def retry( retries, delaySecs, fn, *args, **keywords ):
"""Try something several times before giving up.
n: number of times to retry
delaySecs: wait this long between tries
fn: function to call
args: args to apply to function call"""
tries = 0
while not fn( *args, **keywords ) and tries < retries:
sleep( delaySecs )
tries += 1
if tries >= retries:
lg.error( "*** gave up after %i retries\n" % tries )
exit( 1 )
def moveIntfNoRetry( intf, node, printError=False ):
"""Move interface to node, without retrying.
intf: string, interface
node: Node object
printError: if true, print error"""
cmd = 'ip link set ' + intf + ' netns ' + repr( node.pid )
quietRun( cmd )
links = node.cmd( 'ip link show' )
if not intf in links:
if printError:
lg.error( '*** Error: moveIntf: ' + intf +
' not successfully moved to ' + node.name + '\n' )
return False
return True
def planning():
start_time = datetime.now()
random.seed(2)
mdl = Model("Charging-cluster_Management")
# Sets
space_range = range(50)
connector_range = range(4)
vehicle_range = range(500)
time_range = range(24)
# Parameters
SOC_0 = {}
F = {}
for j in vehicle_range:
SOC_0[j] = random.randint(20, 70)
F[j] = random.randint(SOC_0[j] + 10, 100)
A = {}
U = {}
D = {}
for j in vehicle_range:
A[j] = random.randint(1, time_range[-1] - 2)
D[j] = random.randint(A[j] + 1, time_range[-1] - 1)
for t in time_range:
if A[j] <= t <= D[j]:
U[j, t] = 1
else:
U[j, t] = 0
SOC_0 = {}
F = {}
for j in vehicle_range:
SOC_0[j] = random.randint(20, 70)
F[j] = min(
random.randint(SOC_0[j] + 10, SOC_0[j] + 10 + (D[j] - A[j]) * 5),
100)
# Variables
x = mdl.binary_var_dict(space_range, name='x')
y = mdl.binary_var_matrix(connector_range, space_range, name='y')
h = mdl.continuous_var_matrix(space_range, time_range, name='h')
z = mdl.binary_var_cube(space_range, vehicle_range, time_range, name='z')
b = mdl.binary_var_cube(space_range, range(0, 5), time_range, name='b')
w = mdl.binary_var_cube(space_range, vehicle_range, time_range, name='w')
p = mdl.continuous_var_cube(space_range,
vehicle_range,
time_range,
lb=0,
name='p')
SOC = {}
P = 22
# Constraints
for j in vehicle_range:
SOC[j] = mdl.integer_var_dict(range(A[j], D[j] + 1),
lb=0,
name=f'SOC_{j}')
mdl.add_constraint(mdl.sum(x[k] for k in space_range) <= 300, 'C1')
mdl.add_constraint(
mdl.sum(y[i, k] for i in connector_range for k in space_range) <= 500,
'C2')
for k in space_range:
mdl.add_constraint(
mdl.sum(y[i, k] for i in connector_range) <= 4 * x[k], 'C3')
for j in vehicle_range:
mdl.add_constraint(SOC[j][A[j]] == SOC_0[j], 'C4')
for j in vehicle_range:
for t in range(A[j] + 1, D[j] + 1):
mdl.add_constraint(
SOC[j][t] == SOC[j][t - 1] + mdl.sum(p[k, j, t]
for k in space_range),
'C5')
for j in vehicle_range:
for k in space_range:
for t in range(A[j] + 1, D[j] + 1):
mdl.add_constraint(p[k, j, t] <= h[k, t], 'C6')
for j in vehicle_range:
for k in space_range:
for t in range(A[j] + 1, D[j] + 1):
mdl.add_constraint(p[k, j, t] <= z[k, j, t] * P, 'C20')
# IF static power
'''for j in vehicle_range:
for t in range(A[j] + 1, D[j] + 1):
mdl.add_constraint(SOC[j][t] == SOC[j][t - 1] + mdl.sum(10 * z[k, j, t] for k in space_range), 'C5')'''
# IF SOC is a full matrix
'''for j in vehicle_range:
for t in range(D[j] + 1, time_range[-1] + 1):
mdl.add_constraint(SOC[j][t] == SOC[j][t - 1], 'C6')'''
for k in space_range:
for t in time_range:
mdl.add_constraint(
