power_grid_model

class power_grid_model.PowerGridModel

Bases: object

Attributes

all_component_count: Get count of number of elements per component type.
cache_topology
independent

Methods

`calculate_power_flow`(*, symmetric, ...)	Calculate power flow once with the current model attributes.
`calculate_state_estimation`(*, symmetric, ...)	Calculate state estimation once with the current model attributes.
`copy`()	Copy the current model
`get_indexer`(component_type, ids)	Get array of indexers given array of ids for component type
`update`(*, update_data)	Update the model with changes. Args: update_data: update data dictionary key: component type name value: 1D numpy structured array for this component update Returns: None.

__init__()

Initialize the model from an input data set.

Args:

input_data: input data dictionary: key: component type name value: 1D numpy structured array for this component input

system_frequency: frequency of the power system, default 50 Hz

all_component_count

Get count of number of elements per component type. If the count for a component type is zero, it will not be in the returned dictionary. Returns:

a dictionary with
key: component type name value: integer count of elements of this type

cache_topology

calculate_power_flow(*, symmetric, error_tolerance, max_iterations, calculation_method: Union[CalculationMethod, str], update_data: Optional[Dict[str, Union[ndarray, Dict[str, ndarray]]]], threading) → Dict[str, ndarray]

Calculate power flow once with the current model attributes. Or calculate in batch with the given update dataset in batch

Args:

symmetric:

True: three-phase symmetric calculation, even for asymmetric loads/generations False: three-phase asymmetric calculation

error_tolerance:

error tolerance for voltage in p.u., only applicable when iterative=True

max_iterations:

maximum number of iterations, only applicable when iterative=True

calculation_method: an enumeration or string

newton_raphson: use Newton-Raphson iterative method (default) linear: use linear method

update_data:

None: calculate power flow once with the current model attributes A dictionary for batch calculation with batch update

key: component type name to be updated in batch value:

a 2D numpy structured array for homogeneous update batch
Dimension 0: each batch Dimension 1: each updated element per batch for this component type

or a dictionary containing two keys, for inhomogeneous update batch

indptr: a 1D integer numpy array with length n_batch + 1
given batch number k, the update array for this batch is data[indptr[k]:indptr[k + 1]] This is the concept of compressed sparse structure https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html

data: 1D numpy structured array in flat

threading:

only applicable for batch calculation < 0 sequential = 0 parallel, use number of hardware threads > 0 specify number of parallel threads

Returns:

dictionary of results of all components: key: component type name to be updated in batch value:

for single calculation: 1D numpy structured array for the results of this component type for batch calculation: 2D numpy structured array for the results of this component type

Dimension 0: each batch Dimension 1: the result of each element for this component type
Error handling:: in case an error in the core occurs, an exception will be thrown

calculate_state_estimation(*, symmetric, error_tolerance, max_iterations, calculation_method: Union[CalculationMethod, str], update_data: Optional[Dict[str, Union[ndarray, Dict[str, ndarray]]]], threading) → Dict[str, ndarray]

Calculate state estimation once with the current model attributes. Or calculate in batch with the given update dataset in batch

Args:

symmetric:

True: three-phase symmetric calculation, even for asymmetric loads/generations False: three-phase asymmetric calculation

error_tolerance:

error tolerance for voltage in p.u., only applicable when iterative=True

max_iterations:

maximum number of iterations, only applicable when iterative=True

calculation_method: an enumeration

iterative_linear: use iterative linear method

update_data:

None: calculate state estimation once with the current model attributes A dictionary for batch calculation with batch update

key: component type name to be updated in batch value:

a 2D numpy structured array for homogeneous update batch
Dimension 0: each batch Dimension 1: each updated element per batch for this component type

or a dictionary containing two keys, for inhomogeneous update batch

indptr: a 1D integer numpy array with length n_batch + 1
given batch number k, the update array for this batch is data[indptr[k]:indptr[k + 1]] This is the concept of compressed sparse structure https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html

data: 1D numpy structured array in flat

threading:

only applicable for batch calculation < 0 sequential = 0 parallel, use number of hardware threads > 0 specify number of parallel threads

Returns:

dictionary of results of all components: key: component type name to be updated in batch value:

for single calculation: 1D numpy structured array for the results of this component type for batch calculation: 2D numpy structured array for the results of this component type

Dimension 0: each batch Dimension 1: the result of each element for this component type
Error handling:: in case an error in the core occurs, an exception will be thrown

copy() → PowerGridModel

Copy the current model

Returns:: a copy of PowerGridModel

get_indexer(component_type: unicode, ids: ndarray)

Get array of indexers given array of ids for component type

Args:: component_type: type of component ids: array of ids
Returns:: array of inderxers, same shape as input array ids

independent

update(*, update_data: Dict[str, ndarray])

