solph.flows._simple_flow_block.SimpleFlowBlock._objective_expression() - Code Metrics - oemof/oemof-solph - Measure and Improve Code Quality continuously with Scrutinizer

SimpleFlowBlock._objective_expression() F
last analyzed 2025-06-25 12:55 UTC

↳ Parent: solph.flows._simple_flow_block

Complexity

Conditions

Size

Total Lines	125
Code Lines	55

Duplication

Lines	18
Ratio	14.4 %

Importance

Changes

Metric	Value
eloc	55
dl	18
loc	125
rs	2.4
c	0
b	0
f	0
cc	16
nop	1

How to fix Long Method Complexity

Long Method

Small methods make your code easier to understand, in particular if combined with a good name. Besides, if your method is small, finding a good name is usually much easier.

For example, if you find yourself adding comments to a method's body, this is usually a good sign to extract the commented part to a new method, and use the comment as a starting point when coming up with a good name for this new method.

Commonly applied refactorings include:

If many parameters/temporary variables are present:
- Replace temporary variables with Query
- Introduce parameter object; often combined with preserve whole object
- If the above two are insufficient: Replace method with method object
If you have long conditionals: Decompose Conditional
Otherwise: Extract method

Complexity

Complex classes like solph.flows._simple_flow_block.SimpleFlowBlock._objective_expression() often do a lot of different things. To break such a class down, we need to identify a cohesive component within that class. A common approach to find such a component is to look for fields/methods that share the same prefixes, or suffixes.

Once you have determined the fields that belong together, you can apply the Extract Class refactoring. If the component makes sense as a sub-class, Extract Subclass is also a candidate, and is often faster.

# -*- coding: utf-8 -*-

"""Creating sets, variables, constraints and parts of the objective function
for Flow objects with neither nonconvex nor investment options.

SPDX-FileCopyrightText: Uwe Krien <[email protected]>
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-FileCopyrightText: Johannes Kochems

SPDX-License-Identifier: MIT

"""
from pyomo.core import BuildAction
from pyomo.core import Constraint
from pyomo.core import Expression
from pyomo.core import NonNegativeIntegers
from pyomo.core import NonNegativeReals
from pyomo.core import Set
from pyomo.core import Var
from pyomo.core.base.block import ScalarBlock

from oemof.solph._plumbing import valid_sequence


class SimpleFlowBlock(ScalarBlock):
    r"""Flow block with definitions for standard flows.

    See :class:`~oemof.solph.flows._flow.Flow` class for all parameters of the
    *Flow*.

    .. automethod:: _create_constraints
    .. automethod:: _create_variables
    .. automethod:: _create_sets

    .. automethod:: _objective_expression

    Note
    ----
    See the :class:`~oemof.solph.flows._flow.Flow` class for the definition of
    all parameters from the "List of Parameters above.

    """  # noqa: E501

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

    def _create(self, group=None):
        r"""Creates sets, variables and constraints for all standard flows.

        Parameters
        ----------
        group : list
            List containing tuples containing flow (f) objects and the
            associated source (s) and target (t)
            of flow e.g. groups=[(s1, t1, f1), (s2, t2, f2),..]
        """
        if group is None:
            return None

        self._create_sets(group)
        self._create_variables(group)
        self._create_constraints()

    def _create_sets(self, group):
        """
        Creates all sets for standard flows.
        """
        self.FULL_LOAD_TIME_MAX_FLOWS = Set(
            initialize=[
                (g[0], g[1])
                for g in group
                if g[2].full_load_time_max is not None
                and g[2].nominal_capacity is not None
            ]
        )

        self.FULL_LOAD_TIME_MIN_FLOWS = Set(
            initialize=[
                (g[0], g[1])
                for g in group
                if g[2].full_load_time_min is not None
                and g[2].nominal_capacity is not None
            ]
        )

        self.NEGATIVE_GRADIENT_FLOWS = Set(
            initialize=[
                (g[0], g[1])
                for g in group
                if g[2].negative_gradient_limit[0] is not None
            ]
        )

        self.POSITIVE_GRADIENT_FLOWS = Set(
            initialize=[
                (g[0], g[1])
                for g in group
                if g[2].positive_gradient_limit[0] is not None
            ]
        )

        self.INTEGER_FLOWS = Set(
            initialize=[(g[0], g[1]) for g in group if g[2].integer]
        )

        self.LIFETIME_FLOWS = Set(
            initialize=[
                (g[0], g[1])
                for g in group
                if g[2].lifetime is not None and g[2].age is None
            ]
        )

        self.LIFETIME_AGE_FLOWS = Set(
            initialize=[
                (g[0], g[1])
                for g in group
                if g[2].lifetime is not None and g[2].age is not None
            ]
        )

    def _create_variables(self, group):
        r"""Creates all variables for standard flows.

