{
  "abstract": "Modeling to Generate Alternatives (MGA) methods explore the near-optimal solutions of energy system optimization models (ESOMs), i.e., alternative planning pathways whose cost lies within a small predefined slack of the optimum but which can differ substantially in technology composition, regional distribution, and environmental impacts. Among MGA methods, ORACLE uniquely guarantees convergence to the near-optimal set within a targeted tolerance by iteratively refining inner and outer polytope approximations. At each iteration, given a trial point, it solves a problem as hard as the ESOM to find the nearest near-optimal solution to it. However, the trial point is obtained by solving a bilevel problem whose solution time grows rapidly with the number of iterations and eventually dominates the computational cost of the method. To eliminate this bottleneck, we present G-ORACLE, which exploits the fact that an optimal solution of the bilevel problem is always attained at a vertex of the outer approximation. G-ORACLE therefore replaces the bilevel problem with incremental vertex enumeration by the double description method and one convex projection per vertex, while retaining the convergence guarantee of ORACLE. A supporting-hyperplane check further avoids unnecessary recomputation of these projections as the inner approximation grows. We evaluate G-ORACLE on an electricity-only PyPSA-Eur model, exploring the near-optimal space of the installed capacities of six generation and two storage technologies. While ORACLE's bilevel problem exceeds 20 minutes at iteration 64, G-ORACLE performs the trial-point search in at most 14 seconds throughout 240 iterations, leaving the ESOM solves as the only significant computational cost.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.32759",
  "author_total": 2,
  "authors": [
    {
      "name": "Sina Hajikazemi"
    },
    {
      "name": "Florian Steinke"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/00gprw9f0r3x8.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.32759",
  "fulltext_url": null,
  "id": "00gprw9f0r3x8",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.32759",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "math.OC"
    ],
    [
      "openalex",
      "2208|Integrated Energy Systems Optimization"
    ]
  ],
  "openalex": "W7223701895",
  "pdf_url": "https://arxiv.org/pdf/2610.32759",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/00gprw9f0r3x8.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:19Z",
      "native_id": "2610.32759",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.32759"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "G-ORACLE: A Fast Geometric Method To Explore All Near-Optimal Designs for Energy Systems",
  "title_text": "G-ORACLE: A Fast Geometric Method To Explore All Near-Optimal Designs for Energy Systems",
  "type": "preprint",
  "updated_at": "2026-11-09T00:20:45Z",
  "url": "https://research.draconitic.com/paper/00gprw9f0r3x8",
  "venue": "arXiv",
  "versions": []
}