一种邻域搜索整数规划方法用于风电场布局优化

IF 3.6 Q3 GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY Wind Energy Science Pub Date : 2023-09-19 DOI:10.5194/wes-8-1453-2023
Juan-Andrés Pérez-Rúa, Mathias Stolpe, Nicolaos Antonio Cutululis
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引用次数: 1

摘要

摘要提出了两个求解风电场布局优化问题的模型和一种启发式算法。该模型是线性整数规划公式,其中风力涡轮机候选位置由二元变量描述。一种公式考虑用逐步常数函数逼近功率曲线。另一个模型是基于一个无功率曲线模型,其中最小化与总风速赤字密切相关的措施是优化的。与未包含在启发式中的完整模型相比,一个特殊用途的邻域搜索启发式将这些公式包装起来,具有更高的可追溯性和有效性。启发式算法使用分支切断算法迭代地搜索在位者周围的邻域。候选位置的数量和邻域大小自适应调整。在一组公开的基准问题上的数值结果表明,总风速赤字作为目标的代理是一种函数方法,因为当使用后者作为替代目标时,获得了年能源生产度量的高质量解。此外,与将涡轮机位置视为连续变量的大量不同方法相比,所提出的启发式方法能够提供良好的结果。
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A neighborhood search integer programming approach for wind farm layout optimization
Abstract. Two models and a heuristic algorithm to address the wind farm layout optimization problem are presented. The models are linear integer programming formulations where candidate locations of wind turbines are described by binary variables. One formulation considers an approximation of the power curve by means of a stepwise constant function. The other model is based on a power-curve-free model where minimization of a measure closely related to total wind speed deficit is optimized. A special-purpose neighborhood search heuristic wraps these formulations with increasing tractability and effectiveness compared to the full model that is not contained in the heuristic. The heuristic iteratively searches for neighborhoods around the incumbent using a branch-and-cut algorithm. The number of candidate locations and neighborhood sizes are adjusted adaptively. Numerical results on a set of publicly available benchmark problems indicate that a proxy for total wind speed deficit as an objective is a functional approach, since high-quality solutions of the metric of annual energy production are obtained when using the latter function as an substitute objective. Furthermore, the proposed heuristic is able to provide good results compared to a large set of distinctive approaches that consider the turbine positions as continuous variables.
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来源期刊
Wind Energy Science
Wind Energy Science GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY-
CiteScore
6.90
自引率
27.50%
发文量
115
审稿时长
28 weeks
期刊最新文献
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