def solve(self,net,forecast):
# Local variables
params = self.parameters
# Parameters
quiet = params['quiet']
# Problem
self.problem = self.create_problem(net,forecast,params)
if not quiet:
self.problem.show()
# Prediction
Er_list = self.problem.predict_W(self.problem.get_num_stages()-1)
assert(len(Er_list) == self.problem.T)
# Solve certainty equivalent
x,Q,gQ,results = self.problem.solve_stages(0,Er_list,self.problem.x_prev,quiet=quiet)
# Slow generator powers
xfull = results['x']
assert(xfull.size == self.problem.T*self.problem.num_x)
p_list = []
offset = 0
for t in range(self.problem.T):
p_list.append(xfull[offset:offset+self.problem.num_p])
offset += self.problem.num_x
assert(len(p_list) == self.problem.T)
# Construct policy
def apply(cls,t,x_prev,Wt):
assert(0 <= t < cls.problem.T)
assert(len(Wt) == t+1)
p = cls.data[t]
p_prev = x_prev[:cls.problem.num_p]
q,w,s,z = cls.problem.solve_stage_adjustments(t,Wt[-1],p,quiet=True)
x = cls.problem.construct_x(p=p,q=q,w=w,s=s,y=p-p_prev,z=z)
# Check feasibility
if not cls.problem.is_point_feasible(t,x,x_prev,Wt[-1]):
raise ValueError('point not feasible')
# Return
return x
policy = StochProblemMS_Policy(self.problem,data=p_list,name='Two-Stage Certainty-Equivalent')
policy.apply = MethodType(apply,policy)
# Return
return policy
def solve(self,net,forecast):
# Local variables
params = self.parameters
# Parameters
quiet = params['quiet']
# Problem
self.problem = self.create_problem(net,forecast,params)
if not quiet:
self.problem.show()
# Construct policy
def apply(cls,t,x_prev,Wt):
T = cls.problem.T
assert(0 <= t < T)
assert(len(Wt) == t+1)
w_list = Wt[-1:] + cls.problem.predict_W(T-1,t+1,Wt)
x,Q,gQ,results = cls.problem.solve_stages(t,
w_list,
x_prev,
quiet=True)
# Check feasibility
if not cls.problem.is_point_feasible(t,x,x_prev,Wt[-1]):
raise ValueError('infeasible point')
# Return
return x
policy = StochProblemMS_Policy(self.problem,
data=None,
name='Certainty-Equivalent')
policy.apply = MethodType(apply,policy)
# Return
return policy
