I am assistant professor of energy systems modelling at TU Delft, in the Department of Engineering Systems and Services. My current research is on understanding the spatiotemporal variability of renewable generation and on quantifying the technical, economic and political trade-offs between different possible ways to build a 100% clean and renewable energy system. You can reach me by email. Are you a student interested in working with me? See the group page.

Spotlight: Diversity of options to eliminate fossil fuels and reach carbon neutrality across the entire European energy system

The interactive scenario explorer in action.

We show a diversity of untold options to meet all energy demand based on renewable energy, with a complete phase-out of oil and gas imports. The results can be viewed in an interactive scenario explorer. With a marginal increase above optimal cost, the reliance of an energy self-sufficient Europe on specific solutions, like biofuels, battery storage, transmission expansion, or heat electrification, can vary from not being used at all to being key to system stability. → Pickering, Lombardi and Pfenninger (2022), Joule.


My group’s research is on the global transition to a 100% clean and renewable energy system, and on identifying and resolving the technical, economic and policy barriers on the way to that goal. I have a background in environmental science and policy and work on energy as one critical component of the pathway towards an ecological human civilisation.

Open code and data

In the course of my work I created and lead development of the open-source energy system modelling tool Calliope. I am also the creator and lead developer of the Renewables.ninja platform to simulate wind and solar power plants worldwide. I am a member of the Open Energy Modelling Initiative, which promotes openness and transparency in energy system modelling, and of the Open Power System Data project, which provides a free and open data platform for power system modelling.

Recent publications

  • Enhanced Spatio-Temporal Electric Load Forecasts Using Less Data with Active Deep Learning. Arsam Aryandoust, Anthony Patt, Stefan Pfenninger. (2022). Nature Machine Intelligence. doi: 10.1038/s42256-022-00552-x
  • How Offshore Wind Could Become Economically Attractive in Low-Resource Regions like Indonesia. Jannis Langer, Sergio Simanjuntak, Stefan Pfenninger, Antonio Jarquin Laguna, George Lavidas, Henk Polinder, Jaco Quist, Harkunti Pertiwi Rahayu, Kornelis Blok. (2022). iScience. doi: 10.1016/j.isci.2022.104945
  • Meteorologically-Informed Spatial Planning of European PV Deployment to Reduce Multiday Generation Variability. Dirk Mühlemann, Doris Folini, Stefan Pfenninger, Martin Wild, Jan Wohland. (2022). Earth's Future. doi: 10.1029/2022EF002673

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