solph._options.Investment._check_nonconvex() - Code Metrics - Inspection of "Merge pull request #1184 from jfreissmann/fix/inve..." - oemof/oemof-solph - Measure and Improve Code Quality continuously with Scrutinizer

Completed

Push — dev ( 8af210...b94bf1 )

by Patrik

created 2025-04-02 14:54 UTC

solph._options.Investment._check_nonconvex() A

↳ Parent: solph._options

Complexity

Conditions

Size

Total Lines	11
Code Lines	6

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	6
dl	0
loc	11
rs	10
c	0
b	0
f	0
cc	4
nop	1

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

"""Optional classes to be added to a network class.

SPDX-FileCopyrightText: Uwe Krien <[email protected]>
SPDX-FileCopyrightText: Simon Hilpert
SPDX-FileCopyrightText: Cord Kaldemeyer
SPDX-FileCopyrightText: Stephan Günther
SPDX-FileCopyrightText: Patrik Schönfeldt
SPDX-FileCopyrightText: jmloenneberga
SPDX-FileCopyrightText: Johannes Kochems
SPDX-FileCopyrightText: Malte Fritz
SPDX-FileCopyrightText: Jonas Freißmann

SPDX-License-Identifier: MIT

"""
from warnings import warn

from oemof.tools import debugging

from oemof.solph._plumbing import sequence


class Investment:
    """Defines an Investment object holding all the specifications needed
    for investment modeling.

    Parameters
    ----------
    maximum : float, :math:`P_{invest,max}(p)` or :math:`E_{invest,max}(p)`
        Maximum of the additional invested capacity;
        defined per period p for a multi-period model.
    minimum : float, :math:`P_{invest,min}(p)` or :math:`E_{invest,min}(p)`
        Minimum of the additional invested capacity. If `nonconvex` is `True`,
        `minimum` defines the threshold for the invested capacity;
        defined per period p for a multi-period model.
    ep_costs : float, :math:`c_{invest,var}`
        Equivalent periodical costs or investment expenses for the investment

        * For a standard model: equivalent periodical costs for the investment
          per flow capacity, i.e. annuities for investments already calculated.
        * For a multi-period model: Investment expenses for the respective
          period (in nominal terms). Annuities are calculated within the
          objective term, also considering age and lifetime.
    existing : float, :math:`P_{exist}` or :math:`E_{exist}`
        Existing / installed capacity. The invested capacity is added on top
        of this value. Hence, existing capacities come at no additional costs.
        Not applicable if `nonconvex` is set to `True`.
    nonconvex : bool
        If `True`, a binary variable for the status of the investment is
        created. This enables additional fix investment costs (*offset*)
        independent of the invested flow capacity. Therefore, use the `offset`
        parameter.
    offset : float, :math:`c_{invest,fix}`
        Additional fixed investment costs. Only applicable if `nonconvex` is
        set to `True`.
    overall_maximum : float, :math:`P_{overall,max}` or :math:`E_{overall,max}`
        Overall maximum capacity investment, i.e. the amount of capacity
        that can be totally installed at maximum in any period (taking into
        account decommissionings); only applicable for multi-period models
    overall_minimum : float :math:`P_{overall,min}` or :math:`E_{overall,min}`
        Overall minimum capacity investment that needs to be installed
        in the last period of the optimization (taking into account
        decommissionings); only applicable for multi-period models
    lifetime : int, :math:`l`
        Units lifetime, given in years; only applicable for multi-period
        models
    age : int, :math:`a`
        Units start age, given in years at the beginning of the optimization;
        only applicable for multi-period models
    fixed_costs : float or list of float, :math:`c_{fixed}(p)`
        Fixed costs in each period (given in nominal terms);
        only applicable for multi-period models


    For the variables, constraints and parts of the objective function, which
    are created, see
    :py:class:`~oemof.solph.blocks.investment_flow.InvestmentFlow`,
    :py:class:`~oemof.solph.components.generic_storage.GenericInvestmentStorageBlock`
    :py:class:`~oemof.solph.custom.sink_dsm.SinkDSMOemofInvestmentBlock`,
    :py:class:`~oemof.solph.custom.sink_dsm.SinkDSMDLRInvestmentBlock` and
    :py:class:`~oemof.solph.custom.sink_dsm.SinkDSMDIWInvestmentBlock`.

