Interoperation with ANDES¶

One of the most interesting feature of AMS is its interoperation with dynamic simulator ANDES.

Interoperation includes compatible case conversion and data exchange, thus it facilitates dispatch-dynamic co-simulation using AMS and ANDES.

In [1]:

import numpy as np

import andes
import ams

import datetime

In [2]:

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

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

Last run time: 2024-06-18 20:17:19
andes:1.9.2
ams:0.9.8

In [3]:

ams.config_logger(stream_level=20)

Dispatch¶

In [4]:

sp = ams.load(ams.get_case('ieee14/ieee14_uced.xlsx'),
              setup=True,
              no_output=True,)

Parsing input file "/Users/jinningwang/Documents/work/mambaforge/envs/amsre/lib/python3.9/site-packages/ams/cases/ieee14/ieee14_uced.xlsx"...
Input file parsed in 0.1177 seconds.
System set up in 0.0017 seconds.

In [5]:

sp.RTED.init()

<RTED> initialized in 0.0572 seconds.

Out[5]:

True

In [6]:

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

<RTED> solved as optimal in 0.0157 seconds, converged in 10 iterations with CLARABEL.

Out[6]:

True

Convert to ANDES¶

The built-in ANDES interface can convert an AMS case to ANDES case in memory.

The bridge between AMS and converted ANDES is the shared power flow devices, Bus, PQ, PV, Slack, Line, and Shunt.

In [7]:

sa = sp.to_andes(setup=True,
                 addfile=andes.get_case('ieee14/ieee14_full.xlsx'))

Parsing additional file "/Users/jinningwang/Documents/work/mambaforge/envs/amsre/lib/python3.9/site-packages/andes/cases/ieee14/ieee14_full.xlsx"...
Following PFlow models in addfile will be overwritten: <Bus>, <PQ>, <PV>, <Slack>, <Shunt>, <Line>, <Area>
Addfile parsed in 0.0474 seconds.
System converted to ANDES in 0.1402 seconds.
/Users/jinningwang/Documents/work/mambaforge/envs/amsre/lib/python3.9/site-packages/ams/interop/andes.py:933: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
  ssa_key0 = ssa_key0.fillna(value=False)
AMS system 0x107bf5640 is linked to the ANDES system 0x1591bc310.
<PFlow> initialized in 0.0014 seconds.
 0: |F(x)| = 0.4665790376
 1: |F(x)| = 0.01697226536
 2: |F(x)| = 3.214367637e-05
 3: |F(x)| = 1.533567526e-10
<PFlow> solved in 0.0102 seconds, converged in 3 iterations with PYPOWER-Newton.
Power flow results are consistent.

If you wish to add devices to the converted ANDES system, set setup=False to skip the ANDES setup process.

As indicated by the output information, in the conversion process, ANDES power flow devices will be overwritten by AMS ones, if exists.

Upon a successful conversion, you are ready to enjoy full capability of ANDES.

help command can give a quick reference.

In [8]:

help(sp.to_andes)

Help on method to_andes in module ams.system:

to_andes(setup=True, addfile=None, **kwargs) method of ams.system.System instance
    Convert the AMS system to an ANDES system.
    
    A preferred dynamic system file to be added has following features:
    1. The file contains both power flow and dynamic models.
    2. The file can run in ANDES natively.
    3. Power flow models are in the same shape as the AMS system.
    4. Dynamic models, if any, are in the same shape as the AMS system.
    
    Parameters
    ----------
    setup : bool, optional
        Whether to call `setup()` after the conversion. Default is True.
    addfile : str, optional
        The additional file to be converted to ANDES dynamic mdoels.
    **kwargs : dict
        Keyword arguments to be passed to `andes.system.System`.
    
    Returns
    -------
    andes : andes.system.System
        The converted ANDES system.
    
    Examples
    --------
    >>> import ams
    >>> import andes
    >>> sp = ams.load(ams.get_case('ieee14/ieee14_rted.xlsx'), setup=True)
    >>> sa = sp.to_andes(setup=False,
    ...                  addfile=andes.get_case('ieee14/ieee14_wt3.xlsx'),
    ...                  overwrite=True, no_keep=True, no_output=True)

Interoperation with ANDES¶

In the interface class dyn, the link table is stored in dyn.link.

It describes the mapping relationships between power flow devices and dynamic devices.

In [9]:

sp.dyn.link

Out[9]:

	stg_idx	bus_idx	syg_idx	gov_idx	dg_idx	rg_idx	gammap	gammaq
0	Slack_1	1	GENROU_1	TGOV1_1	NaN	NaN	1.0	1.0
1	PV_5	8	GENROU_5	TGOV1_5	NaN	NaN	1.0	1.0
2	PV_4	6	GENROU_4	TGOV1_4	NaN	NaN	1.0	1.0
3	PV_3	3	GENROU_3	TGOV1_3	NaN	NaN	1.0	1.0
4	PV_2	2	GENROU_2	TGOV1_2	NaN	NaN	1.0	1.0

Send¶

As there is a gap between DC-based dispatch and AC-based TDS, a conversion is required to ensure the TDS initialization.

In [10]:

sp.RTED.dc2ac()

<ACOPF> initialized in 0.0037 seconds.
<ACOPF> solved in 0.1872 seconds, converged in 12 iterations with PYPOWER-PIPS.
<RTED> converted to AC.

Out[10]:

True

In the RTED routine, there are two mapping dictionaries to define the data exchange, namely, map1 for receiving data from ANDES and map2 for sending data to ANDES.

In [11]:

sp.RTED.map2

Out[11]:

OrderedDict([('vBus', ('Bus', 'v0')),
             ('ug', ('StaticGen', 'u')),
             ('pg', ('StaticGen', 'p0'))])

In [12]:

sp.dyn.send(adsys=sa, routine='RTED')

Send <RTED> results to ANDES <0x1591bc310>...
*Send <vBus> to StaticGen.v0
Send <vBus> to Bus.v0
Send <ug> to StaticGen.u
Send <pg> to StaticGen.p0

Out[12]:

True

Run ANDES¶

Sometimes, the ANDES TDS initialization may fail due to inapproriate limits.

Here, we alleviate the TGOV1 limit issue by enlarging the Pmax and Pmin to the same value.

In [13]:

sa.TGOV1.set(src='VMAX', attr='v', idx=sa.TGOV1.idx.v, value=100*np.ones(sa.TGOV1.n))
sa.TGOV1.set(src='VMIN', attr='v', idx=sa.TGOV1.idx.v, value=np.zeros(sa.TGOV1.n))

Out[13]:

True

Run power flow.

In [14]:

sa.PFlow.run()

Out[14]:

True

Try to init TDS.

In [15]:

_ = sa.TDS.init()

Run TDS.

In [16]:

sa.TDS.config.no_tqdm = True # disable progress bar
sa.TDS.run()

Out[16]:

True

Receive¶

In [17]:

sp.RTED.map1

Out[17]:

OrderedDict([('ug', ('StaticGen', 'u')), ('pg0', ('StaticGen', 'p'))])

In [18]:

sp.dyn.receive(adsys=sa, routine='RTED')

Receive <ug> from SynGen.u
Receive <pg0> from SynGen.Pe

Out[18]:

True

The RTED parameter pg0, is retrieved from ANDES as the corresponding generator output power.

In [19]:

sp.RTED.pg0.v

Out[19]:

array([1.79492221, 0.48428986, 0.01000094, 0.02000094, 0.01000095])