solph._models.Model._add_parent_block_sets() - Code Metrics - Inspection of "Merge pull request #1080 from oemof/revision/repla..." - oemof/oemof-solph - Measure and Improve Code Quality continuously with Scrutinizer

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solph._models.Model._add_parent_block_sets() B

↳ Parent: solph._models

Complexity

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Total Lines	84
Code Lines	50

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eloc	50
dl	0
loc	84
rs	8.6363
c	0
b	0
f	0
cc	4
nop	1

How to fix Long Method

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

"""Solph Optimization Models.

SPDX-FileCopyrightText: Uwe Krien <[email protected]>
SPDX-FileCopyrightText: Simon Hilpert
SPDX-FileCopyrightText: Cord Kaldemeyer
SPDX-FileCopyrightText: gplssm
SPDX-FileCopyrightText: Patrik Schönfeldt
SPDX-FileCopyrightText: Saeed Sayadi
SPDX-FileCopyrightText: Johannes Kochems
SPDX-FileCopyrightText: Lennart Schürmann

SPDX-License-Identifier: MIT

"""
import logging
import warnings
from logging import getLogger

from oemof.tools import debugging
from pyomo import environ as po
from pyomo.core.plugins.transform.relax_integrality import RelaxIntegrality
from pyomo.opt import SolverFactory

from oemof.solph import processing
from oemof.solph.buses._bus import BusBlock
from oemof.solph.components._converter import ConverterBlock
from oemof.solph.flows._invest_non_convex_flow_block import (
    InvestNonConvexFlowBlock,
)
from oemof.solph.flows._investment_flow_block import InvestmentFlowBlock
from oemof.solph.flows._non_convex_flow_block import NonConvexFlowBlock
from oemof.solph.flows._simple_flow_block import SimpleFlowBlock


class LoggingError(BaseException):
    """Raised when the wrong logging level is used."""

    pass


class Model(po.ConcreteModel):
    """An energy system model for operational and/or investment
    optimization.

    Parameters
    ----------
    energysystem : EnergySystem object
        Object that holds the nodes of an oemof energy system graph.
    constraint_groups : list
        Solph looks for these groups in the given energy system and uses them
        to create the constraints of the optimization problem.
        Defaults to `Model.CONSTRAINT_GROUPS`
    discount_rate : float or None
        The rate used for discounting in a multi-period model.
        A 2% discount rate needs to be defined as 0.02.
    objective_weighting : array like (optional)
        Weights used for temporal objective function
        expressions. If nothing is passed, `timeincrement` will be used which
        is calculated from the freq length of the energy system timeindex or
        can be directly passed as a sequence.
    auto_construct : boolean
        If this value is true, the set, variables, constraints, etc. are added,
        automatically when instantiating the model. For sequential model
        building process set this value to False
        and use methods `_add_parent_block_sets`,
        `_add_parent_block_variables`, `_add_blocks`, `_add_objective`

    Attributes
    ----------
    timeincrement : sequence
        Time increments
    flows : dict
        Flows of the model
    name : str
        Name of the model
    es : solph.EnergySystem
        Energy system of the model
    meta : `pyomo.opt.results.results_.SolverResults` or None
        Solver results
    dual : `pyomo.core.base.suffix.Suffix` or None
        Store the dual variables of the model if pyomo suffix is set to IMPORT
    rc : `pyomo.core.base.suffix.Suffix` or None
        Store the reduced costs of the model if pyomo suffix is set to IMPORT

    Note
    ----

    * The discount rate is only applicable for a multi-period model.
    * If you want to work with costs data in nominal terms,
      you should specify a discount rate.
    * By default, there is a discount rate of 2% in a multi-period model.
    * If you want to provide your costs data in real terms,
      just specify `discount_rate = 0`, i.e. effectively there will be
      no discounting.


    **The following basic sets are created**:

    NODES
        A set with all nodes of the given energy system.