    """  # noqa: E501

    def __init__(
        self,
        maximum=float("+inf"),
        minimum=0,
        ep_costs=0,
        existing=0,
        nonconvex=False,
        offset=0,
        overall_maximum=None,
        overall_minimum=None,
        lifetime=None,
        age=0,
        fixed_costs=None,
        custom_attributes=None,
    ):
        if custom_attributes is None:
            custom_attributes = {}
        self.maximum = sequence(maximum)
        self.minimum = sequence(minimum)
        self.ep_costs = sequence(ep_costs)
        self.existing = existing
        self.nonconvex = nonconvex
        self.offset = sequence(offset)
        self.overall_maximum = overall_maximum
        self.overall_minimum = overall_minimum
        self.lifetime = lifetime
        self.age = age
        self.fixed_costs = sequence(fixed_costs)

        for attribute in custom_attributes.keys():
            value = custom_attributes.get(attribute)
            setattr(self, attribute, value)

        self._check_invest_attributes()
        self._check_invest_attributes_maximum()
        self._check_invest_attributes_offset()
        self._check_age_and_lifetime()
        self._check_invest_attributes_nonconvex()
        self._check_nonconvex()

    def _check_invest_attributes(self):
        """Throw an error if existing is other than 0 and nonconvex is True"""
        if (self.existing != 0) and (self.nonconvex is True):
            e1 = (
                "Values for 'offset' and 'existing' are given in"
                " investement attributes. \n These two options cannot be "
                "considered at the same time."
            )
            raise AttributeError(e1)

    def _check_invest_attributes_maximum(self):
        """Throw an error if maximum is infinite and nonconvex is True"""
        if (self.maximum[0] == float("+inf")) and (self.nonconvex is True):
            e2 = (
                "Please provide a maximum investment value in case of"
                " nonconvex investment (nonconvex=True), which is in the"
                " expected magnitude."
                " \nVery high maximum values (> 10e8) as maximum investment"
                " limit might lead to numeric issues, so that no investment"
                " is done, although it is the optimal solution!"
            )
            raise AttributeError(e2)

    def _check_invest_attributes_offset(self):
        """Throw an error if offset is given without nonconvex=True"""
        if (self.offset[0] != 0) and (self.nonconvex is False):
            e3 = (
                "If `nonconvex` is `False`, the `offset` parameter will be"
                " ignored."
            )
            raise AttributeError(e3)

    def _check_age_and_lifetime(self):
        """Throw an error if age is chosen greater or equal to lifetime;
        only applicable for multi-period models
        """
        if self.lifetime is not None:
            if self.age >= self.lifetime:
                e4 = (
                    "A unit's age must be smaller than its "
                    "expected lifetime."
                )
                raise AttributeError(e4)

    def _check_invest_attributes_nonconvex(self):
        """Throw an error if nonconvex is not of type boolean."""
        if not isinstance(self.nonconvex, bool):
            e5 = (
                "The `nonconvex` parameter of the `Investment` class has to be"
                + f" of type boolean, not {type(self.nonconvex)}."
            )
            raise AttributeError(e5)

    def _check_nonconvex(self):
        """Checking for unnecessary setting of nonconvex"""
        if self.nonconvex:
            if (self.minimum.min() == 0) and (self.offset.min() == 0):
                msg = (
                    "It is not necessary to set the investment to `nonconvex` "
                    "if `minimum` and `offset` are 0.\n"
                    "This can lead to the `invest_status` variable becoming "
                    "1, even if the `nominal_capacity` is optimized to 0."
                )
                warn(msg, debugging.SuspiciousUsageWarning)


class NonConvex:
    """Defines a NonConvex object holding all the specifications for NonConvex
    Flows, i.e. Flows with binary variables associated to them.

    Parameters
    ----------
    startup_costs : numeric (iterable or scalar)
        Costs associated with a start of the flow (representing a unit).
    shutdown_costs : numeric (iterable or scalar)
        Costs associated with the shutdown of the flow (representing a unit).
    activity_costs : numeric (iterable or scalar)
        Costs associated with the active operation of the flow, independently
        from the actual output.
    inactivity_costs : numeric (iterable or scalar)
        Costs associated with not operating the flow.
    minimum_uptime : numeric or list of numeric (1 or positive integer)
        Minimum number of time steps that a flow must be greater then its
        minimum flow after startup. Be aware that minimum up and downtimes
        can contradict each other and may lead to infeasible problems.
    minimum_downtime : numeric or list of numeric (1 or positive integer)
        Minimum number of time steps a flow is forced to zero after
        shutting down. Be aware that minimum up and downtimes can
        contradict each other and may to infeasible problems.
    maximum_startups : numeric (0 or positive integer)
        Maximum number of start-ups in the optimization timeframe.
    maximum_shutdowns : numeric (0 or positive integer)
        Maximum number of shutdowns in the optimization timeframe.
    initial_status : numeric (0 or 1)
        Integer value indicating the status of the flow in the first time step
        (0 = off, 1 = on). For minimum up and downtimes, the initial status
        is set for the respective values in the beginning e.g. if a
        minimum uptime of four timesteps is defined and the initial status is
        set to one, the initial status is fixed for the four first timesteps
        of the optimization period. Otherwise if the initial status is set to
        zero and the first timesteps are fixed for the number of minimum
        downtime steps.
    negative_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.
    positive_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.
    """

