Dominance-Aware Generation of Near-Optimal Alternatives in Energy System Models
L.W.L. van de Laar (TU Delft - Electrical Engineering, Mathematics and Computer Science)
MM de Weerdt – Mentor (TU Delft - Algorithmics)
Germán A. Morales-España – Mentor (TU Delft - Algorithmics)
G. Neustroev – Mentor (TU Delft - Algorithmics)
José Luis Rueda Torres – Graduation committee member (TU Delft - Intelligent Electrical Power Grids)
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Abstract
Optimization models are widely used in energy system planning to identify cost-effective investment strategies. However, relying solely on a single optimal solution can be misleading, as it fails to account for model uncertainty, competing objectives, and stakeholder preferences. To address this, near-optimal alternatives, solutions that are close in cost to the optimum but structurally different, are increasingly used to support robust and flexible decision-making.
This thesis explores the generation and evaluation of near-optimal alternatives within energy systems, with a focus on improving the decision relevance of the generated alternatives. This thesis introduces a unified analytical framework, formalizing existing Modeling to Generate Alternatives (MGA) methods using weight vector formulations. This formulation enables a clearer comparison of different techniques that generate these alternatives. This analysis highlights the limitations of current evaluation metrics, particularly their inability to distinguish decision-relevant alternatives from decision-irrelevant ones.
To overcome this gap, the thesis proposes a novel evaluation metric based on dominance relations from multi-objective optimization. This metric identifies non-dominated alternatives, those not strictly worse than any other across all decision variables, as decision-relevant. The thesis introduces a new method that uses Directionally Weighted Variables to generate alternatives aligned with this dominance criterion.
The proposed approach is evaluated using a stylized energy investment model and benchmarked against existing MGA techniques. Results show that traditional methods tend to generate fewer non-dominated alternatives, while the new method generates more non-dominated alternatives within the near-optimal space. This work contributes a new perspective on alternative generation, bridging the gap between mathematical optimality and practical decision support.