    TIMESTEPS
        A set with all timesteps of the given time horizon.

    PERIODS
        A set with all investment periods of the given time horizon.

    TIMEINDEX
        A set with all time indices of the given time horizon, whereby
        time indices are defined as a tuple consisting of the period and the
        timestep. E.g. (2, 10) would be timestep 10 (which is exactly the same
        as in the TIMESTEPS set) and which is in period 2.

    FLOWS
        A 2 dimensional set with all flows. Index: `(source, target)`

    **The following basic variables are created**:

    flow
        Flow from source to target indexed by FLOWS, TIMEINDEX.
        Note: Bounds of this variable are set depending on attributes of
        the corresponding flow object.

    """

    CONSTRAINT_GROUPS = [
        BusBlock,
        ConverterBlock,
        InvestmentFlowBlock,
        SimpleFlowBlock,
        NonConvexFlowBlock,
        InvestNonConvexFlowBlock,
    ]

    def __init__(self, energysystem, discount_rate=None, **kwargs):
        super().__init__()

        # Check root logger. Due to a problem with pyomo the building of the
        # model will take up to a 100 times longer if the root logger is set
        # to DEBUG

        if getLogger().level <= 10 and kwargs.get("debug", False) is False:
            msg = (
                "The root logger level is 'DEBUG'.\nDue to a communication "
                "problem between solph and the pyomo package,\nusing the "
                "DEBUG level will slow down the modelling process by the "
                "factor ~100.\nIf you need the debug-logging you can "
                "initialise the Model with 'debug=True`\nYou should only do "
                "this for small models. To avoid the slow-down use the "
                "logger\nfunction of oemof.tools (read docstring) or "
                "change the level of the root logger:\n\nimport logging\n"
                "logging.getLogger().setLevel(logging.INFO)"
            )
            raise LoggingError(msg)

        # ########################  Arguments #################################

        self.name = kwargs.get("name", type(self).__name__)

        self.es = energysystem

        if kwargs.get("timeincrement"):
            msg = "Resetting timeincrement from EnergySystem in Model."
            warnings.warn(msg, debugging.SuspiciousUsageWarning)

            self.timeincrement = kwargs.get("timeincrement")
        else:
            self.timeincrement = self.es.timeincrement

        self.objective_weighting = kwargs.get(
            "objective_weighting", self.timeincrement
        )

        self._constraint_groups = type(self).CONSTRAINT_GROUPS + kwargs.get(
            "constraint_groups", []
        )

        self._constraint_groups += [
            i
            for i in self.es.groups
            if hasattr(i, "CONSTRAINT_GROUP")
            and i not in self._constraint_groups
        ]

        self.flows = self.es.flows()

        self.solver_results = None
        self.dual = None
        self.rc = None

        if discount_rate is not None:
            self.discount_rate = discount_rate
        elif energysystem.periods is not None:
            self._set_discount_rate_with_warning()
        else:
            pass

        if kwargs.get("auto_construct", True):
            self._construct()

    def _construct(self):
        """Construct a Model by adding parent block sets and variables
        as well as child blocks and variables to it.
        """
        self._add_parent_block_sets()
        self._add_parent_block_variables()
        self._add_child_blocks()
        self._add_objective()

    def _set_discount_rate_with_warning(self):
        """
        Sets the discount rate to the standard value and raises a warning.
        """
        self.discount_rate = 0.02
        msg = (
            f"By default, a discount_rate of {self.discount_rate} "
            f"is used for a multi-period model. "
            f"If you want to use another value, "
            f"you have to specify the `discount_rate` attribute."
        )
        warnings.warn(msg, debugging.SuspiciousUsageWarning)

    def _add_parent_block_sets(self):
        """Add all basic sets to the model, i.e. NODES, TIMESTEPS and FLOWS.
        Also create sets PERIODS and TIMEINDEX used for multi-period models.
        """
        self.nodes = list(self.es.nodes)

