def def_bilinear_dynamic_cost(bl, var_in='p_in', var_out='p_out'):
"""
Method for adding bilinear dynamic cost to a block associated to variables 'var_in', 'var_out'.
Names of costs are 'cost_in' 'cost_out' and are not tunable for the moment.
Final instantaneous cost expression is called "inst_cost"
:param bl: Block
:param str var_in: Name of the input variable
:param str var_out: Name of the input variable
:return: Expression
"""
# bl.cost_in = Param(bl.time, default=0, mutable=True,
# doc=f'buying cost of variable {var_in} with respect to time')
# bl.cost_out = Param(bl.time, default=0, mutable=True,
# doc=f'selling cost of variable {var_out} with respect to time')
from lms2.base.base_units import fix_profile
fix_profile(bl, flow_name='cost_in', index_name='cost_in_index', profile_name='cost_in_value',
doc_index=f'index for the dynamic cost related to variable {var_in}',
doc_value=f'values for dynamic cost related to variable {var_in}')
fix_profile(bl, flow_name='cost_out', index_name='cost_out_index', profile_name='cost_out_value',
doc_index=f'index for the dynamic cost related to variable {var_out}',
doc_value=f'values for dynamic cost related to variable {var_out}')
def _instant_cost(m, t):
return (m.find_component(var_out)[t] * m.cost_out[t] - m.find_component(var_in)[t] * m.cost_in[t])/3600
return Expression(bl.time, rule=_instant_cost,
doc=f'instantaneous bilinear and dynamic cost, associated with variable {var_in} and {var_out}')
def def_linear_dyn_cost(m, var_name='p'):
"""
Method for adding a linear dynamic cost associated to variable 'p', to a block
Final instantaneous cost expression is called "inst_cost"
.. math::
m.instantcost(t) = m.var(t) \\times m.cost(t), \ \\forall t \\in m.time
:param m: Block
:param str var_name: Names of the expensive variable
:return: pyomo Expression
"""
from lms2.base.base_units import fix_profile
fix_profile(m,
flow_name='cost',
index_name='cost_index',
profile_name='cost_value')
def _instant_cost(m, t):
return m.find_component(var_name)[t] * m.cost[t] / 3600
return Expression(
m.time,
rule=_instant_cost,
doc=
f'instantaneous linear cost (euros/s), associated with variable {var_name}'
)
def __init__(self, *args, flow_name='p', **kwds):
from lms2.base.base_units import fix_profile
from pyomo.environ import Var
super().__init__(*args, flow_name=flow_name, **kwds)
fix_profile(self,
flow_name='pp',
profile_name='profile_value',
index_name='profile_index')
self.u = Var(
self.time,
within=Binary,
doc='binary, equals to 1 when the load is ON, 0 otherwise.')
@self.Constraint(self.time, doc='curtailement constraint')
def _curtailing(m, t):
return m.p[t], m.u[t] * m.pp[t]
def def_bilinear_dynamic_cost(bl, var_in='p_in', var_out='p_out'):
"""
Method for adding bilinear dynamic cost to a block associated to variables 'var_in', 'var_out'.
Names of costs are 'cost_in' 'cost_out' and are not tunable for the moment.
Final instantaneous cost expression is called "inst_cost"
.. warning :: should only be used for convex cost ! i.e. cost_out > -cost_in
:param bl: Block
:param str var_in: Name of the input variable
:param str var_out: Name of the input variable
:return: Expression
"""
from lms2.base.base_units import fix_profile
fix_profile(
bl,
flow_name='cost_in',
index_name='cost_in_index',
profile_name='cost_in_value',
doc_index=f'index for the dynamic cost related to variable {var_in}',
doc_value=f'values for dynamic cost related to variable {var_in}')
fix_profile(
bl,
flow_name='cost_out',
index_name='cost_out_index',
profile_name='cost_out_value',
doc_index=f'index for the dynamic cost related to variable {var_out}',
doc_value=f'values for dynamic cost related to variable {var_out}')
def _instant_cost(m, t):
return (m.find_component(var_out)[t] * m.cost_out[t] -
m.find_component(var_in)[t] * m.cost_in[t]) / 3600
return Expression(
bl.time,
rule=_instant_cost,
doc=
f'instantaneous bilinear and dynamic cost, associated with variable {var_in} and {var_out}'
)
def __init__(self, *args, flow_name='p', **kwds):
from lms2 import fix_profile
from pyomo.core import Binary
from pyomo.environ import Param, Var, Set
super().__init__(*args, flow_name=flow_name, **kwds)
def _bound_u(m, t):
if m.window.bounds()[0] <= t <= m.window.bounds()[-1]:
return 0, 1
else:
return 0, 0
fix_profile(self,
flow_name='pp',
profile_name='profile_value',
index_name='profile_index')
self.w1 = Param()
self.w2 = Param()
self.window = Set(doc='time window where load can be turned ON.')
self.u = Var(
self.time,
bounds=_bound_u,
within=Binary,
doc='binary, equals to 1 when the load is turned ON, 0 otherwise.')
@self.Constraint(doc='the load is turned on only once')
def _turned_on(m):
return sum([m.u[t] for t in m.time]) == 1
@self.Constraint(
self.time,
doc='the load is contraint to be off outside the time range')
def _bound_p(m, t):
if m.window.bounds()[0] <= t <= m.window.bounds()[-1]:
return Constraint.Skip
else:
return 0, m.p[t], 0