# -*- coding: utf-8 -*-
"""
solph version of oemof.network.Edge
SPDX-FileCopyrightText: Uwe Krien <krien@uni-bremen.de>
SPDX-FileCopyrightText: Simon Hilpert
SPDX-FileCopyrightText: Cord Kaldemeyer
SPDX-FileCopyrightText: Stephan Günther
SPDX-FileCopyrightText: Birgit Schachler
SPDX-FileCopyrightText: jnnr
SPDX-FileCopyrightText: jmloenneberga
SPDX-FileCopyrightText: Pierre-François Duc
SPDX-FileCopyrightText: Saeed Sayadi
SPDX-License-Identifier: MIT
"""
import math
from collections.abc import Iterable
from warnings import warn
import numpy as np
from oemof.network import network as on
from oemof.solph._plumbing import sequence
[docs]class Flow(on.Edge):
r"""Defines a flow between two nodes.
Keyword arguments are used to set the attributes of this flow. Parameters
which are handled specially are noted below.
For the case where a parameter can be either a scalar or an iterable, a
scalar value will be converted to a sequence containing the scalar value at
every index. This sequence is then stored under the paramter's key.
Parameters
----------
nominal_value : numeric, :math:`P_{nom}`
The nominal value of the flow. If this value is set the corresponding
optimization variable of the flow object will be bounded by this value
multiplied with min(lower bound)/max(upper bound).
variable_costs : numeric (iterable or scalar), default: 0, :math:`c`
The costs associated with one unit of the flow per hour. The
costs for each timestep (:math:`P_t \cdot c \cdot \delta(t)`)
will be added to the objective expression of the optimization problem.
max : numeric (iterable or scalar), :math:`f_{max}`
Normed maximum value of the flow. The flow absolute maximum will be
calculated by multiplying :attr:`nominal_value` with :attr:`max`
min : numeric (iterable or scalar), :math:`f_{min}`
Normed minimum value of the flow (see :attr:`max`).
fix : numeric (iterable or scalar), :math:`f_{fix}`
Normed fixed value for the flow variable. Will be multiplied with the
:attr:`nominal_value` to get the absolute value.
positive_gradient_limit : numeric (iterable, scalar or None)
the normed *upper bound* on the positive difference
(`flow[t-1] < flow[t]`) of two consecutive flow values.
negative_gradient_limit : numeric (iterable, scalar or None)
the normed *upper bound* on the negative difference
(`flow[t-1] > flow[t]`) of two consecutive flow values.
full_load_time_max : numeric, :math:`t_{full\_load,max}`
Upper bound on the summed flow expressed as the equivalent time that
the flow would have to run at full capacity to yield the same sum. The
value will be multiplied with the nominal_value to get the absolute
limit.
full_load_time_min : numeric, :math:`t_{full\_load,min}`
Lower bound on the summed flow expressed as the equivalent time that
the flow would have to run at full capacity to yield the same sum. The
value will be multiplied with the nominal_value to get the absolute
limit.
integer : boolean
Set True to bound the flow values to integers.
investment : :class:`Investment <oemof.solph.options.Investment>`
Object indicating if a nominal_value of the flow is determined by
the optimization problem. Note: This will refer all attributes to an
investment variable rather than to the nominal_value. The nominal_value
should not be set (or set to None) if an investment object is used.
nonconvex : :class:`NonConvex <oemof.solph.options.NonConvex>`
If a nonconvex flow object is added here, the flow constraints will
be altered significantly as the mathematical model for the flow
will be different, i.e. constraint etc. from
:class:`~oemof.solph.flows._non_convex_flow_block.NonConvexFlowBlock`
will be used instead of
:class:`~oemof.solph.flows._simple_flow_block.SimpleFlowBlock`.
Notes
-----
See :py:class:`~oemof.solph.flows._simple_flow.SimpleFlowBlock`
for the variables, constraints and objective parts, that are created for
a Flow object.