        # create set with all nodes
        self.NODES = po.Set(initialize=[n for n in self.nodes])

        if self.es.timeincrement is None:
            msg = (
                "The EnergySystem needs to have a valid 'timeincrement' "
                "attribute to build a model."
            )
            raise AttributeError(msg)

        # pyomo set for timesteps of optimization problem
        self.TIMESTEPS = po.Set(
            initialize=range(len(self.es.timeincrement)), ordered=True
        )
        self.TIMEPOINTS = po.Set(
            initialize=range(len(self.es.timeincrement) + 1), ordered=True
        )

        if self.es.periods is None:
            self.TIMEINDEX = po.Set(
                initialize=list(
                    zip(
                        [0] * len(self.es.timeincrement),
                        range(len(self.es.timeincrement)),
                    )
                ),
                ordered=True,
            )
            self.PERIODS = po.Set(initialize=[0])
        else:
            nested_list = [
                [k] * len(self.es.periods[k])
                for k in range(len(self.es.periods))
            ]
            flattened_list = [
                item for sublist in nested_list for item in sublist
            ]
            self.TIMEINDEX = po.Set(
                initialize=list(
                    zip(flattened_list, range(len(self.es.timeincrement)))
                ),
                ordered=True,
            )
            self.PERIODS = po.Set(
                initialize=sorted(list(set(range(len(self.es.periods)))))
            )

        # (Re-)Map timesteps to periods
        timesteps_in_period = {p: [] for p in self.PERIODS}
        for p, t in self.TIMEINDEX:
            timesteps_in_period[p].append(t)
        self.TIMESTEPS_IN_PERIOD = timesteps_in_period

        # previous timesteps
        previous_timesteps = [x - 1 for x in self.TIMESTEPS]
        previous_timesteps[0] = self.TIMESTEPS.last()

        self.previous_timesteps = dict(zip(self.TIMESTEPS, previous_timesteps))

        # pyomo set for all flows in the energy system graph
        self.FLOWS = po.Set(
            initialize=self.flows.keys(), ordered=True, dimen=2
        )

        self.BIDIRECTIONAL_FLOWS = po.Set(
            initialize=[k for (k, v) in self.flows.items() if v.bidirectional],
            ordered=True,
            dimen=2,
            within=self.FLOWS,
        )

        self.UNIDIRECTIONAL_FLOWS = po.Set(
            initialize=[
                k for (k, v) in self.flows.items() if not v.bidirectional
            ],
            ordered=True,
            dimen=2,
            within=self.FLOWS,
        )

    def _add_parent_block_variables(self):
        """Add the parent block variables, which is the `flow` variable,
        indexed by FLOWS and TIMEINDEX."""
        self.flow = po.Var(self.FLOWS, self.TIMESTEPS, within=po.Reals)

        for o, i in self.FLOWS:
            if self.flows[o, i].nominal_value is not None:
                if self.flows[o, i].fix[self.TIMESTEPS.at(1)] is not None:
                    for t in self.TIMESTEPS:
                        self.flow[o, i, t].value = (
                            self.flows[o, i].fix[t]
                            * self.flows[o, i].nominal_value
                        )
                        self.flow[o, i, t].fix()
                else:
                    for t in self.TIMESTEPS:
                        self.flow[o, i, t].setub(
                            self.flows[o, i].max[t]
                            * self.flows[o, i].nominal_value
                        )
                    if not self.flows[o, i].nonconvex:
                        for t in self.TIMESTEPS:
                            self.flow[o, i, t].setlb(
                                self.flows[o, i].min[t]
                                * self.flows[o, i].nominal_value
                            )
                    elif (o, i) in self.UNIDIRECTIONAL_FLOWS:
                        for t in self.TIMESTEPS:
                            self.flow[o, i, t].setlb(0)
            else:
                if (o, i) in self.UNIDIRECTIONAL_FLOWS:
                    for t in self.TIMESTEPS:
                        self.flow[o, i, t].setlb(0)

    def _add_child_blocks(self):
        """Method to add the defined child blocks for components that have
        been grouped in the defined constraint groups. This collects all the
        constraints from the buses, components and flows blocks
        and adds them to the model.
        """
        for group in self._constraint_groups:
            block = group()
            self.add_component(str(block), block)