    def __init__(
        self,
        initial_status=0,
        minimum_uptime=0,
        minimum_downtime=0,
        maximum_startups=None,
        maximum_shutdowns=None,
        startup_costs=None,
        shutdown_costs=None,
        activity_costs=None,
        inactivity_costs=None,
        negative_gradient_limit=None,
        positive_gradient_limit=None,
        custom_attributes=None,
    ):
        if custom_attributes is None:
            custom_attributes = {}

        self.initial_status = initial_status
        self.minimum_uptime = sequence(minimum_uptime)
        self.minimum_downtime = sequence(minimum_downtime)
        self.maximum_startups = maximum_startups
        self.maximum_shutdowns = maximum_shutdowns

        self.startup_costs = sequence(startup_costs)
        self.shutdown_costs = sequence(shutdown_costs)
        self.activity_costs = sequence(activity_costs)
        self.inactivity_costs = sequence(inactivity_costs)
        self.negative_gradient_limit = sequence(negative_gradient_limit)
        self.positive_gradient_limit = sequence(positive_gradient_limit)

        for attribute, value in custom_attributes.items():
            setattr(self, attribute, value)

        if initial_status == 0:
            self.first_flexible_timestep = self.minimum_downtime[0]
        else:
            self.first_flexible_timestep = self.minimum_uptime[0]