        All *Flow* objects are indexed by a starting and ending node
        :math:`(i, o)`, which is omitted in the following for the sake of
        convenience. The creation of some variables depend on the values of
        *Flow* attributes. The following variables are created:

        * :math:`P(p, t)`
            Actual flow value (created in :class:`~oemof.solph._models.Model`).
            The variable is bound to:
            :math:`f_\mathrm{min}(t) \cdot P_\mathrm{nom}
            \le P(p, t)
            \le f_\mathrm{max}(t) \cdot P_\mathrm{nom}`.

            If `Flow.fix` is not None the variable is bound to
            :math:`P(p, t) = f_\mathrm{fix}(t) \cdot P_\mathrm{nom}`.

        * :math:`ve_n` (`Flow.negative_gradient` is not `None`)
            Difference of a flow in consecutive timesteps if flow is reduced.
            The variable is bound to: :math:`0 \ge ve_n \ge ve_n^{max}`.

        * :math:`ve_p` (`Flow.positive_gradient` is not `None`)
            Difference of a flow in consecutive timesteps if flow is increased.
            The variable is bound to: :math:`0 \ge ve_p \ge ve_p^{max}`.

        The following variable is build for Flows with the attribute
        `integer_flows` being not None.

        * :math:`i` (`Flow.integer` is `True`)
            All flow values are integers. Variable is bound to non-negative
            integers.
        """
        m = self.parent_block()

        self.positive_gradient = Var(
            self.POSITIVE_GRADIENT_FLOWS, m.TIMESTEPS, within=NonNegativeReals
        )

        self.negative_gradient = Var(
            self.NEGATIVE_GRADIENT_FLOWS, m.TIMESTEPS, within=NonNegativeReals
        )

        self.integer_flow = Var(
            self.INTEGER_FLOWS, m.TIMESTEPS, within=NonNegativeIntegers
        )
        # set upper bound of gradient variable
        for i, o, f in group:
            if valid_sequence(
                m.flows[i, o].positive_gradient_limit, len(m.TIMESTEPS)
            ):
                for t in m.TIMESTEPS:
                    self.positive_gradient[i, o, t].setub(
                        f.positive_gradient_limit[t] * f.nominal_capacity
                    )
            if valid_sequence(
                m.flows[i, o].negative_gradient_limit, len(m.TIMESTEPS)
            ):
                for t in m.TIMESTEPS:
                    self.negative_gradient[i, o, t].setub(
                        f.negative_gradient_limit[t] * f.nominal_capacity
                    )

    def _create_constraints(self):
        r"""Creates all constraints for standard flows.

        The following constraints are created, if the appropriate attribute of
        the *Flow* (see :class:`~oemof.solph.flows._flow.Flow`) object is set:

        * `Flow.full_load_time_max` is not `None` (full_load_time_max_constr):
            .. math::
                \sum_t P(t) \cdot \tau \leq F_{max} \cdot P_{nom}

        * `Flow.full_load_time_min` is not `None` (full_load_time_min_constr):
            .. math::
                \sum_t P(t) \cdot \tau \geq F_{min} \cdot P_{nom}

        * `Flow.negative_gradient` is not `None` (negative_gradient_constr):
            .. math::
              P(t-1) - P(t) \geq ve_n(t)

        * `Flow.positive_gradient` is not `None` (positive_gradient_constr):
            .. math::
              P(t) - P(t-1) \geq ve_p(t)

        * `Flow.integer` is `True`
            .. math::
              P(t) = i(t)
        """
        m = self.parent_block()

        def _flow_full_load_time_max_rule(model):
            """Rule definition for build action of max. sum flow constraint."""
            for inp, out in self.FULL_LOAD_TIME_MAX_FLOWS:
                lhs = sum(
                    m.flow[inp, out, ts]
                    * m.timeincrement[ts]
                    * m.tsam_weighting[ts]
                    for ts in m.TIMESTEPS
                )
                rhs = (
                    m.flows[inp, out].full_load_time_max
                    * m.flows[inp, out].nominal_capacity
                )
                self.full_load_time_max_constr.add((inp, out), lhs <= rhs)

        self.full_load_time_max_constr = Constraint(
            self.FULL_LOAD_TIME_MAX_FLOWS, noruleinit=True
        )
        self.full_load_time_max_build = BuildAction(
            rule=_flow_full_load_time_max_rule
        )

        def _flow_full_load_time_min_rule(_):
            """Rule definition for build action of min. sum flow constraint."""
            for inp, out in self.FULL_LOAD_TIME_MIN_FLOWS:
                lhs = sum(
                    m.flow[inp, out, ts]
                    * m.timeincrement[ts]
                    * m.tsam_weighting[ts]
                    for ts in m.TIMESTEPS
                )
                rhs = (
                    m.flows[inp, out].full_load_time_min
                    * m.flows[inp, out].nominal_capacity
                )
                self.full_load_time_min_constr.add((inp, out), lhs >= rhs)

