Validation 03 - Two Generator, Two Bus, Two Load, One Branch



In [1]:

    
%matplotlib inline



In [2]:

    
import psst



In [3]:

    
from psst.case import read_matpower
from psst.network import create_network

Validation of case 1



In [4]:

    
case = read_matpower('./cases/case3.m')



In [27]:

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



In [8]:

    
case









    Out[8]:





<psst.case.PSSTCase(name=case5, Generators=2, Buses=2, Branches=1)>



In [30]:

    
case.bus



In [29]:

    
case.branch









    Out[29]:






  
    
      
      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 [9]:

    
case.gen



In [10]:

    
case.gencost



In [11]:

    
case.load



In [12]:

    
from psst.model import build_model



In [13]:

    
model = build_model(case)



In [14]:

    
model









    Out[14]:





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



In [15]:

    
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/tmpYCIRIR.pyomo.lp -import -stat=1 -solve -solu /var/folders/wk/lcf0vgd90bx0vq1873tn04knk_djr3/T/tmpYCIRIR.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/tmpYCIRIR.pyomo.lp
Presolve 7 (-41) rows, 13 (-27) columns and 22 (-77) elements
Statistics for presolved model
Original problem has 2 integers (2 of which binary)


Problem has 7 rows, 13 columns (8 with objective) and 22 elements
There are 8 singletons with objective 
Column breakdown:
10 of type 0.0->inf, 2 of type 0.0->up, 0 of type lo->inf, 
1 of type lo->up, 0 of type free, 0 of type fixed, 
0 of type -inf->0.0, 0 of type -inf->up, 0 of type 0.0->1.0 
Row breakdown:
1 of type E 0.0, 0 of type E 1.0, 0 of type E -1.0, 
2 of type E other, 0 of type G 0.0, 0 of type G 1.0, 
0 of type G other, 2 of type L 0.0, 0 of type L 1.0, 
2 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 1000 - 0.00 seconds
Cgl0004I processed model has 7 rows, 13 columns (0 integer (0 of which binary)) and 20 elements
Cbc3007W No integer variables - nothing to do
Cuts at root node changed objective from 1000 to -1.79769e+308
Probing was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 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:                1000.00000000
Enumerated nodes:               0
Total iterations:               0
Time (CPU seconds):             0.00
Time (Wallclock seconds):       0.00

Total time (CPU seconds):       0.01   (Wallclock seconds):       0.01

Input data



In [16]:

    
import pandas as pd



In [17]:

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



In [18]:

    
case.load

Model Results



In [19]:

    
model.results.unit_commitment



In [20]:

    
model.results.power_generated



In [21]:

    
model.results.production_cost









    Out[21]:





1000



In [22]:

    
model.results.line_power



In [23]:

    
model.results.lmp



In [24]:

    
from psst.plot import line_power



In [28]:

    
line_power(network, model.results, hour=0)









    Out[28]:





<matplotlib.axes._subplots.AxesSubplot at 0x110b10510>



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
GenCo0	Bus1	200	0	30	-30	1	100	1	200	0	0	0	0	0	0	0	0	0	0	0	0
GenCo1	Bus2	500	0	30	-30	1	100	1	500	0	0	0	0	0	0	0	0	0	0	0	0

	MODEL	STARTUP	SHUTDOWN	NCOST	COST_1	COST_0
GenCo0	1	0	0	2	10	0
GenCo1	1	0	0	2	14	0

	PMAX
GenCo0	200
GenCo1	500