1			# -- coding: utf-8 --
2
3			"""Optional classes to be added to a network class.
4
5			SPDX-FileCopyrightText: Uwe Krien <[email protected]>
6			SPDX-FileCopyrightText: Simon Hilpert
7			SPDX-FileCopyrightText: Cord Kaldemeyer
8			SPDX-FileCopyrightText: Stephan Günther
9			SPDX-FileCopyrightText: Patrik Schönfeldt
10			SPDX-FileCopyrightText: jmloenneberga
11			SPDX-FileCopyrightText: Johannes Kochems
12			SPDX-FileCopyrightText: Malte Fritz
13			SPDX-FileCopyrightText: Jonas Freißmann
14
15			SPDX-License-Identifier: MIT
16
17			"""
18			from warnings import warn
19
20			from oemof.tools import debugging
21
22			from oemof.solph._plumbing import sequence
23
24
25			class Investment:
26			"""Defines an Investment object holding all the specifications needed
27			for investment modeling.
28
29			Parameters
30			----------
31			maximum : float, :math:`P_{invest,max}(p)` or :math:`E_{invest,max}(p)`
32			Maximum of the additional invested capacity;
33			defined per period p for a multi-period model.
34			minimum : float, :math:`P_{invest,min}(p)` or :math:`E_{invest,min}(p)`
35			Minimum of the additional invested capacity. If `nonconvex` is `True`,
36			`minimum` defines the threshold for the invested capacity;
37			defined per period p for a multi-period model.
38			ep_costs : float, :math:`c_{invest,var}`
39			Equivalent periodical costs or investment expenses for the investment
40
41			* For a standard model: equivalent periodical costs for the investment
42			per flow capacity, i.e. annuities for investments already calculated.
43			* For a multi-period model: Investment expenses for the respective
44			period (in nominal terms). Annuities are calculated within the
45			objective term, also considering age and lifetime.
46			existing : float, :math:`P_{exist}` or :math:`E_{exist}`
47			Existing / installed capacity. The invested capacity is added on top
48			of this value. Hence, existing capacities come at no additional costs.
49			Not applicable if `nonconvex` is set to `True`.
50			nonconvex : bool
51			If `True`, a binary variable for the status of the investment is
52			created. This enables additional fix investment costs (offset)
53			independent of the invested flow capacity. Therefore, use the `offset`
54			parameter.
55			offset : float, :math:`c_{invest,fix}`
56			Additional fixed investment costs. Only applicable if `nonconvex` is
57			set to `True`.
58			overall_maximum : float, :math:`P_{overall,max}` or :math:`E_{overall,max}`
59			Overall maximum capacity investment, i.e. the amount of capacity
60			that can be totally installed at maximum in any period (taking into
61			account decommissionings); only applicable for multi-period models
62			overall_minimum : float :math:`P_{overall,min}` or :math:`E_{overall,min}`
63			Overall minimum capacity investment that needs to be installed
64			in the last period of the optimization (taking into account
65			decommissionings); only applicable for multi-period models
66			lifetime : int, :math:`l`
67			Units lifetime, given in years; only applicable for multi-period
68			models
69			age : int, :math:`a`
70			Units start age, given in years at the beginning of the optimization;
71			only applicable for multi-period models
72			fixed_costs : float or list of float, :math:`c_{fixed}(p)`
73			Fixed costs in each period (given in nominal terms);
74			only applicable for multi-period models
75
76
77			For the variables, constraints and parts of the objective function, which
78			are created, see
79			:py:class:`~oemof.solph.blocks.investment_flow.InvestmentFlow`,
80			:py:class:`~oemof.solph.components.generic_storage.GenericInvestmentStorageBlock`
81			:py:class:`~oemof.solph.custom.sink_dsm.SinkDSMOemofInvestmentBlock`,
82			:py:class:`~oemof.solph.custom.sink_dsm.SinkDSMDLRInvestmentBlock` and
83			:py:class:`~oemof.solph.custom.sink_dsm.SinkDSMDIWInvestmentBlock`.
84
85			""" # noqa: E501
86
87			def __init__(
88			self,
89			maximum=float("+inf"),
90			minimum=0,
91			ep_costs=0,
92			existing=0,
93			nonconvex=False,
94			offset=0,
95			overall_maximum=None,
96			overall_minimum=None,
97			lifetime=None,
98			age=0,
99			fixed_costs=None,
100			custom_attributes=None,
101			):
102			if custom_attributes is None:
103			custom_attributes = {}
104			self.maximum = sequence(maximum)
105			self.minimum = sequence(minimum)
106			self.ep_costs = sequence(ep_costs)
107			self.existing = existing
108			self.nonconvex = nonconvex
109			self.offset = sequence(offset)
110			self.overall_maximum = overall_maximum
111			self.overall_minimum = overall_minimum
112			self.lifetime = lifetime
113			self.age = age
114			self.fixed_costs = sequence(fixed_costs)
115
116			for attribute in custom_attributes.keys():
117			value = custom_attributes.get(attribute)
118			setattr(self, attribute, value)
119
120			self._check_invest_attributes()
121			self._check_invest_attributes_maximum()
122			self._check_invest_attributes_offset()
123			self._check_age_and_lifetime()
124			self._check_invest_attributes_nonconvex()
125			self._check_nonconvex()
126
127			def _check_invest_attributes(self):
128			"""Throw an error if existing is other than 0 and nonconvex is True"""
129			if (self.existing != 0) and (self.nonconvex is True):
130			e1 = (
131			"Values for 'offset' and 'existing' are given in"
132			" investement attributes. \n These two options cannot be "
133			"considered at the same time."
134			)
135			raise AttributeError(e1)
136
137			def _check_invest_attributes_maximum(self):
138			"""Throw an error if maximum is infinite and nonconvex is True"""
139			if (self.maximum[0] == float("+inf")) and (self.nonconvex is True):
140			e2 = (
141			"Please provide a maximum investment value in case of"
142			" nonconvex investment (nonconvex=True), which is in the"
