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)

: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)

self.es = energysystem self.timeincrement = kwargs.get("timeincrement", self.es.timeincrement) self.objective_weighting = kwargs.get( "objective_weighting", - [1] * len(self.timeincrement) - if self.timeincrement is not None - else [1], + ( + [1] * len(self.timeincrement) + if self.timeincrement is not None + else [1] + ), ) self._constraint_groups = type(self).CONSTRAINT_GROUPS + kwargs.get( "constraint_groups", [] )

# Construct weighting from occurrences and order self.tsam_weighting = list( self.es.tsa_parameters[p]["occurrences"][k] for p in self.PERIODS for k in range(len(self.es.tsa_parameters[p]["occurrences"])) - for _ in range( - self.es.tsa_parameters[p]["timesteps"] - ) + for _ in range(self.es.tsa_parameters[p]["timesteps"]) ) self.CLUSTERS = po.Set( initialize=list( range( sum(

""" return [ (p, k, t) for p in range(len(self.es.tsa_parameters)) for k in range(len(self.es.tsa_parameters[p][cluster_type])) - for t in range( - self.es.tsa_parameters[p]["timesteps"] + offset - ) + for t in range(self.es.tsa_parameters[p]["timesteps"] + offset) ]

: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)

def _outputs_tsam(): OUTPUTS = po.Set(initialize=output_levels.keys()) setattr(model, f"{name}_OUTPUTS", OUTPUTS) active_output = po.Var( - OUTPUTS, model.TIMEINDEX_TYPICAL_CLUSTER_OFFSET, domain=po.Binary, bounds=(0, 1) + OUTPUTS, + model.TIMEINDEX_TYPICAL_CLUSTER_OFFSET, + domain=po.Binary, + bounds=(0, 1), ) setattr(model, f"{name}_active_output", active_output) constraint_name = f"{name}_output_active_constraint"

t = m.get_timestep_from_tsam_timestep(p, k, g) for o in output_levels: getattr(m, constraint_name).add( (o, p, k, g), m.GenericStorageBlock.storage_content_intra[ - storage_component, p, k, g + 1 + storage_component, p, k, g + 1 ] / storage_component.nominal_storage_capacity >= active_output[o, p, k, g] * output_levels[o], )

def _inputs_tsam(): INPUTS = po.Set(initialize=input_levels.keys()) setattr(model, f"{name}_INPUTS", INPUTS) inactive_input = po.Var( - INPUTS, model.TIMEINDEX_TYPICAL_CLUSTER_OFFSET, domain=po.Binary, bounds=(0, 1) + INPUTS, + model.TIMEINDEX_TYPICAL_CLUSTER_OFFSET, + domain=po.Binary, + bounds=(0, 1), ) setattr(model, f"{name}_active_input", inactive_input) constraint_name = f"{name}_input_active_constraint"

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 )

bounds = ( n.nominal_storage_capacity * n.min_storage_level[t], n.nominal_storage_capacity * n.max_storage_level[t], ) return bounds + def _storage_content_bound_intra_rule(block, n, p, k, g): """ Rule definition for bounds of storage_content variable of storage n in timestep t. """

bounds = ( n.nominal_storage_capacity * n.min_storage_level[t], n.nominal_storage_capacity * n.max_storage_level[t], ) return bounds + if not m.TSAM_MODE: self.storage_content = Var( self.STORAGES, m.TIMEPOINTS, bounds=_storage_content_bound_rule )

