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Hybrid global-local optimisation algorithms for the layout design of tidal turbine arrays

机译:用于潮汐涡轮机阵列布局设计的混合全局局部优化算法

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摘要

Tidal stream power generation represents a promising source of renewable energy. In order to extract an economically useful amount of power, tens to hundreds of tidal turbines need to be placed within an array. The layout of these turbines can have a significant impact on the power extracted and hence on the viability of the site. Funke et al. formulated the question of the best turbine layout as an optimisation problem constrained by the shallow water equations and solved it using a local, gradient-based optimisation algorithm. Given the local nature of this approach, the question arises of how optimal the layouts actually are. This becomes particularly important for scenarios with complex bathymetry and layout constraints, both of which typically introduce locally optimal layouts. Optimisation algorithms which find the global optima generally require orders of magnitude more iterations than local optimisation algorithms and are thus infeasible in combination with an expensive flow model. This paper presents an analytical wake model to act as an efficient proxy to the shallow water model. Based upon this, a hybrid global-local two-stage optimisation approach is presented in which turbine layouts are first optimised with the analytical wake model via a global optimisation algorithm, and further optimised with the shallow water model via a local gradient-based optimisation algorithm. This procedure is applied to a number of idealised cases and a more realistic case with complex bathymetry in the Pentland Firth, Scotland. It is shown that in cases where bathymetry is considered, the two-stage optimisation procedure is able to improve the power extracted from the array by as much as 25% compared to local optimisation for idealised scenarios and by as much as 12% for the more realistic Pentland Firth scenario whilst in many cases reducing the overall computation time by approximately 35%.
机译:潮流发电代表了可再生能源的有希望的来源。为了提取经济上有用的功率,需要在阵列中放置数十到数百个潮汐涡轮机。这些涡轮机的布局可能对提取的功率有很大影响,从而对场地的生存能力也有很大影响。 Funke等。将最佳水轮机布置问题表述为受浅水方程约束的优化问题,并使用基于梯度的局部优化算法进行求解。考虑到这种方法的局部性质,提出了这样一个问题,即实际的布局如何最佳。这对于具有复杂测深和布局约束的场景尤为重要,这两种情况通常都会引入局部最优布局。找到全局最优值的优化算法通常需要比局部优化算法多几个数量级的迭代,因此与昂贵的流量模型结合使用是不可行的。本文提出了一种解析唤醒模型,可以作为浅水模型的有效代理。在此基础上,提出了一种混合的全局-局部两阶段优化方法,其中首先通过全局优化算法用解析唤醒模型对涡轮机布局进行优化,然后通过基于局部梯度的优化算法用浅水模型进行进一步优化。此过程适用于苏格兰Pentland Firth的许多理想情况,以及更复杂的测深仪。结果表明,在考虑测深的情况下,与理想情况下的局部优化相比,两步优化程序能够将从阵列中提取的功率提高多达25%,而对于理想方案,则可以提高多达12%。现实的Pentland Firth场景,同时在许多情况下将整体计算时间减少了大约35%。

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