solph._models.Model.get_timestep_from_tsam_timestep() - Code Metrics - Inspection of "Integrate TSAM mode in oemof.solph" - oemof/oemof-solph - Measure and Improve Code Quality continuously with Scrutinizer

Passed

Pull Request — dev (#980)

by Patrik

created 2025-03-10 11:23 UTC

Model.get_timestep_from_tsam_timestep() A

↳ Parent: solph._models

Complexity

Conditions

Size

Total Lines	8
Code Lines	6

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	6
dl	0
loc	8
rs	10
c	0
b	0
f	0
cc	1
nop	4

# -*- 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`
    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


    **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, **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 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

        # Set up disaggregated timesteps from original timeseries
        self.TSAM_MODE = False
        if self.es.tsa_parameters is None:
            self.tsam_weighting = [1] * len(self.timeincrement)
        else:
            self.TSAM_MODE = True

            # Construct weighting from occurrences and order
            self.tsam_weighting = list(
                self.es.tsa_parameters[p]["occurrences"][k]
                for p in self.PERIODS
                for k in range(len(self.es.tsa_parameters[p]["occurrences"]))
                for _ in range(self.es.tsa_parameters[p]["timesteps"])
            )
            self.CLUSTERS = po.Set(
                initialize=list(
                    range(
                        sum(
                            len(self.es.tsa_parameters[p]["order"])
                            for p in self.PERIODS
                        )
                    )
                )
            )
            self.CLUSTERS_OFFSET = po.Set(
                initialize=list(
                    range(
                        sum(
                            len(self.es.tsa_parameters[p]["order"])
                            for p in self.PERIODS
                        )
                        + 1
                    )
                )
            )
            self.TYPICAL_CLUSTERS = po.Set(
                initialize=[
                    (p, i)
                    for p in self.PERIODS
                    for i in range(
                        len(self.es.tsa_parameters[p]["occurrences"])
                    )
                ]
            )

            self.TIMEINDEX_CLUSTER = self.get_cluster_index("order", 0)
            self.TIMEINDEX_TYPICAL_CLUSTER = self.get_cluster_index(
                "occurrences", 0
            )
            self.TIMEINDEX_TYPICAL_CLUSTER_OFFSET = self.get_cluster_index(
                "occurrences", 1
            )

        # 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_capacity 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_capacity
                        )
                        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_capacity
                        )
                    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_capacity
                            )
                    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", allow_nonoptimal=False, **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.Status
        termination_condition = solver_results.Solver.Termination_condition

        self.es.results = solver_results
        self.solver_results = solver_results

        if status == "ok" and termination_condition == "optimal":
            logging.info("Optimization successful...")
        else:
            msg = (
                f"The solver did not return an optimal solution. "
                f"Instead the optimization ended with\n "
                f"      - status: {status}\n"
                f"       - termination condition: {termination_condition}"
            )

            if allow_nonoptimal:
                warnings.warn(
                    msg.format(status, termination_condition), UserWarning
                )
            else:
                raise RuntimeError(msg)

        return solver_results

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

        return self

    def get_timestep_from_tsam_timestep(self, p, ik, g):
        """Return original timestep from cluster-based timestep"""
        t = (
            p * len(self.TIMESTEPS_IN_PERIOD[p])
            + ik * self.es.tsa_parameters[p]["timesteps"]
            + g
        )
        return t

    def get_cluster_index(self, cluster_type, offset):
        """
        Return cluster index for original or typical periods with or
        without offset
        """
        return [
            (p, k, t)
            for p in range(len(self.es.tsa_parameters))
            for k in range(len(self.es.tsa_parameters[p][cluster_type]))
            for t in range(self.es.tsa_parameters[p]["timesteps"] + offset)
        ]