self.storage_content[n, 0].fix() else: for n in group: if n.multiple_tsam_timegrid: self.storage_content_inter = Var( - self.STORAGES, m.CLUSTERS_OFFSET, within=NonNegativeReals + self.STORAGES, + m.CLUSTERS_OFFSET, + within=NonNegativeReals, ) self.storage_content_intra = Var( self.STORAGES, m.TIMEINDEX_TYPICAL_CLUSTER_OFFSET ) else: self.storage_content_intra = Var( - self.STORAGES, m.TIMEINDEX_TYPICAL_CLUSTER_OFFSET, bounds=_storage_content_bound_intra_rule + self.STORAGES, + m.TIMEINDEX_TYPICAL_CLUSTER_OFFSET, + bounds=_storage_content_bound_intra_rule, ) # set the initial intra storage content # first timestep in intra storage is always zero for n in group: for p, k in m.TYPICAL_CLUSTERS: if n.multiple_tsam_timegrid: self.storage_content_intra[n, p, k, 0] = 0 self.storage_content_intra[n, p, k, 0].fix() else: if n.initial_storage_level is not None: - self.storage_content_intra[n, p, k, 0] = n.initial_storage_level * \ - n.nominal_storage_capacity + self.storage_content_intra[n, p, k, 0] = ( + n.initial_storage_level + * n.nominal_storage_capacity + ) self.storage_content_intra[n, p, k, 0].fix() - if n.initial_storage_level is not None and n.multiple_tsam_timegrid: - self.storage_content_inter[n, 0] = ( - n.initial_storage_level * n.nominal_storage_capacity - ) - self.storage_content_inter[n, 0].fix() - + if ( + n.initial_storage_level is not None + and n.multiple_tsam_timegrid + ): + self.storage_content_inter[n, 0] = ( + n.initial_storage_level * n.nominal_storage_capacity + ) + self.storage_content_inter[n, 0].fix() # ************* Constraints *************************** def _storage_inter_minimum_level_rule(block): # See FINE implementation at

k = m.es.tsa_parameters[p]["order"][ii] # Calculate inter losses over whole typical period t0 = m.get_timestep_from_tsam_timestep(p, k, 0) losses = math.prod( - (1 - n.loss_rate[t0 + s]) - ** m.es.tsa_parameters[p]["segments"][(k, s)] - if "segments" in m.es.tsa_parameters[p] - else 1 - n.loss_rate[t0 + s] + ( + (1 - n.loss_rate[t0 + s]) + ** m.es.tsa_parameters[p]["segments"][(k, s)] + if "segments" in m.es.tsa_parameters[p] + else 1 - n.loss_rate[t0 + s] + ) for s in range(m.es.tsa_parameters[p]["timesteps"]) ) expr = 0 expr += block.storage_content_inter[n, i + 1]

) else: for n in self.STORAGES: if n.multiple_tsam_timegrid: self.balanced_cstr = Constraint( - self.STORAGES_BALANCED, rule=_balanced_inter_storage_rule + self.STORAGES_BALANCED, + rule=_balanced_inter_storage_rule, ) def _power_coupled(block): """ Rule definition for constraint to connect the input power

) expr = 0 expr += block.storage_content_inter[n, i + 1] expr += -block.storage_content_inter[n, i] * ( 1 - n.loss_rate[t] - ) ** ( - m.timeincrement[t] - * m.es.tsa_parameters[p]["timesteps"] - ) + ) ** (m.timeincrement[t] * m.es.tsa_parameters[p]["timesteps"]) expr += -self.storage_content_intra[ n, p, k, m.es.tsa_parameters[p]["timesteps"] ] return expr == 0

# 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.tsa_parameters:

inter_value = soc["inter"].iloc[i_offset + i]["value"] inter_series = ( pd.Series( itertools.accumulate( ( - (1 - storage.loss_rate[t]) - ** tsa_period["segments"][(k, t - t0)] - if "segments" in tsa_period - else 1 - storage.loss_rate[t] + ( + (1 - storage.loss_rate[t]) + ** tsa_period["segments"][(k, t - t0)] + if "segments" in tsa_period + else 1 - storage.loss_rate[t] + ) for t in range( - t0, - t0 + timesteps - 1, - ) + t0, + t0 + timesteps - 1, + ) ), operator.mul, initial=1, ) )

soc_frame[:-1] if is_last_timestep else soc_frame, tsa_period["segments"], k, ) if is_last_timestep: - soc_disaggregated.loc[ - len(soc_disaggregated) - ] = soc_frame.iloc[-1] + soc_disaggregated.loc[len(soc_disaggregated)] = ( + soc_frame.iloc[-1] + ) soc_frame = soc_disaggregated soc_frames.append(soc_frame) i_offset += len(tsa_period["order"]) t_offset += i_offset * tsa_period["timesteps"]

