Validation 11 - Global Reserves



In [1]:

    
%matplotlib inline



In [2]:

    
import psst



In [3]:

    
from psst.case import read_matpower
from psst.network import create_network
import pandas as pd

Validation of case 1



In [4]:

    
case = read_matpower('./cases/case7.m')



In [5]:

    
case.reserve_factor = 0.5



In [6]:

    
case.load = pd.read_csv('./cases/case7.csv', index_col=0)



In [7]:

    
network = create_network(case, prog='neato')
network.draw()



In [8]:

    
case









    Out[8]:





<psst.case.PSSTCase(name=casematpower, Generators=2, Buses=2, Branches=1)>



In [9]:

    
case.bus



In [10]:

    
case.branch









    Out[10]:






  
    
      
      F_BUS
      T_BUS
      BR_R
      BR_X
      BR_B
      RATE_A
      RATE_B
      RATE_C
      TAP
      SHIFT
      BR_STATUS
      ANGMIN
      ANGMAX
    
  
  
    
      0
      Bus1
      Bus2
      0.00281
      0.0281
      0.00712
      800
      800
      800
      0
      0
      1
      -360
      360



In [11]:

    
case.gen









    Out[11]:






  
    
      
      GEN_BUS
      PG
      QG
      QMAX
      QMIN
      VG
      MBASE
      GEN_STATUS
      PMAX
      PMIN
      PC1
      PC2
      QC1MIN
      QC1MAX
      QC2MIN
      QC2MAX
      RAMP_AGC
      RAMP_10
      RAMP_30
      RAMP_Q
      APF
      STARTUP_RAMP
      SHUTDOWN_RAMP
      MINIMUM_UP_TIME
      MINIMUM_DOWN_TIME
    
  
  
    
      GenCo0
      Bus1
      200
      0
      30
      -30
      1
      100
      1
      200
      0
      0
      0
      0
      0
      0
      0
      0
      200
      0
      0
      0
      200
      200
      0
      0
    
    
      GenCo1
      Bus2
      500
      0
      30
      -30
      1
      100
      1
      500
      0
      0
      0
      0
      0
      0
      0
      0
      500
      0
      0
      0
      500
      500
      0
      0



In [12]:

    
case.gencost



In [13]:

    
import matplotlib.pyplot as plt



In [14]:

    
fig, axs = plt.subplots(1, 1, figsize=(8, 5))
ax = axs
case.load['Bus2'].plot.bar(ax=ax)
ax.set_ylim(0, 500);



In [15]:

    
from psst.model import build_model



In [16]:

    
model = build_model(case)



In [17]:

    
model









    Out[17]:





<psst.model.PSSTModel(status=None)>



In [18]:

    
model.solve(solver='cbc', verbose=True)









    



Welcome to the CBC MILP Solver 
Version: 2.9.6 
Build Date: May 27 2016 

command line - /usr/local/bin/cbc -mipgap 0.01 -printingOptions all -import /var/folders/wk/lcf0vgd90bx0vq1873tn04knk_djr3/T/tmpkarPPc.pyomo.lp -import -stat=1 -solve -solu /var/folders/wk/lcf0vgd90bx0vq1873tn04knk_djr3/T/tmpkarPPc.pyomo.soln (default strategy 1)
No match for mipgap - ? for list of commands
No match for 0.01 - ? for list of commands
Option for printingOptions changed from normal to all
Current default (if $ as parameter) for import is /var/folders/wk/lcf0vgd90bx0vq1873tn04knk_djr3/T/tmpkarPPc.pyomo.lp
Presolve 262 (-637) rows, 383 (-370) columns and 901 (-1386) elements
Statistics for presolved model
Original problem has 48 integers (48 of which binary)
Presolved problem has 24 integers (24 of which binary)
==== 190 zero objective 3 different
190 variables have objective of 0
49 variables have objective of 1
144 variables have objective of 1e+06
==== absolute objective values 3 different
190 variables have objective of 0
49 variables have objective of 1
144 variables have objective of 1e+06
==== for integers 24 zero objective 1 different
24 variables have objective of 0
==== for integers absolute objective values 1 different
24 variables have objective of 0
===== end objective counts