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			objective_weighting : array like (optional)
56			Weights used for temporal objective function
57			expressions. If nothing is passed, `timeincrement` will be used which
58			is calculated from the freq length of the energy system timeindex or
59			can be directly passed as a sequence.
60			auto_construct : boolean
61			If this value is true, the set, variables, constraints, etc. are added,
62			automatically when instantiating the model. For sequential model
63			building process set this value to False
64			and use methods `_add_parent_block_sets`,
65			`_add_parent_block_variables`, `_add_blocks`, `_add_objective`
66
67			Attributes
68			----------
69			timeincrement : sequence
70			Time increments
71			flows : dict
72			Flows of the model
73			name : str
74			Name of the model
75			es : solph.EnergySystem
76			Energy system of the model
77			meta : `pyomo.opt.results.results_.SolverResults` or None
78			Solver results
79			dual : `pyomo.core.base.suffix.Suffix` or None
80			Store the dual variables of the model if pyomo suffix is set to IMPORT
81			rc : `pyomo.core.base.suffix.Suffix` or None
82			Store the reduced costs of the model if pyomo suffix is set to IMPORT
83
84
85			The following basic sets are created:
86
87			NODES
88			A set with all nodes of the given energy system.
89
90			TIMESTEPS
91			A set with all timesteps of the given time horizon.
92
93			PERIODS
94			A set with all investment periods of the given time horizon.
95
96			TIMEINDEX
97			A set with all time indices of the given time horizon, whereby
98			time indices are defined as a tuple consisting of the period and the
99			timestep. E.g. (2, 10) would be timestep 10 (which is exactly the same
100			as in the TIMESTEPS set) and which is in period 2.
101
102			FLOWS
103			A 2 dimensional set with all flows. Index: `(source, target)`
104
105			The following basic variables are created:
106
107			flow
108			Flow from source to target indexed by FLOWS, TIMEINDEX.
109			Note: Bounds of this variable are set depending on attributes of
110			the corresponding flow object.
111
112			"""
113
114			CONSTRAINT_GROUPS = [
115			BusBlock,
116			ConverterBlock,
117			InvestmentFlowBlock,
118			SimpleFlowBlock,
119			NonConvexFlowBlock,
120			InvestNonConvexFlowBlock,
121			]
122
123			def __init__(self, energysystem, **kwargs):
124			super().__init__()
125
126			# Check root logger. Due to a problem with pyomo the building of the
127			# model will take up to a 100 times longer if the root logger is set
128			# to DEBUG
129
130			if getLogger().level <= 10 and kwargs.get("debug", False) is False:
131			msg = (
132			"The root logger level is 'DEBUG'.\nDue to a communication "
133			"problem between solph and the pyomo package,\nusing the "
134			"DEBUG level will slow down the modelling process by the "
135			"factor ~100.\nIf you need the debug-logging you can "
136			"initialise the Model with 'debug=True`\nYou should only do "
137			"this for small models. To avoid the slow-down use the "
138			"logger\nfunction of oemof.tools (read docstring) or "
139			"change the level of the root logger:\n\nimport logging\n"
140			"logging.getLogger().setLevel(logging.INFO)"
141			)
142			raise LoggingError(msg)
143
144			# ######################## Arguments #################################
145
146			self.name = kwargs.get("name", type(self).__name__)
147
148			self.es = energysystem
149
150			if kwargs.get("timeincrement"):
151			msg = "Resetting timeincrement from EnergySystem in Model."
152			warnings.warn(msg, debugging.SuspiciousUsageWarning)
153
154			self.timeincrement = kwargs.get("timeincrement")
155			else:
156			self.timeincrement = self.es.timeincrement
157
158			self.objective_weighting = kwargs.get(