        self.full_load_time_min_constr = Constraint(
            self.FULL_LOAD_TIME_MIN_FLOWS, noruleinit=True
        )
        self.full_load_time_min_build = BuildAction(
            rule=_flow_full_load_time_min_rule
        )

        def _positive_gradient_flow_rule(_):
            """Rule definition for positive gradient constraint."""
            for inp, out in self.POSITIVE_GRADIENT_FLOWS:
                for index in range(1, len(m.TIMESTEPS) + 1):
                    if m.TIMESTEPS.at(index) > 0:
                        lhs = (
                            m.flow[
                                inp,
                                out,
                                m.TIMESTEPS.at(index),
                            ]
                            - m.flow[
                                inp,
                                out,
                                m.TIMESTEPS.at(index - 1),
                            ]
                        )
                        rhs = self.positive_gradient[
                            inp, out, m.TIMESTEPS.at(index)
                        ]
                        self.positive_gradient_constr.add(
                            (inp, out, m.TIMESTEPS.at(index)),
                            lhs <= rhs,
                        )
                    else:
                        lhs = self.positive_gradient[inp, out, 0]
                        rhs = 0
                        self.positive_gradient_constr.add(
                            (inp, out, m.TIMESTEPS.at(index)),
                            lhs == rhs,
                        )

        self.positive_gradient_constr = Constraint(
            self.POSITIVE_GRADIENT_FLOWS, m.TIMESTEPS, noruleinit=True
        )
        self.positive_gradient_build = BuildAction(
            rule=_positive_gradient_flow_rule
        )

        def _negative_gradient_flow_rule(model):
            """Rule definition for negative gradient constraint."""
            for inp, out in self.NEGATIVE_GRADIENT_FLOWS:
                for index in range(1, len(m.TIMESTEPS) + 1):
                    if m.TIMESTEPS.at(index) > 0:
                        lhs = (
                            m.flow[inp, out, m.TIMESTEPS.at(index - 1)]
                            - m.flow[inp, out, m.TIMESTEPS.at(index)]
                        )
                        rhs = self.negative_gradient[
                            inp, out, m.TIMESTEPS.at(index)
                        ]
                        self.negative_gradient_constr.add(
                            (inp, out, m.TIMESTEPS.at(index)),
                            lhs <= rhs,
                        )
                    else:
                        lhs = self.negative_gradient[inp, out, 0]
                        rhs = 0
                        self.negative_gradient_constr.add(
                            (inp, out, m.TIMESTEPS.at(index)),
                            lhs == rhs,
                        )

        self.negative_gradient_constr = Constraint(
            self.NEGATIVE_GRADIENT_FLOWS, m.TIMESTEPS, noruleinit=True
        )
        self.negative_gradient_build = BuildAction(
            rule=_negative_gradient_flow_rule
        )

        def _integer_flow_rule(_, ii, oi, ti):
            """Force flow variable to NonNegativeInteger values."""
            return self.integer_flow[ii, oi, ti] == m.flow[ii, oi, ti]

        self.integer_flow_constr = Constraint(
            self.INTEGER_FLOWS, m.TIMESTEPS, rule=_integer_flow_rule
        )

        if m.es.periods is not None:

            def _lifetime_output_rule(_):
                """Force flow value to zero when lifetime is reached"""
                for inp, out in self.LIFETIME_FLOWS:
                    for p, ts in m.TIMEINDEX:
                        if m.flows[inp, out].lifetime <= m.es.periods_years[p]:
                            lhs = m.flow[inp, out, ts]
                            rhs = 0
                            self.lifetime_output.add(
                                (inp, out, p, ts), (lhs == rhs)
                            )

            self.lifetime_output = Constraint(
                self.LIFETIME_FLOWS, m.TIMEINDEX, noruleinit=True
            )
            self.lifetime_output_build = BuildAction(
                rule=_lifetime_output_rule
            )

            def _lifetime_age_output_rule(block):
                """Force flow value to zero when lifetime is reached
                considering initial age
                """
                for inp, out in self.LIFETIME_AGE_FLOWS:
                    for p, ts in m.TIMEINDEX:
                        if (
                            m.flows[inp, out].lifetime - m.flows[inp, out].age
                            <= m.es.periods_years[p]
                        ):
                            lhs = m.flow[inp, out, ts]
                            rhs = 0
                            self.lifetime_age_output.add(
                                (inp, out, p, ts), (lhs == rhs)
                            )

            self.lifetime_age_output = Constraint(
                self.LIFETIME_AGE_FLOWS, m.TIMEINDEX, noruleinit=True
            )
            self.lifetime_age_output_build = BuildAction(
                rule=_lifetime_age_output_rule
            )

    def _objective_expression(self):
        r"""Objective expression for all standard flows with fixed costs
        and variable costs.

        Depending on the attributes of the `Flow` object the following parts of
        the objective function are created for a standard model:

        * `Flow.variable_costs` is not `None`:
            .. math::
              \sum_{(i,o)} \sum_t P(t) \cdot w(t) \cdot c_{var}(i, o, t)

        where :math:`w(t)` is the objective weighting.