            # create constraints etc. related with block for all nodes
            # in the group
            block._create(group=self.es.groups.get(group))

    def _add_objective(self, sense=po.minimize, update=False):
        """Method to sum up all objective expressions from the child blocks
        that have been created. This method looks for `_objective_expression`
        attribute in the block definition and will call this method to add
        their return value to the objective function.
        """
        if update:
            self.del_component("objective")

        expr = 0

        for block in self.component_data_objects():
            if hasattr(block, "_objective_expression"):
                expr += block._objective_expression()

        self.objective = po.Objective(sense=sense, expr=expr)

    def receive_duals(self):
        """Method sets solver suffix to extract information about dual
        variables from solver. Shadow prices (duals) and reduced costs (rc) are
        set as attributes of the model.
        """
        # shadow prices
        self.dual = po.Suffix(direction=po.Suffix.IMPORT)
        # reduced costs
        self.rc = po.Suffix(direction=po.Suffix.IMPORT)

    def results(self):
        """Returns a nested dictionary of the results of this optimization.
        See the processing module for more information on results extraction.
        """
        return processing.results(self)

    def solve(self, solver="cbc", solver_io="lp", **kwargs):
        r"""Takes care of communication with solver to solve the model.

        Parameters
        ----------
        solver : string
            solver to be used e.g. "cbc", "glpk", "gurobi", "cplex"
        solver_io : string
            pyomo solver interface file format: "lp", "python", "nl", etc.
        \**kwargs : keyword arguments
            Possible keys can be set see below:

        Other Parameters
        ----------------
        solve_kwargs : dict
            Other arguments for the pyomo.opt.SolverFactory.solve() method
            Example : {"tee":True}
        cmdline_options : dict
            Dictionary with command line options for solver e.g.
            {"mipgap":"0.01"} results in "--mipgap 0.01"
            \{"interior":" "} results in "--interior"
            \Gurobi solver takes numeric parameter values such as
            {"method": 2}
        """
        solve_kwargs = kwargs.get("solve_kwargs", {})
        solver_cmdline_options = kwargs.get("cmdline_options", {})

        opt = SolverFactory(solver, solver_io=solver_io)
        # set command line options
        options = opt.options
        for k in solver_cmdline_options:
            options[k] = solver_cmdline_options[k]

        solver_results = opt.solve(self, **solve_kwargs)

        status = solver_results["Solver"][0]["Status"]
        termination_condition = solver_results["Solver"][0][
            "Termination condition"
        ]

        if status == "ok" and termination_condition == "optimal":
            logging.info("Optimization successful...")
        else:
            msg = (
                "Optimization ended with status {0} and termination "
                "condition {1}"
            )
            warnings.warn(
                msg.format(status, termination_condition), UserWarning
            )
        self.es.results = solver_results
        self.solver_results = solver_results

        return solver_results

    def relax_problem(self):
        """Relaxes integer variables to reals of optimization model self."""
        relaxer = RelaxIntegrality()
        relaxer._apply_to(self)