159			"objective_weighting", self.timeincrement
160			)
161
162			self._constraint_groups = type(self).CONSTRAINT_GROUPS + kwargs.get(
163			"constraint_groups", []
164			)
165
166			self._constraint_groups += [
167			i
168			for i in self.es.groups
169			if hasattr(i, "CONSTRAINT_GROUP")
170			and i not in self._constraint_groups
171			]
172
173			self.flows = self.es.flows()
174
175			self.solver_results = None
176			self.dual = None
177			self.rc = None
178
179			if energysystem.periods is not None:
180			self._set_discount_rate_with_warning()
181			else:
182			pass
183
184			if kwargs.get("auto_construct", True):
185			self._construct()
186
187			def _construct(self):
188			"""Construct a Model by adding parent block sets and variables
189			as well as child blocks and variables to it.
190			"""
191			self._add_parent_block_sets()
192			self._add_parent_block_variables()
193			self._add_child_blocks()
194			self._add_objective()
195
196			def _set_discount_rate_with_warning(self):
197			"""
198			Sets the discount rate to the standard value and raises a warning.
199			"""
200			self.discount_rate = 0.02
201			msg = (
202			f"By default, a discount_rate of {self.discount_rate} "
203			f"is used for a multi-period model. "
204			f"If you want to use another value, "
205			f"you have to specify the `discount_rate` attribute."
206			)
207			warnings.warn(msg, debugging.SuspiciousUsageWarning)
208
209			def _add_parent_block_sets(self):
210			"""Add all basic sets to the model, i.e. NODES, TIMESTEPS and FLOWS.
211			Also create sets PERIODS and TIMEINDEX used for multi-period models.
212			"""
213			self.nodes = list(self.es.nodes)
214
215			# create set with all nodes
216			self.NODES = po.Set(initialize=[n for n in self.nodes])
217
218			if self.es.timeincrement is None:
219			msg = (
220			"The EnergySystem needs to have a valid 'timeincrement' "
221			"attribute to build a model."
222			)
223			raise AttributeError(msg)
224
225			# pyomo set for timesteps of optimization problem
226			self.TIMESTEPS = po.Set(
227			initialize=range(len(self.es.timeincrement)), ordered=True
228			)
229			self.TIMEPOINTS = po.Set(
230			initialize=range(len(self.es.timeincrement) + 1), ordered=True
231			)
232
233			if self.es.periods is None:
234			self.TIMEINDEX = po.Set(
235			initialize=list(
236			zip(
237			[0] * len(self.es.timeincrement),
238			range(len(self.es.timeincrement)),
239			)
240			),
241			ordered=True,
242			)
243			self.PERIODS = po.Set(initialize=[0])
244			else:
245			nested_list = [
246			[k] * len(self.es.periods[k])
247			for k in range(len(self.es.periods))
248			]
249			flattened_list = [
250			item for sublist in nested_list for item in sublist
251			]
252			self.TIMEINDEX = po.Set(
253			initialize=list(
254			zip(flattened_list, range(len(self.es.timeincrement)))
255			),
256			ordered=True,
257			)
258			self.PERIODS = po.Set(
259			initialize=sorted(list(set(range(len(self.es.periods)))))
260			)
261
262			# (Re-)Map timesteps to periods
263			timesteps_in_period = {p: [] for p in self.PERIODS}
264			for p, t in self.TIMEINDEX:
265			timesteps_in_period[p].append(t)
266			self.TIMESTEPS_IN_PERIOD = timesteps_in_period
267
268			# Set up disaggregated timesteps from original timeseries
269			self.TSAM_MODE = False
270			if self.es.tsa_parameters is None:
271			self.tsam_weighting = [1] * len(self.timeincrement)
272			else:
273			self.TSAM_MODE = True
274
275			# Construct weighting from occurrences and order
276			self.tsam_weighting = list(
277			self.es.tsa_parameters[p]["occurrences"][k]
278			for p in self.PERIODS
279			for k in range(len(self.es.tsa_parameters[p]["occurrences"]))
280			for _ in range(self.es.tsa_parameters[p]["timesteps"])
281			)
282			self.CLUSTERS = po.Set(
