Manipulate the Simulation#

This example shows how to play with the simulation, such as contingency analysis and manipulate the constraints.

[1]:

import ams

[2]:

ams.config_logger(stream_level=20)

Manipulate the Simulation#

Load Case#

[3]:

sp = ams.load(ams.get_case('5bus/pjm5bus_demo.xlsx'),
              setup=True,
              no_output=True,)

Parsing input file "/Users/jinningwang/work/ams/ams/cases/5bus/pjm5bus_demo.xlsx"...
Input file parsed in 0.1880 seconds.
Zero line rates detacted in rate_b, rate_c, adjusted to 999.
System set up in 0.0032 seconds.

The system load are defined in model PQ.

[4]:

sp.PQ.as_df()

[4]:

	idx	u	name	bus	Vn	p0	q0	vmax	vmin	owner	ctrl
uid
0	PQ_1	1.0	PQ 1	1	230.0	3.0	0.9861	1.1	0.9	None	1.0
1	PQ_2	1.0	PQ 2	2	230.0	3.0	0.9861	1.1	0.9	None	1.0
2	PQ_3	1.0	PQ 3	3	230.0	4.0	1.3147	1.1	0.9	None	1.0

In RTED, system load is referred as pd.

[5]:

sp.RTED.pd.v

[5]:

array([3., 3., 4.])

Run Simulation#

RTED can be solved and we can inspect the results as discussed in previous example.

[6]:

sp.RTED.run(solver='CLARABEL')

Building system matrices
Parsing OModel for <RTED>
Evaluating OModel for <RTED>
Finalizing OModel for <RTED>
<RTED> initialized in 0.0169 seconds.
<RTED> solved as optimal in 0.0169 seconds, converged in 11 iterations with CLARABEL.

[6]:

True

Power generation pg and line flow plf can be accessed as follows.

[7]:

sp.RTED.pg.v

[7]:

array([0.2       , 1.43998388, 0.6       , 5.76001612, 2.        ])

[8]:

sp.RTED.plf.v

[8]:

array([-0.38000806,  0.78912153,  0.17089459,  2.        ,  0.43998388,
       -0.77089459, -0.38000806])

Change Load#

The load values can be manipulated in the model PQ.

Note the difference between Model.set and Model.alter:

set: This method WILL NOT modify the input values from the case file that have not been converted to the system base. As a result, changes applied by this method WILL NOT affect the dumped case file.

alter: If the method operates on an input parameter, the new data should be in the same base as that in the input file. This function will convert value to per unit in the system base whenever necessary. The values for storing the input data, i.e., the parameter’s vin field, will be overwritten. As a result, altered values WILL BE reflected in the dumped case file.

[9]:

sp.PQ.alter(src='p0', idx=['PQ_1', 'PQ_2'], value=[3.2, 3.2])

According parameters need to be updated to make the changes effective in the optimization model. If not sure which parameters need to be updated, we can use update() to update all parameters.

[10]:

sp.RTED.update('pd')

[10]:

True

After manipulation, the routined can be solved again.

[11]:

sp.RTED.run(solver='CLARABEL')

<RTED> solved as optimal in 0.0051 seconds, converged in 11 iterations with CLARABEL.

[11]:

True

[12]:

sp.RTED.pg.v

[12]:

array([0.2       , 1.63998388, 0.6       , 5.96001612, 2.        ])

An alternative way is to alter the load through RTED.

As pd has owner StaticLoad and soruce p0, the parameter update through RTED actually happens to StaticLoad.p0.

[13]:

sp.RTED.pd.owner

[13]:

StaticLoad (3 devices) at 0x1047a20e0

[14]:

sp.RTED.pd.src

[14]:

'p0'

Similarly, the load can be changed using set method.

[15]:

sp.RTED.set(src='pd', attr='v', idx=['PQ_1', 'PQ_2'], value=[0.2, 0.2])

[15]:

True

Remember to update the optimization parameters after the change.

[16]:

sp.RTED.update('pd')

[16]:

True

We can see that the original load is also updated.

[17]:

sp.PQ.as_df()

[17]:

	idx	u	name	bus	Vn	p0	q0	vmax	vmin	owner	ctrl
uid
0	PQ_1	1.0	PQ 1	1	230.0	0.2	0.9861	1.1	0.9	None	1.0
1	PQ_2	1.0	PQ 2	2	230.0	0.2	0.9861	1.1	0.9	None	1.0
2	PQ_3	1.0	PQ 3	3	230.0	4.0	1.3147	1.1	0.9	None	1.0

[18]:

sp.RTED.run(solver='CLARABEL')

<RTED> solved as optimal in 0.0022 seconds, converged in 11 iterations with CLARABEL.

[18]:

True

As expected, the power generation also changed.

