solph._options.Investment._check_invest_attributes_offset() - Code Metrics - Inspection of "Steamline investment periods" - oemof/oemof-solph - Measure and Improve Code Quality continuously with Scrutinizer

Passed

Pull Request — dev (#1183)

by Patrik

created 2025-08-29 13:17 UTC

Investment._check_invest_attributes_offset() A

↳ Parent: solph._options

Complexity

Conditions

Size

Total Lines	8
Code Lines	5

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	5
dl	0
loc	8
rs	10
c	0
b	0
f	0
cc	3
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


    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,
        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

        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_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_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
67
68			For the variables, constraints and parts of the objective function, which
69			are created, see
70			:py:class:`~oemof.solph.blocks.investment_flow.InvestmentFlow`,
71			:py:class:`~oemof.solph.components.generic_storage.GenericInvestmentStorageBlock`
72			:py:class:`~oemof.solph.custom.sink_dsm.SinkDSMOemofInvestmentBlock`,
73			:py:class:`~oemof.solph.custom.sink_dsm.SinkDSMDLRInvestmentBlock` and
74			:py:class:`~oemof.solph.custom.sink_dsm.SinkDSMDIWInvestmentBlock`.
75
76			""" # noqa: E501
77
78			def __init__(
79			self,
80			maximum=float("+inf"),
81			minimum=0,
82			ep_costs=0,
83			existing=0,
84			nonconvex=False,
85			offset=0,
86			overall_maximum=None,
87			overall_minimum=None,
88			custom_attributes=None,
89			):
90			if custom_attributes is None:
91			custom_attributes = {}
92			self.maximum = sequence(maximum)
93			self.minimum = sequence(minimum)
94			self.ep_costs = sequence(ep_costs)
95			self.existing = existing
96			self.nonconvex = nonconvex
97			self.offset = sequence(offset)
98			self.overall_maximum = overall_maximum
99			self.overall_minimum = overall_minimum
100
101			for attribute in custom_attributes.keys():
102			value = custom_attributes.get(attribute)
103			setattr(self, attribute, value)
104
105			self._check_invest_attributes()
106			self._check_invest_attributes_maximum()
107			self._check_invest_attributes_offset()
108			self._check_invest_attributes_nonconvex()
109			self._check_nonconvex()
110
111			def _check_invest_attributes(self):
112			"""Throw an error if existing is other than 0 and nonconvex is True"""
113			if (self.existing != 0) and (self.nonconvex is True):
114			e1 = (
115			"Values for 'offset' and 'existing' are given in"
116			" investement attributes. \n These two options cannot be "
117			"considered at the same time."
118			)
119			raise AttributeError(e1)
120
121			def _check_invest_attributes_maximum(self):
122			"""Throw an error if maximum is infinite and nonconvex is True"""
123			if (self.maximum[0] == float("+inf")) and (self.nonconvex is True):
124			e2 = (
125			"Please provide a maximum investment value in case of"
126			" nonconvex investment (nonconvex=True), which is in the"
127			" expected magnitude."
128			" \nVery high maximum values (> 10e8) as maximum investment"
129			" limit might lead to numeric issues, so that no investment"
130			" is done, although it is the optimal solution!"
131			)
132			raise AttributeError(e2)
133
134			def _check_invest_attributes_offset(self):
135			"""Throw an error if offset is given without nonconvex=True"""
136			if (self.offset[0] != 0) and (self.nonconvex is False):
137			e3 = (
138			"If `nonconvex` is `False`, the `offset` parameter will be"
139			" ignored."
140			)
141			raise AttributeError(e3)
142
143			def _check_invest_attributes_nonconvex(self):
144			"""Throw an error if nonconvex is not of type boolean."""
145			if not isinstance(self.nonconvex, bool):
146			e5 = (
147			"The `nonconvex` parameter of the `Investment` class has to be"
148			+ f" of type boolean, not {type(self.nonconvex)}."
149			)
150			raise AttributeError(e5)
151
152			def _check_nonconvex(self):
153			"""Checking for unnecessary setting of nonconvex"""
154			if self.nonconvex:
155			if (self.minimum.min() == 0) and (self.offset.min() == 0):
156			msg = (
157			"It is not necessary to set the investment to `nonconvex` "
