def test_basic_functionality():
times = range(1,4)
consumption = lambda t: t * 1.5
V0 = 10
p = Producer(name='Producer')
c = Consumer(consumption, name='Consumer')
s = Storage(RESOURCE, capacity=15, name='Storage')
s.volume(0).value = V0
rn = FlowNetwork(RESOURCE)
rn.connect(p, s)
rn.connect(s, c)
prob = Problem()
prob.add_constraints(chain(*(rn.constraints(t) for t in times)))
prob.objective = Minimize(sum(p.cost(t) for t in times))
solution = default_solver.solve(prob)
for t in times:
c.activity(t).take_value(solution)
p.activity(t).take_value(solution)
s.volume(t).take_value(solution)
for t in times:
assert approx(p.activity(t).value, 0)
assert approx(c.consumption[RESOURCE](t).value, consumption(t))
assert approx(s.volume(t).value, s.volume(t-1).value + s.accumulation[RESOURCE](t-1).value)
def test_basic_functionality():
times = range(1, 4)
consumption = lambda t: t * 1.5
V0 = 10
p = Producer(name='Producer')
c = Consumer(consumption, name='Consumer')
s = Storage(RESOURCE, capacity=15, name='Storage')
s.volume(0).value = V0
rn = FlowNetwork(RESOURCE)
rn.connect(p, s)
rn.connect(s, c)
prob = Problem()
prob.add_constraints(chain(*(rn.constraints(t) for t in times)))
prob.objective = Minimize(sum(p.cost(t) for t in times))
solution = default_solver.solve(prob)
for t in times:
c.activity(t).take_value(solution)
p.activity(t).take_value(solution)
s.volume(t).take_value(solution)
for t in times:
assert approx(p.activity(t).value, 0)
assert approx(c.consumption[RESOURCE](t).value, consumption(t))
assert approx(
s.volume(t).value,
s.volume(t - 1).value + s.accumulation[RESOURCE](t - 1).value)
def test_balance_simple():
time = 0
consumption = lambda t: 1
c = Consumer(consumption, name='Consumer')
prob = fs.Problem()
prob.add_constraints(c.constraints(time))
prob.objective = fs.Minimize(c.consumption[RESOURCE](time))
# Raises SolverError because the consumer wants to consume but noone delivers
solution = default_solver.solve(prob)
def run_and_save():
p = Producer(name='Producer')
c = Consumer(consumption, name='Consumer')
cl = fs.Cluster(p, c, resource=RESOURCE, name='Cluster')
run_model(p, c, cl, TIMES_1)
with open(FILENAME, 'wb') as f:
dill.dump((p, c, cl), f)
def test_state_vars():
consumption = lambda t: t * 1.5
p = Producer(name='Producer')
c = Consumer(consumption, name='Consumer')
cl = Cluster(p, c, resource=RESOURCE, name='Cluster')
m = fs.models.MyopicDispatchModel(t0=0, step=3, horizon=7)
m.add_part(cl)
m.solver = default_solver
m.advance()
m.advance()
def test_cluster():
times = list(range(1,4))
consumption = lambda t: t * 1.5
p = Producer(name='Producer')
c = Consumer(consumption, name='Consumer')
cl = Cluster(p, c, resource=RESOURCE, name='Cluster')
prob = fs.Problem()
prob.add_constraints(chain(*(cl.constraints(t) for t in times)))
prob.objective = fs.Minimize(sum(p.cost(t) for t in times))
solution = default_solver.solve(prob)
for t in times:
c.activity(t).take_value(solution)
p.activity(t).take_value(solution)
for t in times:
assert approx(p.production[RESOURCE](t).value, consumption(t))
assert approx(c.consumption[RESOURCE](t).value, consumption(t))
def test_cluster():
times = list(range(1, 4))
consumption = lambda t: t * 1.5
p = Producer(name="Producer")
c = Consumer(consumption, name="Consumer")
cl = Cluster(p, c, resource=RESOURCE, name="Cluster")
prob = fs.Problem()
prob += (part.constraints.make(t) for part, t in product(cl.descendants_and_self, times))
prob.objective = fs.Minimize(sum(p.cost(t) for t in times))
solution = default_solver.solve(prob)
for t in times:
c.activity(t).take_value(solution)
p.activity(t).take_value(solution)
for t in times:
assert approx(p.production[RESOURCE](t).value, consumption(t))
assert approx(c.consumption[RESOURCE](t).value, consumption(t))
