Making jet fuel from sun and wind: when will it be cost effective?
We invite you to a free webinar on June 17, 9-10 AM Pacific, where Dr. Evan Sherwin, Stanford Postdoctoral Research Fellow, will discuss his research (and Analytica model) to answer the question: when will it be cost effective to make jet fuel from sun and wind? Read Dr. Sherwin’s new 2021 paper in Environmental Science and Technology here.
Webinar Abstract: As the energy system moves toward variable renewable electricity, sectors such as aviation may require energy-dense low-carbon liquid fuels to dramatically reduce emissions. Electrofuels, synthesized from CO2 from direct air capture and hydrogen from electrolysis of water, powered primarily by solar or wind electricity, may present a cost-effective path forward. However, this approach will require operating capital-intensive equipment using variable renewable electricity. Dr. Sherwin employed an optimization-based techno-economic analysis, implemented in Analytica, to assess the prospects for large cost reductions, accounting for changes in optimal system operation as component technologies advance.
About the author: Dr. Evan Sherwin is a data-informed energy policy analyst assessing the role of hydrocarbon fuels in the transition to a net-zero energy system. Much of Evan’s research focuses in two areas: techno-economic assessment of emerging low-carbon hydrocarbon technologies and prediction, detection, and quantification of methane emissions from the oil and gas value chain. Evan combines approaches from optimization, data science, machine learning, and systems engineering with an eye toward informing policy and industry decision-making. Evan is a Postdoctoral Research Fellow at Stanford University’s department of Energy Resources Engineering and is Programs Chair of Climate Change AI, an international organization catalyzing impactful work that uses AI to help tackle climate change. Evan is also the founder and chair of the Methane Emissions Technology Alliance international seminar series. Evan holds a PhD in Engineering and Public Policy and an MS in Machine Learning from Carnegie Mellon University and bachelor’s degrees in Physics and Applied Mathematics from UC Berkeley. Evan got his start in energy systems modeling as an energy analyst at Lumina Decision Systems.