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Fetch from upstream #2

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ae781c3
Update README.md
bingqingchen Sep 15, 2021
35f5057
Replace inverter_policy with version in private repo
bingqingchen Sep 15, 2021
18cfb88
Merge branch 'main' of https://github.com/bingqingchen/PROF into main
bingqingchen Sep 15, 2021
3624690
Merge branch 'main' into main
bingqingchen Sep 15, 2021
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69 changes: 40 additions & 29 deletions agents/inverter_policy.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,6 +15,7 @@
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"


### Can move to utils.network if appropriate
class Net(nn.Module):
def __init__(self, n_bus, n_inverters, shared_hidden_layer_sizes, indiv_hidden_layer_sizes, n_input = 3):
super(Net, self).__init__()
Expand Down Expand Up @@ -44,8 +45,8 @@ def __init__(self, n_bus, n_inverters, shared_hidden_layer_sizes, indiv_hidden_l
# # Set up non-linear network of Linear -> BatchNorm -> ReLU -> Dropout layers
# self.n_inverters = n_inverters
# layer_sizes = [4 * n_inverters] + shared_hidden_layer_sizes
# layers = reduce(operator.add,
# [[nn.Linear(a,b), nn.BatchNorm1d(b), nn.ReLU(), nn.Dropout(p=0.2)]
# layers = reduce(operator.add,
# [[nn.Linear(a,b), nn.BatchNorm1d(b), nn.ReLU(), nn.Dropout(p=0.2)]
# for a,b in zip(layer_sizes[0:-1], layer_sizes[1:])])
# layers += [nn.Linear(layer_sizes[-1], 2 * n_inverters)]
# self.nn = nn.Sequential(*layers)
Expand Down Expand Up @@ -80,11 +81,11 @@ def __init__(self, network, memory, lr, lam = 10, scaler = 1000, **env_params):

n_bus = env_params['n_bus']
self.gen_idx = env_params['gen_idx']
self.other_idx = [i for i in range(self.n_bus) if i not in self.gen_idx]
self.other_idx = [i for i in range(n_bus) if i not in self.gen_idx]

H = env_params['H']
R = H[:, :self.n_bus]
B = H[:, self.n_bus:]
R = H[:, :n_bus]
B = H[:, n_bus:]
R_new = np.vstack([np.hstack([R[self.gen_idx][:, self.gen_idx],
R[self.gen_idx][:, self.other_idx]]),
np.hstack([R[self.other_idx][:, self.gen_idx],
Expand Down Expand Up @@ -129,7 +130,7 @@ def __init__(self, network, memory, lr, lam = 10, scaler = 1000, **env_params):
P_av = cp.Parameter(len(self.gen_idx))

# Voltage: Apply to All Buses
z = cp.hstack([self.P, self.P_nc, self.Q, self.Q_nc]) # z: (70, )
z = cp.hstack([P, P_nc, Q, Q_nc]) # z: (70, )
constraints = [self.V_lower - self.V0 <= H_new@z,
H_new@z <= self.V_upper - self.V0]

Expand All @@ -152,55 +153,66 @@ def __init__(self, network, memory, lr, lam = 10, scaler = 1000, **env_params):
def forward(self, state, Sbus, P_av, inference_flag = True):
'''
Input:
state: [dV(k-1), P_nc, Q_nc]
state: [dV(k-1), P_nc, Q_nc]
where,
Z_nc = Z - Z0
May get (n, dim) or (dim);
Output:
P, Q (with repsect to the reference point)
'''
## Get information for non-controllable loads
P_nc = Sbus.real[self.other_idx] / self.scaler
Q_nc = Sbus.imag[self.other_idx] / self.scaler
self.P_nc.value = P_nc
self.Q_nc.value = Q_nc
self.P_av.value = P_av

if P_tilde.ndimension() == 1:
P_tilde, Q_tilde = self.nn(state.to(DEVICE)) # n x n_inverter
P_all = Sbus.real /self.scaler
Q_all = Sbus.imag /self.scaler
if len(Sbus.shape)==1:
P_nc = Sbus.real[self.other_idx] / self.scaler
Q_nc = Sbus.imag[self.other_idx] / self.scaler
elif len(Sbus.shape)==2:
P_nc = Sbus.real[:, self.other_idx] / self.scaler
Q_nc = Sbus.imag[:, self.other_idx] / self.scaler
else:
P_tilde, Q_tilde = self.nn(state.to(DEVICE)) # n x n_inverter
print("Well, not expected to happen")

