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Yusuf, Jubair

Publications and source records attributed to Yusuf, Jubair.

Systemic Drivers of Electric-Grid-Caused Catastrophic Wildfires: Implications for Resilience in the United States

Wildfires are projected to increase in severity and frequency due to climate change, and the electric grid is both a cause of wildfires and is vulnerable to wildfires. Equipment from the electric grid accounts for 10% of fires burned in California and 3% of fires nationally. Recent catastrophic wildfires, such as the Lahaina Fire, Camp Fire, Marshall Fire, and Smokehouse Creek fires, were all started by electrical equipment and show how devastating these events can be because they threaten lives and structures. Vegetation structure, weather and winds, climate and vegetation response, land use, and human activities all impact the likelihood of severe wildfires. We explore the relationship between the built environment, electric grid infrastructure specifically, and its role in causing catastrophic wildfires to find lessons learned for increasing resilience. Electric grid utility companies currently employ multiple methods to mitigate fire, including (1) early detection, (2) grid hardening, (3) vegetation management, and (4) pre-emptive shutoffs. Utility companies need to consider the conditions for wildfire and the impact that each mitigation strategy has on drivers of wildfire behavior, as a single solution will not be adequate. Utility companies need to work with stakeholders to develop a holistic strategy to reduce ignition likelihood and spread likelihood to reduce catastrophic wildfires and improve resiliency.

Eagleston, Holly (ORCID:0000000178175116)↗

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

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.

14 SOLAR ENERGY↗

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

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.

14 SOLAR ENERGY↗