[19]:

sp.RTED.pg.v

[19]:

array([0.2 , 0.5 , 0.6 , 2.69, 0.41])

Trip a Generator#

We can see that there are three PV generators in the system.

Warning: in MatProcessor, StaticGen online status is NOT considered in its connectivity matrix Cg. The same applies for PQ, Line, and Shunt.

[20]:

sp.PV.as_df()

[20]:

	idx	u	name	Sn	Vn	bus	busr	p0	q0	pmax	...	Qc2min	Qc2max	Ragc	R10	R30	Rq	apf	pg0	td1	td2
uid
0	PV_1	1.0	Alta	100.0	230.0	0	None	1.0000	0.0	2.1	...	0.0	0.0	999.0	999.0	999.0	999.0	0.0	0.0	0.5	0.0
1	PV_3	1.0	Solitude	100.0	230.0	2	None	3.2349	0.0	5.2	...	0.0	0.0	999.0	999.0	999.0	999.0	0.0	0.0	0.5	0.0
2	PV_5	1.0	Brighton	100.0	230.0	4	None	4.6651	0.0	6.0	...	0.0	0.0	999.0	999.0	999.0	999.0	0.0	0.0	0.5	0.0
3	PV_2	1.0	PV 2	100.0	230.0	1	None	0.1000	0.0	99.0	...	0.0	0.0	999.0	999.0	999.0	999.0	0.0	0.0	0.5	0.0

4 rows × 34 columns

PV_1 is tripped by setting its connection status u to 0.

[21]:

sp.StaticGen.set(src='u', idx='PV_1', attr='v', value=0)

[21]:

True

In AMS, some parameters are defiend as constants in the numerical optimization model to follow the CVXPY DCP and DPP rules. Once non-parametric parameters are changed, the optimization model will be re-initialized to make the changes effective.

More details can be found at CVXPY - Disciplined Convex Programming.

[22]:

sp.RTED.update()

Building system matrices
<RTED> reinit OModel due to non-parametric change.
Evaluating OModel for <RTED>
Finalizing OModel for <RTED>

[22]:

True

Then we can re-solve the model.

[23]:

sp.RTED.run(solver='CLARABEL')

<RTED> solved as optimal in 0.0175 seconds, converged in 10 iterations with CLARABEL.

[23]:

True

We can see that the tripped generator has no power generation.

[24]:

sp.RTED.pg.v.round(2)

[24]:

array([-0.  ,  0.5 ,  0.6 ,  2.97,  0.33])

Trip a Line#

We can inspect the Line model to check the system topology.

[25]:

sp.Line.as_df()

[25]:

	idx	u	name	bus1	bus2	Sn	fn	Vn1	Vn2	r	...	tap	phi	rate_a	rate_b	rate_c	owner	xcoord	ycoord	amin	amax
uid
0	Line_1	1.0	Line AB	0	1	100.0	60.0	230.0	230.0	0.00281	...	1.0	0.0	4.0	999.0	999.0	None	None	None	-6.283185	6.283185
1	Line_2	1.0	Line AD	0	3	100.0	60.0	230.0	230.0	0.00304	...	1.0	0.0	2.0	999.0	999.0	None	None	None	-6.283185	6.283185
2	Line_3	1.0	Line AE	0	4	100.0	60.0	230.0	230.0	0.00064	...	1.0	0.0	2.0	999.0	999.0	None	None	None	-6.283185	6.283185
3	Line_4	1.0	Line BC	1	2	100.0	60.0	230.0	230.0	0.00108	...	1.0	0.0	2.0	999.0	999.0	None	None	None	-6.283185	6.283185
4	Line_5	1.0	Line CD	2	3	100.0	60.0	230.0	230.0	0.00297	...	1.0	0.0	2.0	999.0	999.0	None	None	None	-6.283185	6.283185
5	Line_6	1.0	Line DE	3	4	100.0	60.0	230.0	230.0	0.00297	...	1.0	0.0	2.4	999.0	999.0	None	None	None	-6.283185	6.283185
6	Line_7	1.0	Line AB2	0	1	100.0	60.0	230.0	230.0	0.00281	...	1.0	0.0	4.0	999.0	999.0	None	None	None	-6.283185	6.283185

7 rows × 28 columns

Here line 2 is tripped by setting its connection status u to 0.

Note that in ANDES, dynamic simulation of line tripping should use model ``Toggle``.

[26]:

sp.Line.alter(src='u', idx='Line_1', value=0)

[27]:

sp.RTED.update()

Building system matrices
<RTED> reinit OModel due to non-parametric change.
Evaluating OModel for <RTED>
Finalizing OModel for <RTED>

[27]:

True

[28]:

sp.RTED.run(solver='CLARABEL')

<RTED> solved as optimal in 0.0173 seconds, converged in 10 iterations with CLARABEL.