# Disaggregate segments by linear interpolation and remove # last timestep afterwards (only needed for interpolation) interpolated_soc = soc_ts.interpolate() return interpolated_soc.iloc[:-1] + def _calculate_multiplexer_actives(values, multiplexer, tsa_parameters): """Calculate multiplexer actives""" actives_frames = [] for p, tsa_period in enumerate(tsa_parameters): for i, k in enumerate(tsa_period["order"]): timesteps = tsa_period["timesteps"] - actives_frames.append(pd.DataFrame( - values[(p, k)].iloc[0:timesteps], - columns=["value"]) + actives_frames.append( + pd.DataFrame( + values[(p, k)].iloc[0:timesteps], columns=["value"] ) + ) actives_frames_ts = pd.concat(actives_frames) - actives_frames_ts["variable_name"] = values[(p, k)]["variable_name"].values[0] + actives_frames_ts["variable_name"] = values[(p, k)][ + "variable_name" + ].values[0] actives_frames_ts["timestep"] = range(len(actives_frames_ts)) return actives_frames_ts + def _get_storage_soc_flows_and_keys(flow_dict): """Detect storage flows in flow dict""" storages = {} storage_keys = []

multiplexer = {} multiplexer_keys = [] for oemof_tuple, data in flow_dict.items(): if oemof_tuple[1] is not None and not isinstance(oemof_tuple[1], int): continue - if 'multiplexer_active' in data['variable_name'].values[0]: - multiplexer.setdefault(oemof_tuple[0],{}) + if "multiplexer_active" in data["variable_name"].values[0]: + multiplexer.setdefault(oemof_tuple[0], {}) multiplexer_keys.append(oemof_tuple) - multiplexer[oemof_tuple[0]][(oemof_tuple[1], oemof_tuple[2])] = data + multiplexer[oemof_tuple[0]][ + (oemof_tuple[1], oemof_tuple[2]) + ] = data return multiplexer, multiplexer_keys + + def _disaggregate_tsa_timeindex(period_index, tsa_parameters): """Disaggregate aggregated period timeindex by using TSA parameters""" return pd.date_range( start=period_index[0], periods=tsa_parameters["timesteps_per_period"]

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

result_index = processing._disaggregate_tsa_timeindex( periods[p], period_data ) else: result_index = result_index.union( - processing._disaggregate_tsa_timeindex( - periods[p], period_data - ) + processing._disaggregate_tsa_timeindex(periods[p], period_data) ) assert all(result_index == ti)

: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)

: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)

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)

self.es = energysystem self.timeincrement = kwargs.get("timeincrement", self.es.timeincrement) self.objective_weighting = kwargs.get( "objective_weighting", - [1] * len(self.timeincrement) - if self.timeincrement is not None - else [1], + ( + [1] * len(self.timeincrement) + if self.timeincrement is not None + else [1] + ), ) self._constraint_groups = type(self).CONSTRAINT_GROUPS + kwargs.get( "constraint_groups", [] )

# Construct weighting from occurrences and order self.tsam_weighting = list( self.es.tsa_parameters[p]["occurrences"][k] for p in self.PERIODS for k in range(len(self.es.tsa_parameters[p]["occurrences"])) - for _ in range( - self.es.tsa_parameters[p]["timesteps"] - ) + for _ in range(self.es.tsa_parameters[p]["timesteps"]) ) self.CLUSTERS = po.Set( initialize=list( range( sum(

""" return [ (p, k, t) for p in range(len(self.es.tsa_parameters)) for k in range(len(self.es.tsa_parameters[p][cluster_type])) - for t in range( - self.es.tsa_parameters[p]["timesteps"] + offset - ) + for t in range(self.es.tsa_parameters[p]["timesteps"] + offset) ]

: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)

: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)

def _outputs_tsam(): OUTPUTS = po.Set(initialize=output_levels.keys()) setattr(model, f"{name}_OUTPUTS", OUTPUTS) active_output = po.Var( - OUTPUTS, model.TIMEINDEX_TYPICAL_CLUSTER_OFFSET, domain=po.Binary, bounds=(0, 1) + OUTPUTS, + model.TIMEINDEX_TYPICAL_CLUSTER_OFFSET, + domain=po.Binary, + bounds=(0, 1), ) setattr(model, f"{name}_active_output", active_output) constraint_name = f"{name}_output_active_constraint"

t = m.get_timestep_from_tsam_timestep(p, k, g) for o in output_levels: getattr(m, constraint_name).add( (o, p, k, g), m.GenericStorageBlock.storage_content_intra[ - storage_component, p, k, g + 1 + storage_component, p, k, g + 1 ] / storage_component.nominal_storage_capacity >= active_output[o, p, k, g] * output_levels[o], )

def _inputs_tsam(): INPUTS = po.Set(initialize=input_levels.keys()) setattr(model, f"{name}_INPUTS", INPUTS) inactive_input = po.Var( - INPUTS, model.TIMEINDEX_TYPICAL_CLUSTER_OFFSET, domain=po.Binary, bounds=(0, 1) + INPUTS, + model.TIMEINDEX_TYPICAL_CLUSTER_OFFSET, + domain=po.Binary, + bounds=(0, 1), ) setattr(model, f"{name}_active_input", inactive_input) constraint_name = f"{name}_input_active_constraint"