283			initialize=list(
284			range(
285			sum(
286			len(self.es.tsa_parameters[p]["order"])
287			for p in self.PERIODS
288			)
289			)
290			)
291			)
292			self.CLUSTERS_OFFSET = po.Set(
293			initialize=list(
294			range(
295			sum(
296			len(self.es.tsa_parameters[p]["order"])
297			for p in self.PERIODS
298			)
299			+ 1
300			)
301			)
302			)
303			self.TYPICAL_CLUSTERS = po.Set(
304			initialize=[
305			(p, i)
306			for p in self.PERIODS
307			for i in range(
308			len(self.es.tsa_parameters[p]["occurrences"])
309			)
310			]
311			)
312
313			self.TIMEINDEX_CLUSTER = self.get_cluster_index("order", 0)
314			self.TIMEINDEX_TYPICAL_CLUSTER = self.get_cluster_index(
315			"occurrences", 0
316			)
317			self.TIMEINDEX_TYPICAL_CLUSTER_OFFSET = self.get_cluster_index(
318			"occurrences", 1
319			)
320
321			# previous timesteps
322			previous_timesteps = [x - 1 for x in self.TIMESTEPS]
323			previous_timesteps[0] = self.TIMESTEPS.last()
324
325			self.previous_timesteps = dict(zip(self.TIMESTEPS, previous_timesteps))
326
327			# pyomo set for all flows in the energy system graph
328			self.FLOWS = po.Set(
329			initialize=self.flows.keys(), ordered=True, dimen=2
330			)
331
332			self.BIDIRECTIONAL_FLOWS = po.Set(
333			initialize=[k for (k, v) in self.flows.items() if v.bidirectional],
334			ordered=True,
335			dimen=2,
336			within=self.FLOWS,
337			)
338
339			self.UNIDIRECTIONAL_FLOWS = po.Set(
340			initialize=[
341			k for (k, v) in self.flows.items() if not v.bidirectional
342			],
343			ordered=True,
344			dimen=2,
345			within=self.FLOWS,
346			)
347
348			def _add_parent_block_variables(self):
349			"""Add the parent block variables, which is the `flow` variable,
350			indexed by FLOWS and TIMEINDEX."""
351			self.flow = po.Var(self.FLOWS, self.TIMESTEPS, within=po.Reals)
352
353			for o, i in self.FLOWS:
354			if self.flows[o, i].nominal_capacity is not None:
355			if self.flows[o, i].fix[self.TIMESTEPS.at(1)] is not None:
356			for t in self.TIMESTEPS:
357			self.flow[o, i, t].value = (
358			self.flows[o, i].fix[t]
359			* self.flows[o, i].nominal_capacity
360			)
361			self.flow[o, i, t].fix()
362			else:
363			for t in self.TIMESTEPS:
364			self.flow[o, i, t].setub(
365			self.flows[o, i].max[t]
366			* self.flows[o, i].nominal_capacity
367			)
368			if not self.flows[o, i].nonconvex:
369			for t in self.TIMESTEPS:
370			self.flow[o, i, t].setlb(
371			self.flows[o, i].min[t]
372			* self.flows[o, i].nominal_capacity
373			)
374			elif (o, i) in self.UNIDIRECTIONAL_FLOWS:
375			for t in self.TIMESTEPS:
376			self.flow[o, i, t].setlb(0)
377			else:
378			if (o, i) in self.UNIDIRECTIONAL_FLOWS:
379			for t in self.TIMESTEPS:
380			self.flow[o, i, t].setlb(0)
381
382			def _add_child_blocks(self):
383			"""Method to add the defined child blocks for components that have
384			been grouped in the defined constraint groups. This collects all the
385			constraints from the buses, components and flows blocks
386			and adds them to the model.
387			"""
388			for group in self._constraint_groups:
389			block = group()
390			self.add_component(str(block), block)
391
392			# create constraints etc. related with block for all nodes
393			# in the group
394			block._create(group=self.es.groups.get(group))
395
396			def _add_objective(self, sense=po.minimize, update=False):
397			"""Method to sum up all objective expressions from the child blocks
398			that have been created. This method looks for `_objective_expression`
399			attribute in the block definition and will call this method to add
400			their return value to the objective function.
401			"""
402			if update:
403			self.del_component("objective")
404
405			expr = 0
406
407			for block in self.component_data_objects():
408			if hasattr(block, "_objective_expression"):