[28]:

True

Here we can see the tripped line has no flow.

[29]:

sp.RTED.plf.v.round(2)

[29]:

array([ 0.  ,  0.99,  0.34,  1.44,  1.74, -0.94, -1.33])

Disable Constraints#

In addition to the system parameters, the constraints can also be manipulated.

Here, we load the case to a new system.

[30]:

spc = ams.load(ams.get_case('5bus/pjm5bus_demo.xlsx'),
               setup=True,
               no_output=True,)

Parsing input file "/Users/jinningwang/work/ams/ams/cases/5bus/pjm5bus_demo.xlsx"...
Input file parsed in 0.0544 seconds.
Zero line rates detacted in rate_b, rate_c, adjusted to 999.
System set up in 0.0042 seconds.

[31]:

spc.RTED.init()

Building system matrices
Parsing OModel for <RTED>
Evaluating OModel for <RTED>
Finalizing OModel for <RTED>
<RTED> initialized in 0.0167 seconds.

[31]:

True

[32]:

spc.RTED.set(src='rate_a', idx=['Line_3'], attr='v', value=0.6)

[32]:

True

[33]:

spc.RTED.update('rate_a')

[33]:

True

We can inspect the constraints status as follows. All constraints are turned on by default.

[34]:

spc.RTED.constrs

[34]:

OrderedDict([('pb', Constraint: pb [ON]),
             ('sba', Constraint: sbus [ON]),
             ('pglb', Constraint: pglb [ON]),
             ('pgub', Constraint: pgub [ON]),
             ('plflb', Constraint: plflb [ON]),
             ('plfub', Constraint: plfub [ON]),
             ('alflb', Constraint: alflb [ON]),
             ('alfub', Constraint: alfub [ON]),
             ('rbu', Constraint: rbu [ON]),
             ('rbd', Constraint: rbd [ON]),
             ('rru', Constraint: rru [ON]),
             ('rrd', Constraint: rrd [ON]),
             ('rgu', Constraint: rgu [ON]),
             ('rgd', Constraint: rgd [ON])])

Then, solve the dispatch and inspect the line flow.

[35]:

spc.RTED.run(solver='CLARABEL')

<RTED> solved as optimal in 0.0189 seconds, converged in 11 iterations with CLARABEL.

[35]:

True

[36]:

spc.RTED.plf.v.round(2)

[36]:

array([-0.38,  0.79,  0.17,  2.  ,  0.44, -0.77, -0.38])

In the next, we can disable specific constraints, and the parameter name takes both single constraint name or a list of constraint names.

[37]:

spc.RTED.disable(['plflb', 'plfub'])

Turn off constraints: plflb, plfub

[37]:

True

Now, it can be seen that the two constraints are disabled.

[38]:

spc.RTED.constrs

[38]:

OrderedDict([('pb', Constraint: pb [ON]),
             ('sba', Constraint: sbus [ON]),
             ('pglb', Constraint: pglb [ON]),
             ('pgub', Constraint: pgub [ON]),
             ('plflb', Constraint: plflb [OFF]),
             ('plfub', Constraint: plfub [OFF]),
             ('alflb', Constraint: alflb [ON]),
             ('alfub', Constraint: alfub [ON]),
             ('rbu', Constraint: rbu [ON]),
             ('rbd', Constraint: rbd [ON]),
             ('rru', Constraint: rru [ON]),
             ('rrd', Constraint: rrd [ON]),
             ('rgu', Constraint: rgu [ON]),
             ('rgd', Constraint: rgd [ON])])

[39]:

spc.RTED.run(solver='CLARABEL')

Disabled constraints: plflb, plfub
Finalizing OModel for <RTED>
<RTED> initialized in 0.0131 seconds.
<RTED> solved as optimal in 0.0359 seconds, converged in 9 iterations with CLARABEL.

[39]:

True

We can see that now the line flow limits are not in effect.

[40]:

spc.RTED.plf.v.round(2)

[40]:

array([-0.9 ,  1.36,  0.65,  3.48,  0.98, -1.25, -0.9 ])

Similarly, you can also enable the constraints again.

[41]:

spc.RTED.enable(['plflb', 'plfub'])

Turn on constraints: plflb, plfub

[41]:

True

[42]:

spc.RTED.constrs

[42]:

OrderedDict([('pb', Constraint: pb [ON]),
             ('sba', Constraint: sbus [ON]),
             ('pglb', Constraint: pglb [ON]),
             ('pgub', Constraint: pgub [ON]),
             ('plflb', Constraint: plflb [ON]),
             ('plfub', Constraint: plfub [ON]),
             ('alflb', Constraint: alflb [ON]),
             ('alfub', Constraint: alfub [ON]),
             ('rbu', Constraint: rbu [ON]),
             ('rbd', Constraint: rbd [ON]),
             ('rru', Constraint: rru [ON]),
             ('rrd', Constraint: rrd [ON]),
             ('rgu', Constraint: rgu [ON]),
             ('rgd', Constraint: rgd [ON])])

[43]:

spc.RTED.run(solver='CLARABEL')

Finalizing OModel for <RTED>
<RTED> initialized in 0.0018 seconds.
<RTED> solved as optimal in 0.0189 seconds, converged in 11 iterations with CLARABEL.