        return self


1			# -- coding: utf-8 --
2
3			"""Solph Optimization Models.
4
5			SPDX-FileCopyrightText: Uwe Krien <[email protected]>
6			SPDX-FileCopyrightText: Simon Hilpert
7			SPDX-FileCopyrightText: Cord Kaldemeyer
8			SPDX-FileCopyrightText: gplssm
9			SPDX-FileCopyrightText: Patrik Schönfeldt
10			SPDX-FileCopyrightText: Saeed Sayadi
11			SPDX-FileCopyrightText: Johannes Kochems
12			SPDX-FileCopyrightText: Lennart Schürmann
13
14			SPDX-License-Identifier: MIT
15
16			"""
17			import logging
18			import warnings
19			from logging import getLogger
20
21			from oemof.tools import debugging
22			from pyomo import environ as po
23			from pyomo.core.plugins.transform.relax_integrality import RelaxIntegrality
24			from pyomo.opt import SolverFactory
25
26			from oemof.solph import processing
27			from oemof.solph.buses._bus import BusBlock
28			from oemof.solph.components._converter import ConverterBlock
29			from oemof.solph.flows._invest_non_convex_flow_block import (
30			InvestNonConvexFlowBlock,
31			)
32			from oemof.solph.flows._investment_flow_block import InvestmentFlowBlock
33			from oemof.solph.flows._non_convex_flow_block import NonConvexFlowBlock
34			from oemof.solph.flows._simple_flow_block import SimpleFlowBlock
35
36
37			class LoggingError(BaseException):
38			"""Raised when the wrong logging level is used."""
39
40			pass
41
42
43			class Model(po.ConcreteModel):
44			"""An energy system model for operational and/or investment
45			optimization.
46
47			Parameters
48			----------
49			energysystem : EnergySystem object
50			Object that holds the nodes of an oemof energy system graph.
51			constraint_groups : list
52			Solph looks for these groups in the given energy system and uses them
53			to create the constraints of the optimization problem.
54			Defaults to `Model.CONSTRAINT_GROUPS`
55			discount_rate : float or None
56			The rate used for discounting in a multi-period model.
57			A 2% discount rate needs to be defined as 0.02.
58			objective_weighting : array like (optional)
59			Weights used for temporal objective function
60			expressions. If nothing is passed, `timeincrement` will be used which
61			is calculated from the freq length of the energy system timeindex or
62			can be directly passed as a sequence.
63			auto_construct : boolean
64			If this value is true, the set, variables, constraints, etc. are added,
65			automatically when instantiating the model. For sequential model
66			building process set this value to False
67			and use methods `_add_parent_block_sets`,
68			`_add_parent_block_variables`, `_add_blocks`, `_add_objective`
69
70			Attributes
71			----------
72			timeincrement : sequence
73			Time increments
74			flows : dict
75			Flows of the model
76			name : str
77			Name of the model
78			es : solph.EnergySystem
79			Energy system of the model
80			meta : `pyomo.opt.results.results_.SolverResults` or None
81			Solver results
82			dual : `pyomo.core.base.suffix.Suffix` or None
83			Store the dual variables of the model if pyomo suffix is set to IMPORT
84			rc : `pyomo.core.base.suffix.Suffix` or None
85			Store the reduced costs of the model if pyomo suffix is set to IMPORT
86
87			Note
88			----
89
90			* The discount rate is only applicable for a multi-period model.
91			* If you want to work with costs data in nominal terms,
92			you should specify a discount rate.
93			* By default, there is a discount rate of 2% in a multi-period model.
94			* If you want to provide your costs data in real terms,
95			just specify `discount_rate = 0`, i.e. effectively there will be
96			no discounting.
97
98
99			The following basic sets are created:
100
101			NODES
102			A set with all nodes of the given energy system.
103
104			TIMESTEPS
105			A set with all timesteps of the given time horizon.
106
107			PERIODS
