solph._options.Investment._check_nonconvex() - Code Metrics - Inspection of "Increment alpha version counter" - oemof/oemof-solph - Measure and Improve Code Quality continuously with Scrutinizer

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Push — dev ( f78b4e...7790ca )

by Patrik

created 2025-03-24 13:13 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_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_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_nonconvex()
125
126			def _check_invest_attributes(self):
127			"""Throw an error if existing is other than 0 and nonconvex is True"""
128			if (self.existing != 0) and (self.nonconvex is True):
129			e1 = (
130			"Values for 'offset' and 'existing' are given in"
131			" investement attributes. \n These two options cannot be "
132			"considered at the same time."
133			)
134			raise AttributeError(e1)
135
136			def _check_invest_attributes_maximum(self):
137			"""Throw an error if maximum is infinite and nonconvex is True"""
138			if (self.maximum[0] == float("+inf")) and (self.nonconvex is True):
139			e2 = (
140			"Please provide a maximum investment value in case of"
141			" nonconvex investment (nonconvex=True), which is in the"
142			" expected magnitude."
143			" \nVery high maximum values (> 10e8) as maximum investment"
144			" limit might lead to numeric issues, so that no investment"
145			" is done, although it is the optimal solution!"
146			)
147			raise AttributeError(e2)
148
149			def _check_invest_attributes_offset(self):
150			"""Throw an error if offset is given without nonconvex=True"""
151			if (self.offset[0] != 0) and (self.nonconvex is False):
152			e3 = (
153			"If `nonconvex` is `False`, the `offset` parameter will be"
154			" ignored."
155			)
156			raise AttributeError(e3)
157
158			def _check_age_and_lifetime(self):
159			"""Throw an error if age is chosen greater or equal to lifetime;
160			only applicable for multi-period models
161			"""
162			if self.lifetime is not None:
163			if self.age >= self.lifetime:
164			e4 = (
165			"A unit's age must be smaller than its "
166			"expected lifetime."
167			)
168			raise AttributeError(e4)
169
170			def _check_nonconvex(self):
171			"""Checking for unnecessary setting of nonconvex"""
172			if self.nonconvex:
173			if (self.minimum.min() == 0) and (self.offset.min() == 0):
174			msg = (
175			"It is not necessary to set the investment to `nonconvex` "
176			"if `minimum` and `offset` are 0.\n"
177			"This can lead to the `invest_status` variable becoming "
178			"1, even if the `nominal_capacity` is optimized to 0."
179			)
180			warn(msg, debugging.SuspiciousUsageWarning)
181
182
183			class NonConvex:
184			"""Defines a NonConvex object holding all the specifications for NonConvex
185			Flows, i.e. Flows with binary variables associated to them.
186
187			Parameters
188			----------
189			startup_costs : numeric (iterable or scalar)
190			Costs associated with a start of the flow (representing a unit).
191			shutdown_costs : numeric (iterable or scalar)
192			Costs associated with the shutdown of the flow (representing a unit).
193			activity_costs : numeric (iterable or scalar)
194			Costs associated with the active operation of the flow, independently
195			from the actual output.
196			inactivity_costs : numeric (iterable or scalar)
197			Costs associated with not operating the flow.
198			minimum_uptime : numeric or list of numeric (1 or positive integer)
199			Minimum number of time steps that a flow must be greater then its
200			minimum flow after startup. Be aware that minimum up and downtimes
201			can contradict each other and may lead to infeasible problems.
202			minimum_downtime : numeric or list of numeric (1 or positive integer)
203			Minimum number of time steps a flow is forced to zero after
204			shutting down. Be aware that minimum up and downtimes can
205			contradict each other and may to infeasible problems.
206			maximum_startups : numeric (0 or positive integer)
207			Maximum number of start-ups in the optimization timeframe.
208			maximum_shutdowns : numeric (0 or positive integer)
209			Maximum number of shutdowns in the optimization timeframe.
210			initial_status : numeric (0 or 1)
211			Integer value indicating the status of the flow in the first time step
212			(0 = off, 1 = on). For minimum up and downtimes, the initial status
213			is set for the respective values in the beginning e.g. if a
214			minimum uptime of four timesteps is defined and the initial status is
215			set to one, the initial status is fixed for the four first timesteps
216			of the optimization period. Otherwise if the initial status is set to
217			zero and the first timesteps are fixed for the number of minimum
218			downtime steps.
219			negative_gradient_limit : numeric (iterable, scalar or None)
220			the normed upper bound on the positive difference
221			(`flow[t-1] < flow[t]`) of two consecutive flow values.
222			positive_gradient_limit : numeric (iterable, scalar or None)
223			the normed upper bound on the negative difference
224			(`flow[t-1] > flow[t]`) of two consecutive flow values.
225			"""
226
227			def __init__(
228			self,
229			initial_status=0,
230			minimum_uptime=0,
231			minimum_downtime=0,
232			maximum_startups=None,
233			maximum_shutdowns=None,
234			startup_costs=None,
235			shutdown_costs=None,
236			activity_costs=None,
237			inactivity_costs=None,
238			negative_gradient_limit=None,
239			positive_gradient_limit=None,
240			custom_attributes=None,
241			):
242			if custom_attributes is None:
243			custom_attributes = {}
244
245			self.initial_status = initial_status
246			self.minimum_uptime = sequence(minimum_uptime)
247			self.minimum_downtime = sequence(minimum_downtime)
248			self.maximum_startups = maximum_startups
249			self.maximum_shutdowns = maximum_shutdowns
250
251			self.startup_costs = sequence(startup_costs)
252			self.shutdown_costs = sequence(shutdown_costs)
253			self.activity_costs = sequence(activity_costs)
254			self.inactivity_costs = sequence(inactivity_costs)
255			self.negative_gradient_limit = sequence(negative_gradient_limit)
256			self.positive_gradient_limit = sequence(positive_gradient_limit)
257
258			for attribute, value in custom_attributes.items():
259			setattr(self, attribute, value)
260
261			if initial_status == 0:
262			self.first_flexible_timestep = self.minimum_downtime[0]
263			else:
264			self.first_flexible_timestep = self.minimum_uptime[0]
265

oemof / oemof-solph

Push — dev ( f78b4e...7790ca )

solph._options.Investment._check_nonconvex() A

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