Reno, Matthew J.; Blakely, Logan; Trevizan, Rodrigo D.; Pena, Bethany D.; Lave, Matthew S.; Azzolini, Joseph A.; Yusuf, Jubair Y.; Jones, Christian B.; Furlani Bastos, Alvaro F.; Chalamala, Rohit C.; Korkali, Mert K.; Sun, Chih-Che S.; Donadee, Jonathan D.; Stewart, Emma M.; Donde, Vaibhav D.; Peppanen, Jouni P.; Hernandez, Miguel H.; Deboever, Jeremiah D.; Rocha, Celso R.; Rylander, Matthew R.; Siratarnsophon, Piyapath S.; Grijalva, Santiago G.; Talkington, Samuel T.; Mason, Karl M.; Vejdan, Sadegh V.; Khan, Ahmad U.; Mbeleg, Jordan S.; Ashok, Kavya A.; Divan, Deepak D.; Li, Feng L.; Therrien, Francis T.; Jacques, Patrick J.; Rao, Vittal R.; Francis, Cody F.; Zaragoza, Nicholas Z.; Nordy, David N.; Glass, Jim G.; Holman, Derek H.; Mannon, Tim M.; Pinney, David P.
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.
Substantial decreases in the cost of solar and energy storage systems create suitable conditions for implementing microgrids that operate independently from the main transmission/distribution grids. Such microgrids concept is particularly of interest for islanded and remote communities, which oftentimes rely on expensive energy resources to supply their demand. This paper presents the design of a microgrid for an island community, in which transmission infrastructure (an aging subsea cable that connects to the mainland grid) is replaced by solar and energy storage systems. Based on historical demand data and solar generation forecasts, an optimization framework is proposed to determine sizes of the microgrid components such that the local generation resources are self-sufficient and reliable. Results of this analysis show that, indeed, solar and energy storage systems are viable choices for implementing a microgrid and replacing transmission infrastructure.
Reno, Matthew J.; Blakely, Logan; Trevizan, Rodrigo D.; Pena, Bethany D.; Lave, Matthew S.; Azzolini, Joseph A.; Yusuf, Jubair Y.; Jones, Christian B.; Furlani Bastos, Alvaro F.; Chalamala, Rohit C.; Korkali, Mert K.; Sun, Chih-Che S.; Donadee, Jonathan D.; Stewart, Emma M.; Donde, Vaibhav D.; Peppanen, Jouni P.; Hernandez, Miguel H.; Deboever, Jeremiah D.; Rocha, Celso R.; Rylander, Matthew R.; Siratarnsophon, Piyapath S.; Grijalva, Santiago G.; Talkington, Samuel T.; Gomez-Peces, Cristian G.; Mason, Karl M.; Vejdan, Sadegh V.; Khan, Ahmad U.; Mbeleg, Jordan S.; Ashok, Kavya A.; Divan, Deepak D.; Li, Feng L.; Therrien, Francis T.; Jacques, Patrick J.; Rao, Vittal S.; Francis, Cody F.; Zaragoza, Nicholas Z.; Nordy, David N.; Glass, Jim G.
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.
Energy storage systems (ESS) can provide multiple services to the electric grid, each with a unique charge/discharge profile. One category of such services comprises power quality applications, where ESS is deployed to protect downstream customers from events or disturbances that might result in poor power quality. This paper analyzes ESS usage to simultaneously mitigate two power quality issues: harmonic distortion and low power factor. Techniques for solving each one of these issues are already known by utilities; however, the main contribution of this paper is the utilization of a single asset to mitigate both power quality issues simultaneously. An optimization model was developed to determine the ESS dispatch that would satisfy the requirements for these stacked applications. Through case studies of a medium-size commercial customer, it was demonstrated that ESS can, indeed, correct and/or mitigate poor power quality issues.