[43]:

True

[44]:

spc.RTED.plf.v.round(2)

[44]:

array([-0.38,  0.79,  0.17,  2.  ,  0.44, -0.77, -0.38])

Alternatively, you can also force init the dispatch to rebuild the system matrices, enable all constraints, and re-init the optimization models.

[45]:

spc.RTED.disable(['plflb', 'plfub', 'rgu', 'rgd'])

Turn off constraints: plflb, plfub, rgu, rgd

[45]:

True

[46]:

spc.RTED.init(force=True)

Disabled constraints: plflb, plfub, rgu, rgd
Finalizing OModel for <RTED>
<RTED> initialized in 0.0022 seconds.

[46]:

True

[47]:

spc.RTED.constrs

[47]:

OrderedDict([('pb', Constraint: pb [ON]),
             ('sba', Constraint: sbus [ON]),
             ('pglb', Constraint: pglb [ON]),
             ('pgub', Constraint: pgub [ON]),
             ('plflb', Constraint: plflb [OFF]),
             ('plfub', Constraint: plfub [OFF]),
             ('alflb', Constraint: alflb [ON]),
             ('alfub', Constraint: alfub [ON]),
             ('rbu', Constraint: rbu [ON]),
             ('rbd', Constraint: rbd [ON]),
             ('rru', Constraint: rru [ON]),
             ('rrd', Constraint: rrd [ON]),
             ('rgu', Constraint: rgu [OFF]),
             ('rgd', Constraint: rgd [OFF])])

Alter Config#

Routines have an config attribute as configuration settings.

[48]:

spf = ams.load(ams.get_case('5bus/pjm5bus_demo.xlsx'),
               setup=True,
               no_output=True,)

Parsing input file "/Users/jinningwang/work/ams/ams/cases/5bus/pjm5bus_demo.xlsx"...
Input file parsed in 0.1086 seconds.
Zero line rates detacted in rate_b, rate_c, adjusted to 999.
System set up in 0.0032 seconds.

In RTED, the default interval is 5/60 [hour], and the formulations has been adjusted to fit the interval.

[49]:

spf.RTED.config

[49]:

OrderedDict([('t', 0.08333333333333333)])

[50]:

spf.RTED.run(solver='CLARABEL')

Building system matrices
Parsing OModel for <RTED>
Evaluating OModel for <RTED>
Finalizing OModel for <RTED>
<RTED> initialized in 0.0163 seconds.
<RTED> solved as optimal in 0.0178 seconds, converged in 11 iterations with CLARABEL.

[50]:

True

[51]:

spf.RTED.obj.v

[51]:

0.07988294371985226

We can update the interval to 1 [hour] and re-solve the dispatch.

Note that in this senario, compared to DCOPF, RTED has extra costs for pru and prd.

[52]:

spf.RTED.config.t = 60/60

Remember to update the parameters after the change.

[53]:

spf.RTED.update()

Building system matrices
<RTED> reinit OModel due to non-parametric change.
Evaluating OModel for <RTED>
Finalizing OModel for <RTED>

[53]:

True

[54]:

spf.RTED.run(solver='SCS')

<RTED> solved as optimal in 0.0208 seconds, converged in 2450 iterations with SCS.

[54]:

True

We can then get the objective value.

[55]:

spf.RTED.obj.v

[55]:

0.9585953504440567

Note that in this build-in case, the cru and crd are defined as zero.

[56]:

spf.RTED.cru.v

[56]:

array([0., 0., 0., 0., 0.])

[57]:

spf.RTED.crd.v

[57]:

array([0., 0., 0., 0., 0.])

As benchmark, we can solve the DCOPF.

[58]:

spf.DCOPF.run(solver='SCS')

Parsing OModel for <DCOPF>
Evaluating OModel for <DCOPF>
Finalizing OModel for <DCOPF>
<DCOPF> initialized in 0.0089 seconds.
<DCOPF> solved as optimal in 0.0099 seconds, converged in 375 iterations with SCS.

[58]:

True

As expected, the DCOPF has a similar objective value.

[59]:

spf.DCOPF.obj.v

[59]:

0.9585828177364673

Manipulate the Simulation#

Manipulate the Simulation#

Load Case#

Run Simulation#

Change Load#

Trip a Generator#

Trip a Line#

Disable Constraints#

Alter Config#

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