The Whiting School of Engineering (WSE) and Ralph O’Connor Sustainable Energy Institute (ROSEI) will sponsor the 2024 ARPA-E Innovation Summit, Read more
This is the ROSEI bi-weekly funding digest summarizing external funding opportunities of interest to ROSEI and the JHU community. ROSEI Read more
Supported by a new grant from U.S. Department of Energy Advanced Research Projects Agency-Energy (ARPA-E), a Johns Hopkins group is Read more
Researchers from the Ralph O’Connor Sustainable Energy Institute (ROSEI) and Morgan State University are teaming up with the State of Read more
A team of Johns Hopkins researchers affiliated with the Whiting School of Engineering’s Ralph O’Connor Sustainable Energy Institute (ROSEI) is Read more
Click here for more information and to apply to the position. The Electric Power Innovation for a Carbon-free Society (EPICS) Read more

Events

1:30 pm
March 28
Title: Reengineering civil infrastructure systems for decarbonization Abstract: Re-engineering civil infrastructure systems requires radical but practical new thought in how we design and operate them. Many societies are poised to make massive, costly, and technically challenging investments to decarbonize and electrify existing infrastructure. Demand management options, exercised on an unprecedented scale, can reduce the level of investment required. These options include storage, smart scheduling, and much greater efficiency and flexibility in energy end use. We do not need to overbuild our energy systems. Instead, automation, distributed sensors, smart modeling, optimization, and software can all be used to provide services with the same quality to humans with less infrastructure and less energy consumption. With my collaborators, I have already implemented some of our ideas in live environments and shown how they can be rapidly scaled. Our city-scale decarbonization experiments with electrified, integrated energy systems explored the diverse roles of thermal storage, demonstrated megawatt-scale flexibility, and generated revenue. We also developed an 85,000 m2 experimentation testbed at Stanford to “stress test” multi-zone commercial buildings and show how modest changes to room temperature settings could be used to avoid multi-million-dollar investments. Bio: I am an Independent Research Consultant at Stanford for TotalEnergies and an Adjunct Professor in the Energy Science & Engineering department. My research approach draws on technical engineering, mathematical modeling, and software engineering. My background has led me to work on solutions across traditional disciplinary barriers, in productive collaborations with engineers, mathematicians, and economists. I create 1) mathematical models of integrated energy systems; 2) computational tools to design and operate them in new ways; and 3) software prototypes to conduct real-world efficiency and flexibility experiments with cyber-physical systems. I wrote my PhD dissertation in Stanford’s Energy Science & Engineering department, advised by Profs. Sally Benson and Peter Glynn. I am also an Ingénieur Polytechnicien from the French Ecole Polytechnique (X2011).
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1:30 pm
March 29
Title: Evaluating hardware and soft technology for rapid energy system decarbonization Abstract: Technology hardware and deployment processes (“soft technology”) seem fundamentally different, but little work examines the nature of this difference and its implications for technology improvement. This talk introduces a conceptual and quantitative model to study the roles of hardware and soft technology in cost evolution and applies this model to solar photovoltaic (PV) systems. Differing properties of hardware and soft technology help explain solar PV’s cost decline, where rapid improvements in hardware affected globally traded components that lowered both hardware and deployment costs (“soft costs”). Improvements in soft technology occurred more slowly, were not shared as readily across locations, and only affected soft costs, ultimately contributing less than previously estimated. As a result, initial differences in soft technology across countries persisted and the share of soft costs rose. Complementing the case of solar PV, insights on cost dynamics in other technologies, including nuclear fission and electrolytic hydrogen production systems, will be used to outline conditions under which hardware and soft technology can enable and hinder cost reductions. More generally, this talk illustrates the usefulness of modeling dependencies between technology costs and features to understand past drivers of cost change and inform future technology development and adoption strategies. Bio: Magdalena Klemun is an assistant professor in the Division of Public Policy at the Hong Kong University of Science and Technology (HKUST). She is also affiliated with the HKUST Energy Institute. Magdalena’s research interests are in understanding how the economic and environmental performance of technologies evolves as a function of policy and engineering design choices, with a particular interest in the role of hardware vs. non-hardware innovations. Magdalena received her Ph.D. from the Institute of Data, Systems, and Society (IDSS) at MIT, her M.S. in Earth Resources Engineering from Columbia University, where she studied as a Fulbright Scholar, and her B.S. in Electrical Engineering and Information Technology from Vienna University of Technology.
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11:00 am
April 3
Title: Differentiable Programming for Data-driven Modeling, Optimization, and Control Abstract: This talk will present a different programming perspective for physics-informed machine learning (PIML) of dynamical system models, learning to optimize, and learning to control methods.  We will discuss the opportunity to develop a unified PIML framework by leveraging the conceptual similarities between these distinct approaches. Specifically, we introduce differentiable predictive control (DPC) as a sampling-based learning to control method that integrates the principles of parametric model predictive control (MPC) with physics-informed neural networks (PINNs).  We also show how to use recent developments in control barrier functions and neural Lyapunov functions to obtain online performance guarantees for learning-based control policies. We demonstrate the performance of these PIML methods in a range of simulation case studies, including modeling of networked dynamical systems, robotics, building control, and dynamic economic dispatch problem in power systems. Bio: Jan is a senior data scientist and the principal investigator in the Physics and Computational Sciences Division at Pacific Northwest National Laboratory (PNNL). Jan has a PhD in Control Engineering from the Slovak University of Technology in Bratislava, Slovakia, and before joining PNNL, he was a postdoc at the mechanical engineering department at Katholieke Universiteit (KU) Leuven in Belgium. His current research is focused on physics-informed machine learning for dynamical systems, constrained optimization, and model-based optimal control with applications in the energy sector.
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Carbon / Grid / Storage / Wind
April 22
After a great turnout in 2023, ROSEI's 2024 Earth Day and Birthday celebration will be hosted in the Glass Pavilion on the Homewood Campus! Check this page at a later date for more information about the event.
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May 22
The ARPA-E Energy Innovation Summit (The Summit) is an annual conference and technology showcase that brings together experts from different technical disciplines and professional communities to think about America's energy challenges in new and innovative ways. Now in its thirteenth year, the Summit offers a unique, three-day program aimed at moving transformational energy technologies out of the lab and into the market. Click here to learn more about the 2023 Summit, for which ROSEI and the Whiting School of Engineering served as a platinum partner. Check this page at a later date for more information about the 2024 iteration of the Summit.
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