143			" expected magnitude."
144			" \nVery high maximum values (> 10e8) as maximum investment"
145			" limit might lead to numeric issues, so that no investment"
146			" is done, although it is the optimal solution!"
147			)
148			raise AttributeError(e2)
149
150			def _check_invest_attributes_offset(self):
151			"""Throw an error if offset is given without nonconvex=True"""
152			if (self.offset[0] != 0) and (self.nonconvex is False):
153			e3 = (
154			"If `nonconvex` is `False`, the `offset` parameter will be"
155			" ignored."
156			)
157			raise AttributeError(e3)
158
159			def _check_age_and_lifetime(self):
160			"""Throw an error if age is chosen greater or equal to lifetime;
161			only applicable for multi-period models
162			"""
163			if self.lifetime is not None:
164			if self.age >= self.lifetime:
165			e4 = (
166			"A unit's age must be smaller than its "
167			"expected lifetime."
168			)
169			raise AttributeError(e4)
170
171			def _check_invest_attributes_nonconvex(self):
172			"""Throw an error if nonconvex is not of type boolean."""
173			if not isinstance(self.nonconvex, bool):
174			e5 = (
175			"The `nonconvex` parameter of the `Investment` class has to be"
176			+ f" of type boolean, not {type(self.nonconvex)}."
177			)
178			raise AttributeError(e5)
179
180			def _check_nonconvex(self):
181			"""Checking for unnecessary setting of nonconvex"""
182			if self.nonconvex:
183			if (self.minimum.min() == 0) and (self.offset.min() == 0):
184			msg = (
185			"It is not necessary to set the investment to `nonconvex` "
186			"if `minimum` and `offset` are 0.\n"
187			"This can lead to the `invest_status` variable becoming "
188			"1, even if the `nominal_capacity` is optimized to 0."
189			)
190			warn(msg, debugging.SuspiciousUsageWarning)
191
192
193			class NonConvex:
194			"""Defines a NonConvex object holding all the specifications for NonConvex
195			Flows, i.e. Flows with binary variables associated to them.
196
197			Parameters
198			----------
199			startup_costs : numeric (iterable or scalar)
200			Costs associated with a start of the flow (representing a unit).
201			shutdown_costs : numeric (iterable or scalar)
202			Costs associated with the shutdown of the flow (representing a unit).
203			activity_costs : numeric (iterable or scalar)
204			Costs associated with the active operation of the flow, independently
205			from the actual output.
206			inactivity_costs : numeric (iterable or scalar)
207			Costs associated with not operating the flow.
208			minimum_uptime : numeric or list of numeric (1 or positive integer)
209			Minimum number of time steps that a flow must be greater then its
210			minimum flow after startup. Be aware that minimum up and downtimes
211			can contradict each other and may lead to infeasible problems.
212			minimum_downtime : numeric or list of numeric (1 or positive integer)
213			Minimum number of time steps a flow is forced to zero after
214			shutting down. Be aware that minimum up and downtimes can
215			contradict each other and may to infeasible problems.
216			maximum_startups : numeric (0 or positive integer)
217			Maximum number of start-ups in the optimization timeframe.
218			maximum_shutdowns : numeric (0 or positive integer)
219			Maximum number of shutdowns in the optimization timeframe.
220			initial_status : numeric (0 or 1)
221			Integer value indicating the status of the flow in the first time step
222			(0 = off, 1 = on). For minimum up and downtimes, the initial status
223			is set for the respective values in the beginning e.g. if a
224			minimum uptime of four timesteps is defined and the initial status is
225			set to one, the initial status is fixed for the four first timesteps
226			of the optimization period. Otherwise if the initial status is set to
227			zero and the first timesteps are fixed for the number of minimum
228			downtime steps.
229			negative_gradient_limit : numeric (iterable, scalar or None)
230			the normed upper bound on the positive difference
231			(`flow[t-1] < flow[t]`) of two consecutive flow values.
232			positive_gradient_limit : numeric (iterable, scalar or None)
233			the normed upper bound on the negative difference
234			(`flow[t-1] > flow[t]`) of two consecutive flow values.
235			"""
236
237			def __init__(
238			self,
239			initial_status=0,
240			minimum_uptime=0,
241			minimum_downtime=0,
242			maximum_startups=None,
243			maximum_shutdowns=None,
244			startup_costs=None,
245			shutdown_costs=None,
246			activity_costs=None,
247			inactivity_costs=None,
248			negative_gradient_limit=None,
249			positive_gradient_limit=None,
250			custom_attributes=None,
251			):
252			if custom_attributes is None:
253			custom_attributes = {}
254
255			self.initial_status = initial_status
256			self.minimum_uptime = sequence(minimum_uptime)
257			self.minimum_downtime = sequence(minimum_downtime)
258			self.maximum_startups = maximum_startups
259			self.maximum_shutdowns = maximum_shutdowns
260
261			self.startup_costs = sequence(startup_costs)
262			self.shutdown_costs = sequence(shutdown_costs)
263			self.activity_costs = sequence(activity_costs)
264			self.inactivity_costs = sequence(inactivity_costs)
265			self.negative_gradient_limit = sequence(negative_gradient_limit)
266			self.positive_gradient_limit = sequence(positive_gradient_limit)
267
268			for attribute, value in custom_attributes.items():
269			setattr(self, attribute, value)
270
271			if initial_status == 0:
272			self.first_flexible_timestep = self.minimum_downtime[0]
273			else:
274			self.first_flexible_timestep = self.minimum_uptime[0]
275

oemof / oemof-solph

Push — dev ( 8af210...b94bf1 )

solph._options.Investment._check_nonconvex() A

Complexity

Size

Duplication

Importance

Duplication Side-by-Side

Filter issues like