108			A set with all investment periods of the given time horizon.
109
110			TIMEINDEX
111			A set with all time indices of the given time horizon, whereby
112			time indices are defined as a tuple consisting of the period and the
113			timestep. E.g. (2, 10) would be timestep 10 (which is exactly the same
114			as in the TIMESTEPS set) and which is in period 2.
115
116			FLOWS
117			A 2 dimensional set with all flows. Index: `(source, target)`
118
119			The following basic variables are created:
120
121			flow
122			Flow from source to target indexed by FLOWS, TIMEINDEX.
123			Note: Bounds of this variable are set depending on attributes of
124			the corresponding flow object.
125
126			"""
127
128			CONSTRAINT_GROUPS = [
129			BusBlock,
130			ConverterBlock,
131			InvestmentFlowBlock,
132			SimpleFlowBlock,
133			NonConvexFlowBlock,
134			InvestNonConvexFlowBlock,
135			]
136
137			def __init__(self, energysystem, discount_rate=None, **kwargs):
138			super().__init__()
139
140			# Check root logger. Due to a problem with pyomo the building of the
141			# model will take up to a 100 times longer if the root logger is set
142			# to DEBUG
143
144			if getLogger().level <= 10 and kwargs.get("debug", False) is False:
145			msg = (
146			"The root logger level is 'DEBUG'.\nDue to a communication "
147			"problem between solph and the pyomo package,\nusing the "
148			"DEBUG level will slow down the modelling process by the "
149			"factor ~100.\nIf you need the debug-logging you can "
150			"initialise the Model with 'debug=True`\nYou should only do "
151			"this for small models. To avoid the slow-down use the "
152			"logger\nfunction of oemof.tools (read docstring) or "
153			"change the level of the root logger:\n\nimport logging\n"
154			"logging.getLogger().setLevel(logging.INFO)"
155			)
156			raise LoggingError(msg)
157
158			# ######################## Arguments #################################
159
160			self.name = kwargs.get("name", type(self).__name__)
161
162			self.es = energysystem
163
164			if kwargs.get("timeincrement"):
165			msg = "Resetting timeincrement from EnergySystem in Model."
166			warnings.warn(msg, debugging.SuspiciousUsageWarning)
167
168			self.timeincrement = kwargs.get("timeincrement")
169			else:
170			self.timeincrement = self.es.timeincrement
171
172			self.objective_weighting = kwargs.get(
173			"objective_weighting", self.timeincrement
174			)
175
176			self._constraint_groups = type(self).CONSTRAINT_GROUPS + kwargs.get(
177			"constraint_groups", []
178			)
179
180			self._constraint_groups += [
181			i
182			for i in self.es.groups
183			if hasattr(i, "CONSTRAINT_GROUP")
184			and i not in self._constraint_groups
185			]
186
187			self.flows = self.es.flows()
188
189			self.solver_results = None
190			self.dual = None
191			self.rc = None
192
193			if discount_rate is not None:
194			self.discount_rate = discount_rate
195			elif energysystem.periods is not None:
196			self._set_discount_rate_with_warning()
197			else:
198			pass
199
200			if kwargs.get("auto_construct", True):
201			self._construct()
202
203			def _construct(self):
204			"""Construct a Model by adding parent block sets and variables
205			as well as child blocks and variables to it.
206			"""
207			self._add_parent_block_sets()
208			self._add_parent_block_variables()
209			self._add_child_blocks()
210			self._add_objective()
211
212			def _set_discount_rate_with_warning(self):
213			"""
214			Sets the discount rate to the standard value and raises a warning.
215			"""
216			self.discount_rate = 0.02
217			msg = (
218			f"By default, a discount_rate of {self.discount_rate} "
219			f"is used for a multi-period model. "
220			f"If you want to use another value, "
221			f"you have to specify the `discount_rate` attribute."
222			)
223			warnings.warn(msg, debugging.SuspiciousUsageWarning)
224
