OEDI SI/Scenarios/Weighted Least Squares DSSE PV Estimator IEEE123 test feeder

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Weighted Least Squares DSSE PV Estimator IEEE123 test feeder​ Summary

  • Date Created: 2024/07/15
  • Organization: PNNL
  • Objective: Objective: Distribution system state estimation (DSSE) refers to the process of estimating the current conditions of the system’s state, such as voltage/current magnitudes and angles, power flows, etc. in a distribution system using available field measurements from sensors installed at substations, feeders, and other strategic points in the distribution network. However, for modern distribution systems, operators also need accurate information about the locations and injections of solar rooftop PVs due to their increased integration and potential to introduce operational challenges. In this context, we present a state estimation algorithm for distribution systems that can accurately estimate photovoltaic system injections. Specifically, the algorithm 1) detects unregistered rooftop photovoltaics by estimating the injections of hypothetical systems at behind-the-meter locations, 2) identifies inverter settings for unregistered systems, and 3) detects incorrect inverter settings for registered systems. In the developed algorithm, the Weighted Least Squares (WLS) state estimation method is used, where 3-phase LinDistFlow model is used to define the measurement function. The WLS method a robust technique widely used in power systems to determine the state of the system, such as voltage magnitudes and phase angles at different buses. It minimizes the sum of the squared differences between the measured and estimated values, weighted by the inverse of the measurement variances. This method accounts for different accuracy levels of measurements by assigning higher weights to more accurate data, ensuring a reliable estimation. Here we have leveraged the robustness of the WLS method to detect and estimate the PV parameters. The method is non-iterative, as the measurement function is linear.
  • Use Case: Distribution System State Estimation
  • Methodology
    • Inputs
      • Outputs
        • Configuration
          • Webinars


          Docker Container

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          Run Locally

          http://localhost:8080/edit_scenario?Weighted Least Squares DSSE PV Estimator IEEE123 test feeder
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          Input Data

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            Output Data

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              Component Raw Data

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              IEEE 123-bus Distribution Network Data

              References


                Back to Distribution System State Estimation



                "Objective: Distribution system state estimation (DSSE) refers to the process of estimating the current conditions of the system’s state, such as voltage/current magnitudes and angles, power flows, etc. in a distribution system using available field measurements from sensors installed at substations, feeders, and other strategic points in the distribution network. However, for modern distribution systems, operators also need accurate information about the locations and injections of solar rooftop PVs due to their increased integration and potential to introduce operational challenges. In this context, we present a state estimation algorithm for distribution systems that can accurately estimate photovoltaic system injections. Specifically, the algorithm 1) detects unregistered rooftop photovoltaics by estimating the injections of hypothetical systems at behind-the-meter locations, 2) identifies inverter settings for unregistered systems, and 3) detects incorrect inverter settings for registered systems. In the developed algorithm, the Weighted Least Squares (WLS) state estimation method is used, where 3-phase LinDistFlow model is used to define the measurement function. The WLS method a robust technique widely used in power systems to determine the state of the system, such as voltage magnitudes and phase angles at different buses. It minimizes the sum of the squared differences between the measured and estimated values, weighted by the inverse of the measurement variances. This method accounts for different accuracy levels of measurements by assigning higher weights to more accurate data, ensuring a reliable estimation. Here we have leveraged the robustness of the WLS method to detect and estimate the PV parameters. The method is non-iterative, as the measurement function is linear." cannot be used as a page name in this wiki.


              1. This scenario employs an WLS method for DSSE. The WLS method has only one step. Note that the PV active power and reactive power injections are the states to be estimated.
              2. The DSSE algorithm is integrated in OEDI SI as an independent federate, where HELICS maintains a message queue. This allows each federate to move at their own pace. The use case represents a time series analysis at 15-minute intervals for 24 hours. The sensor federate generates the measurements using the power flow results generated by the power flow federate. At each time step, the WLS algorithm (DSSE) federate generates the PV parameter estimates by using the measurement set and topology data. Results are logged by the recorder federate as well as written in the .csv files. They are also plotted using the post-processing scripts.
              3. System topology consisting of node names, nominal node voltages and angles, Y-bus matrix in sparse format, location of source bus and nominal active and reactive power loads at all nodes [Topology] -- The estimator federate subscribes the system topology from the publications of the feeder federate.
              4. Voltage magnitude measurements [VoltagesMagnitude] -- The estimator federate subscribes the voltage magnitude measurements from the publications of the measuring federate responsible to record voltage magnitude measurement.
              5. Real power measurements [PowersReal] -- The estimator federate subscribes the real power measurements from the publications of the measuring federate responsible to record real power measurement.
              6. Reactive power measurements [PowersImaginary] -- The estimator federate subscribes the reactive power measurements from the publications of the measuring federate responsible to record reactive power measurement. Location of all measurements.
              7. Estimated PV locations through updated Injection equipment types
              8. Estimated active and reactive power injections from the PVs[Injection]
              9. The user is not required to manipulate the internal contents of this image. To run the image, the user needs to follow the instructions in the readme file of the Docker container.
              10. https://hub.docker.com/r/openenergydatainitiative/pnnl-dsse-wsl-pv-ieee-123
              11. https://data.openei.org/s3_viewer?bucket=gadal&prefix=gadal_ieee123%2F
              12. Output are saved to the local folder /outputs or the designated mounted volume for the single container setup.
                13. Retrieved from "https://openei.org/w/index.php?title=OEDI_SI/Scenarios/Weighted_Least_Squares_DSSE_PV_Estimator_IEEE123_test_feeder&oldid=1084778"