Update the model with changes. Args:

update_data: update data dictionary
key: component type name value: 1D numpy structured array for this component update

Returns:: None

power_grid_model.initialize_array(data_type: unicode, component_type: unicode, shape: Union[tuple, int], empty=False)

Initializes an array for use in Power Grid Model calculations

Args:

data_type: input, update, sym_output, or asym_output component_type: one component type, e.g. node shape: shape of initialization

integer, it is a 1-dimensional array tuple, it is an N-dimensional (tuple.shape) array

empty: if leave the memory block un-initialized

Returns:

np structured array with all entries as null value

enum

Common Enumerations

Note: these enumeration match the C++ arithmetic core, so dont change the values unless you change thmn in C++ as well

class power_grid_model.enum.LoadGenType(value)

Bases: IntEnum

Load and Generator Types

const_power = 0

const_impedance = 1

const_current = 2

class power_grid_model.enum.WindingType(value)

Bases: IntEnum

Transformer Winding Types

wye = 0

wye_n = 1

delta = 2

zigzag = 3

zigzag_n = 4

class power_grid_model.enum.BranchSide(value)

Bases: IntEnum

Branch Sides

from_side = 0

to_side = 1

class power_grid_model.enum.Branch3Side(value)

Bases: IntEnum

Branch3 Sides

side_1 = 0

side_2 = 1

side_3 = 2

class power_grid_model.enum.CalculationType(value)

Bases: Enum

Calculation Types

power_flow = 'power_flow'

state_estimation = 'state_estimation'

class power_grid_model.enum.CalculationMethod(value)

Bases: IntEnum

Calculation Methods

linear = 0

newton_raphson = 1

iterative_linear = 2

iterative_current = 3

linear_current = 4

class power_grid_model.enum.MeasuredTerminalType(value)

Bases: IntEnum

The type of asset measured by a (power) sensor

branch_from = 0: Measuring the from-terminal between a branch and a node

branch_to = 1: Measuring the to-terminal between a branch and a node

source = 2: Measuring the terminal between a source and a node

shunt = 3: Measuring the terminal between a shunt and a node

load = 4: Measuring the terminal between a load and a node

generator = 5: Measuring the terminal between a generator and a node

branch3_1 = 6: Measuring the terminal-1 between a branch3 and a node

branch3_2 = 7: Measuring the terminal-2 between a branch3 and a node

branch3_3 = 8: Measuring the terminal-3 between a branch3 and a node

validation

power_grid_model.validation.validate_input_data(input_data: Dict[str, ndarray], calculation_type: Optional[CalculationType] = None, symmetric: bool = True) → Optional[List[ValidationError]]

Validates the entire input dataset:

Is the data structure correct? (checking data types and numpy array shapes)

Are all required values provided? (checking NaNs)

Are all ID’s unique? (checking object identifiers across all components)

Are the supplied values valid? (checking limits and other logic as described in “Graph Data Model”)

Args:: input_data: A power-grid-model input dataset calculation_type: Supply a calculation method, to allow missing values for unused fields symmetric: A boolean to state whether input data will be used for a symmetric or asymmetric calculation
Raises:: KeyError, TypeError or ValueError if the data structure is invalid.
Returns:: None if the data is valid, or a list containing all validation errors.

power_grid_model.validation.validate_batch_data(input_data: Dict[str, ndarray], update_data: Dict[str, Union[ndarray, Dict[str, ndarray]]], calculation_type: Optional[CalculationType] = None, symmetric: bool = True) → Optional[Dict[int, List[ValidationError]]]

Ihe input dataset is validated:

Is the data structure correct? (checking data types and numpy array shapes)

Are all input data ID’s unique? (checking object identifiers across all components)

For each batch the update data is validated:

Is the update data structure correct? (checking data types and numpy array shapes)
Are all update ID’s valid? (checking object identifiers across update and input data)

Then (for each batch independently) the input dataset is updated with the batch’s update data and validated:

Are all required values provided? (checking NaNs)
Are the supplied values valid? (checking limits and other logic as described in “Graph Data Model”)

Args:

input_data: a power-grid-model input dataset update_data: a power-grid-model update dataset (one or more batches) calculation_type: Supply a calculation method, to allow missing values for unused fields symmetric: A boolean to state whether input data will be used for a symmetric or asymmetric calculation

Raises:

KeyError, TypeError or ValueError if the data structure is invalid.

