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Constrained multi-objective wind farm layout optimization: Novel constraint handling approach based on constraint programming

机译：约束多目标风电场布局优化：基于约束规划的新型约束处理方法

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摘要

Wind farms are frequently located in proximity to human dwellings, natural habitats, and infrastructure making land use constraints and noise matters of increasing concern for all stakeholders. In this study, we perform a constrained multi-objective wind farm layout optimization considering energy and noise as objective functions, and considering land use constraints arising from landowner participation, environmental setbacks and proximity to existing infrastructure. A multi-objective, continuous variable Genetic Algorithm (NSGA-II) is combined with a novel constraint handling approach to solve the optimization problem. This constraint handling approach uses a combination of penalty functions and Constraint Programming to balance local and global exploration to find feasible solutions. The proposed approach is used to solve the wind farm layout optimization problem with different numbers of turbines and under different levels of land availability (constraint severity). Our results show increasing land availability and/or number of turbines, increases energy generation, noise production, and computational cost. Results also illustrate the potential of the proposed constraint handling approach to outperform existing methods in the context of evolutionary optimization, yielding better solutions at a lower computational cost. (C) 2018 Elsevier Ltd. All rights reserved.
机译：风电场通常位于人类住所，自然栖息地和基础设施附近，这使得土地使用限制和噪音问题日益受到所有利益相关者的关注。在这项研究中，我们以能源和噪声为目标函数，并考虑了由于土地所有者的参与，环境挫折和对现有基础设施的邻近性而产生的土地利用约束条件，进行了受限的多目标风电场布局优化。将多目标连续变量遗传算法（NSGA-II）与新颖的约束处理方法相结合，以解决优化问题。这种约束处理方法结合使用惩罚函数和约束编程来平衡本地和全局探索，以找到可行的解决方案。所提出的方法用于解决风力涡轮机布局优化问题，该问题具有不同数量的涡轮机和不同水平的土地可用性（约束严重性）。我们的结果表明，土地的可用性和/或涡轮机数量的增加，增加了能源的产生，噪声的产生和计算成本。结果还说明了所提出的约束处理方法在进化优化方面优于现有方法的潜力，从而以较低的计算成本产生了更好的解决方案。 （C）2018 Elsevier Ltd.保留所有权利。

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