Problem has 262 rows, 383 columns (193 with objective) and 901 elements
There are 193 singletons with objective 
Column breakdown:
287 of type 0.0->inf, 48 of type 0.0->up, 0 of type lo->inf, 
24 of type lo->up, 0 of type free, 0 of type fixed, 
0 of type -inf->0.0, 0 of type -inf->up, 24 of type 0.0->1.0 
Row breakdown:
24 of type E 0.0, 0 of type E 1.0, 0 of type E -1.0, 
48 of type E other, 0 of type G 0.0, 0 of type G 1.0, 
0 of type G other, 119 of type L 0.0, 0 of type L 1.0, 
71 of type L other, 0 of type Range 0.0->1.0, 0 of type Range other, 
0 of type Free 
Continuous objective value is 54200 - 0.00 seconds
Cgl0003I 0 fixed, 0 tightened bounds, 23 strengthened rows, 0 substitutions
Cgl0004I processed model has 238 rows, 382 columns (24 integer (24 of which binary)) and 760 elements
Cbc0038I Initial state - 16 integers unsatisfied sum - 2.75
Cbc0038I Pass   1: suminf.    0.00000 (0) obj. 1.37505e+09 iterations 38
Cbc0038I Solution found of 1.37505e+09
Cbc0038I Relaxing continuous gives 1.37505e+09
Cbc0038I Before mini branch and bound, 8 integers at bound fixed and 240 continuous
Cbc0038I Full problem 238 rows 382 columns, reduced to 50 rows 44 columns
Cbc0038I Mini branch and bound improved solution from 1.37505e+09 to 80700 (0.03 seconds)
Cbc0038I Round again with cutoff of 78050
Cbc0038I Pass   2: suminf.    2.54996 (15) obj. 78050 iterations 30
Cbc0038I Pass   3: suminf.    1.11579 (16) obj. 78050 iterations 60
Cbc0038I Pass   4: suminf.    2.69997 (15) obj. 78050 iterations 44
Cbc0038I Pass   5: suminf.    2.49997 (14) obj. 78050 iterations 1
Cbc0038I Pass   6: suminf.    0.48125 (4) obj. 78050 iterations 43
Cbc0038I Pass   7: suminf.    0.36250 (16) obj. 78050 iterations 29
Cbc0038I Pass   8: suminf.    2.06875 (18) obj. 78050 iterations 3
Cbc0038I Pass   9: suminf.    2.06875 (18) obj. 78050 iterations 0
Cbc0038I Pass  10: suminf.    2.64997 (14) obj. 78050 iterations 43
Cbc0038I Pass  11: suminf.    2.44997 (13) obj. 78050 iterations 1
Cbc0038I Pass  12: suminf.    1.01000 (4) obj. 78050 iterations 43
Cbc0038I Pass  13: suminf.    0.53125 (3) obj. 78050 iterations 17
Cbc0038I Pass  14: suminf.    0.14999 (3) obj. 78050 iterations 4
Cbc0038I Pass  15: suminf.    1.11579 (16) obj. 78050 iterations 27
Cbc0038I Pass  16: suminf.    2.74995 (16) obj. 78050 iterations 41
Cbc0038I Pass  17: suminf.    2.54996 (15) obj. 78050 iterations 1
Cbc0038I Pass  18: suminf.    2.74999 (16) obj. 78050 iterations 12
Cbc0038I Pass  19: suminf.    2.54999 (15) obj. 78050 iterations 1
Cbc0038I Pass  20: suminf.    1.97625 (17) obj. 78050 iterations 69
Cbc0038I Pass  21: suminf.    1.97625 (17) obj. 78050 iterations 0
Cbc0038I Pass  22: suminf.    2.74995 (16) obj. 78050 iterations 44
Cbc0038I Pass  23: suminf.    2.54996 (15) obj. 78050 iterations 1
Cbc0038I Pass  24: suminf.    1.11579 (16) obj. 78050 iterations 66
Cbc0038I Pass  25: suminf.    1.11579 (16) obj. 78050 iterations 0
Cbc0038I Pass  26: suminf.    1.11579 (16) obj. 78050 iterations 0
Cbc0038I Pass  27: suminf.    1.97625 (17) obj. 78050 iterations 2
Cbc0038I Pass  28: suminf.    2.54998 (12) obj. 78050 iterations 36
Cbc0038I Pass  29: suminf.    2.34998 (11) obj. 78050 iterations 1
Cbc0038I Pass  30: suminf.    1.97625 (17) obj. 78050 iterations 48
Cbc0038I Pass  31: suminf.    2.74995 (16) obj. 78050 iterations 45
Cbc0038I No solution found this major pass
Cbc0038I Before mini branch and bound, 5 integers at bound fixed and 229 continuous
Cbc0038I Full problem 238 rows 382 columns, reduced to 18 rows 22 columns
Cbc0038I Mini branch and bound did not improve solution (0.07 seconds)
Cbc0038I After 0.07 seconds - Feasibility pump exiting with objective of 80700 - took 0.05 seconds
Cbc0012I Integer solution of 80700 found by feasibility pump after 0 iterations and 0 nodes (0.07 seconds)
Cbc0038I Full problem 238 rows 382 columns, reduced to 201 rows 338 columns - 1 fixed gives 199, 335 - still too large
Cbc0006I The LP relaxation is infeasible or too expensive
Cbc0013I At root node, 0 cuts changed objective from 54200 to 80700 in 1 passes
Cbc0014I Cut generator 0 (Probing) - 2 row cuts average 2.0 elements, 13 column cuts (13 active)  in 0.001 seconds - new frequency is 1
Cbc0014I Cut generator 1 (Gomory) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.000 seconds - new frequency is -100
Cbc0014I Cut generator 2 (Knapsack) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.000 seconds - new frequency is -100
Cbc0014I Cut generator 3 (Clique) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.000 seconds - new frequency is -100
Cbc0014I Cut generator 4 (MixedIntegerRounding2) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.000 seconds - new frequency is -100
Cbc0014I Cut generator 5 (FlowCover) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.000 seconds - new frequency is -100
Cbc0014I Cut generator 6 (TwoMirCuts) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.000 seconds - new frequency is -100
Cbc0001I Search completed - best objective 80700, took 19 iterations and 0 nodes (0.08 seconds)
Cbc0035I Maximum depth 0, 0 variables fixed on reduced cost
Cuts at root node changed objective from 54200 to 80700
Probing was tried 1 times and created 15 cuts of which 0 were active after adding rounds of cuts (0.001 seconds)
Gomory was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)
Knapsack was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)
Clique was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)
MixedIntegerRounding2 was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)
FlowCover was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)
TwoMirCuts was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)

