Multi-period Scheduling Simulation¶

Multi-period economic dispatch (ED) and unit commitment (UC) are also available.

In this case, we will show a 24-hour ED simulation.

In [1]:

import ams

import datetime

In [2]:

print("Last run time:", datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))

print(f'ams:{ams.__version__}')

Last run time: 2024-11-24 17:47:13
ams:0.9.12

In [3]:

ams.config_logger(stream_level=20)

Load Case¶

In [4]:

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

Working directory: "/Users/jinningwang/work/ams/examples"
Parsing input file "/Users/jinningwang/work/miniconda3/envs/amsre/lib/python3.12/site-packages/ams/cases/5bus/pjm5bus_demo.xlsx"...
Input file parsed in 0.0222 seconds.
Zero line rates detacted in rate_b, rate_c, adjusted to 999.
System set up in 0.0019 seconds.

Zonal Design¶

The scheduling models in AMS has develoepd with zonal structure, and it can be inspected in device Zone.

Note: Since version 0.9.14, model Region is renamed to Zone for clarity.

In [ ]:

sp.Zone.as_df()

Out[ ]:

	idx	u	name
uid
0	ZONE_1	1.0	ZONE 1
1	ZONE_2	1.0	ZONE 2

In device Bus, the Param zone indicates the zone of the bus. Correspondingly, the region of generator and load are determined by the bus they connected.

In [6]:

sp.Bus.as_df()

Out[6]:

	idx	u	name	Vn	vmax	vmin	v0	a0	xcoord	ycoord	area	zone	owner	type
uid
0	Bus_1	1.0	A	230.0	1.1	0.9	1.0	0.0	0	0	1	ZONE_1	None	1
1	Bus_2	1.0	B	230.0	1.1	0.9	1.0	0.0	0	0	1	ZONE_1	None	1
2	Bus_3	1.0	C	230.0	1.1	0.9	1.0	0.0	0	0	2	ZONE_1	None	1
3	Bus_4	1.0	D	230.0	1.1	0.9	1.0	0.0	0	0	2	ZONE_1	None	1
4	Bus_5	1.0	E	230.0	1.1	0.9	1.0	0.0	0	0	3	ZONE_1	None	1

Multi-period Dispatch Base¶

In AMS, multi-period dispatch involves devices in group Horizon. This group is developed to provide time-series data for multi-period dispatch.

In [7]:

sp.Horizon.models

Out[7]:

OrderedDict([('TimeSlot', TimeSlot (0 devices) at 0x15b8bc530),
             ('EDTSlot', EDTSlot (6 devices) at 0x15b8bcb30),
             ('UCTSlot', UCTSlot (6 devices) at 0x15b8bcec0)])

We can get the idx of StaticGens.

In [8]:

sp.StaticGen.get_all_idxes()

Out[8]:

['Slack_4', 'PV_1', 'PV_3', 'PV_5']

In EDTSlot, Param sd refers the load factors of each region in each time slot, and Param ug represents the generator commitment status in each time slot.

To be more specific, EDT1 has sd=0.0793,0.0, which means the load factor of region 1 is 0.0793 in the first time slot, and 0.0 in the second time slot.

Next, EDT1 has ug=1,1,1,1, and it means the commitment status of generator PV_1, PV_3, PV_5, and Slack_4 are all online.

In [9]:

sp.EDTSlot.as_df()

Out[9]:

	idx	u	name	sd	ug
uid
0	EDT1	1.0	EDT1	0.793,0.0	1,1,1,1
1	EDT2	1.0	EDT2	0.756,0.0	1,1,1,1
2	EDT3	1.0	EDT3	0.723,0.0	1,1,1,1
3	EDT4	1.0	EDT4	0.708,0.0	1,1,1,1
4	EDT5	1.0	EDT5	0.7,0.0	1,1,1,1
5	EDT6	1.0	EDT6	0.706,0.0	1,1,1,1

Solve and Result¶

In [10]:

sp.ED.init()

Building system matrices
Parsing OModel for <ED>
Evaluating OModel for <ED>
Finalizing OModel for <ED>
<ED> initialized in 0.0138 seconds.

Out[10]:

True

In [11]:

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

<ED> solved as optimal in 0.0139 seconds, converged in 9 iterations with CLARABEL.

Out[11]:

True

All decision variables are collected in the dict vars.

In [12]:

sp.ED.vars

Out[12]:

OrderedDict([('pg', Var: StaticGen.pg),
             ('aBus', Var: Bus.aBus),
             ('pru', Var: StaticGen.pru),
             ('prd', Var: StaticGen.prd),
             ('prs', Var: StaticGen.prs)])

As we can see, the generator output pg is a 2D array, and the first dimension is the generator index, and the second dimension is the time slot.

In [13]:

sp.ED.pg.v

Out[13]:

array([[2.  , 2.  , 2.  , 2.  , 2.  , 2.  ],
       [2.1 , 2.1 , 2.1 , 2.1 , 2.1 , 2.1 ],
       [3.23, 2.86, 2.53, 2.38, 2.3 , 2.36],
       [0.6 , 0.6 , 0.6 , 0.6 , 0.6 , 0.6 ]])

Partial results can be accessed with desired time slot. In the retrieved result, the first dimension is the generator index, and the second dimension is the time slot.

In [14]:

sp.ED.get(src='pg', attr='v', idx='PV_1', horizon=['EDT1'])

Out[14]:

2.0999999983486095

Or, get multiple variables in mutliple time slots.

In [15]:

sp.ED.get(src='pg', attr='v', idx=['PV_1', 'PV_3'], horizon=['EDT1', 'EDT2', 'EDT3'])

Out[15]:

array([[2.1 , 2.1 , 2.1 ],
       [3.23, 2.86, 2.53]])