def __init__(self,
scenario_values=None,
costcurveequation='0',
fuelcost=1,
bid_points=None,
noloadcost=0,
mustrun=False,
faststart=False,
pmin=0,
pmax=None,
name='',
index=None,
bus=None,
kind='wind',
observed_values=None,
schedule=None,
sheddingallowed=False,
**kwargs):
update_attributes(self, locals()) # load in inputs
self.is_controllable = False
self.is_stochastic = not \
(user_config.perfect_solve or user_config.deterministic_solve)
self.build_cost_model()
self.init_optimization()
self.startupcost = 0
self.shutdowncost = 0
self.fuelcost = float(fuelcost)
self.shedding_mode = sheddingallowed and user_config.economic_wind_shed
def __init__(self, power_system, times, datadir='.', is_stochastic=False):
update_attributes(self, locals())
self._resolved = False
self._setup_powersystem()
self._get_problem_info()
self._get_outputs()
self._get_costs()
self._get_prices()
def __init__(self, *args, **kwargs):
'''
Individual class defined.
Initialize the object. Often this just means assigning
all of the keyword arguments to self.
The :meth:`~OptimizationObject.init_optimization` method
should be called in __init__.
'''
update_attributes(self, locals()) # load in inputs
self.init_optimization()
def __init__(self,
name='', index=None, bus=None, schedule=None,
sheddingallowed=True,
cost_shedding=None,
):
update_attributes(self, locals()) # load in inputs
if cost_shedding is None:
self.cost_shedding = user_config.cost_load_shedding
self.init_optimization()
self.shedding_mode = False
def __init__(self, *args, **kwargs):
'''
Individual class defined.
Initialize the object. Often this just means assigning
all of the keyword arguments to self.
The :meth:`~OptimizationObject.init_optimization` method
should be called in __init__.
'''
update_attributes(self, locals()) # load in inputs
self.init_optimization()
def __init__(self,
name='',
index=None,
frombus=None,
tobus=None,
reactance=0.05,
pmin=None,
pmax=9999,
**kwargs):
update_attributes(self, locals()) # load in inputs
if self.pmin is None:
self.pmin = -1 * self.pmax # default is -1*pmax
self.init_optimization()
def __init__(self,
kind='generic',
pmin=0,
pmax=500,
minuptime=0,
mindowntime=0,
rampratemax=None,
rampratemin=None,
costcurveequation='20P',
heatrateequation=None,
fuelcost=1,
bid_points=None,
noloadcost=0,
startupcost=0,
shutdowncost=0,
startupramplimit=None,
shutdownramplimit=None,
faststart=False,
mustrun=False,
name='',
index=None,
bus=None):
update_attributes(self, locals()) # load in inputs
# The formulation below requires that startup ramp limits are set.
# The defaults are the normal ramp rate limits,
# unless Pmin so high and Rmin/max small that startup is infeasible.
# In that case the default is Pmin.
if (self.startupramplimit is None) and (self.rampratemax is not None):
if self.pmin > self.rampratemax:
self.startupramplimit = self.pmin
else:
self.startupramplimit = self.rampratemax
if (self.shutdownramplimit is None) and (self.rampratemin is not None):
if (-1 * self.pmin < self.rampratemin):
self.shutdownramplimit = -1 * self.pmin
else:
self.shutdownramplimit = self.rampratemin
self.fuelcost = float(fuelcost)
self.is_controllable = True
self.is_stochastic = False
self.commitment_problem = True
self.build_cost_model()
self.init_optimization()
def __init__(self, generators, loads, lines=None):
# load in inputs
update_attributes(self, locals(), exclude=["generators", "loads", "lines"])
self.reserve_fixed = user_config.reserve_fixed
self.reserve_load_fraction = user_config.reserve_load_fraction
self.reserve_required = (self.reserve_fixed > 0) or (self.reserve_load_fraction > 0.0)
if lines is None: # pragma: no cover
lines = []
buses = self.make_buses_list(loads, generators)
self.create_admittance_matrix(buses, lines)
self.init_optimization()
self.add_children(buses, "buses")
self.add_children(lines, "lines")
self.is_stochastic = len(filter(lambda gen: gen.is_stochastic, generators)) > 0
self.shedding_mode = False
def __init__(self, power_system, times, datadir='.', is_stochastic=True):
update_attributes(self, locals())
self._setup_powersystem()
self.scenarios = sorted(self.power_system._scenario_instances.keys())
self.stage_start = times.strings.index[0]
gen = self.power_system.get_generator_with_scenarios()
try: # single stage setup
self.probability = gen.scenario_values.probability.dropna()
