solph._options

Complexity

Total Complexity

25

Size/Duplication

Total Lines	275
Duplicated Lines	0 %

Importance

Changes

0

8 Methods

Rating	Name	Duplication	Size	Complexity
A	Investment._check_invest_attributes_offset()	0	8	3
A	Investment._check_invest_attributes()	0	9	3
A	Investment._check_invest_attributes_maximum()	0	12	3
A	Investment._check_age_and_lifetime()	0	11	3
A	Investment.__init__()	0	39	3
A	NonConvex.__init__()	0	38	4
A	Investment._check_nonconvex()	0	11	4
A	Investment._check_invest_attributes_nonconvex()	0	8	2

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

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

SPDX-FileCopyrightText: Uwe Krien <krien@uni-bremen.de>
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]