        In a multi-period model, in contrast, the following parts of
        the objective function are created:

        * `Flow.variable_costs` is not `None`:
            .. math::
              \sum_{(i,o)} \sum_{p, t} P(p, t) \cdot w(t)
              \cdot c_{var}(i, o, t)

        * `Flow.fixed_costs` is not `None` and flow has no lifetime limit
            .. math::
              \sum_{(i,o)} \displaystyle \sum_{pp=0}^{year_{max}}
              P_{nominal} \cdot c_{fixed}(i, o, pp) \cdot DF^{-pp}

        * `Flow.fixed_costs` is not `None` and flow has a lifetime limit,
           but not an initial age
            .. math::
              \sum_{(i,o)} \displaystyle \sum_{pp=0}^{limit_{exo}}
              P_{nominal} \cdot c_{fixed}(i, o, pp) \cdot DF^{-pp}

        * `Flow.fixed_costs` is not `None` and flow has a lifetime limit,
           and an initial age
            .. math::
              \sum_{(i,o)} \displaystyle \sum_{pp=0}^{limit_{exo}} P_{nominal}
              \cdot c_{fixed}(i, o, pp) \cdot DF^{-pp}

        Hereby

        * :math:`DF(p) = (1 + dr)` is the discount factor for period :math:`p`
          and :math:`dr` is the discount rate.
        * :math:`n` is the unit lifetime and :math:`a` is the initial age.
        * :math:`year_{max}` denotes the last year of the optimization
          horizon, i.e. at the end of the last period.
        * :math:`limit_{exo}=min\{year_{max}, n - a\}` is used as an
          upper bound to ensure fixed costs for existing capacities to occur
          within the optimization horizon. :math:`a` is the initial age
          of an asset (or 0 if not specified).
        """
        m = self.parent_block()

        variable_costs = 0
        fixed_costs = 0

        if m.es.periods is None:
            for i, o in m.FLOWS:
                if valid_sequence(
                    m.flows[i, o].variable_costs, len(m.TIMESTEPS)
                ):
                    for t in m.TIMESTEPS:
                        variable_costs += (
                            m.flow[i, o, t]
                            * m.objective_weighting[t]
                            * m.tsam_weighting[t]
                            * m.flows[i, o].variable_costs[t]
                        )

        else:
            for i, o in m.FLOWS:
                if valid_sequence(
                    m.flows[i, o].variable_costs, len(m.TIMESTEPS)
                ):
                    for p, t in m.TIMEINDEX:
                        variable_costs += (
                            m.flow[i, o, t]
                            * m.objective_weighting[t]
                            * m.tsam_weighting[t]
                            * m.flows[i, o].variable_costs[t]
                            * ((1 + m.discount_rate) ** -m.es.periods_years[p])
                        )

                # Fixed costs for units with no lifetime limit
                if (
                    m.flows[i, o].fixed_costs[0] is not None
                    and m.flows[i, o].nominal_capacity is not None
                    and (i, o) not in self.LIFETIME_FLOWS
                    and (i, o) not in self.LIFETIME_AGE_FLOWS
                ):
                    fixed_costs += sum(
                        m.flows[i, o].nominal_capacity
                        * m.flows[i, o].fixed_costs[pp]
                        for pp in range(m.es.end_year_of_optimization)
                    )

            # Fixed costs for units with limited lifetime
            for i, o in self.LIFETIME_FLOWS:
                if valid_sequence(m.flows[i, o].fixed_costs, len(m.TIMESTEPS)):

                    range_limit = min(
                        m.es.end_year_of_optimization,
                        m.flows[i, o].lifetime,
                    )
                    fixed_costs += sum(
                        m.flows[i, o].nominal_capacity
                        * m.flows[i, o].fixed_costs[pp]
                        for pp in range(range_limit)
                    )

            for i, o in self.LIFETIME_AGE_FLOWS:
                if valid_sequence(m.flows[i, o].fixed_costs, len(m.TIMESTEPS)):

                    range_limit = min(
                        m.es.end_year_of_optimization,
                        m.flows[i, o].lifetime - m.flows[i, o].age,
                    )
                    fixed_costs += sum(
                        m.flows[i, o].nominal_capacity
                        * m.flows[i, o].fixed_costs[pp]
                        for pp in range(range_limit)
                    )

        self.variable_costs = Expression(expr=variable_costs)
        self.fixed_costs = Expression(expr=fixed_costs)
        self.costs = Expression(expr=variable_costs + fixed_costs)

