Model is infeasible

Hi,
i created a model for scheduling, in which processsteps and timesteps are implemented.
The model works fine until i add constraints which limit the processsteps to a maximum of 2 at one timestep.

for t in range(0,T):
m.addConstr(quicksum([v[(p,t)] for p in processsteps]) <= 2)

After this the model is infeasible. You can uncomment this line and run the code to see what the result would look like. Do you have any idea why it doesnt work after adding?

here is the full code:

from gurobipy import *

P = 5
T = 14

timeperiods = range(T)
processsteps = ['Form_saeubern','Form_versiegeln','Form_eintrennen','Gewebelagen_zuschneiden','Gewebelagen_drapieren']
time = {'Form_saeubern':2,'Form_versiegeln':2,'Form_eintrennen':3,'Gewebelagen_zuschneiden':5,'Gewebelagen_drapieren':2}

m = Model()

v = m.addVars(processsteps, timeperiods, vtype=GRB.BINARY)
v_i = m.addVars(processsteps, timeperiods, vtype=GRB.BINARY)
v_e = m.addVars(processsteps, timeperiods, vtype=GRB.BINARY)
w = m.addVars(processsteps, timeperiods, vtype=GRB.BINARY)
z = m.addVars(timeperiods, vtype=GRB.BINARY)

m.addConstr(quicksum([v[(p,0)] for p in processsteps]) >= 1)

m.addConstrs(v_i[(p,t)] == (v[(p,t)]-v[(p,t+1)]) for p in processsteps for t in range(0,T-1))
m.addConstrs(v_e[(p,t)] == max_(v_i[(p,t)], 0.0) for p in processsteps for t in range(0,T-1))
for p in processsteps:
m.addSOS(GRB.SOS_TYPE1, [v_e[(p,t)] for t in range(0,T-1)])

for p in processsteps:
m.addConstr(quicksum([(v[(p,t)]) for t in timeperiods]) == time[p])

for p in processsteps:
for t in range(0,T):
m.addConstr(w[(p,t)] == 1-v[(p,t)])

for t in range(0,T):
m.addGenConstrAnd(z[(t)], [w[(p,t)] for p in processsteps])

for t in range(0,T):
m.addConstr(quicksum([v[(p,t)] for p in processsteps]) <= 2)

m.setObjective(quicksum([(z[(t)]) for t in timeperiods]), GRB.MAXIMIZE)

m.update()

m.optimize()

print(m.ObjVal)

for p in processsteps:
x = []
for t in timeperiods:
x.append(int(v[(p,t)].X))
print(p,x)
print()

for p in processsteps:
x = []
for t in timeperiods:
x.append(int(w[(p,t)].X))
print(x)
print()

for p in processsteps:
x = []
for t in timeperiods:
x.append(int(v_e[(p,t)].X))
print(x)
print()

u =[]
for t in timeperiods:
u.append(int(z[(t)].X))
print(u)

Nice Regards

Sascha