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Little bit of a code review
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21 files changed

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‎models/pitt/Reduced_Order_Model/arl.py‎

Lines changed: 95 additions & 89 deletions
Original file line numberDiff line numberDiff line change
@@ -9,22 +9,25 @@
99
import numpy as np
1010
from process_input_data import process_data_norm
1111

12+
1213
class arl:
1314
"""
1415
Aggregate responsive load (ARL) agent
1516
Attributes：
16-
(1) house_number : The number of hvac to be aggragted
17-
(2) Bid RNN
18-
(3) Response RNN
17+
(1) house_number : The number of hvac to be aggregated
18+
(2) Bid RNN
19+
(3) Response RNN
1920
2021
Functions:
2122
1) Formulate bid RNN
22-
Input：RNN input features (temp_rnn,schedule_rnn,limit_rnn,ramp_rnn,Hm_rnn,Ca_rnn,Cm_rnn,Ua_rnn)
23+
Input：RNN input features (temp_rnn, schedule_rnn, limit_rnn, ramp_rnn,
24+
Hm_rnn, Ca_rnn, Cm_rnn, Ua_rnn)
2325
historical input features
2426
Output: bids
2527
2628
2) Response to the clearing price (adjust the aggregated load)
27-
Input：RNN input features (price_rnn,temp_rnn,schedule_rnn,limit_rnn,ramp_rnn,Hm_rnn,Ca_rnn,Cm_rnn,Ua_rnn,tint_rnn,sqft_rnn)
29+
Input： RNN input features (price_rnn, temp_rnn, schedule_rnn, limit_rnn, ramp_rnn,
30+
Hm_rnn, Ca_rnn, Cm_rnn, Ua_rnn, tint_rnn, sqft_rnn)
2831
Output: aggregate load
2932
3033
3) obtain clearing price
@@ -38,15 +41,15 @@ class arl:
3841
3942
"""
4043

