OEDI SI/Use Cases/Distribution System State Estimation

From Open Energy Information

Distribution System State Estimation Summary


Description

  • State estimation is a statistical analysis that generates a 'true’ estimate of a system by combining the mathematical model of the system with (noisy) measurements from sensors on the system. Typically, it is assumed that the number of measurements (data points) are larger than the number of unknown variables (states of the system). The ratio of number of measurements to the number of unknowns is known as the data redundancy. A (desired) redundancy of larger than unity enables the analysis to filter the noise/errors in the measurements by accounting for the uncertainties in the model and the data.
  • The power state estimation is typically the first analysis/function that is run for real-time operation and control of electrical power systems. Its role is to provide a complete, coherent, and reliable real-time state of the power network. The correctness and effect of other downstream real-time operation and control functions depend on the accuracy of the power system state estimation algorithms.
  • Transmission system state estimation (TSSE) is a mature tool that has been commonly used by system operators to ensure secure system operations at utility and independent system operator (ISO) control centers. Distribution System State Estimation (DSSE) is on the other hand has drawn attention only recently due to the traditional 'passive’ nature of distribution networks.
  • In literature, the DSSE problem is solved largely through model-based approaches such as Weighted Least Squares (WLS) algorithm [3], data driven methods such as learning based approaches [4], [5], and forecasting aided algorithms [6], [7]. This use case presents different DSSE algorithms.
  • The figure below depicts the simulation capabilities that are offered to enable different what-if scenarios with this use case. The flow starts with solving a power flow case using OpenDSS to create 'perfect’ state variables for the network model that is picked by the user. Sensor Federate creates different types of measurements using the power flow results and introduces user defined noise into these measurements. The users can edit certain input data and algorithm parameters to modify the 'what-if’ conditions.
  • The measurement redundancy and can either run OEDI SI reference DSSE algorithm or swap it with their own DSSE algorithm.
  • The users need to make sure that their algorithm follows the input and output requirements set up by OEDI SI for this use case. Finally, Recorder Federate prints out the results locally on the user’s workstation.
  • Please note that the workflow is defined by the OEDI SI administrators, and the users cannot arbitrarily modify it. The users can only manipulate some of the input data and its configuration parameters; and can swap their own DSSE algorithm with the reference algorithm, only if they follow the I/O context and format.


Scenarios


Workflow


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References




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    Distribution System State Estimation


  1. A. Abur and A. G. Exposito, Power system state estimation: theory and implementation. CRC press, 2004.
  2. M. Fotopoulou, S. Petridis, I. Karachalios, and D. Rakopoulos, “A review on distribution system state estimation algorithms,” Applied Sciences, vol. 12, no. 21, p. 11073, 2022.
  3. A. Angioni, J. Shang, F. Ponci, and A. Monti, “Real-time monitoring of distribution system based on state estimation,” IEEE Transactions on Instrumentation and Measurement, vol. 65, no. 10, pp. 2234–2243, 2016.
  4. A. Bernieri, G. Betta, C. Liguori, and A. Losi, “Neural networks and pseudo-measurements for real-time monitoring of distribution systems,” IEEE transactions on instrumentation and measurement, vol. 45, no. 2, pp. 645–650, 1996.
  5. M. Ferdowsi, A. Benigni, A. L ̈owen, B. Zargar, A. Monti, and F. Ponci, “A scalable data-driven monitoring approach for distribution systems,” IEEE Transactions on Instrumentation and Measurement, vol. 64, no. 5, pp. 1292–1305, 2015.
  6. M. Shafiei, G. Ledwich, G. Nourbakhsh, A. Arefi, and H. Pezeshki, “Layered based augmented complex kalman filter for fast forecasting-aided state estimation of distribution networks,” arXiv preprint arXiv:1804.08298, 2018.
  7. G. Durgaprasad and S. Thakur, “Robust dynamic state estimation of power systems based on m-estimation and realistic modeling of system dynamics,” IEEE Transactions on Power Systems, vol. 13, no. 4, pp. 1331–1336, 1998.
  8. https://openei.org/w/images/d/d7/OEDI_SI_Use_Case_01_Chart.png
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