TY - BOOK
T1 - IMoFi (Intelligent Model Fidelity): Physics-Based Data-Driven Grid Modeling to Accelerate Accurate PV Integration Updated Accomplishments
AU - Reno, Matthew J.
AU - Blakely, Logan
AU - Trevizan, Rodrigo D.
AU - Pena, Bethany
AU - Lave, Matthew
AU - Azzolini, Joseph A.
AU - Yusuf, Jubair
AU - Jones, Christian Birk
AU - Furlani-Bastos, Alvaro
AU - Chalamala, Rohit
AU - Korkali, Mert
AU - Sun, Chih-Che
AU - Donadee, Jonathan
AU - Stewart, Emma M.
AU - Donde, Vaibhav
AU - Peppanen, Jouni
AU - Hernandez, Miguel
AU - Deboever, Jeremiah
AU - Rocha, Celso
AU - Rylander, Matthew
AU - Siratarnsophon, Piyapath
AU - Grijalva, Santiago
AU - Talkington, Samuel
AU - Gomez-Peces, Christian
AU - Mason, Karl
AU - Vejdan, Sadegh
AU - Khan, Ahmad Usman
AU - Mbeleg, Jordan Sihno
AU - Ashok, Kavya
AU - Divan, Deepak
AU - Li, Feng
AU - Therrien, Francis
AU - Jacques, Patrick
AU - Rao, Vittal
AU - Francis, Cody
AU - Zaragoza, Nicholas
AU - Nordy, David
AU - Glass, Jim
AU - Holman, Derek
AU - Mannon, Tim
AU - Pinney, David
PY - 2022/9/1
Y1 - 2022/9/1
N2 - 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.
AB - 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.
U2 - 10.2172/1888157
DO - 10.2172/1888157
M3 - Technical Report
BT - IMoFi (Intelligent Model Fidelity): Physics-Based Data-Driven Grid Modeling to Accelerate Accurate PV Integration Updated Accomplishments
ER -