except AttributeError: # staged setup
self.probability = gen._get_scenario_probabilities(times)
self.probability.index = self.scenarios
self._get_problem_info()
self._get_outputs()
self._get_costs()
self._get_prices()
def __init__(self, power_system, stage_times, stage_solutions):
update_attributes(self, locals(),
exclude=['stage_solutions', 'stage_times'])
self._resolved = power_system.is_stochastic \
or user_config.deterministic_solve or user_config.perfect_solve
self.is_stochastic = any(sln.is_stochastic for sln in stage_solutions)
times = pd.concat(
[times.non_overlap().strings for times in stage_times]).index
self.times = TimeIndex(times)
self.times.set_initial(stage_times[0].initialTime)
self.objective = self._sum_over('objective', stage_solutions)
self.solve_time = self._sum_over('solve_time', stage_solutions)
self.mipgaps = pd.Series([sln.mipgap for sln in stage_solutions])
self._get_outputs(stage_solutions)
self._get_costs(stage_solutions)
self._get_prices(stage_solutions)
def __init__(self, kind='generic',
pmin=0, pmax=500,
minuptime=0, mindowntime=0,
rampratemax=None, rampratemin=None,
costcurveequation='20P',
heatrateequation=None, fuelcost=1,
bid_points=None,
noloadcost=0,
startupcost=0, shutdowncost=0,
startupramplimit=None,
shutdownramplimit=None,
faststart=False,
mustrun=False,
name='', index=None, bus=None):
update_attributes(self, locals()) # load in inputs
# The formulation below requires that startup ramp limits are set.
# The defaults are the normal ramp rate limits,
# unless Pmin so high and Rmin/max small that startup is infeasible.
# In that case the default is Pmin.
if (self.startupramplimit is None) and (self.rampratemax is not None):
if self.pmin > self.rampratemax:
self.startupramplimit = self.pmin
else:
self.startupramplimit = self.rampratemax
if (self.shutdownramplimit is None) and (self.rampratemin is not None):
if (-1 * self.pmin < self.rampratemin):
self.shutdownramplimit = -1 * self.pmin
else:
self.shutdownramplimit = self.rampratemin
self.fuelcost = float(fuelcost)
self.is_controllable = True
self.is_stochastic = False
self.commitment_problem = True
self.build_cost_model()
self.init_optimization()
def __init__(self,
polynomial='10P',
bid_points=None,
constant_term=0,
owner=None,
times=None,
input_variable=0,
min_input=0,
max_input=1000,
num_breakpoints=user_config.breakpoints,
status_variable=True,
fixed_input=False):
update_attributes(self, locals(), exclude=['owner'])
self._parent_problem = owner._parent_problem
self.owner_id = str(owner)
self.is_pwl = (self.bid_points is not None)
self.is_linear = is_linear(self.polynomial)
if not fixed_input:
self.build_model()
def __init__(self, generators, loads, lines=None):
# load in inputs
update_attributes(self, locals(),
exclude=['generators', 'loads', 'lines'])
self.reserve_fixed = user_config.reserve_fixed
self.reserve_load_fraction = user_config.reserve_load_fraction
self.reserve_required = (self.reserve_fixed > 0) or \
(self.reserve_load_fraction > 0.0)
if lines is None: # pragma: no cover
lines = []
buses = self.make_buses_list(loads, generators)
self.create_admittance_matrix(buses, lines)
self.init_optimization()
self.add_children(buses, 'buses')
self.add_children(lines, 'lines')
self.is_stochastic = len(
filter(lambda gen: gen.is_stochastic, generators)) > 0
self.shedding_mode = False
def __init__(self,
schedule=None,
fuelcost=1,
costcurveequation='0',
bid_points=None,
noloadcost=0,
mustrun=False,
faststart=False,
sheddingallowed=False,
pmin=0, pmax=None,
name='', index=None, bus=None, kind='wind',
observed_values=None,
**kwargs):
update_attributes(self, locals()) # load in inputs
self.is_controllable = False
self.startupcost = 0
self.shutdowncost = 0
self.fuelcost = float(fuelcost)
self.build_cost_model()
self.init_optimization()
self.is_stochastic = False
self.shedding_mode = sheddingallowed and user_config.economic_wind_shed
def __init__(self, name='', index=None, frombus=None, tobus=None,
reactance=0.05, pmin=None, pmax=9999, **kwargs):
update_attributes(self, locals()) # load in inputs
if self.pmin is None:
self.pmin = -1 * self.pmax # default is -1*pmax
self.init_optimization()
def __init__(self, name=None, index=None, isSwing=False):
update_attributes(self, locals()) # load in inputs
self.generators, self.loads = [], []
self.init_optimization()