41-
def __init__(self,house_num,rnn1,rnn2,aucObj,hn):
44+
def __init__(self, house_num, rnn1, rnn2, aucObj, hn):
4245
""" Initializes the class
4346
"""
4447
self.house_number = house_num
4548
self.bid_RNN = rnn1
4649
self.response_RNN = rnn2
4750
self.res_load = 0
4851
self.unres_load = 0
49-
self.agg_resp_max = 0
52+
self.agg_resp_max = 0
5053
self.states = np.zeros(self.house_number)
5154
self.bid_q = np.zeros(self.house_number)
5255
self.bid_q.fill(0.5)
@@ -58,26 +61,43 @@ def __init__(self,house_num,rnn1,rnn2,aucObj,hn):
5861
self.step = 288
5962
self.input_num = 13
6063
self.max_num = 1500
61-
62-
self.inputs = np.zeros((self.house_number,self.day,self.step,self.input_num))
63-
self.inputs[:,:,:,1] = np.tile(np.load('input/inputs.npy', allow_pickle=True).item().get('temp'), (self.house_number, 1, 1))
64-
self.inputs[:,:,:,2] = np.load('input/inputs.npy', allow_pickle=True).item().get('schedule')[:self.house_number]
65-
self.inputs[:,:,:,3] = np.tile(np.load('input/inputs.npy', allow_pickle=True).item().get('limit').reshape(self.max_num, 1, 1), (1, self.day, self.step))[:self.house_number]
66-
self.inputs[:,:,:,4] = np.tile(np.load('input/inputs.npy', allow_pickle=True).item().get('ramp').reshape(self.max_num, 1, 1), (1, self.day, self.step))[:self.house_number]
67-
self.inputs[:,:,:,5] = np.tile(np.load('input/inputs.npy', allow_pickle=True).item().get('Hm').reshape(self.max_num, 1, 1), (1, self.day, self.step))[:self.house_number]
68-
self.inputs[:,:,:,6] = np.tile(np.load('input/inputs.npy', allow_pickle=True).item().get('Ca').reshape(self.max_num, 1, 1), (1, self.day, self.step))[:self.house_number]
69-
self.inputs[:,:,:,7] = np.tile(np.load('input/inputs.npy', allow_pickle=True).item().get('Cm').reshape(self.max_num, 1, 1), (1, self.day, self.step))[:self.house_number]
70-
self.inputs[:,:,:,8] = np.tile(np.load('input/inputs.npy', allow_pickle=True).item().get('Ua').reshape(self.max_num, 1, 1), (1, self.day, self.step))[:self.house_number]
71-
self.inputs[:,:,:,9] = np.full((self.house_number,self.day,self.step), self.house_number)
72-
self.inputs[:,:,:,10] = np.load('input/inputs.npy', allow_pickle=True).item().get('p')[:self.house_number]
73-
self.inputs[:,:,:,11] = np.load('input/inputs.npy', allow_pickle=True).item().get('h')[:self.house_number]
74-
self.inputs[:,:,:,12] = np.tile(np.load('input/inputs.npy', allow_pickle=True).item().get('sqft').reshape(self.max_num, 1, 1), (1, self.day, self.step))[:self.house_number]
75-
76-
self.unresponsive_load = (np.load('input/aul.npy', allow_pickle=True).item().get(str(hn))) * (self.house_number / hn)
77-
self.rnn_q=np.load('input/rnn_q.npy', allow_pickle=True).item().get(str(hn))
78-
self.agg_un_tesp=self.unresponsive_load.reshape(self.day,self.step)
79-
self.unres_load_list = self.unresponsive_load
80-
64+
65+
self.inputs = np.zeros((self.house_number, self.day, self.step, self.input_num))
66+
self.inputs[:, :, :, 1] = np.tile(np.load('input/inputs.npy', allow_pickle=True).item().get('temp'),
67+
(self.house_number, 1, 1))
68+
self.inputs[:, :, :, 2] = np.load('input/inputs.npy', allow_pickle=True).item().get('schedule')[
69+
:self.house_number]
70+
self.inputs[:, :, :, 3] = np.tile(
71+
np.load('input/inputs.npy', allow_pickle=True).item().get('limit').reshape(self.max_num, 1, 1),
72+
(1, self.day, self.step))[:self.house_number]
73+
self.inputs[:, :, :, 4] = np.tile(
74+
np.load('input/inputs.npy', allow_pickle=True).item().get('ramp').reshape(self.max_num, 1, 1),
75+
(1, self.day, self.step))[:self.house_number]
76+
self.inputs[:, :, :, 5] = np.tile(
77+
np.load('input/inputs.npy', allow_pickle=True).item().get('Hm').reshape(self.max_num, 1, 1),
78+
(1, self.day, self.step))[:self.house_number]
79+
self.inputs[:, :, :, 6] = np.tile(
80+
np.load('input/inputs.npy', allow_pickle=True).item().get('Ca').reshape(self.max_num, 1, 1),
81+
(1, self.day, self.step))[:self.house_number]
82+
self.inputs[:, :, :, 7] = np.tile(
83+
np.load('input/inputs.npy', allow_pickle=True).item().get('Cm').reshape(self.max_num, 1, 1),
84+
(1, self.day, self.step))[:self.house_number]
85+
self.inputs[:, :, :, 8] = np.tile(
86+
np.load('input/inputs.npy', allow_pickle=True).item().get('Ua').reshape(self.max_num, 1, 1),
87+
(1, self.day, self.step))[:self.house_number]
88+
self.inputs[:, :, :, 9] = np.full((self.house_number, self.day, self.step), self.house_number)
89+
self.inputs[:, :, :, 10] = np.load('input/inputs.npy', allow_pickle=True).item().get('p')[:self.house_number]
90+
self.inputs[:, :, :, 11] = np.load('input/inputs.npy', allow_pickle=True).item().get('h')[:self.house_number]
91+
self.inputs[:, :, :, 12] = np.tile(
92+
np.load('input/inputs.npy', allow_pickle=True).item().get('sqft').reshape(self.max_num, 1, 1),
93+
(1, self.day, self.step))[:self.house_number]
94+
95+
self.unresponsive_load = (np.load('input/aul.npy', allow_pickle=True).item().get(str(hn))) * (
96+
self.house_number / hn)
97+
self.rnn_q = np.load('input/rnn_q.npy', allow_pickle=True).item().get(str(hn))
98+
self.agg_un_tesp = self.unresponsive_load.reshape(self.day, self.step)
99+
self.unres_load_list = self.unresponsive_load
100+
81101
self.Y_bid_max = np.load('input/minmax.npy', allow_pickle=True).item().get('outp_max')
82102
self.Y_bid_min = np.load('input/minmax.npy', allow_pickle=True).item().get('outp_min')
83103
self.X_response_max = np.load('input/minmax.npy', allow_pickle=True).item().get('inl_max')
@@ -86,81 +106,67 @@ def __init__(self,house_num,rnn1,rnn2,aucObj,hn):
86106
self.Y_response_min = np.load('input/minmax.npy', allow_pickle=True).item().get('outl_min')
87107
self.input_max = np.load('input/input_max.npy')
88108
self.input_min = np.load('input/input_min.npy')
89-
90-
self.inputs_norm = process_data_norm(self.inputs,self.input_max,self.input_min)
91-
92-
def inform_bid (self,price):
109+
110+
self.inputs_norm = process_data_norm(self.inputs, self.input_max, self.input_min)
111+
112+
def inform_bid(self, price):
93113
""" Set the cleared_price attribute
94114
"""
95115
self.cleared_price = price
96116