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 )

bounds = ( n.nominal_storage_capacity * n.min_storage_level[t], n.nominal_storage_capacity * n.max_storage_level[t], ) return bounds + def _storage_content_bound_intra_rule(block, n, p, k, g): """ Rule definition for bounds of storage_content variable of storage n in timestep t. """

bounds = ( n.nominal_storage_capacity * n.min_storage_level[t], n.nominal_storage_capacity * n.max_storage_level[t], ) return bounds + if not m.TSAM_MODE: self.storage_content = Var( self.STORAGES, m.TIMEPOINTS, bounds=_storage_content_bound_rule )

self.storage_content[n, 0].fix() else: for n in group: if n.multiple_tsam_timegrid: self.storage_content_inter = Var( - self.STORAGES, m.CLUSTERS_OFFSET, within=NonNegativeReals + self.STORAGES, + m.CLUSTERS_OFFSET, + within=NonNegativeReals, ) self.storage_content_intra = Var( self.STORAGES, m.TIMEINDEX_TYPICAL_CLUSTER_OFFSET ) else: self.storage_content_intra = Var( - self.STORAGES, m.TIMEINDEX_TYPICAL_CLUSTER_OFFSET, bounds=_storage_content_bound_intra_rule + self.STORAGES, + m.TIMEINDEX_TYPICAL_CLUSTER_OFFSET, + bounds=_storage_content_bound_intra_rule, ) # set the initial intra storage content # first timestep in intra storage is always zero for n in group: for p, k in m.TYPICAL_CLUSTERS: if n.multiple_tsam_timegrid: self.storage_content_intra[n, p, k, 0] = 0 self.storage_content_intra[n, p, k, 0].fix() else: if n.initial_storage_level is not None: - self.storage_content_intra[n, p, k, 0] = n.initial_storage_level * \ - n.nominal_storage_capacity + self.storage_content_intra[n, p, k, 0] = ( + n.initial_storage_level + * n.nominal_storage_capacity + ) self.storage_content_intra[n, p, k, 0].fix() - if n.initial_storage_level is not None and n.multiple_tsam_timegrid: - self.storage_content_inter[n, 0] = ( - n.initial_storage_level * n.nominal_storage_capacity - ) - self.storage_content_inter[n, 0].fix() - + if ( + n.initial_storage_level is not None + and n.multiple_tsam_timegrid + ): + self.storage_content_inter[n, 0] = ( + n.initial_storage_level * n.nominal_storage_capacity + ) + self.storage_content_inter[n, 0].fix() # ************* Constraints *************************** def _storage_inter_minimum_level_rule(block): # See FINE implementation at

k = m.es.tsa_parameters[p]["order"][ii] # Calculate inter losses over whole typical period t0 = m.get_timestep_from_tsam_timestep(p, k, 0) losses = math.prod( - (1 - n.loss_rate[t0 + s]) - ** m.es.tsa_parameters[p]["segments"][(k, s)] - if "segments" in m.es.tsa_parameters[p] - else 1 - n.loss_rate[t0 + s] + ( + (1 - n.loss_rate[t0 + s]) + ** m.es.tsa_parameters[p]["segments"][(k, s)] + if "segments" in m.es.tsa_parameters[p] + else 1 - n.loss_rate[t0 + s] + ) for s in range(m.es.tsa_parameters[p]["timesteps"]) ) expr = 0 expr += block.storage_content_inter[n, i + 1]

) else: for n in self.STORAGES: if n.multiple_tsam_timegrid: self.balanced_cstr = Constraint( - self.STORAGES_BALANCED, rule=_balanced_inter_storage_rule + self.STORAGES_BALANCED, + rule=_balanced_inter_storage_rule, ) def _power_coupled(block): """ Rule definition for constraint to connect the input power

