def linear():
'''
Create a linear bid curve for one generators.
Ensure that correct cost is valued for the load.
'''
a = 5
b = 30
Pd = 221
generators = [Generator(costcurveequation='{}+{}P'.format(a, b))]
_, times = solve_problem(generators, **make_loads_times(Pd))
cost = generators[0].bids.output(times[0], evaluate=True)
assert cost == a + b * Pd
def rolling():
'''
Run a basic unit commitment over 72hrs
Ensure that the generation meets the load for each time.
'''
generators = [Generator(costcurveequation='10P+.01P^2')]
Pdt = [random.randrange(0, 200) for i in range(0, 72)]
power_system, times = solve_problem(generators,
**make_loads_times(Pdt=Pdt))
load = power_system.loads()[0]
load_balanced = all(generators[0].power(t) == load.power(t) for t in times)
assert load_balanced
def cubic_non_convex():
'''
Create a cubic, but non-convex (negative cubic term) bid curve for one generators.
Ensure that linearized cost is within +5% of the true cost
'''
Pd = 221
a = 5
b = 30
c = .2
d = .0001
generators = [
Generator(costcurveequation='{}+{}P+{}P^2 - {}P^3'.format(a, b, c, d))
]
power_system, times = solve_problem(generators, **make_loads_times(Pd))
cost = generators[0].bids.output(times[0], evaluate=True)
actual_cost = a + b * Pd + c * Pd**2 + -1 * d * Pd**3
assert actual_cost <= cost <= 1.05 * actual_cost
def coverage():
'''
Make sure that the bid range covers the whole pmin-pmax range
'''
Pd = 221
a = 5
b = 30
c = .2
d = .1
pmin = 10
pmax = 559
generators = [
Generator(costcurveequation='{}+{}P+{}P^2+{}P^3'.format(a, b, c, d),
pmin=pmin,
pmax=pmax)
]
_, times = solve_problem(generators, **make_loads_times(Pd))
bid_points = generators[0].bids.discrete_input_points
assert pmin == bid_points[0] and pmax == bid_points[-1]
def cubic_convex():
"""
Create a cubic, convex bid curve for one generators.
Ensure that linearized cost is within +5% of the true cost
"""
Pd = 221
a = 5
b = 30
c = 0.2
d = 0.1
user_config.breakpoints = 10
generators = [Generator(costcurveequation="{}+{}P+{}P^2+{}P^3".format(a, b, c, d))]
_, times = solve_problem(
generators, **make_loads_times(Pd)
) # ,problem_filename='bidproblem.lp')
cost = value(generators[0].bids.output(times[0], evaluate=True))
actual_cost = a + b * Pd + c * Pd ** 2 + d * Pd ** 3
assert actual_cost <= cost and cost <= 1.05 * actual_cost
def make_expensive_gen(**kwargs):
if 'costcurveequation' not in kwargs:
kwargs['costcurveequation'] = '{}P'.format(gen_costs['expensive'])
return Generator(name='expensive gen', **kwargs)
def make_mid_gen(**kwargs):
return Generator(name='middle-range gen',
costcurveequation='{}P'.format(gen_costs['mid']),
**kwargs)
def make_cheap_gen(**kwargs):
return Generator(name='cheap gen',
costcurveequation='{}P'.format(gen_costs['cheap']),
**kwargs)
def make_expensive_gen(**kwargs):
if "costcurveequation" not in kwargs:
kwargs["costcurveequation"] = "{}P".format(gen_costs["expensive"])
return Generator(name="expensive gen", **kwargs)
def make_mid_gen(**kwargs):
return Generator(name="middle-range gen",
costcurveequation="{}P".format(gen_costs["mid"]),
**kwargs)
def make_cheap_gen(**kwargs):
return Generator(name="cheap gen",
costcurveequation="{}P".format(gen_costs["cheap"]),
**kwargs)
