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Department of Computer Science and Technology

 

Energy and Environment Group (EEG)

The Energy and Environment Research Group applies computer science to address renewable energy integration, energy demand reduction, and the assessment and management of environmental impact (e.g. climate change, biodiversity loss, deforestation) from anthropogenic activities.

We operate in an interdisciplinary manner, collaborating with climate scientists, ecologists, engineers, lawyers, regulators, and economists, and conducting wide engagement with external partners to effect evidence-based outcomes.

Goal

Our primary goal is to have a measurable impact on tools and techniques for de-risking our future. To do so, we share recent advances at the intersection of computer science, energy, and the environment through seminars, workshops, and scientific publications. We also help form collaborations between group members to coordinate interdisciplinary initiatives across University departments. 

Membership

EEG members are, in the first instance, faculty members in the Department for Computer Science and Technology and their students. We also invite membership from Postdocs, PhDs, Lab Visitors and Master’s students primarily from other departments, as appropriate.

Seminars

A list of talks for the current term can be found below; talks from prior terms are linked to this page. Seminar details can also be found at Talks.cam. Recordings from the EEG seminar series are available to watch online. We thank the Institute of Computing for Climate Science for their sponsorship of this series.


Partners


Upcoming seminars

Lent term

  • 01Mar
    Jerome Neufeld, University of Camridge

    Understanding a predicting the observed changes in key climatic systems, or predicting and de-risking climate solutions pose challenges, particularly in assessing poorly constrained properties of the system whose behaviour at small scales often determines the response. In this talk I’ll give two physical examples, and an approach of reduced modelling which focuses on the key uncertainties.

    The two large global ice sheets are loosing significant mass, but Greenland predominantly looses mass by surface melting, while Antarctica looses mass by melting of ice shelves and in the zone where ice becomes grounded. In both cases, the response is dictated by the temporal and spatial evolution of a complex subglacial hydrological system, whose properties are difficult to observe remotely. We’ll use reduced models of the subglacial system to understand the key role of subglacial systems and pose the question of how best to constrain this subglacial hydrological system.

    Geological storage of CO2 is one method of reducing anthropogenic CO2 emissions, and involves the injection of large volumes of CO2 into the subsurface. In order to remain trapped in the subsurface, buoyant CO2 is typically injected beneath a relatively impermeable cap rock. Observations of CO2 stored in various storage sites demonstrates that the subsequent flow of CO2 is dominated by variability in caprice topography and variations in permeability which are not readily observed remotely. We’ll again use reduced models of CO2 spreading to understand the sensitivity to geological heterogeneity, and pose the question of how best to estimate the uncertainties associated with the storage of large volumes of CO2 in the subsurface.

    Bio:

    Jerome Neufeld is Professor of Earth and Planetary Fluid Dynamics jointly appointed at the Institute for Energy and Environmental Flows, the Department of Earth Sciences and the Department of Applied Mathematics and Theoretical Physics at the University of Cambridge. The research in his group focuses on using mathematical models, laboratory experiments and field observations to understand the fluid dynamical behaviour of the Earth and other planetary bodies. Current research interests include the consequences of subglacial hydrology on supraglacial lake drainage and the tidal modulation of ice streams, the solidification of magma oceans and the early generation of magnetic fields on planetary bodies, the erosive dynamics of idealised river systems, the emplacement and solidification of magmatic flows, viscous tectonic mountain building, and the fluid dynamics of geological carbon storage.

  • 08Mar
    Livia Capol, ETH Zurich

    Livia Capol is a Computer Science student from ETH Zurich. She has been working on her Master's thesis with Keshav at the University of Cambridge since last September.

  • 15Mar
    Yashar Ghiassi-Farrokhfal, Rotterdam School of Management, Erasmus University

    The expansion of renewable energy generation increases the need for short-term reserve facilities to compensate for their short-term variations. This makes reserve markets increasingly profitable and attractive for renewable energy producers (REPs), who are facing diminishing subsidies and returns. Policymakers would also value REPs as reserve providers because conventional reserves are generally carbon-intensive. The major hurdle is, however, that reserve markets require reliability, while renewables are intermittent. This brings financial risks to REPs and reliability risks to the system operators. Two remedies to alleviate these risks are intraday trading and storage. The open question is whether, these hedging instruments, individually or combined, can resolve the financial and reliability risks and facilitate REP'S participation in reserve markets. This is currently unknown and, among others, depends on the micro-structure of the intraday markets, mainly distinguished as discrete (D-ID) or continuous (C-ID) markets. We study this problem by formulating the operation of a profit-maximizing REP, with and without storage, providing reserve services as a multi-stage stochastic integer program, separately with the support of D-ID or C-ID markets. We combine the Benders decomposition and stochastic dual dynamic programming algorithm (SDDP) to solve the problem efficiently. Our analysis of real data from the German market provides interesting insights into REPs' participation in short-term reserve markets. Importantly, we find that C-ID trading is the best enabler among all, facilitating the profitable and reliable participation of REPs in the FCR market. In this case, batteries not only do not help FCR participation but also worsen the reliability. Conversely, D-ID markets do not help FCR participation and REPs need batteries for reliable and profitable FCR participation. Thus, system operators should discourage the use of batteries (for REPs) in the case of C-ID markets and encourage it in the case of D-ID markets.

