Stanford Doerr School of Sustainability
Showing 11-20 of 73 Results
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Dylan Marshall Crain
Ph.D. Student in Energy Resources Engineering, admitted Autumn 2022
Current Research and Scholarly InterestsMy current research revolves around optimizing the monitoring design of Carbon Capture and Sequestration (CCS) projects in such a way that the posterior (after data assimilation) predictions are as close to reality as can be hoped for.
In CCS projects within the U.S., it is important to have monitoring plan, which can consist of wells with pressure, saturation, salinity, et cetera sensors, seismic lines, or gravimetric above-ground measurements, before any injection has begun into the subsurface. This is due to the permitting requirements that must be satisfied before operations are begun.
Due to this constraint, any monitoring optimization (at least initially) needs to be determined using only a prior (highly uncertain) understanding of the subsurface. This makes the optimization much more challenging. We utilize a prior optimization scheme from a previous student which allows us to optimize a monitoring plan using only prior information to get the minimized, expected uncertainty reduction in the posterior models for a given quantity of interest. This scheme is limited by some Gaussian assumptions. We optimize it using a genetic algorithm.
From this point, with the monitoring plan established, the information gathered from the optimized monitoring scheme (using only monitoring wells at the moment) is used to history match (data assimilate) our understanding of the subsurface. The results can be used to predict the CO2 plume flow and behavior into the future.
This work was initially developed to assist a project in Illinois that is currently seeking Class VI injection well permits in the self-same state in order to begin injecting CO2 produced from two companies paying for the work from the Illinois Geological Survey. -
Victoria Dinov
Masters Student in Energy Science and Engineering, admitted Autumn 2025
BioHi, my name is Vicky and I am a graduate student at Stanford studying energy science and engineering. At Stanford, I worked in the INES research group, focused primarily on creating a capacity expansion model with improved spatial, temporal, and geographical resolution.
I am passionate about capacity expansion and transmission planning, power markets, utility rate structure, load forecasting, microgrids and more. I am also curious to explore the ways in which we will harness DERs and technological tools at our fingertips to create more resilient communities and energy systems. This can have broad implications for developing regions and more generally energy scarce areas.
Outside of school, you will find me running, climbing, backpacking, skiing, etc. I like everything outdoors, have an affinity for art/music, and love to salsa too. Please message me with questions or just to connect! -
Lama El Halabi
Ph.D. Student in Energy Science and Engineering, admitted Spring 2022
BioI am a PhD candidate in the Department of Energy Sciences and Engineering and a Data Science Scholar, advised by Adam Brandt. My research is driven by the crucial role renewable energy must play in sustainably meeting our energy demands. The major challenge in transitioning to renewable energy lies in the intermittent and inherently uncertain nature of these energy sources. My current research focuses on predicting energy outputs from these stochastically behaving sources, with an emphasis on uncertainty quantification and volatility. Specifically, I employ computer vision models and statistical techniques to develop short-term probabilistic photovoltaic (PV) power forecasts from sky images and time-series PV data. I hold an MS in Energy Resources Engineering from Stanford and a BE in Mechanical Engineering and a BS in Physics from the American University of Beirut. Previously, my research involved using machine learning to model water resources.
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Julia Frohmann
Ph.D. Student in Energy Science and Engineering, admitted Autumn 2023
BioJulia Frohmann is a PhD candidate in Energy Science and Engineering, and leads the demand side technology utilization modeling at STEER. Her research focuses on the feasibility of large-scale grid storage deployment in the US by evaluating battery energy storage revenue generation potential. Julia's key areas of interest are electricity market operations, and macro-scale energy systems modeling. Previous work focused on renewable grid penetration effect on electricity exchange variability and modeling optimal integration of power-to-heat-to-power storage. Julia obtained her bachelors in Mechanical Engineering and masters in Energy Engineering from RWTH Aachen, Germany.