mdl.sum(z[k, j, t] for j in vehicle_range) == mdl.sum(
i * b[k, i, t] for i in range(0, 5)), 'C7')
for k in space_range:
for t in time_range:
mdl.add_constraint(
mdl.sum(b[k, i, t] for i in range(0, 5)) == 1, 'C8')
for k in space_range:
for t in time_range:
mdl.add_constraint(h[k, t] <= P, 'C9')
for k in space_range:
for i in range(0, 5):
for t in time_range:
mdl.add_constraint((i * h[k, t] - 100 * (1 - b[k, i, t])) <= P,
'C10')
for k in space_range:
for j in vehicle_range:
for t in time_range:
mdl.add_constraint(w[k, j, t] <= x[k], 'C11')
for k in space_range:
for t in time_range:
mdl.add_constraint(
mdl.sum(w[k, j, t] for j in vehicle_range) <= mdl.sum(
y[i, k] for i in connector_range), 'C7')
for j in vehicle_range:
for t in time_range:
mdl.add_constraint(
mdl.sum(w[k, j, t] for k in space_range) <= 1, 'C12')
for k in space_range:
for j in vehicle_range:
for t in time_range:
mdl.add_constraint(w[k, j, t] <= U[j, t], 'C13')
for k in space_range:
for j in vehicle_range:
for t in time_range:
mdl.add_constraint(z[k, j, t] <= w[k, j, t], 'C14')
for j in vehicle_range:
mdl.add_constraint(F[j] - SOC[j][D[j]] <= 20, 'C15')
for k in space_range:
for j in vehicle_range:
for t in range(A[j] + 1, D[j] + 1):
mdl.add_constraint(w[k, j, t] == w[k, j, t - 1], 'C16')
mdl.minimize(
mdl.sum(1000 * x[k]
for k in space_range) + mdl.sum(100 * y[i, k]
for k in space_range
for i in connector_range))
# + mdl.sum(F[j] - SOC[j][D[j]] for j in vehicle_range) * 1 )
mdl.print_information()
# assert mdl.solve(), "!!! Solve of the model fails"
mdl.solve()
mdl.report()
for k in space_range:
if x[k].solution_value != 0:
lg.error(f'x_{k} = {x[k].solution_value}')
for i in connector_range:
for k in space_range:
if y[i, k].solution_value != 0:
lg.error(f'y_{i, k} = {y[i, k].solution_value}')
for j in vehicle_range:
for t in time_range:
for k in space_range:
if w[k, j, t].solution_value != 0:
lg.error(
f'w_{k, j, t} = {w[k, j, t].solution_value} , '
f'z_{k, j, t} = {z[k, j, t].solution_value}, A_{j}={A[j]}, D_{j}={D[j]}'
)
for i in range(0, 5):
for t in time_range:
for k in space_range:
if b[k, i, t].solution_value != 0:
lg.error(
f'b_{k, i, t} = {b[k, i, t].solution_value}, '
f'z = {mdl.sum(z[k, j, t].solution_value for j in vehicle_range)}'
f', w = {mdl.sum(w[k, j, t].solution_value for j in vehicle_range)}'
)
for t in time_range:
for k in space_range:
if h[k, t].solution_value != 0:
lg.error(
f'h_{k, t} = {h[k, t].solution_value}, '
f'sum = {mdl.sum(z[k, j, t].solution_value for j in vehicle_range)}'
)
for j in vehicle_range:
lg.error(
f'SOC_0_{j} = {SOC_0[j]}, SOC_{j, D[j]} = {SOC[j][D[j]].solution_value}, F_{j}={F[j]}'
)
# print(sum([F[j] - SOC[j][D[j]].solution_value]))
end_time = datetime.now()
lg.error('Duration: {}'.format(end_time - start_time))
def planning():
start_time = datetime.now()
random.seed(2)
mdl = Model("Charging-cluster_Management")
# Sets
space_range = range(20)
connector_range = range(4)
vehicle_range = range(100)
time_range = range(24)
# Parameters
S = 50
N = 4
C_plug = 100
C_EVSE = 1000
C_grid = 10
P_EVSE = 22
P_grid = 10000
n_s = 0.9
l_star = 150
T_e = {}
l = {}
for t in time_range:
T_e[t] = random.randint(25, 80) / 100
l[t] = 100
e_d = {}
A = {}
U = {}
D = {}
for j in vehicle_range:
e_d[j] = random.randint(20, 40)
A[j] = random.randint(1, time_range[-1] - 2)
D[j] = random.randint(A[j] + 1, time_range[-1] - 1)