    Bio:

    Yashar Ghiassi-Farrokhfal is an Associate Professor at the Rotterdam School of Management, Erasmus University. Additionally, he serves as the academic director of smart energy and sustainability at the Erasmus Center for Data Analytics (ECDA) and holds the position of Erasmus Uni. scientific lead at the Centre of Energy System Intelligence (CESI). He obtained his Ph.D. from the University of Toronto, Canada in Electrical and Computer Engineering. He started his research in the energy domain when he was a post-doctoral fellow under the supervision of Prof. S. Keshav at UWaterloo, Canada. Since then, he has published numerous articles in esteemed journals and has organized numerous conferences and workshops in the field of energy transition. His involvement extends to various European and Dutch projects, including FlexSUS (municipal heat transition), MAGPIE (Energy transition in Ports), HyTROS (Hydrogen market), and Com2Heat (composite-based heat network). Employing a multi-disciplinary approach, he has delved into diverse facets of energy transitions such as sector coupling, microgrids, electric vehicles, hydrogen, energy storage, and market mechanisms at retail, wholesale, peer-to-peer, and bilateral levels.

  • 22Mar
    Dr. Shivkumar Kalyanaraman, CTO, Energy Industry, Asia, Microsoft. // Dr. Srinivasan Iyengar, Senior Program Manager, Asia, Microsoft.

    Deep decarbonization and rapid electrification of energy will require greater penetration of renewables of energy supply, and electrification of energy demand. As renewables penetration crosses 10-20% of the grid electricity demand (and other supply sources correspondingly adjust), the intermittency and volatility of renewable supply & new electrified demand will increasingly dominate the market. Renewable supply and grid electricity demand needs to be matched through a combination of multiple markets, energy storage and an orchestrated portfolio of flexibility resources. The future of renewables will fundamentally be driven by software and AI on the cloud to manage this transition. This talk will unwrap the various challenges and opportunities around this transition. We will also cover examples of application of multi-modal Generative AI for advanced remote operations, health & safety and multi-lingual knowledge access in the context of problem solving in field operations.

    Shivkumar's Bio:

    Shiv is CTO, Energy Industry, Asia at Microsoft. Previously he was Executive General Manager of Growth Offerings at GE Power Conversion responsible for new Line of Business development in e-Mobility, Commercial & Industrial Solar and digital/AI innovations. Earlier he was at IBM Research - India, and the Chief Scientist of IBM Research - Australia. Before IBM, he was a tenured Full Professor at Rensselaer Polytechnic Institute in Troy, NY, USA. Shiv has degrees from Indian Institute of Technology, Madras (B.Tech, CS), Ohio State University (MS, PhD) and RPI (Executive MBA). Shiv is a Distinguished Alumnus Awardee of IIT Madras (2021, recognizing 0.3% of IITM's alumni over the years) & Ohio State University (2021), Fellow of the IEEE (2010), Fellow of Indian National Academy of Engineering (2015), ACM Distinguished Scientist (2010), MIT Technology Review TR100 young innovator (1999).

    Srinivasan Iyengar's Bio:

    Srinivasan Iyengar is a Senior Program Manager at Microsoft's Energy Industry Asia Team. Previously he was a Post-doc Researcher at Microsoft Research India Lab. He completed his masters and doctoral studies in Computer Science from the University of Massachusetts Amherst. Before that he worked as a researcher at TCS India Innovation Labs. His interests are in energy systems, distributed systems, sensing, IoT, cloud and edge computing.

  • 29Mar
    Konstantin Klemmer, Microsoft Research

    Abstract not available

Easter term

Michaelmas term

  • 18Oct
    Astrid Nieße, Carl von Ossietzky Universität Oldenburg

    Abstract not available

  • 15Nov
    Ronita Bardhan, University of Cambridge

    Abstract not available

  • 06Dec
    Femke Nijsse, University of Exeter

    Solar power has seen massive and unexpected growth over the last decade. It developed from a niche technology used by ambitious citizens to a utility-scale resource used all over the world. Net-zero plans across the globe aim for 2050 or 2060. Solar energy is the most widely available energy resource on Earth, and its economic attractiveness is improving fast in a cycle of increasing investments.

    During my talk, I will discuss a data-driven technology and economic forecasting model to establish which zero carbon power sources could become dominant worldwide. The simulation models seeks to explore likely future scenarios, based on historical trends, rather than exploring “least-cost” configurations of a future clean energy system, as is usually done in energy modelling.

    We find that, due to technological trajectories set in motion by past policy, a global irreversible solar tipping point may have passed where solar energy comes to dominate global electricity markets, without any additional policies directly supporting solar. Uncertainties arise, however, over grid stability in a renewables-dominated power system, the availability of sufficient finance in poorer economies and the capacity of supply chains. Policies resolving these barriers may be more effective than price instruments to accelerate the transition to clean energy.

    Bio:

    Dr. Femke Nijsse specializes in modelling climate, energy systems, and the economy. With a background in climate physics, they earned a Ph.D. in mathematics, focusing on multi-model comparisons and statistical techniques related to decadal variability, historical warming, and climate sensitivity. In energy research, Dr. Nijsse contributed to the Economics of Energy Innovation and System Transition project, informing energy policies in China, Brazil, India, the UK, and the EU. They improved the E3ME-FTT model's power sector representation, using evolutionary economics for technology diffusion. Currently, they're working on cascading tipping points across sectors and a stronger implementation of hourly supply and demand in E3ME-FTT.