发表机构
University of British Columbia（不列颠哥伦比亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对风电场复杂地形道路设计成本高的问题，提出三阶段优化框架TriPhase，结合斯坦纳树与凸优化自动完成走廊选择、水平及垂直线形优化，在真实场地节省高达14%成本。
AI 中文摘要
在复杂地形中为风电场设计道路网络是一项具有挑战性的任务，尤其是在施工预算紧张的情况下。传统的人工方法耗时且可能产生次优结果。我们提出一个结构化的三阶段优化框架TriPhase，以自动化并最小化从走廊选择到土方工程的道路建设成本。第一阶段将走廊选择建模为基于地形感知图上的斯坦纳最小树问题，并纳入坡度和曲率约束。第二阶段，每个路段通过双层模型进行水平线形优化，其中混合整数规划评估垂直线形成本。为确保求解器兼容性和性能，该模型被显式重构以适应Gurobi。最后阶段应用网络级凸优化模型来细化垂直线形。在真实场地上的数值实验表明，与行业标准的人工设计相比，成本节省高达14%，验证了该框架的有效性和实际相关性。
英文摘要
Designing road networks for wind farms in complex terrain is a challenging task, especially under tight construction budgets. Traditional manual methods are time-consuming and may yield suboptimal results. We propose a structured three-phase optimization framework, TriPhase, to automate and minimize road construction costs from corridor selection to earthwork. Phase one formulates corridor selection as a Steiner minimum tree problem over a terrain-aware graph, incorporating slope and curvature constraints. In phase two, each road segment undergoes horizontal alignment optimization using a bilevel model, where a mixed-integer program evaluates vertical alignment costs. To ensure solver compatibility and performance, the model is reformulated explicitly for Gurobi. The final phase applies a network-wide convex optimization model to refine vertical alignment. Numerical experiments on real-world sites demonstrate up to 14% cost savings compared to industry-standard manual designs, validating the framework's effectiveness and practical relevance.