        return self.costs


1		# -- coding: utf-8 --
2
3		"""Creating sets, variables, constraints and parts of the objective function
4		for Flow objects with neither nonconvex nor investment options.
5
6		SPDX-FileCopyrightText: Uwe Krien <[email protected]>
7		SPDX-FileCopyrightText: Simon Hilpert
8		SPDX-FileCopyrightText: Cord Kaldemeyer
9		SPDX-FileCopyrightText: Stephan Günther
10		SPDX-FileCopyrightText: Birgit Schachler
11		SPDX-FileCopyrightText: jnnr
12		SPDX-FileCopyrightText: jmloenneberga
13		SPDX-FileCopyrightText: Pierre-François Duc
14		SPDX-FileCopyrightText: Saeed Sayadi
15		SPDX-FileCopyrightText: Johannes Kochems
16
17		SPDX-License-Identifier: MIT
18
19		"""
20		from pyomo.core import BuildAction
21		from pyomo.core import Constraint
22		from pyomo.core import Expression
23		from pyomo.core import NonNegativeIntegers
24		from pyomo.core import NonNegativeReals
25		from pyomo.core import Set
26		from pyomo.core import Var
27		from pyomo.core.base.block import ScalarBlock
28
29		from oemof.solph._plumbing import valid_sequence
30
31
32		class SimpleFlowBlock(ScalarBlock):
33		r"""Flow block with definitions for standard flows.
34
35		See :class:`~oemof.solph.flows._flow.Flow` class for all parameters of the
36		Flow.
37
38		.. automethod:: _create_constraints
39		.. automethod:: _create_variables
40		.. automethod:: _create_sets
41
42		.. automethod:: _objective_expression
43
44		Note
45		----
46		See the :class:`~oemof.solph.flows._flow.Flow` class for the definition of
47		all parameters from the "List of Parameters above.
48
49		""" # noqa: E501
50
51		def __init__(self, args, *kwargs):
52		super().__init__(args, *kwargs)
53
54		def _create(self, group=None):
55		r"""Creates sets, variables and constraints for all standard flows.
56
57		Parameters
58		----------
59		group : list
60		List containing tuples containing flow (f) objects and the
61		associated source (s) and target (t)
62		of flow e.g. groups=[(s1, t1, f1), (s2, t2, f2),..]
63		"""
64		if group is None:
65		return None
66
67		self._create_sets(group)
68		self._create_variables(group)
69		self._create_constraints()
70
71		def _create_sets(self, group):
72		"""
73		Creates all sets for standard flows.
74		"""
75		self.FULL_LOAD_TIME_MAX_FLOWS = Set(
76		initialize=[
77		(g[0], g[1])
78		for g in group
79		if g[2].full_load_time_max is not None
80		and g[2].nominal_capacity is not None
81		]
82		)
83
84		self.FULL_LOAD_TIME_MIN_FLOWS = Set(
85		initialize=[
86		(g[0], g[1])
87		for g in group
88		if g[2].full_load_time_min is not None
89		and g[2].nominal_capacity is not None
90		]
91		)
92
93		self.NEGATIVE_GRADIENT_FLOWS = Set(
94		initialize=[
95		(g[0], g[1])
96		for g in group
97		if g[2].negative_gradient_limit[0] is not None
98		]
99		)
100
101		self.POSITIVE_GRADIENT_FLOWS = Set(
102		initialize=[
103		(g[0], g[1])
104		for g in group
105		if g[2].positive_gradient_limit[0] is not None
106		]
107		)
108
109		self.INTEGER_FLOWS = Set(
110		initialize=[(g[0], g[1]) for g in group if g[2].integer]
111		)
112
113		self.LIFETIME_FLOWS = Set(
114		initialize=[
115		(g[0], g[1])
116		for g in group
117		if g[2].lifetime is not None and g[2].age is None
118		]
119		)
120
121		self.LIFETIME_AGE_FLOWS = Set(
122		initialize=[
123		(g[0], g[1])
124		for g in group
125		if g[2].lifetime is not None and g[2].age is not None
126		]
127		)
128
129		def _create_variables(self, group):
130		r"""Creates all variables for standard flows.
131
132		All Flow objects are indexed by a starting and ending node
133		:math:`(i, o)`, which is omitted in the following for the sake of
134		convenience. The creation of some variables depend on the values of
135		Flow attributes. The following variables are created:
136
137		* :math:`P(p, t)`
138		Actual flow value (created in :class:`~oemof.solph._models.Model`).
139		The variable is bound to:
140		:math:`f_\mathrm{min}(t) \cdot P_\mathrm{nom}
141		\le P(p, t)
142		\le f_\mathrm{max}(t) \cdot P_\mathrm{nom}`.
143
144		If `Flow.fix` is not None the variable is bound to
