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SUMMARY:MechE Seminar: Insights\, not numbers: leveraging sector-coupled m
 odels for planning net-zero energy systems
DTSTART:20260211T130000
DTEND:20260211T140000
DTSTAMP:20260405T200513Z
UID:20ed2378b5f3e457d17da11a8cef15e53d99f7c1b4aaa0761ae10ec9
CATEGORIES:Conferences - Seminars
DESCRIPTION:Dr. Evren Mert Turan\, Department of Mechanical and Process E
 ngineering\, ETH Zürich\nAbstract: Achieving a net-zero society is one 
 of the most significant challenges of our time. Optimization-based pred
 ictive models have become central to decision support for the energy sy
 stem because they translate techno-economic and environmental targets 
 into concrete designs and operating decisions. However\, these models
  are not crystal balls. The optimal solution of a model is rarely the
  optimal solution in the real world\, and the greatest risk is not being
  wrong\; it is being confidently wrong.  \nThis seminar argues for 
 a shift from reporting a single or a few deterministic least-cost opti
 ons to systematically extracting insights from sector-coupled energy sy
 stem models. First\, a multi-model comparison under uncertainty for Switz
 erland is introduced\, revealing where models agree and disagree and the
 reby exposing modelling biases and artifacts. As part of this\, we sho
 w how machine learning can compress high-dimensional results into interpre
 table\, decision-relevant “strategies” for stakeholders. \nSecond\
 , we introduce ORACLE\, a rigorous method for mapping the full space o
 f near-optimal energy systems: solutions that are close to optimal but 
 otherwise differ in technology choices and system structure. In the l
 iterature\, near-optimal solutions are primarily found using heuristics
  that do not fully explore the space\, leaving out important solutions an
 d potentially skewing decision-making by leaving viable energy options.
  By using a metric that quantifies the extent of exploration\, ORACLE ena
 bles systematic\, comprehensive exploration of the near-optimal space\, ac
 hieving equivalent coverage to other methods in significantly fewer ite
 rations. \nTogether\, these efforts reposition energy system models as to
 ols to reveal the futures we can still choose from.  \n\nBiography: Evr
 en Turan is a postdoctoral researcher and lecturer at ETH Zurich. His curr
 ent research interests are the development of rigorous\, interpretable\, a
 nd uncertainty-aware optimization frameworks for the design and operation 
 of complex systems\, currently with a focus on energy and process systems.
  He earned his BSc and MSc in Chemical Engineering from the University of
  Cape Town and completed his PhD in Chemical Engineering at the Norwegian 
 University of Science and Technology (NTNU)\, where he focused on proce
 ss control\, optimization\, and decision-making under uncertainty. 
LOCATION:BM 5202 https://plan.epfl.ch/?room==BM%205202 https://epfl.zoom.u
 s/j/61360740951
STATUS:CONFIRMED
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