September 19, 2024
16:15
Louvain-la-Neuve
Place Sainte Barbe, auditorium BARB 91
The global imperative to reduce anthropogenic greenhouse gas (GHG) emissions necessitates adaptive energy transition strategies. This thesis examines uncertainty-aware energy transition pathways, focusing on the Belgian energy system. The research aims to understand how varying degrees of foresight and uncertainties in technology, resource, and policy influence transition outcomes.
The study employs a multi-faceted approach, integrating advanced energy system modeling with uncertainty quantification. The EnergyScope Pathway model optimizes energy transition pathways, analyzing how different energy sources, including renewables and electrofuels, can meet defossilization targets. Polynomial Chaos Expansion (PCE) is used to address uncertainties, offering a computationally efficient method to explore how variations in key inputs impact the model's outputs.
Additionally, the research incorporates agent-based reinforcement learning (RL) to model decision-making with limited foresight. This approach explores realistic, myopic strategies where actions are optimized based on short-term outcomes. The comparison between perfect foresight and myopic strategies reveals significant trade-offs between immediate costs and long-term sustainability.
Principal Component Analysis (PCA) is applied to reduce data complexity and highlight the most influential variables affecting energy transition outcomes. PCA enables a focused interpretation of how factors such as technology costs and energy demand contribute to overall uncertainty, aiding in more informed decision-making.
Key findings emphasize the critical role of renewable electrofuels in deep defossilization, particularly when domestic renewable energy is insufficient. The results suggest that the success 
of RL-based myopic strategies depends on early emissions reductions and infrastructure development for energy imports. With PCA, this work shows that massively integrating local renewables (solar and wind) thanks to a deeper electrification of the demand, enhances the robustness of the technological roadmap.
This thesis provides insights into the complexities of energy transition planning under uncertainty, offering guidance for policymakers and researchers on the importance of adaptive strategies that can respond to evolving technological, economic, and environmental conditions.
Jury members :
- Prof. Francesco Contino (UCLouvain, Belgium), supervisor
- Prof. Hervé Jeanmart (UCLouvain, Belgium), supervisor
- Prof. Paul Fisette (UCLouvain, Belgium), chairperson
- Dr. Stefano Moret (ETH Zurich, Switzerland)
- Prof. Sylvain Quoilin (ULiège, Belgium)
- Prof. Stefan Pfenninger (TU Delft, The Netherlands)
- Prof. Christophe De Vleeschouwer (UClouvain, Belgium)