145		:math:`P(p, t) = f_\mathrm{fix}(t) \cdot P_\mathrm{nom}`.
146
147		* :math:`ve_n` (`Flow.negative_gradient` is not `None`)
148		Difference of a flow in consecutive timesteps if flow is reduced.
149		The variable is bound to: :math:`0 \ge ve_n \ge ve_n^{max}`.
150
151		* :math:`ve_p` (`Flow.positive_gradient` is not `None`)
152		Difference of a flow in consecutive timesteps if flow is increased.
153		The variable is bound to: :math:`0 \ge ve_p \ge ve_p^{max}`.
154
155		The following variable is build for Flows with the attribute
156		`integer_flows` being not None.
157
158		* :math:`i` (`Flow.integer` is `True`)
159		All flow values are integers. Variable is bound to non-negative
160		integers.
161		"""
162		m = self.parent_block()
163
164		self.positive_gradient = Var(
165		self.POSITIVE_GRADIENT_FLOWS, m.TIMESTEPS, within=NonNegativeReals
166		)
167
168		self.negative_gradient = Var(
169		self.NEGATIVE_GRADIENT_FLOWS, m.TIMESTEPS, within=NonNegativeReals
170		)
171
172		self.integer_flow = Var(
173		self.INTEGER_FLOWS, m.TIMESTEPS, within=NonNegativeIntegers
174		)
175		# set upper bound of gradient variable
176		for i, o, f in group:
177		if valid_sequence(
178		m.flows[i, o].positive_gradient_limit, len(m.TIMESTEPS)
179		):
180		for t in m.TIMESTEPS:
181		self.positive_gradient[i, o, t].setub(
182		f.positive_gradient_limit[t] * f.nominal_capacity
183		)
184		if valid_sequence(
185		m.flows[i, o].negative_gradient_limit, len(m.TIMESTEPS)
186		):
187		for t in m.TIMESTEPS:
188		self.negative_gradient[i, o, t].setub(
189		f.negative_gradient_limit[t] * f.nominal_capacity
190		)
191
192		def _create_constraints(self):
193		r"""Creates all constraints for standard flows.
194
195		The following constraints are created, if the appropriate attribute of
196		the Flow (see :class:`~oemof.solph.flows._flow.Flow`) object is set:
197
198		* `Flow.full_load_time_max` is not `None` (full_load_time_max_constr):
199		.. math::
200		\sum_t P(t) \cdot \tau \leq F_{max} \cdot P_{nom}
201
202		* `Flow.full_load_time_min` is not `None` (full_load_time_min_constr):
203		.. math::
204		\sum_t P(t) \cdot \tau \geq F_{min} \cdot P_{nom}
205
206		* `Flow.negative_gradient` is not `None` (negative_gradient_constr):
207		.. math::
208		P(t-1) - P(t) \geq ve_n(t)
209
210		* `Flow.positive_gradient` is not `None` (positive_gradient_constr):
211		.. math::
212		P(t) - P(t-1) \geq ve_p(t)
213
214		* `Flow.integer` is `True`
215		.. math::
216		P(t) = i(t)
217		"""
218		m = self.parent_block()
219
220		def _flow_full_load_time_max_rule(model):
221		"""Rule definition for build action of max. sum flow constraint."""
222		for inp, out in self.FULL_LOAD_TIME_MAX_FLOWS:
223		lhs = sum(
224		m.flow[inp, out, ts]
225		* m.timeincrement[ts]
226		* m.tsam_weighting[ts]
227		for ts in m.TIMESTEPS
228		)
229		rhs = (
230		m.flows[inp, out].full_load_time_max
231		* m.flows[inp, out].nominal_capacity
232		)
233		self.full_load_time_max_constr.add((inp, out), lhs <= rhs)
234
235		self.full_load_time_max_constr = Constraint(
236		self.FULL_LOAD_TIME_MAX_FLOWS, noruleinit=True
237		)
238		self.full_load_time_max_build = BuildAction(
239		rule=_flow_full_load_time_max_rule
240		)
241
242		def _flow_full_load_time_min_rule(_):
243		"""Rule definition for build action of min. sum flow constraint."""
244		for inp, out in self.FULL_LOAD_TIME_MIN_FLOWS:
245		lhs = sum(
246		m.flow[inp, out, ts]
247		* m.timeincrement[ts]
248		* m.tsam_weighting[ts]
249		for ts in m.TIMESTEPS
250		)
251		rhs = (
252		m.flows[inp, out].full_load_time_min
253		* m.flows[inp, out].nominal_capacity
254		)
255		self.full_load_time_min_constr.add((inp, out), lhs >= rhs)
256
257		self.full_load_time_min_constr = Constraint(
258		self.FULL_LOAD_TIME_MIN_FLOWS, noruleinit=True
259		)
260		self.full_load_time_min_build = BuildAction(
261		rule=_flow_full_load_time_min_rule
262		)
263
264		def _positive_gradient_flow_rule(_):
265		"""Rule definition for positive gradient constraint."""