Result - Optimal solution found

Objective value:                80700.00000000
Enumerated nodes:               0
Total iterations:               19
Time (CPU seconds):             0.09
Time (Wallclock seconds):       0.10

Total time (CPU seconds):       0.10   (Wallclock seconds):       0.12

Input data



In [19]:

    
import pandas as pd



In [20]:

    
pd.DataFrame(case.gen['PMAX'])



In [21]:

    
case.load

Model Results



In [22]:

    
model.results.unit_commitment



In [23]:

    
model.results.power_generated



In [24]:

    
model.results.commitment_cost









    Out[24]:





6000



In [25]:

    
model.results.production_cost









    Out[25]:





42700



In [26]:

    
model.results.noload_cost









    Out[26]:





32000.0



In [27]:

    
model.results.line_power



In [28]:

    
from psst.plot import line_power, stacked_power_generation



In [32]:

    
ax = stacked_power_generation(model.results, legend=True)

(model.results.power_generated.sum(axis=1) + model.results.regulating_reserve_up_available.sum(axis=1)).plot(ax=ax)









    Out[32]:





<matplotlib.axes._subplots.AxesSubplot at 0x10ff11a90>



In [ ]:



In [ ]:



In [ ]:

	TYPE	PD	QD	GS	BS	AREA	VM	VA	BASEKV	ZONE	VMAX	VMIN
Bus1	3	0	131.47	0	0	1	1	0	230	1	1.1	0.9
Bus2	2	100	0.00	0	0	1	1	0	230	1	1.1	0.9