225			def _add_parent_block_sets(self):
226			"""Add all basic sets to the model, i.e. NODES, TIMESTEPS and FLOWS.
227			Also create sets PERIODS and TIMEINDEX used for multi-period models.
228			"""
229			self.nodes = list(self.es.nodes)
230
231			# create set with all nodes
232			self.NODES = po.Set(initialize=[n for n in self.nodes])
233
234			if self.es.timeincrement is None:
235			msg = (
236			"The EnergySystem needs to have a valid 'timeincrement' "
237			"attribute to build a model."
238			)
239			raise AttributeError(msg)
240
241			# pyomo set for timesteps of optimization problem
242			self.TIMESTEPS = po.Set(
243			initialize=range(len(self.es.timeincrement)), ordered=True
244			)
245			self.TIMEPOINTS = po.Set(
246			initialize=range(len(self.es.timeincrement) + 1), ordered=True
247			)
248
249			if self.es.periods is None:
250			self.TIMEINDEX = po.Set(
251			initialize=list(
252			zip(
253			[0] * len(self.es.timeincrement),
254			range(len(self.es.timeincrement)),
255			)
256			),
257			ordered=True,
258			)
259			self.PERIODS = po.Set(initialize=[0])
260			else:
261			nested_list = [
262			[k] * len(self.es.periods[k])
263			for k in range(len(self.es.periods))
264			]
265			flattened_list = [
266			item for sublist in nested_list for item in sublist
267			]
268			self.TIMEINDEX = po.Set(
269			initialize=list(
270			zip(flattened_list, range(len(self.es.timeincrement)))
271			),
272			ordered=True,
273			)
274			self.PERIODS = po.Set(
275			initialize=sorted(list(set(range(len(self.es.periods)))))
276			)
277
278			# (Re-)Map timesteps to periods
279			timesteps_in_period = {p: [] for p in self.PERIODS}
280			for p, t in self.TIMEINDEX:
281			timesteps_in_period[p].append(t)
282			self.TIMESTEPS_IN_PERIOD = timesteps_in_period
283
284			# previous timesteps
285			previous_timesteps = [x - 1 for x in self.TIMESTEPS]
286			previous_timesteps[0] = self.TIMESTEPS.last()
287
288			self.previous_timesteps = dict(zip(self.TIMESTEPS, previous_timesteps))
289
290			# pyomo set for all flows in the energy system graph
291			self.FLOWS = po.Set(
292			initialize=self.flows.keys(), ordered=True, dimen=2
293			)
294
295			self.BIDIRECTIONAL_FLOWS = po.Set(
296			initialize=[k for (k, v) in self.flows.items() if v.bidirectional],
297			ordered=True,
298			dimen=2,
299			within=self.FLOWS,
300			)
301
302			self.UNIDIRECTIONAL_FLOWS = po.Set(
303			initialize=[
304			k for (k, v) in self.flows.items() if not v.bidirectional
305			],
306			ordered=True,
307			dimen=2,
308			within=self.FLOWS,
309			)
310
311			def _add_parent_block_variables(self):
312			"""Add the parent block variables, which is the `flow` variable,
313			indexed by FLOWS and TIMEINDEX."""
314			self.flow = po.Var(self.FLOWS, self.TIMESTEPS, within=po.Reals)
315
316			for o, i in self.FLOWS:
317			if self.flows[o, i].nominal_value is not None:
318			if self.flows[o, i].fix[self.TIMESTEPS.at(1)] is not None:
319			for t in self.TIMESTEPS:
320			self.flow[o, i, t].value = (
321			self.flows[o, i].fix[t]
322			* self.flows[o, i].nominal_value
323			)
324			self.flow[o, i, t].fix()
325			else:
326			for t in self.TIMESTEPS:
327			self.flow[o, i, t].setub(
328			self.flows[o, i].max[t]
329			* self.flows[o, i].nominal_value
330			)
331			if not self.flows[o, i].nonconvex:
332			for t in self.TIMESTEPS:
333			self.flow[o, i, t].setlb(
334			self.flows[o, i].min[t]
335			* self.flows[o, i].nominal_value
336			)
337			elif (o, i) in self.UNIDIRECTIONAL_FLOWS:
338			for t in self.TIMESTEPS:
339			self.flow[o, i, t].setlb(0)
340			else:
341			if (o, i) in self.UNIDIRECTIONAL_FLOWS:
342			for t in self.TIMESTEPS:
343			self.flow[o, i, t].setlb(0)
344
345			def _add_child_blocks(self):
346			"""Method to add the defined child blocks for components that have
347			been grouped in the defined constraint groups. This collects all the