158			"if `minimum` and `offset` are 0.\n"
159			"This can lead to the `invest_status` variable becoming "
160			"1, even if the `nominal_capacity` is optimized to 0."
161			)
162			warn(msg, debugging.SuspiciousUsageWarning)
163
164
165			class NonConvex:
166			"""Defines a NonConvex object holding all the specifications for NonConvex
167			Flows, i.e. Flows with binary variables associated to them.
168
169			Parameters
170			----------
171			startup_costs : numeric (iterable or scalar)
172			Costs associated with a start of the flow (representing a unit).
173			shutdown_costs : numeric (iterable or scalar)
174			Costs associated with the shutdown of the flow (representing a unit).
175			activity_costs : numeric (iterable or scalar)
176			Costs associated with the active operation of the flow, independently
177			from the actual output.
178			inactivity_costs : numeric (iterable or scalar)
179			Costs associated with not operating the flow.
180			minimum_uptime : numeric or list of numeric (1 or positive integer)
181			Minimum number of time steps that a flow must be greater then its
182			minimum flow after startup. Be aware that minimum up and downtimes
183			can contradict each other and may lead to infeasible problems.
184			minimum_downtime : numeric or list of numeric (1 or positive integer)
185			Minimum number of time steps a flow is forced to zero after
186			shutting down. Be aware that minimum up and downtimes can
187			contradict each other and may to infeasible problems.
188			maximum_startups : numeric (0 or positive integer)
189			Maximum number of start-ups in the optimization timeframe.
190			maximum_shutdowns : numeric (0 or positive integer)
191			Maximum number of shutdowns in the optimization timeframe.
192			initial_status : numeric (0 or 1)
193			Integer value indicating the status of the flow in the first time step
194			(0 = off, 1 = on). For minimum up and downtimes, the initial status
195			is set for the respective values in the beginning e.g. if a
196			minimum uptime of four timesteps is defined and the initial status is
197			set to one, the initial status is fixed for the four first timesteps
198			of the optimization period. Otherwise if the initial status is set to
199			zero and the first timesteps are fixed for the number of minimum
200			downtime steps.
201			negative_gradient_limit : numeric (iterable, scalar or None)
202			the normed upper bound on the positive difference
203			(`flow[t-1] < flow[t]`) of two consecutive flow values.
204			positive_gradient_limit : numeric (iterable, scalar or None)
205			the normed upper bound on the negative difference
206			(`flow[t-1] > flow[t]`) of two consecutive flow values.
207			"""
208
209			def __init__(
210			self,
211			initial_status=0,
212			minimum_uptime=0,
213			minimum_downtime=0,
214			maximum_startups=None,
215			maximum_shutdowns=None,
216			startup_costs=None,
217			shutdown_costs=None,
218			activity_costs=None,
219			inactivity_costs=None,
220			negative_gradient_limit=None,
221			positive_gradient_limit=None,
222			custom_attributes=None,
223			):
224			if custom_attributes is None:
225			custom_attributes = {}
226
227			self.initial_status = initial_status
228			self.minimum_uptime = sequence(minimum_uptime)
229			self.minimum_downtime = sequence(minimum_downtime)
230			self.maximum_startups = maximum_startups
231			self.maximum_shutdowns = maximum_shutdowns
232
233			self.startup_costs = sequence(startup_costs)
234			self.shutdown_costs = sequence(shutdown_costs)
235			self.activity_costs = sequence(activity_costs)
236			self.inactivity_costs = sequence(inactivity_costs)
237			self.negative_gradient_limit = sequence(negative_gradient_limit)
238			self.positive_gradient_limit = sequence(positive_gradient_limit)
239
240			for attribute, value in custom_attributes.items():
241			setattr(self, attribute, value)
242
243			if initial_status == 0:
244			self.first_flexible_timestep = self.minimum_downtime[0]
245			else:
246			self.first_flexible_timestep = self.minimum_uptime[0]
247

oemof / oemof-solph

Pull Request — dev (#1183)

Investment._check_invest_attributes_offset() A

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