) expr = 0 expr += block.storage_content_inter[n, i + 1] expr += -block.storage_content_inter[n, i] * ( 1 - n.loss_rate[t] - ) ** ( - m.timeincrement[t] - * m.es.tsa_parameters[p]["timesteps"] - ) + ) ** (m.timeincrement[t] * m.es.tsa_parameters[p]["timesteps"]) expr += -self.storage_content_intra[ n, p, k, m.es.tsa_parameters[p]["timesteps"] ] return expr == 0

# 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.tsa_parameters:

inter_value = soc["inter"].iloc[i_offset + i]["value"] inter_series = ( pd.Series( itertools.accumulate( ( - (1 - storage.loss_rate[t]) - ** tsa_period["segments"][(k, t - t0)] - if "segments" in tsa_period - else 1 - storage.loss_rate[t] + ( + (1 - storage.loss_rate[t]) + ** tsa_period["segments"][(k, t - t0)] + if "segments" in tsa_period + else 1 - storage.loss_rate[t] + ) for t in range( - t0, - t0 + timesteps - 1, - ) + t0, + t0 + timesteps - 1, + ) ), operator.mul, initial=1, ) )

soc_frame[:-1] if is_last_timestep else soc_frame, tsa_period["segments"], k, ) if is_last_timestep: - soc_disaggregated.loc[ - len(soc_disaggregated) - ] = soc_frame.iloc[-1] + soc_disaggregated.loc[len(soc_disaggregated)] = ( + soc_frame.iloc[-1] + ) soc_frame = soc_disaggregated soc_frames.append(soc_frame) i_offset += len(tsa_period["order"]) t_offset += i_offset * tsa_period["timesteps"]

# Disaggregate segments by linear interpolation and remove # last timestep afterwards (only needed for interpolation) interpolated_soc = soc_ts.interpolate() return interpolated_soc.iloc[:-1] + def _calculate_multiplexer_actives(values, multiplexer, tsa_parameters): """Calculate multiplexer actives""" actives_frames = [] for p, tsa_period in enumerate(tsa_parameters): for i, k in enumerate(tsa_period["order"]): timesteps = tsa_period["timesteps"] - actives_frames.append(pd.DataFrame( - values[(p, k)].iloc[0:timesteps], - columns=["value"]) + actives_frames.append( + pd.DataFrame( + values[(p, k)].iloc[0:timesteps], columns=["value"] ) + ) actives_frames_ts = pd.concat(actives_frames) - actives_frames_ts["variable_name"] = values[(p, k)]["variable_name"].values[0] + actives_frames_ts["variable_name"] = values[(p, k)][ + "variable_name" + ].values[0] actives_frames_ts["timestep"] = range(len(actives_frames_ts)) return actives_frames_ts + def _get_storage_soc_flows_and_keys(flow_dict): """Detect storage flows in flow dict""" storages = {} storage_keys = []

multiplexer = {} multiplexer_keys = [] for oemof_tuple, data in flow_dict.items(): if oemof_tuple[1] is not None and not isinstance(oemof_tuple[1], int): continue - if 'multiplexer_active' in data['variable_name'].values[0]: - multiplexer.setdefault(oemof_tuple[0],{}) + if "multiplexer_active" in data["variable_name"].values[0]: + multiplexer.setdefault(oemof_tuple[0], {}) multiplexer_keys.append(oemof_tuple) - multiplexer[oemof_tuple[0]][(oemof_tuple[1], oemof_tuple[2])] = data + multiplexer[oemof_tuple[0]][ + (oemof_tuple[1], oemof_tuple[2]) + ] = data return multiplexer, multiplexer_keys + + def _disaggregate_tsa_timeindex(period_index, tsa_parameters): """Disaggregate aggregated period timeindex by using TSA parameters""" return pd.date_range( start=period_index[0], periods=tsa_parameters["timesteps_per_period"]

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

result_index = processing._disaggregate_tsa_timeindex( periods[p], period_data ) else: result_index = result_index.union( - processing._disaggregate_tsa_timeindex( - periods[p], period_data - ) + processing._disaggregate_tsa_timeindex(periods[p], period_data) ) assert all(result_index == ti)

: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)

: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)

Node.js 16 actions are deprecated. Please update the following actions to use Node.js 20: actions/checkout@v3, github/codeql-action/init@v2, github/codeql-action/autobuild@v2, github/codeql-action/analyze@v2. For more information see: https://github.blog/changelog/2023-09-22-github-actions-transitioning-from-node-16-to-node-20/.