The job opening is for a full time PhD position at the Energy & Resources group of the department of Sustainable Development at the Faculty of Geosciences of Utrecht University. This PhD position is part of a larger research project – Next Generation Sector Coupling Models for Optimal Investments and Operation (NextGenOpt) – which is funded by the Dutch Research Council (NWO) within the Energy System Integration, Towards Futureproof, Affordable and Reliable Energy Systems (ESI-FAR) call.
Accordingly, the candidate will be embedded in an interdisciplinary team consisting of engineers, computer scientists, and mathematicians from TNO, Utrecht University, TU Delft, and industry partners. The candidate will closely cooperate with the other project team members and spend part of the research time at TNO (flexible internship).
Societal relevance: The transition to a CO2 net-zero society requires a major rethinking of the energy system that calls for coordination among the different sectors. In this context, supporting investment and operation decisions requires reliable and accurate large-scale optimization models that include electricity, heat and (green) hydrogen networks, and their associated demand, supply, and flexibility properties. However, current models for large-scale energy systems are very simplistic and therefore not reliable for decision-making. Conversely, the quality of sector-specific small-scale models has evolved considerably, but real-life instances of these models are computationally very demanding and cannot be easily scaled to the required size.
Research focus: Overall, the NextGenOpt research project aims at combining different sector-specific models into a single computational tool that integrates hydrogen, heat and electricity systems, and their associated generation, demand, conversion and storage technologies. This will be done by bringing together state-of-the-art understanding of mathematical modeling of power systems with energy system components and state-of-the-art optimization and machine learning techniques. With this goal in mind, this PhD will specifically work on improving cross-sectorial energy systems modeling and on testing the new framework on local, regional and national case studies. You will investigate inter- and intra-grids dependencies to properly describe heat and hydrogen networks within a mixed-integer linear programming environment while retaining the more mature description of the electricity grid. You will investigate the possibility of using iterative design to construct aggregate technology models, where key constraints are identified and embedded in simplified, yet reliable, models. Finally, you will investigate how the model granularity can be flexibly selected depending on the problem at hand, and how this can be used to reconcile (inter)national case studies with those involving municipalities and provinces. Multiscale modeling, time-series analysis, and other decomposition methods will be used to iterate between long-term large-scale and short-term small-scale perspectives.
During your PhD journey you will be supervised by Prof. Madeleine Gibescu (promotor) and Dr. Matteo Gazzani (co-promotor).
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