Returns:

None if the data is valid, or a dictionary containing all validation errors, where the key is the batch number (0-indexed).

power_grid_model.validation.assert_valid_input_data(input_data: Dict[str, ndarray], calculation_type: Optional[CalculationType] = None, symmetric: bool = True)

Validates the entire input dataset:

Is the data structure correct? (checking data types and numpy array shapes)

Are all required values provided? (checking NaNs)

Are all ID’s unique? (checking object identifiers across all components)

Are the supplied values valid? (checking limits and other logic as described in “Graph Data Model”)

Args:: input_data: A power-grid-model input dataset calculation_type: Supply a calculation method, to allow missing values for unused fields symmetric: A boolean to state whether input data will be used for a symmetric or asymmetric calculation
Raises:: KeyError, TypeError or ValueError if the data structure is invalid. ValidationException if the contents are invalid.

power_grid_model.validation.assert_valid_batch_data(input_data: Dict[str, ndarray], update_data: Dict[str, Union[ndarray, Dict[str, ndarray]]], calculation_type: Optional[CalculationType] = None, symmetric: bool = True)

Ihe input dataset is validated:

Is the data structure correct? (checking data types and numpy array shapes)

Are all input data ID’s unique? (checking object identifiers across all components)

For each batch the update data is validated:

Is the update data structure correct? (checking data types and numpy array shapes)
Are all update ID’s valid? (checking object identifiers across update and input data)

Then (for each batch independently) the input dataset is updated with the batch’s update data and validated:

Are all required values provided? (checking NaNs)
Are the supplied values valid? (checking limits and other logic as described in “Graph Data Model”)

Args:

input_data: a power-grid-model input dataset update_data: a power-grid-model update dataset (one or more batches) calculation_type: Supply a calculation method, to allow missing values for unused fields symmetric: A boolean to state whether input data will be used for a symmetric or asymmetric calculation

Raises:

KeyError, TypeError or ValueError if the data structure is invalid. ValidationException if the contents are invalid.

power_grid_model.validation.errors_to_string(errors: Optional[Union[List[ValidationError], Dict[int, List[ValidationError]]]], name: str = 'the data', details: bool = False, id_lookup: Optional[Union[List[str], Dict[int, str]]] = None) → str

Convert a set of errors (list or dict) to a human readable string representation. Args:

errors: The error objects. List for input_data only, dict for batch data. name: Human understandable name of the dataset, e.g. input_data, or update_data. details: Display object ids and error specific information. id_lookup: A list or dict (int->str) containing textual object ids

Returns: A human readable string representation of a set of errors.

errors

class power_grid_model.validation.errors.ValidationError

The Validation Error is an abstract base class which should be extended by all validation errors. It supplies three public member variables: component, field and ids; storing information about the origin of the validation error. Error classes can extend the public members. For example:

NotBetweenError(ValidationError):
component = ‘vehicle’ field = ‘direction’ id = [3, 14, 15, 92, 65, 35] ref_value = (-3.1416, 3.1416)

For convenience, a human readable representation of the error is supplied using the str() function. I.e. print(str(error)) will print a human readable error message like:

Field direction is not between -3.1416 and 3.1416 for 6 vehicles

Attributes

component
component_str: A string representation of the component to which this error applies
field
field_str: A string representation of the field to which this error applies
ids

Methods

get_context([id_lookup])

Returns a dictionary that supplies (human readable) information about this error.

component: Optional[Union[str, List[str]]] = None: The component, or components, to which the error applies.

field: Optional[Union[str, List[str], List[Tuple[str, str]]]] = None: The field, or fields, to which the error applies. A field can also be a tuple (component, field) when multiple components are being addressed.

ids: Optional[Union[List[int], List[Tuple[str, int]]]] = None: The object identifiers to which the error applies. A field object identifier can also be a tuple (component, id) when multiple components are being addressed.

property component_str: str: A string representation of the component to which this error applies

property field_str: str: A string representation of the field to which this error applies

get_context(id_lookup: Optional[Union[List[str], Dict[int, str]]] = None) → Dict[str, Any]

Returns a dictionary that supplies (human readable) information about this error. Each member variable is included in the dictionary. If a function {field_name}_str() exists, the value is overwritten by that function.

Args:: id_lookup: A list or dict (int->str) containing textual object ids

utils

power_grid_model.utils.import_json_data(json_file: Path, data_type: str, ignore_extra: bool = False) → Union[Dict[str, ndarray], Dict[str, Union[ndarray, Dict[str, ndarray]]]]

import json data Args:

json_file: path to the json file data_type: type of data: input, update, sym_output, or asym_output ignore_extra: Allow (and ignore) extra attributes in the json file

Returns:: A single or batch dataset for power-grid-model

power_grid_model.utils.export_json_data(json_file: Path, data: Union[Dict[str, ndarray], Dict[str, Union[ndarray, Dict[str, ndarray]]]], indent: Optional[int] = 2, compact: bool = False)

export json data Args:

json_file: path to json file data: a single or batch dataset for power-grid-model indent: indent of the file, default 2 compact: write components on a single line

Returns:: Save to file