# -*- coding: utf-8 -*-
"""Optional classes to be added to a network class.
This file is part of project oemof (github.com/oemof/oemof). It's copyrighted
by the contributors recorded in the version control history of the file,
available from its original location oemof/oemof/solph/options.py
SPDX-License-Identifier: MIT
"""
from oemof.solph.plumbing import sequence
[docs]class Investment:
"""
Parameters
----------
maximum : float
Maximum of the additional invested capacity
minimum : float
Minimum of the additional invested capacity
ep_costs : float
Equivalent periodical costs for the investment, if period is one
year these costs are equal to the equivalent annual costs.
existing : float
Existing / installed capacity. The invested capacity is added on top
of this value.
"""
def __init__(self, maximum=float('+inf'), minimum=0, ep_costs=0,
existing=0):
self.maximum = maximum
self.minimum = minimum
self.ep_costs = ep_costs
self.existing = existing
[docs]class NonConvex:
"""
Parameters
----------
startup_costs : numeric (sequence or scalar)
Costs associated with a start of the flow (representing a unit).
shutdown_costs : numeric (sequence or scalar)
Costs associated with the shutdown of the flow (representing a unit).
activity_costs : numeric (sequence or scalar)
Costs associated with the active operation of the flow, independently
from the actual output.
minimum_uptime : numeric (1 or positive integer)
Minimum time 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 (1 or positive integer)
Minimum time 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.
maximum_shutdowns : numeric (0 or positive integer)
Maximum number of shutdowns.
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 edge regions e.g. if a
minimum uptime of four timesteps is defined, the initial status is
fixed for the four first and last timesteps of the optimization period.
If both, up and downtimes are defined, the initial status is set for
the maximum of both e.g. for six timesteps if a minimum downtime of
six timesteps is defined in addition to a four timestep minimum uptime.
"""
def __init__(self, **kwargs):
scalars = ['minimum_uptime', 'minimum_downtime', 'initial_status',
'maximum_startups', 'maximum_shutdowns']
sequences = ['startup_costs', 'shutdown_costs', 'activity_costs']
defaults = {'initial_status': 0}
for attribute in set(scalars + sequences + list(kwargs)):
value = kwargs.get(attribute, defaults.get(attribute))
setattr(self, attribute,
sequence(value) if attribute in sequences else value)
self._max_up_down = None
def _calculate_max_up_down(self):
"""
Calculate maximum of up and downtime for direct usage in constraints.
The maximum of both is used to set the initial status for this
number of timesteps within the edge regions.
"""
if self.minimum_uptime is not None and self.minimum_downtime is None:
max_up_down = self.minimum_uptime
elif self.minimum_uptime is None and self.minimum_downtime is not None:
max_up_down = self.minimum_downtime
else:
max_up_down = max(self.minimum_uptime, self.minimum_downtime)
self._max_up_down = max_up_down
@property
def max_up_down(self):
"""Compute or return the _max_up_down attribute."""
if self._max_up_down is None:
self._calculate_max_up_down()
return self._max_up_down