Managing Physical Units

Managing Physical Units#

  • In GDM, all physical quantities are wrapped in pint objects (https://pypi.org/project/Pint/).

  • Users import physical quantities when building new models or updating model parameters.

    • Units are validated for all physical quantities.

    • All quantities are imported from gdm.quantities.

Creating Quantities#

from gdm.quantities import ReactivePower

power = ReactivePower(800, "kilovar")
print(power)

800 kilovar

The to method can be used for unit conversion

print(power.to("var"))

800000.0 var

The quantitiy models can be used with list and numpy arrays

powers = ReactivePower([400, 200, 100], "kilovar")
print(powers)
print(powers.to("var"))

[400 200 100] kilovar
[400000.0 200000.0 100000.0] var

In certain cases, additional validation may be performed. e.g. PV systems can only provide active power hence represented by PossitiveActivePower

from gdm.quantities import PositiveActivePower

try:
    PositiveActivePower(-700, "kilowatt")
except Exception as e:
    print(e)

Active power (-700, kilowatt) must be positive.

Validation Limitations#

Warning

Unit validation does not occur until the quantity is added to a data model. In the example below, the rated_voltage object is built with incorrect units. It is only validated when we instantiate the DistributionBus component.

from gdm.distribution.enums import LimitType, Phase, VoltageTypes
from gdm.distribution.components import DistributionSubstation
from gdm.distribution.common.limitset import VoltageLimitSet
from gdm.distribution.components import DistributionFeeder
from gdm.distribution.components import DistributionBus
from gdm.quantities import PositiveVoltage
from infrasys import Location

rated_voltage = PositiveVoltage(400, "ampere")

/opt/hostedtoolcache/Python/3.12.10/x64/lib/python3.12/site-packages/pydantic/_internal/_generate_schema.py:502: UserWarning: Ellipsis is not a Python type (it may be an instance of an object), Pydantic will allow any object with no validation since we cannot even enforce that the input is an instance of the given type. To get rid of this error wrap the type with `pydantic.SkipValidation`.
  warn(

bus = DistributionBus(
    voltage_type=VoltageTypes.LINE_TO_LINE,
    phases=[Phase.A, Phase.B, Phase.C],
    rated_voltage=rated_voltage,
    name="DistBus1",
    substation=DistributionSubstation.example(),
    feeder=DistributionFeeder.example(),
    voltagelimits=[
        VoltageLimitSet(
            limit_type=LimitType.MIN, value=PositiveVoltage(400 * 0.9, "volt")
        ),
        VoltageLimitSet(
            limit_type=LimitType.MAX, value=PositiveVoltage(400 * 1.1, "volt")
        ),
    ],
    coordinate=Location(x=20.0, y=30.0),
)

---------------------------------------------------------------------------
ValidationError                           Traceback (most recent call last)
Cell In[6], line 1
----> 1 bus = DistributionBus(
      2     voltage_type=VoltageTypes.LINE_TO_LINE,
      3     phases=[Phase.A, Phase.B, Phase.C],
      4     rated_voltage=rated_voltage,
      5     name="DistBus1",
      6     substation=DistributionSubstation.example(),
      7     feeder=DistributionFeeder.example(),
      8     voltagelimits=[
      9         VoltageLimitSet(
     10             limit_type=LimitType.MIN, value=PositiveVoltage(400 * 0.9, "volt")
     11         ),
     12         VoltageLimitSet(
     13             limit_type=LimitType.MAX, value=PositiveVoltage(400 * 1.1, "volt")
     14         ),
     15     ],
     16     coordinate=Location(x=20.0, y=30.0),
     17 )

File /opt/hostedtoolcache/Python/3.12.10/x64/lib/python3.12/site-packages/pydantic/main.py:214, in BaseModel.__init__(self, **data)
    212 # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
    213 __tracebackhide__ = True
--> 214 validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self)
    215 if self is not validated_self:
    216     warnings.warn(
    217         'A custom validator is returning a value other than `self`.\n'
    218         "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n"
    219         'See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.',
    220         stacklevel=2,
    221     )

ValidationError: 1 validation error for DistributionBus
rated_voltage
  Assertion failed, Unit must be compatible with volt [type=assertion_error, input_value=<Quantity(400, 'ampere')>, input_type=PositiveVoltage]
    For further information visit https://errors.pydantic.dev/2.10/v/assertion_error
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