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Title: IMoFi - Intelligent Model Fidelity: Physics-Based Data-Driven Grid Modeling to Accelerate Accurate PV Integration (Final Report)

Abstract

This report summarizes the work performed under a project funded by U.S. DOE Solar Energy Technologies Office (SETO) to use grid edge measurements to calibrate distribution system models for improved planning and grid integration of solar PV. Several physics-based data-driven algorithms are developed to identify inaccuracies in models and to bring increased visibility into distribution system planning. This includes phase identification, secondary system topology and parameter estimation, meter-to-transformer pairing, medium-voltage reconfiguration detection, determination of regulator and capacitor settings, PV system detection, PV parameter and setting estimation, PV dynamic models, and improved load modeling. Each of the algorithms is tested using simulation data and demonstrated on real feeders with our utility partners. The final algorithms demonstrate the potential for future planning and operations of the electric power grid to be more automated and data-driven, with more granularity, higher accuracy, and more comprehensive visibility into the system.

Authors:
 [1];  [1];  [1];  [1];  [1];  [1];  [1];  [1];  [1];  [1];  [2];  [2];  [2];  [2];  [2];  [3];  [3];  [3];  [3];  [3] more »;  [3];  [4];  [4];  [4];  [4];  [4];  [4];  [4];  [4];  [4];  [5];  [5];  [5];  [6];  [6];  [6];  [7];  [7] « less
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  3. Electric Power Research Inst., Knoxville, TN (United States)
  4. Georgia Inst. of Technology, Atlanta, GA (United States)
  5. CYME International T&D, Quebec (Canada)
  6. Texas Tech Univ., Lubbock, TX (United States)
  7. Electric Power Board Chattanooga, TN (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1855058
Report Number(s):
SAND2022-0215
702727
DOE Contract Number:  
NA0003525
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
14 SOLAR ENERGY