266		for inp, out in self.POSITIVE_GRADIENT_FLOWS:
267		for index in range(1, len(m.TIMESTEPS) + 1):
268		if m.TIMESTEPS.at(index) > 0:
269		lhs = (
270		m.flow[
271		inp,
272		out,
273		m.TIMESTEPS.at(index),
274		]
275		- m.flow[
276		inp,
277		out,
278		m.TIMESTEPS.at(index - 1),
279		]
280		)
281		rhs = self.positive_gradient[
282		inp, out, m.TIMESTEPS.at(index)
283		]
284		self.positive_gradient_constr.add(
285		(inp, out, m.TIMESTEPS.at(index)),
286		lhs <= rhs,
287		)
288		else:
289		lhs = self.positive_gradient[inp, out, 0]
290		rhs = 0
291		self.positive_gradient_constr.add(
292		(inp, out, m.TIMESTEPS.at(index)),
293		lhs == rhs,
294		)
295
296		self.positive_gradient_constr = Constraint(
297		self.POSITIVE_GRADIENT_FLOWS, m.TIMESTEPS, noruleinit=True
298		)
299		self.positive_gradient_build = BuildAction(
300		rule=_positive_gradient_flow_rule
301		)
302
303		def _negative_gradient_flow_rule(model):
304		"""Rule definition for negative gradient constraint."""
305		for inp, out in self.NEGATIVE_GRADIENT_FLOWS:
306		for index in range(1, len(m.TIMESTEPS) + 1):
307		if m.TIMESTEPS.at(index) > 0:
308		lhs = (
309		m.flow[inp, out, m.TIMESTEPS.at(index - 1)]
310		- m.flow[inp, out, m.TIMESTEPS.at(index)]
311		)
312		rhs = self.negative_gradient[
313		inp, out, m.TIMESTEPS.at(index)
314		]
315		self.negative_gradient_constr.add(
316		(inp, out, m.TIMESTEPS.at(index)),
317		lhs <= rhs,
318		)
319		else:
320		lhs = self.negative_gradient[inp, out, 0]
321		rhs = 0
322		self.negative_gradient_constr.add(
323		(inp, out, m.TIMESTEPS.at(index)),
324		lhs == rhs,
325		)
326
327		self.negative_gradient_constr = Constraint(
328		self.NEGATIVE_GRADIENT_FLOWS, m.TIMESTEPS, noruleinit=True
329		)
330		self.negative_gradient_build = BuildAction(
331		rule=_negative_gradient_flow_rule
332		)
333
334		def _integer_flow_rule(_, ii, oi, ti):
335		"""Force flow variable to NonNegativeInteger values."""
336		return self.integer_flow[ii, oi, ti] == m.flow[ii, oi, ti]
337
338		self.integer_flow_constr = Constraint(
339		self.INTEGER_FLOWS, m.TIMESTEPS, rule=_integer_flow_rule
340		)
341
342		if m.es.periods is not None:
343
344		def _lifetime_output_rule(_):
345		"""Force flow value to zero when lifetime is reached"""
346		for inp, out in self.LIFETIME_FLOWS:
347		for p, ts in m.TIMEINDEX:
348		if m.flows[inp, out].lifetime <= m.es.periods_years[p]:
349		lhs = m.flow[inp, out, ts]
350		rhs = 0
351		self.lifetime_output.add(
352		(inp, out, p, ts), (lhs == rhs)
353		)
354
355		self.lifetime_output = Constraint(
356		self.LIFETIME_FLOWS, m.TIMEINDEX, noruleinit=True
357		)
358		self.lifetime_output_build = BuildAction(
359		rule=_lifetime_output_rule
360		)
361
362		def _lifetime_age_output_rule(block):
363		"""Force flow value to zero when lifetime is reached
364		considering initial age
365		"""
366		for inp, out in self.LIFETIME_AGE_FLOWS:
367		for p, ts in m.TIMEINDEX:
368		if (
369		m.flows[inp, out].lifetime - m.flows[inp, out].age
370		<= m.es.periods_years[p]
371		):
372		lhs = m.flow[inp, out, ts]
373		rhs = 0
374		self.lifetime_age_output.add(
375		(inp, out, p, ts), (lhs == rhs)
376		)
377
378		self.lifetime_age_output = Constraint(
379		self.LIFETIME_AGE_FLOWS, m.TIMEINDEX, noruleinit=True
380		)
381		self.lifetime_age_output_build = BuildAction(
382		rule=_lifetime_age_output_rule
383		)
384
385		def _objective_expression(self):
386		r"""Objective expression for all standard flows with fixed costs
387		and variable costs.
388
389		Depending on the attributes of the `Flow` object the following parts of
390		the objective function are created for a standard model:
391
392		* `Flow.variable_costs` is not `None`:
393		.. math::
394		\sum_{(i,o)} \sum_t P(t) \cdot w(t) \cdot c_{var}(i, o, t)
395
396		where :math:`w(t)` is the objective weighting.
397
398		In a multi-period model, in contrast, the following parts of
399		the objective function are created:
400
401		* `Flow.variable_costs` is not `None`:
402		.. math::
403		\sum_{(i,o)} \sum_{p, t} P(p, t) \cdot w(t)
404		\cdot c_{var}(i, o, t)