409			expr += block._objective_expression()
410
411			self.objective = po.Objective(sense=sense, expr=expr)
412
413			def receive_duals(self):
414			"""Method sets solver suffix to extract information about dual
415			variables from solver. Shadow prices (duals) and reduced costs (rc) are
416			set as attributes of the model.
417			"""
418			# shadow prices
419			self.dual = po.Suffix(direction=po.Suffix.IMPORT)
420			# reduced costs
421			self.rc = po.Suffix(direction=po.Suffix.IMPORT)
422
423			def results(self):
424			"""Returns a nested dictionary of the results of this optimization.
425			See the processing module for more information on results extraction.
426			"""
427			return processing.results(self)
428
429			def solve(
430			self, solver="cbc", solver_io="lp", allow_nonoptimal=False, **kwargs
431			):
432			r"""Takes care of communication with solver to solve the model.
433
434			Parameters
435			----------
436			solver : string
437			solver to be used e.g. "cbc", "glpk", "gurobi", "cplex"
438			solver_io : string
439			pyomo solver interface file format: "lp", "python", "nl", etc.
440			\**kwargs : keyword arguments
441			Possible keys can be set see below:
442
443			Other Parameters
444			----------------
445			solve_kwargs : dict
446			Other arguments for the pyomo.opt.SolverFactory.solve() method
447			Example : {"tee":True}
448			cmdline_options : dict
449			Dictionary with command line options for solver e.g.
450			{"mipgap":"0.01"} results in "--mipgap 0.01"
451			\{"interior":" "} results in "--interior"
452			\Gurobi solver takes numeric parameter values such as
453			{"method": 2}
454			"""
455			solve_kwargs = kwargs.get("solve_kwargs", {})
456			solver_cmdline_options = kwargs.get("cmdline_options", {})
457			opt = SolverFactory(solver, solver_io=solver_io)
458
459			# set command line options
460			options = opt.options
461			for k in solver_cmdline_options:
462			options[k] = solver_cmdline_options[k]
463
464			solver_results = opt.solve(self, **solve_kwargs)
465
466			status = solver_results.Solver.Status
467			termination_condition = solver_results.Solver.Termination_condition
468
469			self.es.results = solver_results
470			self.solver_results = solver_results
471
472			if status == "ok" and termination_condition == "optimal":
473			logging.info("Optimization successful...")
474			else:
475			msg = (
476			f"The solver did not return an optimal solution. "
477			f"Instead the optimization ended with\n "
478			f" - status: {status}\n"
479			f" - termination condition: {termination_condition}"
480			)
481
482			if allow_nonoptimal:
483			warnings.warn(
484			msg.format(status, termination_condition), UserWarning
485			)
486			else:
487			raise RuntimeError(msg)
488
489			return solver_results
490
491			def relax_problem(self):
492			"""Relaxes integer variables to reals of optimization model self."""
493			relaxer = RelaxIntegrality()
494			relaxer._apply_to(self)
495
496			return self
497
498			def get_timestep_from_tsam_timestep(self, p, ik, g):
499			"""Return original timestep from cluster-based timestep"""
500			t = (
501			p * len(self.TIMESTEPS_IN_PERIOD[p])
502			+ ik * self.es.tsa_parameters[p]["timesteps"]
503			+ g
504			)
505			return t
506
507			def get_cluster_index(self, cluster_type, offset):
508			"""
509			Return cluster index for original or typical periods with or
510			without offset
511			"""
512			return [
513			(p, k, t)
514			for p in range(len(self.es.tsa_parameters))
515			for k in range(len(self.es.tsa_parameters[p][cluster_type]))
516			for t in range(self.es.tsa_parameters[p]["timesteps"] + offset)
517			]
518

oemof / oemof-solph

Pull Request — dev (#980)

Model.get_timestep_from_tsam_timestep() A

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