P_tilde, Q_tilde = self.nn(state.to(DEVICE)) # n x n_inverter

## During inference if the action is already feasible, not need to project
if inference_flag:
if self.is_feasible(P_tilde.detach().clone()/self.scaler, Q_tilde.detach().clone()/self.scaler,
P_nc, Q_nc, P_av):
return P_tilde/self.scaler, Q_tilde/self.scaler
P_tilde = P_tilde.squeeze()
Q_tilde = Q_tilde.squeeze()
if self.is_feasible(P_tilde.detach().clone()/self.scaler,
Q_tilde.detach().clone()/self.scaler,
P_nc, Q_nc, P_av):
P_all[self.gen_idx] = P_tilde.detach().cpu().numpy() / self.scaler
Q_all[self.gen_idx] = Q_tilde.detach().cpu().numpy() / self.scaler
return P_all, Q_all
else:
try:
try:
P, Q = self.proj_layer(P_tilde/self.scaler, Q_tilde/self.scaler,
torch.tensor(P_nc).float().to(DEVICE),
torch.tensor(Q_nc).float().to(DEVICE),
torch.tensor(P_av).float().to(DEVICE))
self.proj_count += 1
P_all[self.gen_idx] = P.detach().cpu().numpy()
Q_all[self.gen_idx] = Q.detach().cpu().numpy()
except: # The solver dies for some reason
P = torch.zeros_like(P_tilde)
Q = torch.zeros_like(Q_tilde)
return P, Q
P_all[self.gen_idx] = 0
Q_all[self.gen_idx] = 0
return P_all, Q_all
else:
#pdb.set_trace()
P, Q = self.proj_layer(P_tilde/self.scaler, Q_tilde/self.scaler,
torch.tensor(P_nc).float().to(DEVICE),
torch.tensor(Q_nc).float().to(DEVICE),
torch.tensor(P_av).float().to(DEVICE))
proj_loss = self.mse(P.detach(), P_tilde/self.scaler) + self.mse(Q.detach(), Q_tilde/self.scaler)
proj_loss = self.mse(P.detach(), P_tilde/self.scaler) \
+ self.mse(Q.detach(), Q_tilde/self.scaler)
return P, Q, proj_loss

def update(self, batch_size = 64, n_batch = 16):
for _ in range(n_batch):
state, Sbus, P_av = self.memory.sample_batch(batch_size = batch_size)
P, Q, proj_loss = self.forward(state, Sbus, P_av, inference_flag = False)
#pdb.set_trace()
curtail = self.ReLU(torch.tensor(P_av).to(DEVICE) - P[:, self.gen_idx])
curtail = self.ReLU(torch.tensor(P_av).to(DEVICE) - P)
loss = curtail.mean() + self.lam * proj_loss
print(f'curtail = {curtail.mean().item()}, proj_loss = {proj_loss.item()}')

Expand All @@ -222,14 +234,13 @@ def is_feasible(self, P, Q, P_nc, Q_nc, P_av):
if torch.any(v < self.V_lower_torch -self.V0_torch - eps) | torch.any(v > self.V_upper_torch-self.V0_torch+eps):
return False

P = P[self.gen_idx] + self.P0_torch[self.gen_idx]
Q = Q[self.gen_idx] + self.Q0_torch[self.gen_idx]
P = P + self.P0_torch[self.gen_idx]
Q = Q + self.Q0_torch[self.gen_idx]
PQ = torch.stack([P, Q]) # (2, 21)
if torch.any(torch.norm(PQ, dim = 0) > self.S_rating_torch + eps):
return False

if torch.any(P < 0-eps) | torch.any(P > torch.tensor(P_av).to(DEVICE)+eps):
return False
else:
return True

return True