Citation Formats

Reno, Matthew J., Blakely, Logan, Trevizan, Rodrigo D., Pena, Bethany, Lave, Matthew, Azzolini, Joseph A., Yusuf, Jubair, Jones, Christian Birk, Bastos, Alvaro Furlani, Chalamala, Rohit, Korkali, Mert, Sun, Chih-Che, Donadee, Jonathan, Stewart, Emma M., Donde, Vaibhav, Peppanen, Jouni, Hernandez, Miguel, Deboever, Jeremiah, Rocha, Celso, Rylander, Matthew, Siratarnsophon, Piyapath, Grijalva, Santiago, Talkington, Samuel, Gomez-Peces, Cristian, Mason, Karl, Vejdan, Sadegh, Khan, Ahmad Usman, Mbeleg, Jordan Sihno, Ashok, Kavya, Divan, Deepak, Li, Feng, Therrien, Francis, Jacques, Patrick, Rao, Vittal, Francis, Cody, Zaragoza, Nicholas, Nordy, David, and Glass, Jim. IMoFi - Intelligent Model Fidelity: Physics-Based Data-Driven Grid Modeling to Accelerate Accurate PV Integration (Final Report). United States: N. p., 2022. Web. doi:10.2172/1855058.
Reno, Matthew J., Blakely, Logan, Trevizan, Rodrigo D., Pena, Bethany, Lave, Matthew, Azzolini, Joseph A., Yusuf, Jubair, Jones, Christian Birk, Bastos, Alvaro Furlani, Chalamala, Rohit, Korkali, Mert, Sun, Chih-Che, Donadee, Jonathan, Stewart, Emma M., Donde, Vaibhav, Peppanen, Jouni, Hernandez, Miguel, Deboever, Jeremiah, Rocha, Celso, Rylander, Matthew, Siratarnsophon, Piyapath, Grijalva, Santiago, Talkington, Samuel, Gomez-Peces, Cristian, Mason, Karl, Vejdan, Sadegh, Khan, Ahmad Usman, Mbeleg, Jordan Sihno, Ashok, Kavya, Divan, Deepak, Li, Feng, Therrien, Francis, Jacques, Patrick, Rao, Vittal, Francis, Cody, Zaragoza, Nicholas, Nordy, David, & Glass, Jim. IMoFi - Intelligent Model Fidelity: Physics-Based Data-Driven Grid Modeling to Accelerate Accurate PV Integration (Final Report). United States. https://doi.org/10.2172/1855058
Reno, Matthew J., Blakely, Logan, Trevizan, Rodrigo D., Pena, Bethany, Lave, Matthew, Azzolini, Joseph A., Yusuf, Jubair, Jones, Christian Birk, Bastos, Alvaro Furlani, Chalamala, Rohit, Korkali, Mert, Sun, Chih-Che, Donadee, Jonathan, Stewart, Emma M., Donde, Vaibhav, Peppanen, Jouni, Hernandez, Miguel, Deboever, Jeremiah, Rocha, Celso, Rylander, Matthew, Siratarnsophon, Piyapath, Grijalva, Santiago, Talkington, Samuel, Gomez-Peces, Cristian, Mason, Karl, Vejdan, Sadegh, Khan, Ahmad Usman, Mbeleg, Jordan Sihno, Ashok, Kavya, Divan, Deepak, Li, Feng, Therrien, Francis, Jacques, Patrick, Rao, Vittal, Francis, Cody, Zaragoza, Nicholas, Nordy, David, and Glass, Jim. 2022. "IMoFi - Intelligent Model Fidelity: Physics-Based Data-Driven Grid Modeling to Accelerate Accurate PV Integration (Final Report)". United States. https://doi.org/10.2172/1855058. https://www.osti.gov/servlets/purl/1855058.
@article{osti_1855058,
title = {IMoFi - Intelligent Model Fidelity: Physics-Based Data-Driven Grid Modeling to Accelerate Accurate PV Integration (Final Report)},
author = {Reno, Matthew J. and Blakely, Logan and Trevizan, Rodrigo D. and Pena, Bethany and Lave, Matthew and Azzolini, Joseph A. and Yusuf, Jubair and Jones, Christian Birk and Bastos, Alvaro Furlani and Chalamala, Rohit and Korkali, Mert and Sun, Chih-Che and Donadee, Jonathan and Stewart, Emma M. and Donde, Vaibhav and Peppanen, Jouni and Hernandez, Miguel and Deboever, Jeremiah and Rocha, Celso and Rylander, Matthew and Siratarnsophon, Piyapath and Grijalva, Santiago and Talkington, Samuel and Gomez-Peces, Cristian and Mason, Karl and Vejdan, Sadegh and Khan, Ahmad Usman and Mbeleg, Jordan Sihno and Ashok, Kavya and Divan, Deepak and Li, Feng and Therrien, Francis and Jacques, Patrick and Rao, Vittal and Francis, Cody and Zaragoza, Nicholas and Nordy, David and Glass, Jim},
abstractNote = {This report summarizes the work performed under a project funded by U.S. DOE Solar Energy Technologies Office (SETO) to use grid edge measurements to calibrate distribution system models for improved planning and grid integration of solar PV. Several physics-based data-driven algorithms are developed to identify inaccuracies in models and to bring increased visibility into distribution system planning. This includes phase identification, secondary system topology and parameter estimation, meter-to-transformer pairing, medium-voltage reconfiguration detection, determination of regulator and capacitor settings, PV system detection, PV parameter and setting estimation, PV dynamic models, and improved load modeling. Each of the algorithms is tested using simulation data and demonstrated on real feeders with our utility partners. The final algorithms demonstrate the potential for future planning and operations of the electric power grid to be more automated and data-driven, with more granularity, higher accuracy, and more comprehensive visibility into the system.},
doi = {10.2172/1855058},
url = {https://www.osti.gov/biblio/1855058}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2022},
month = {1}
}