_components.py SPDX-License-Identifier: MIT """ + import pytest from oemof import solph

:math:`\omega_{t}` P Objective weighting of the model for timestep t :math:`year_{max}` P Last year of the optimization horizon ================================= ======================== ==== ======================================= """ # noqa: E501 + CONSTRAINT_GROUP = True def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs)

:math:`\mathbb{P}` S Periods of the model :math:`\textrm{TIMEINDEX}` S Timeindex set of the model (periods, timesteps) ================================= ======================== ==== ======================================= """ # noqa: E501 + CONSTRAINT_GROUP = True def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs)

:math:`\omega_{t}` P Objective weighting of the model for timestep t :math:`year_{max}` P Last year of the optimization horizon ================================= ======================== ==== ======================================= """ # noqa E:501 + CONSTRAINT_GROUP = True def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs)

:math:`\mathbb{P}` S Periods of the model :math:`\textrm{TIMEINDEX}` S Timeindex set of the model (periods, timesteps) ================================= ======================== ==== ======================================= """ # noqa: E501 + CONSTRAINT_GROUP = True def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs)

| resp. in the last hours before the current" :math:`year_{max}` P Last year of the optimization horizon =========================================== ================================= ==== ======================================= """ # noqa: E501 + CONSTRAINT_GROUP = True def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs)