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IMoFi (Intelligent Model Fidelity): Physics-Based Data-Driven Grid Modeling to Accelerate Accurate PV Integration Updated Accomplishments

Technical Report ·
DOI:https://doi.org/10.2172/1888157· OSTI ID:1888157
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  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. (EPRI), Palo Alto, CA (United States)
  4. Georgia Institute of Technology, Atlanta, GA (United States)
  5. CYME International T&D, St. Bruno, Quebec (Canada)
  6. Texas Tech Univ., Lubbock, TX (United States)
  7. Electric Power Board Chattanooga, TN (United States)
  8. National Rural Electric Cooperative Association, Arlington, VA (United States)
+ Show Author Affiliations

This report summarizes the work performed under a project funded by U.S. DOE Solar Energy Technologies Office (SETO), including some updates from the previous report SAND2022-0215, 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.

Research Organization:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
DOE Contract Number:
NA0003525; 34226
OSTI ID:
1888157
Report Number(s):
SAND2022-12669; 710000
Country of Publication:
United States
Language:
English

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