openpy-dsse 0.1.4

Open source library for state estimation of a distribution network modeled in OpenDSS

OpenPY_DSSE

It is an open-source library developed in Python for estimating distribution networks (DSSE). It communicates with the free software for the simulation of electrical networks (OpenDSS) and collects the results of power flow and distribution system parameters and executes the DSSE, obtaining an estimated state according to the type and location of measurements.

It is developed within the framework of the OpenREiD project (Integral software for simulation and optimization of electrical distribution networks), of the Instituto de Energía Eléctrica (IEE), UNSJ - CONICET, San Juan - Argentina.

Index

• Weighted Least Squares
• Installation
• How to use
  • Measurements
    • Definition and creation of meters
    • Generate metrics from OpenDSS results
  • Run the state estimation algorithm
  • Sample tests
• License

Weighted Least Squares (WLS)

The library uses a hybrid weighted least squares algorithm that incorporates traditional and D-PMU measurements. It supports single-phase and multiphase networks that are modeled as electromagnetically decoupled positive sequence impedances. More details can be found in docs/methodology

Installation

With pip

pip install py-open-dsse

Without pip, clone or download the repository, in the dist folder is the .whl file, copy the location of the file, and in the CMD:

pip install {path-save-files}/openpy_dsse-{version}-py3-none-any.whl’

How to use

First, in the IDE (Integrated Development Environment) of preference, we import the library:

import openpy_dsse

The object class that contains all the functions of the library is activated as follows:

dsse = openpy_dsse.init_DSSE()

The class init_DSSE(), has default values as shown in table 1 and can be modified as appropriate.

Table 1. Description and attributes of function init_DSSE()

Parameters	Description	Default value
Sbas3ph_MVA	Three-phase system base power	30
tolerance	Convergence tolerance of selected algorithm	1e-3
max_iter	Maximum number of iterations of the selected algorithm	30
init_values	Initial values for state estimation. With flat start with 1.0 p.u. / 0° on all buses and with dss start with OpenDSS voltage and angle results	flat

Once the class is initialized, we can use the functions described below.

Measurements

Definition and creation of meters

The library supports meters and their respective error variance described in Table 2.

Table 2. Measurement type of the openpy_dsse library.

Meter	Description
$\left|V_{i}\right|$	Node voltage magnitude.
$PQ_{ft}$	Branch power flow
$\left|I_{ft}\right|$	Magnitude of branch current.
$PQ_{i}^{SM}$	Injection power or node consumption obtained by a smart meter
$PQ_{i}^{0}$	Passive node or zero injection power.
$PQ_{i}^{PSD}$	Artificial node injection power known as pseudo-measurement
$\left|V_{i}\right|\angle \theta$	Voltage phasor measurement
$\left|I_{ft}\right|\angle \delta$	Current phasor measurement

The measurement data per phase 𝜌 (1, 2, 3) and measurement error variance Rii of a network modeled in OpenDSS. They must be entered in the MEAS_Bus_i.json, MEAS_Elem_ft.json, MEAS_Bus_i_PMU.json and MEAS_Elem_ft_PMU.json files. The .json measurement files without data are generated with the empty_MEAS_files() function and the parameters from table 3 must be entered.

Table 3. Parameters and description of empty_file_MEAS() function

Parameter	Description	Default value
DSS_path	A path of the .DSS files of the circuit modeled in OpenDSS	None
MEAS_path_save	Path where the measurement .json files will be saved	None

The description of the identifiers that can be modified is detailed in tables 4, 5, 6, and 7. The other identifiers in the .json files, are node characteristics or elements extracted from the circuit modeled in OpenDSS, these data should not be modified since they would affect the result of the state estimation algorithm.

Table 4. Description of identifiers of the MEAS_Bus_i.json file.

Identifier	Description
STS_Vm	Status (1: Enabled, 0: Disabled)
Rii_Vm	Variance of voltage magnitude measurement error.
Vρm(pu)	Measurement of voltage magnitude voltage in phase 𝜌.
STS_PQd(SM)	Status (1: Enabled, 0: Disabled)
Rii_SM	Measurement error variance of injection power or consumption of a smart meter.
STS_PQd(0)	Status (1: Enabled, 0: Disabled)
Rii_0	Zero or passive injection power measurement error variance.
STS_PQd(Psd)	Status (1: Enabled, 0: Disabled)
Rii_Psd	Measurement error variance of pseudo power injection measurement
Pρmd(pu)	Measurement of active power injection in phase 𝜌.
Qρmd(pu)	Measurement of reactive power injection in phase 𝜌.

Table 5. Description of identifiers of the MEAS_Elem_ft.json file.

Identifier	Description
STS_PQft	Status (1: Enabled, 0: Disabled)
Rii_PQft	Branch power flow measurement error variance.
Pρmft(pu)	Measurement of branch active power in phase 𝜌.
Qρmft(pu)	Measurement of branch reactive power in phase 𝜌.
STS_Ift	Status (1: Enabled, 0: Disabled)
Rii_Ift	Branch current magnitude error variance.
Iρmft(pu)	Measurement of branch current magnitude in phase 𝜌.