97-
# def generate_bids(self,day,t,input_tensor,input_dim):
98-
#
99-
# input_tensor_day=input_tensor[:,day,0:t+1,:]
100-
# chunks= torch.chunk(input_tensor_day,self.chunk_num, dim=0)
101-
# out = np.zeros((self.chunk_num,(len(chunks[0])),2))
102-
# for k in range(self.chunk_num):
103-
# out[k,:len(chunks[k]),:] = self.bid_RNN(chunks[k][:,0:t+1,:])[:,t,:].cpu().detach().numpy()
104-
#
105-
# bid_pq_list=out.reshape(self.chunk_num*len(chunks[0]),2)[0:len(input_tensor),:]
106-
# bid_p_list=bid_pq_list[:,0]
107-
# bid_q_list=bid_pq_list[:,1]
108-
# state= self.states
109-
#
110-
# return np.array([bid_p_list,bid_q_list,state])
111-
112-
113-
def generate_bidsps(self,day,t):
114-
115-
input_tensor_day=self.inputs_norm[:,day,0:t+1,1:11]
116-
chunks= torch.chunk(input_tensor_day,self.chunk_num, dim=0)
117-
outp = np.zeros((self.chunk_num,len(chunks[0])))
117+
# def generate_bids(self,day,t,input_tensor,input_dim):
118+
#
119+
# input_tensor_day=input_tensor[:,day,0:t+1,:]
120+
# chunks= torch.chunk(input_tensor_day,self.chunk_num, dim=0)
121+
# out = np.zeros((self.chunk_num,(len(chunks[0])),2))
122+
# for k in range(self.chunk_num):
123+
# out[k,:len(chunks[k]),:] = self.bid_RNN(chunks[k][:,0:t+1,:])[:,t,:].cpu().detach().numpy()
124+
#
125+
# bid_pq_list=out.reshape(self.chunk_num*len(chunks[0]),2)[0:len(input_tensor),:]
126+
# bid_p_list=bid_pq_list[:,0]
127+
# bid_q_list=bid_pq_list[:,1]
128+
# state= self.states
129+
#
130+
# return np.array([bid_p_list,bid_q_list,state])
131+
132+
def generate_bidsps(self, day, t):
133+
134+
input_tensor_day = self.inputs_norm[:, day, 0:t + 1, 1:11]
135+
chunks = torch.chunk(input_tensor_day, self.chunk_num, dim=0)
136+
outp = np.zeros((self.chunk_num, len(chunks[0])))
118137

