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A TIGHT UPPER BOUND FOR GRID-BASED WIND FARM LAYOUT OPTIMIZATION

机译:基于网格的风电场布局优化的紧上限

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This paper uses the method developed by Billionnet et al. (1999) to obtain tight upper bounds on the optimal values of mixed integer linear programming (MILP) formulations in grid-based wind farm layout optimization. The MILP formulations in grid-based wind farm layout optimization can be seen as linearized versions of the 0-1 quadratic knapsack problem (QKP) in combinatorial optimization. The QKP is NP-hard, which means the MILP formulations remain difficult problems to solve, especially for large problems with grid sizes of more than 500 points. The upper bound method proposed by Billionnet et al. is particularly well-suited for grid-based wind farm layout optimization problems, and was able to provide tight optimality gaps for a range of numerical experiments with up to 1296 grid points. The results of the numerical experiments also suggest that the greedy algorithm is a promising solution method for large MILP formulations in grid-based layout optimization that cannot be solved using standard branch and bound solvers.
机译:本文使用Billionnet等人开发的方法。 (1999)在基于网格的风电场布局优化中获得混合整数线性规划(MILP)公式的最优值的严格上限。基于网格的风电场布局优化中的MILP公式可以看作组合优化中0-1二次背包问题(QKP)的线性化版本。 QKP是NP难处理的,这意味着MILP公式仍然是难以解决的问题,尤其是对于网格大小超过500点的大问题。 Billionnet等人提出的上限方法。特别适用于基于网格的风电场布局优化问题,并且能够为多达1296个网格点的一系列数值实验提供紧密的最优差距。数值实验结果还表明,贪婪算法是基于网格的布局优化中大型MILP公式的一种有前途的解决方法,该方法无法使用标准分支定界求解器进行求解。

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