for t in time_range:
if A[j] <= t <= D[j]:
U[j, t] = 1
else:
U[j, t] = 0
# Variables
x = mdl.binary_var_dict(space_range, name='x')
y = mdl.binary_var_matrix(connector_range, space_range, name='y')
h = mdl.continuous_var_cube(space_range, vehicle_range, time_range, name='h')
w = mdl.binary_var_cube(space_range, vehicle_range, time_range, name='w')
e = mdl.continuous_var_cube(space_range, vehicle_range, time_range, lb=0, name='e')
p_plus = mdl.continuous_var(ub=0, name='p_plus')
p_star = mdl.continuous_var(ub=0, name='p_star')
# Constraints
mdl.add_constraint(mdl.sum(x[k] for k in space_range) <= S, 'C1')
# mdl.add_constraint(mdl.sum(y[i, k] for i in connector_range for k in space_range) <= S * N, 'C2')
for k in space_range:
mdl.add_constraint(mdl.sum(y[i, k] for i in connector_range) <= N * x[k], 'C3')
mdl.add_constraint(mdl.sum(h[k, j, t] for k in space_range for j in vehicle_range for t in time_range)
>= n_s * mdl.sum(e_d[j] for j in vehicle_range), 'C4')
for j in vehicle_range:
mdl.add_constraint(mdl.sum(h[k, j, t] for k in space_range for t in time_range)
<= e_d[j], 'C5')
for t in time_range:
mdl.add_constraint(mdl.sum(h[k, j, t] for k in space_range for j in vehicle_range) + l[t] <=
P_grid + p_plus, 'C6')
'''for k in space_range:
for j in vehicle_range:
mdl.add_constraint(w[k, j, A[j]] <= 0, 'C7')
mdl.add_constraint(w[k, j, D[j]] <= 0, 'C8')'''
for k in space_range:
for j in vehicle_range:
for t in range(A[j] + 1, D[j] + 1):
mdl.add_constraint(w[k, j, t] <= x[k], 'C9')
for k in space_range:
for t in time_range:
mdl.add_constraint(mdl.sum(w[k, j, t] for j in vehicle_range) <= mdl.sum(y[i, k] for i in connector_range)
, 'C10')
for j in vehicle_range:
for t in range(A[j], D[j] + 1):
mdl.add_constraint(mdl.sum(w[k, j, t] for k in space_range) <= 1, 'C11')
for k in space_range:
for j in vehicle_range:
for t in time_range:
mdl.add_constraint(w[k, j, t] <= U[j, t], 'C12')
for k in space_range:
for j in vehicle_range:
for t in range(A[j] + 1, D[j] + 1):
mdl.add_constraint(w[k, j, t] >= w[k, j, t - 1], 'C13')
mdl.add_constraint(w[k, j, t] <= w[k, j, t - 1], 'C13')
for k in space_range:
for j in vehicle_range:
for t in time_range:
mdl.add_constraint(h[k, j, t] <= w[k, j, t] * P_EVSE, 'C14')
for k in space_range:
for t in time_range:
mdl.add_constraint(mdl.sum(h[k, j, t] for j in vehicle_range) <= P_EVSE, 'C15')
mdl.minimize(mdl.sum(C_EVSE * x[k] for k in space_range) + mdl.sum(C_plug * y[i, k] for k in space_range
for i in connector_range) + C_grid * p_plus +
mdl.sum(T_e[t] * h[k, j, t] for k in space_range for j in vehicle_range for t in time_range))
# + mdl.sum(F[j] - SOC[j][D[j]] for j in vehicle_range) * 1 )
mdl.print_information()
# assert mdl.solve(), "!!! Solve of the model fails"
mdl.solve()
mdl.report()
for k in space_range:
if x[k].solution_value != 0:
lg.error(f'x_{k} = {x[k].solution_value}')
for i in connector_range:
for k in space_range:
if y[i, k].solution_value != 0:
lg.error(f'y_{i, k} = {y[i, k].solution_value}')
for j in vehicle_range:
for t in time_range:
for k in space_range:
if w[k, j, t].solution_value != 0 or h[k, j, t].solution_value != 0:
lg.error(f'w_{k, j, t} = {w[k, j, t].solution_value}, '
f'h_{k, j, t} = {h[k, j, t].solution_value}, '
f'A_{j} = {A[j]}, D_{j} = {D[j]}')
end_time = datetime.now()
lg.error('Duration: {}'.format(end_time - start_time))
def server_error(e):
lg.error(e)
raise e