	GEN_BUS	PG	QG	QMAX	QMIN	VG	MBASE	GEN_STATUS	PMAX	PMIN	PC1	PC2	QC1MIN	QC1MAX	QC2MIN	QC2MAX	RAMP_AGC	RAMP_10	RAMP_30	RAMP_Q	APF	STARTUP_RAMP	SHUTDOWN_RAMP	MINIMUM_UP_TIME	MINIMUM_DOWN_TIME
GenCo0	Bus1	200	0	30	-30	1	100	1	200	0	0	0	0	0	0	0	0	200	0	0	0	200	200	0	0
GenCo1	Bus2	500	0	30	-30	1	100	1	500	0	0	0	0	0	0	0	0	500	0	0	0	500	500	0	0

	Bus1	Bus2
0	0.0	100.0
1	0.0	100.0
2	0.0	100.0
3	0.0	120.0
4	0.0	120.0
5	0.0	120.0
6	0.0	150.0
7	0.0	150.0
8	0.0	150.0
9	0.0	200.0
10	0.0	200.0
11	0.0	200.0
12	0.0	300.0
13	0.0	400.0
14	0.0	300.0
15	0.0	200.0
16	0.0	200.0
17	0.0	200.0
18	0.0	150.0
19	0.0	150.0
20	0.0	150.0
21	0.0	150.0
22	0.0	100.0
23	0.0	100.0

	GenCo0	GenCo1
0	1	0
1	1	0
2	1	0
3	1	0
4	1	0
5	1	0
6	1	1
7	1	1
8	1	1
9	1	1
10	1	1
11	1	1
12	1	1
13	1	1
14	1	1
15	1	1
16	1	1
17	1	1
18	1	1
19	1	1
20	1	1
21	1	1
22	1	0
23	1	0

	GenCo0	GenCo1
0	100	0
1	100	0
2	100	0
3	120	0
4	120	0
5	120	0
6	150	0
7	150	0
8	150	0
9	200	0
10	200	0
11	200	0
12	200	100
13	200	200
14	200	100
15	200	0
16	200	0
17	200	0
18	150	0
19	150	0
20	150	0
21	150	0
22	100	0
23	100	0

	0
0	100
1	100
2	100
3	120
4	120
5	120
6	150
7	150
8	150
9	200
10	200
11	200
12	200
13	200
14	200
15	200
16	200
17	200
18	150
19	150
20	150
21	150
22	100
23	100

	GenCo0	GenCo1
0	1	0
1	1	0
2	1	0
3	1	0
4	1	0
5	1	0
6	1	1
7	1	1
8	1	1
9	1	1
10	1	1
11	1	1
12	1	1
13	1	1
14	1	1
15	1	1
16	1	1
17	1	1
18	1	1
19	1	1
20	1	1
21	1	1
22	1	0
23	1	0

	GenCo0	GenCo1
0	100	0
1	100	0
2	100	0
3	120	0
4	120	0
5	120	0
6	150	0
7	150	0
8	150	0
9	200	0
10	200	0
11	200	0
12	200	100
13	200	200
14	200	100
15	200	0
16	200	0
17	200	0
18	150	0
19	150	0
20	150	0
21	150	0
22	100	0
23	100	0

	0
0	100
1	100
2	100
3	120
4	120
5	120
6	150
7	150
8	150
9	200
10	200
11	200
12	200
13	200
14	200
15	200
16	200
17	200
18	150
19	150
20	150
21	150
22	100
23	100

	GenCo0	GenCo1
0	1	0
1	1	0
2	1	0
3	1	0
4	1	0
5	1	0
6	1	1
7	1	1
8	1	1
9	1	1
10	1	1
11	1	1
12	1	1
13	1	1
14	1	1
15	1	1
16	1	1
17	1	1
18	1	1
19	1	1
20	1	1
21	1	1
22	1	0
23	1	0

	GenCo0	GenCo1
0	100	0
1	100	0
2	100	0
3	120	0
4	120	0
5	120	0
6	150	0
7	150	0
8	150	0
9	200	0
10	200	0
11	200	0
12	200	100
13	200	200
14	200	100
15	200	0
16	200	0
17	200	0
18	150	0
19	150	0
20	150	0
21	150	0
22	100	0
23	100	0

	0
0	100
1	100
2	100
3	120
4	120
5	120
6	150
7	150
8	150
9	200
10	200
11	200
12	200
13	200
14	200
15	200
16	200
17	200
18	150
19	150
20	150
21	150
22	100
23	100