Table 6. Description of identifiers of the MEAS_Elem_ft_PMU.json file.

Identifier	Description
STS_Vm	Status (1: Enabled, 0: Disabled)
Rii_Vm	Variance of voltage phasor measurement error
Vρm(pu)	Measurement of voltage magnitude in phase 𝜌
Angρm(deg)	Measurement of volage angle in phase 𝜌

Table 7. Description of identifiers of the MEAS_Elem_ft_PMU.json file.

Identifier	Description
STS_Ift	Status (1: Enabled, 0: Disabled)
Rii_Ift	Variance of current phasor measurement error
Iρmft(pu)	Measurement of current magnitude in phase 𝜌
Angρm(deg)	Measurement of current angle in phase 𝜌

Generate metrics from OpenDSS results

Initial measurements with uncertainty of measurement error

The empty_init_files_MEAS_Unc() function generates .json files where all the nodes and elements that can participate as measurement in the state estimation algorithm are placed. For this purpose, the parameters of table 8 must be specified.

Table 8. Parameters and description of empty_file_MEAS() function

Parameters	Description	Default value
DSS_path	A path of the .DSS files of the circuit modeled in OpenDSS	None
MEAS_path_save	Path where the measurement .json files will be saved	None

In the MEAS_path_save path, it generates the files Init_Bus_i.json, Init_Elem_ft.json, Init_Bus_i_PMU.json and Init_Elem_ft_PMU.json. Depending on the case study, the STS meter status (1: Enabled, 0: Disabled) and the measurement error rate in Unc(%) can be modified.

Adding random errors and generating measurement files

With the .json files generated by the empty_file_MEAS() function and the changes indicated by the user, with the add_error_file_MEAS() function and the Parameters described in Table 9.

Table 9. Description and attributes of function add_error_files_MEAS()

Parameters	Description	Default value
DSS_path	A path of the .DSS files of the circuit modeled in OpenDSS	None
MEAS_path	Path of initial .json files	None
seed_DS	Random error generation seed	1

Adds random errors from a normal distribution to the OpenDSS power flow results, for use as a measurement. The result of generating random errors is contained in the files MEAS_Bus_i.json, MEAS_Elem_ft.json, MEAS_Bus_i_PMU.json and MEAS_Elem_ft_PMU.json, stored in MEAS_path.

Run the state estimation algorithm

To run the state estimation algorithm, function estimate() is called, it is necessary to enter or change parameters detailed in table 10.

Table 10. Parameters and description of estimate() function

Parameters	Description	Default value
DSS_path	A path of the .DSS files of the circuit modeled in OpenDSS.	None
MEAS_path	Path where measurement files are located.	None
path_save	Path where the results will be saved	None
Typ_cir	Circuit type, can be 1ph or Pos.	None
ALG	Type of algorithm state variables. At the moment NV	NV
coord	Type of coordinates to solve. For the moment polar.	Polar
method	Solution method, can be nonlinear, linear_PMU and nonlinear_PMU.	nonlinear_PMU
name_project	Project name	Default
View_res	Displays the result of the selected algorithm by console	False
DSS_coll	Displays next to the estimated status, the actual status according to OpenDSS	False
summary	Displays a summary of the simulation by console	False
MEAS_Pos	True if it is a Pos circuit, take the .json files in positive sequence.	False

Sample tests

In the path :{Python_library_path}/openpy_dsse/examples, the .DSS and .json files of single-phase (1ph) and positive sequence equivalent (Pos) circuit measurements detailed in Table 11 are located.

Table 11. Sample tests

Circuit	Tpy_circ	Case
4Node	1ph	1
15NodeIEEE	1ph	2
13NodeIEEE	Pos	1
37NodeIEEE	Pos	2

The function test_circuit(Typ_cir, case), returns a dictionary with the keys 'DSS_file', 'MEAS_path', 'save_path', 'name_project' and 'Typ_cir' which correspond to the .DSS file path, measurement file path, path where results will be saved and circuit type respectively.

import openpy_dsse

dsse = openpy_dsse.init_DSSE() #Start the class.

if __name__ == '__main__':
    net = dsse.test_circuit(Typ_cir='1ph', case=1)
    
    #dsse.empty_init_files_MEAS_Unc(DSS_path=net['DSS_file'], MEAS_path=net['MEAS_path'])
    #dsse.add_error_files_MEAS(DSS_path=net['DSS_file'], MEAS_path=net['MEAS_path'])
    #dsse.empty_MEAS_files(DSS_path=net['DSS_file'], MEAS_path=net['MEAS_path'])

    #execute the state estimation algorithm
    Results = dsse.estimate(
        DSS_path=net['DSS_file'],
        MEAS_path=net['MEAS_path'],
        path_save=net['save_path'],
        Typ_cir=net['Typ_cir'],
        name_project=net['name_project'],
        View_res=True,
        summary=True,
        DSS_coll=True,
        #MEAS_Pos=True,
        #method='nonlinear'
        #method='linear_PMU',
    )

License

License: CC BY-NC-SA 4.0

This work has a license Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.