M. Antoine DUBOIS will publicly defend his thesis entitled "Exploring the near-optimal spaces of energy system optimisation models using necessary conditions for better decision making".
Summary
This thesis delves into the pivotal question:
How can modelisation tools enhance decision-making processes?
In the midst of converging economic, social, and ecological crises, decision-makers are confronted with intricate choices demanding thoughtful deliberation. While tools such as mathematical programming offer a structured framework to rationalise these choices, it is imperative to refine traditional methodologies to reflect the underlying ideologies influencing these decisions. To this aim, this thesis explores the concept of near-optimal space analysis through distinct prisms.
Firstly, it introduces an innovative methodology for near-optimal space exploration grounded on necessary conditions. Next, it widens the scope of near-optimal space analysis to encompass multi-objective optimisation. Lastly, it starts a reflection on the influence of model complexity on near-optimal spaces and necessary conditions.
Though the proposed methodologies possess universal applicability, they are empirically tested through case studies focused on the energy transition. Notably, the European electricity grid and Belgium's entire energy system were scrutinised to extract actionable insights. Using these methods, we derived valuable decision-making insights on aspects like the minimum capacities of technologies or necessary energy from diverse sources to ensure constrained deviations from objectives such as cost and invested energy.
The insights garnered accentuate the pitfalls of exclusively emphasising the optimal solution. They have also led to derive a list of promising research avenues, which encompass harmonised approaches to tackle both parametric and structural uncertainties, the quest for more efficient methods for near-optimal space analysis, and their prospective extension into multi-objective and multi-stage programming.
Practical information
Defence will take place on December 14th 2023 at 14:00, at Institut de Montefiore (salle R7, Bât. B28 - Sart Tilman).