405
406		* `Flow.fixed_costs` is not `None` and flow has no lifetime limit
407		.. math::
408		\sum_{(i,o)} \displaystyle \sum_{pp=0}^{year_{max}}
409		P_{nominal} \cdot c_{fixed}(i, o, pp) \cdot DF^{-pp}
410
411		* `Flow.fixed_costs` is not `None` and flow has a lifetime limit,
412		but not an initial age
413		.. math::
414		\sum_{(i,o)} \displaystyle \sum_{pp=0}^{limit_{exo}}
415		P_{nominal} \cdot c_{fixed}(i, o, pp) \cdot DF^{-pp}
416
417		* `Flow.fixed_costs` is not `None` and flow has a lifetime limit,
418		and an initial age
419		.. math::
420		\sum_{(i,o)} \displaystyle \sum_{pp=0}^{limit_{exo}} P_{nominal}
421		\cdot c_{fixed}(i, o, pp) \cdot DF^{-pp}
422
423		Hereby
424
425		* :math:`DF(p) = (1 + dr)` is the discount factor for period :math:`p`
426		and :math:`dr` is the discount rate.
427		* :math:`n` is the unit lifetime and :math:`a` is the initial age.
428		* :math:`year_{max}` denotes the last year of the optimization
429		horizon, i.e. at the end of the last period.
430		* :math:`limit_{exo}=min\{year_{max}, n - a\}` is used as an
431		upper bound to ensure fixed costs for existing capacities to occur
432		within the optimization horizon. :math:`a` is the initial age
433		of an asset (or 0 if not specified).
434		"""
435		m = self.parent_block()
436
437		variable_costs = 0
438		fixed_costs = 0
439
440		if m.es.periods is None:
441		for i, o in m.FLOWS:
442		if valid_sequence(
443		m.flows[i, o].variable_costs, len(m.TIMESTEPS)
444		):
445		for t in m.TIMESTEPS:
446		variable_costs += (
447		m.flow[i, o, t]
448		* m.objective_weighting[t]
449		* m.tsam_weighting[t]
450		* m.flows[i, o].variable_costs[t]
451		)
452
453		else:
454		for i, o in m.FLOWS:
455		if valid_sequence(
456		m.flows[i, o].variable_costs, len(m.TIMESTEPS)
457		):
458		for p, t in m.TIMEINDEX:
459		variable_costs += (
460		m.flow[i, o, t]
461		* m.objective_weighting[t]
462		* m.tsam_weighting[t]
463		* m.flows[i, o].variable_costs[t]
464		* ((1 + m.discount_rate) ** -m.es.periods_years[p])
465		)
466
467		# Fixed costs for units with no lifetime limit
468		if (
469		m.flows[i, o].fixed_costs[0] is not None
470		and m.flows[i, o].nominal_capacity is not None
471		and (i, o) not in self.LIFETIME_FLOWS
472		and (i, o) not in self.LIFETIME_AGE_FLOWS
473		):
474		fixed_costs += sum(
475		m.flows[i, o].nominal_capacity
476		* m.flows[i, o].fixed_costs[pp]
477		for pp in range(m.es.end_year_of_optimization)
478		)
479
480		# Fixed costs for units with limited lifetime
481		for i, o in self.LIFETIME_FLOWS:
482	View Code Duplication	if valid_sequence(m.flows[i, o].fixed_costs, len(m.TIMESTEPS)):
		0 ignored issues – show Duplication introduced 2023-10-13 09:38 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
483		range_limit = min(
484		m.es.end_year_of_optimization,
485		m.flows[i, o].lifetime,
486		)
487		fixed_costs += sum(
488		m.flows[i, o].nominal_capacity
489		* m.flows[i, o].fixed_costs[pp]
490		for pp in range(range_limit)
491		)
492
493		for i, o in self.LIFETIME_AGE_FLOWS:
494	View Code Duplication	if valid_sequence(m.flows[i, o].fixed_costs, len(m.TIMESTEPS)):
		0 ignored issues – show Duplication introduced 2023-10-13 09:38 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
495		range_limit = min(
496		m.es.end_year_of_optimization,
497		m.flows[i, o].lifetime - m.flows[i, o].age,
498		)
499		fixed_costs += sum(
500		m.flows[i, o].nominal_capacity
501		* m.flows[i, o].fixed_costs[pp]
502		for pp in range(range_limit)
503		)
504
505		self.variable_costs = Expression(expr=variable_costs)
506		self.fixed_costs = Expression(expr=fixed_costs)
507		self.costs = Expression(expr=variable_costs + fixed_costs)
508
509		return self.costs
510

oemof / oemof-solph

SimpleFlowBlock._objective_expression() F last analyzed 2025-06-25 12:55 UTC

Complexity

Size

Duplication

Importance

How to fix Long Method Complexity

Long Method

Complexity

Duplication Side-by-Side

Filter issues like

SimpleFlowBlock._objective_expression() F
last analyzed 2025-06-25 12:55 UTC