{"ID":2862890,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.26452","arxiv_id":"2509.26452","title":"ORACLE: A rigorous metric and method to explore all near-optimal designs for energy systems","abstract":"Optimization models are fundamental tools for providing quantitative insights to decision-makers. However, models, objectives, and constraints do not capture all real-world factors accurately. Thus, instead of the single optimal solution, real-world stakeholders are often interested in the near-optimal space -- solutions that lie within a specified margin of the optimal objective value. Solutions in the near-optimal space can then be assessed regarding desirable non-modeled or qualitative aspects. The near-optimal space is usually explored by so-called Modelling to Generate Alternatives (MGA) methods. However, current MGA approaches mainly employ heuristics, which do not measure or guarantee convergence. We propose a method called ORACLE, which guarantees generation and exploration on the \\emph{entire near-optimal} space by exploiting convexity. ORACLE iteratively approximates the near-optimal space by introducing a metric that both measures convergence and suggests exploration directions. Once the approximations are refined to a desired tolerance, any near-optimal designs can be generated with negligible computational effort. We compare our approach with existing methods on a sector-coupled energy system model of Switzerland. ORACLE is the only method able to guarantee convergence within a desired tolerance. Additionally, we show that heuristic MGA methods miss large areas of the near-optimal space, potentially skewing decision-making by leaving viable options for the energy transition off the table.","short_abstract":"Optimization models are fundamental tools for providing quantitative insights to decision-makers. However, models, objectives, and constraints do not capture all real-world factors accurately. Thus, instead of the single optimal solution, real-world stakeholders are often interested in the near-optimal space -- solutio...","url_abs":"https://arxiv.org/abs/2509.26452","url_pdf":"https://arxiv.org/pdf/2509.26452v2","authors":"[\"E. M. Turan\",\"S. Moret\",\"A. Bardow\"]","published":"2025-09-30T16:07:40Z","proceeding":"math.OC","tasks":"[\"math.OC\"]","methods":"[\"LoRA\"]","has_code":false}