119138
for k in range(self.chunk_num):
120-
outp[k,:len(chunks[k])] = self.bid_RNN(chunks[k][:,0:t+1,:10])[:,t,0].cpu().detach().numpy()
121-
122-
bid_p_list=outp.reshape(self.chunk_num*len(chunks[0]))[0:self.house_number]
123-
# bid_q_list=self.rnn_q[:self.house_number,day,t].ravel()
124-
bid_q_list=np.full((self.house_number,),self.rnn_q[day,t])
125-
state_list=self.states
126-
127-
return np.array([bid_p_list,bid_q_list,state_list])
128-
129-
def set_hvac_load(self,day,t):
130-
131-
input_tensor_day=self.inputs_norm[:,day,0:t+1,[0,1,2,3,4,5,6,7,8,12,9,11]]
132-
chunks= torch.chunk(input_tensor_day,self.chunk_num, dim=0)
133-
out = np.zeros((self.chunk_num,(len(chunks[0]))))
134-
for k in range(self.chunk_num):
135-
out[k,:len(chunks[k])] = self.response_RNN(chunks[k][:,0:t+1,:])[:,t,0].cpu().detach().numpy()
136-
137-
load_list=out.reshape(self.chunk_num*len(chunks[0]))[0:self.house_number]
138-
139-
aggregate_load = np.sum(load_list)*self.Y_response_max
140-
self.res_load = aggregate_load
141-
142-
for n in range(self.house_number):
143-
if load_list[n] > 0.25 :
144-
self.states[n] = 1
145-
else:
146-
self.states[n] = 0
147-
148-
return load_list
149-
150-
def update_unresposive_load(self,day,t):
151-
152-
unres_t=self.unres_load_list[self.step*day+t]-150
153-
self.unres_load = unres_t
154-
155-
156-
139+
outp[k, :len(chunks[k])] = self.bid_RNN(chunks[k][:, 0:t + 1, :10])[:, t, 0].cpu().detach().numpy()
157140

141+
bid_p_list = outp.reshape(self.chunk_num * len(chunks[0]))[0:self.house_number]
142+
# bid_q_list=self.rnn_q[:self.house_number,day,t].ravel()
143+
bid_q_list = np.full((self.house_number,), self.rnn_q[day, t])
144+
state_list = self.states
158145

146+
return np.array([bid_p_list, bid_q_list, state_list])
159147

148+
def set_hvac_load(self, day, t):
160149

150+
input_tensor_day = self.inputs_norm[:, day, 0:t + 1, [0, 1, 2, 3, 4, 5, 6, 7, 8, 12, 9, 11]]
151+
chunks = torch.chunk(input_tensor_day, self.chunk_num, dim=0)
152+
out = np.zeros((self.chunk_num, (len(chunks[0]))))
153+
for k in range(self.chunk_num):
154+
out[k, :len(chunks[k])] = self.response_RNN(chunks[k][:, 0:t + 1, :])[:, t, 0].cpu().detach().numpy()
161155

156+
load_list = out.reshape(self.chunk_num * len(chunks[0]))[0:self.house_number]
162157

158+
aggregate_load = np.sum(load_list) * self.Y_response_max
159+
self.res_load = aggregate_load
163160

161+
for n in range(self.house_number):
162+
if load_list[n] > 0.25:
163+
self.states[n] = 1
164+
else:
165+
self.states[n] = 0
164166

167+
return load_list
165168

169+
def update_unresposive_load(self, day, t):
166170

171+
unres_t = self.unres_load_list[self.step * day + t] - 150
172+
self.unres_load = unres_t

‎models/pitt/Reduced_Order_Model/clean.bat‎

Lines changed: 0 additions & 9 deletions
This file was deleted.

‎models/pitt/Reduced_Order_Model/gui.bat‎

Lines changed: 0 additions & 3 deletions
This file was deleted.

‎models/pitt/Reduced_Order_Model/launch_auction.py‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -3,4 +3,4 @@
33

44
import tesp_support.substation as ss
55

6-
ss.substation_loop_f('TE_agent_dict.json', 'TE', 900)
6+
ss.substation_loop('TE_agent_dict.json', 'TE', 900)

‎models/pitt/Reduced_Order_Model/launch_pp.py‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -3,4 +3,4 @@
33

44
import tesp_support.original.tso_PYPOWER_f as pp
55

6-
pp.pypower_loop_f('TE_pp.json', 'TE')
6+
pp.tso_pypower_loop_f('TE_pp.json', 'TE')

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