348			constraints from the buses, components and flows blocks
349			and adds them to the model.
350			"""
351			for group in self._constraint_groups:
352			block = group()
353			self.add_component(str(block), block)
354
355			# create constraints etc. related with block for all nodes
356			# in the group
357			block._create(group=self.es.groups.get(group))
358
359			def _add_objective(self, sense=po.minimize, update=False):
360			"""Method to sum up all objective expressions from the child blocks
361			that have been created. This method looks for `_objective_expression`
362			attribute in the block definition and will call this method to add
363			their return value to the objective function.
364			"""
365			if update:
366			self.del_component("objective")
367
368			expr = 0
369
370			for block in self.component_data_objects():
371			if hasattr(block, "_objective_expression"):
372			expr += block._objective_expression()
373
374			self.objective = po.Objective(sense=sense, expr=expr)
375
376			def receive_duals(self):
377			"""Method sets solver suffix to extract information about dual
378			variables from solver. Shadow prices (duals) and reduced costs (rc) are
379			set as attributes of the model.
380			"""
381			# shadow prices
382			self.dual = po.Suffix(direction=po.Suffix.IMPORT)
383			# reduced costs
384			self.rc = po.Suffix(direction=po.Suffix.IMPORT)
385
386			def results(self):
387			"""Returns a nested dictionary of the results of this optimization.
388			See the processing module for more information on results extraction.
389			"""
390			return processing.results(self)
391
392			def solve(self, solver="cbc", solver_io="lp", **kwargs):
393			r"""Takes care of communication with solver to solve the model.
394
395			Parameters
396			----------
397			solver : string
398			solver to be used e.g. "cbc", "glpk", "gurobi", "cplex"
399			solver_io : string
400			pyomo solver interface file format: "lp", "python", "nl", etc.
401			\**kwargs : keyword arguments
402			Possible keys can be set see below:
403
404			Other Parameters
405			----------------
406			solve_kwargs : dict
407			Other arguments for the pyomo.opt.SolverFactory.solve() method
408			Example : {"tee":True}
409			cmdline_options : dict
410			Dictionary with command line options for solver e.g.
411			{"mipgap":"0.01"} results in "--mipgap 0.01"
412			\{"interior":" "} results in "--interior"
413			\Gurobi solver takes numeric parameter values such as
414			{"method": 2}
415			"""
416			solve_kwargs = kwargs.get("solve_kwargs", {})
417			solver_cmdline_options = kwargs.get("cmdline_options", {})
418
419			opt = SolverFactory(solver, solver_io=solver_io)
420			# set command line options
421			options = opt.options
422			for k in solver_cmdline_options:
423			options[k] = solver_cmdline_options[k]
424
425			solver_results = opt.solve(self, **solve_kwargs)
426
427			status = solver_results["Solver"][0]["Status"]
428			termination_condition = solver_results["Solver"][0][
429			"Termination condition"
430			]
431
432			if status == "ok" and termination_condition == "optimal":
433			logging.info("Optimization successful...")
434			else:
435			msg = (
436			"Optimization ended with status {0} and termination "
437			"condition {1}"
438			)
439			warnings.warn(
440			msg.format(status, termination_condition), UserWarning
441			)
442			self.es.results = solver_results
443			self.solver_results = solver_results
444
445			return solver_results
446
447			def relax_problem(self):
448			"""Relaxes integer variables to reals of optimization model self."""
449			relaxer = RelaxIntegrality()
450			relaxer._apply_to(self)
451
452			return self
453

oemof / oemof-solph

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solph._models.Model._add_parent_block_sets() B

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