Wind Farm Layout Optimization in Complex Terrains Using Computational Fluid Dynamics

Author(s):  
Jim Y. J. Kuo ◽  
I. Amy Wong ◽  
David A. Romero ◽  
J. Christopher Beck ◽  
Cristina H. Amon

The aim of wind farm design is to maximize energy production and minimize cost. In particular, optimizing the placement of turbines in a wind farm is crucial to minimize the wake effects that impact energy production. Most work on wind farm layout optimization has focused on flat terrains and spatially uniform wind regimes. In complex terrains, however, the lack of accurate analytical wake models makes it difficult to evaluate the performance of layouts quickly and accurately as needed for optimization purposes. This paper proposes an algorithm that couples computational fluid dynamics (CFD) with mixed-integer programming (MIP) to optimize layouts in complex terrains. High-fidelity CFD simulations of wake propagation are utilized in the proposed algorithm to constantly improve the accuracy of the predicted wake effects from upstream turbines in complex terrains. By exploiting the deterministic nature of MIP layout solutions, the number of expensive CFD simulations can be reduced significantly. The proposed algorithm is demonstrated on the layout design of a wind farm domain in Carleton-sur-Mer, Quebec, Canada. Results show that the algorithm is capable of producing good wind farm layouts in complex terrains while minimizing the number of computationally expensive wake simulations.

Author(s):  
Ning Quan ◽  
Harrison Kim

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.


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