Examples
--------
Creating a fixed flow object:
>>> f = Flow(nominal_value=2, fix=[10, 4, 4], variable_costs=5)
>>> f.variable_costs[2]
5
>>> f.fix[2]
4
Creating a flow object with time-depended lower and upper bounds:
>>> f1 = Flow(min=[0.2, 0.3], max=0.99, nominal_value=100)
>>> f1.max[1]
0.99
""" # noqa: E501
def __init__(
self,
nominal_value=None,
variable_costs=0,
min=None,
max=None,
fix=None,
positive_gradient_limit=None,
negative_gradient_limit=None,
full_load_time_max=None,
full_load_time_min=None,
integer=False,
bidirectional=False,
investment=None,
nonconvex=None,
# --- BEGIN: To be removed for versions >= v0.6 ---
summed_max=None,
summed_min=None,
# --- END ---
custom_attributes=None,
):
# TODO: Check if we can inherit from pyomo.core.base.var _VarData
# then we need to create the var object with
# pyomo.core.base.IndexedVarWithDomain before any SimpleFlowBlock
# is created. E.g. create the variable in the energy system and
# populate with information afterwards when creating objects.
# --- BEGIN: The following code can be removed for versions >= v0.6 ---
msg = (
"\nThe parameter 'summed_{0}' is deprecated and will be removed "
"in version v0.6.\nRename the parameter to 'full_load_time_{0}', "
"to avoid this warning and future problems. "
)
if summed_max is not None:
warn(msg.format("max"), FutureWarning)
full_load_time_max = summed_max
if summed_min is not None:
warn(msg.format("min"), FutureWarning)
full_load_time_min = summed_min
# --- END ---
super().__init__()
if custom_attributes is not None:
for attribute, value in custom_attributes.items():
setattr(self, attribute, value)
infinite_error_msg = (
"{} must be a finite value. Passing an infinite "
"value is not allowed."
)
if nominal_value is not None and not math.isfinite(nominal_value):
raise ValueError(infinite_error_msg.format("nominal_value"))
self.nominal_value = nominal_value
self.positive_gradient_limit = sequence(positive_gradient_limit)
self.negative_gradient_limit = sequence(negative_gradient_limit)
self.full_load_time_max = full_load_time_max
self.full_load_time_min = full_load_time_min
self.integer = integer
self.investment = investment
self.nonconvex = nonconvex
self.bidirectional = bidirectional
# It is not allowed to define min or max if fix is defined.
if fix is not None and (min is not None or max is not None):
raise AttributeError(
"It is not allowed to define `min`/`max` if `fix` is defined."
)
# Checking for impossible attribute combinations
if self.investment and self.nominal_value is not None:
raise ValueError(
"Using the investment object the nominal_value"
" has to be set to None."
)
need_nominal_value = [
"fix",
"full_load_time_max",
"full_load_time_min",
"min",
"max",
]
sequences = ["fix", "variable_costs", "min", "max"]
if self.investment is None and self.nominal_value is None:
for attr in need_nominal_value:
if isinstance(eval(attr), Iterable):
the_attr = eval(attr)[0]
else:
the_attr = eval(attr)
if the_attr is not None:
raise AttributeError(
"If {} is set in a flow (except InvestmentFlow), "
"nominal_value must be set as well.\n"
"Otherwise, it won't have any effect.".format(attr)
)
# minumum will be set even without nominal limit
# maximum and minimum (absolute values) should be always set,
# as nominal_value or invest might be defined later
if max is None:
max = 1
if min is None:
if bidirectional:
min = -1
else:
min = 0
for attr in sequences:
setattr(self, attr, sequence(eval(attr)))
if self.nominal_value is not None and not math.isfinite(self.max[0]):
raise ValueError(infinite_error_msg.format("max"))
# Checking for impossible gradient combinations
if self.nonconvex:
if self.nonconvex.positive_gradient_limit[0] is not None and (
self.positive_gradient_limit[0] is not None
or self.negative_gradient_limit[0] is not None
):
raise ValueError(
"You specified a positive gradient in your nonconvex "
"option. This cannot be combined with a positive or a "
"negative gradient for a standard flow!"
)
if self.nonconvex.negative_gradient_limit[0] is not None and (
self.positive_gradient_limit[0] is not None
or self.negative_gradient_limit[0] is not None
):
raise ValueError(
"You specified a negative gradient in your nonconvex "
"option. This cannot be combined with a positive or a "
"negative gradient for a standard flow!"
)
if (
self.investment
and self.nonconvex
and not np.isfinite(self.investment.maximum)
):
raise AttributeError(
"Investment into a non-convex flows needs a maximum "
+ "investment to be set."
)