Wind farm optimization with multiple hub heights using gradient-based methods

Ciavarra, Andreas Wolf; Rodrigues, Rafael Valotta; Dykes, Katherine; Réthore, Pierre-Élouan

doi:10.1088/1742-6596/2265/2/022012

Cited by 7 publications

(4 citation statements)

References 21 publications

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“…Furthermore, other features can be coupled to the surrogate to increase its capabilities, for instance, foundation structures and wind farm geometrical archetypes other than rectangular. Hub heights can also be considered in future works, as its importance has been highlighted in previous works (for instance, [23]).…”

Section: Resultsmentioning

confidence: 99%

A surrogate model of offshore wind farm annual energy production to support financial evaluation

Rodrigues

Friis-Møller

Dykes

et al. 2022

J. Phys.: Conf. Ser.

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The preliminary financial evaluation of wind farm profitability requires fast analysis of energy production and costs while having very little specific information around the project. Early in the design process, the selection of specific wind turbines and the layout design may not yet be defined. Techno-economic and financial analysis models have been developed to use input from a small set of high-level project characteristics to estimate major cost elements and energy production for a wind farm to support quick analysis of levelized cost of energy (LCoE), or other financial metrics. Such models are typically based on prior project data and/or very simple analytical models. However, as capabilities for financial analysis of wind farms advance, so does the desire to improve the accuracy of the physical and cost modelling of the system. In this work, we develop a surrogate model of Annual Energy Production (AEP) for offshore wind farms for financial analysis applications in the early stages of development. The surrogate is developed from an parameterized engineering model and covers a large potential wind farm design space addressing different technological and site conditions. The surrogate model uncovers the underlying structure in the model in terms of input-output relationships and achieves a coefficient of determination of 0.994. The method used to develop the surrogate model can be adapted for additional dimensions of inputs as needed.

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Section: Resultsmentioning

confidence: 99%

A surrogate model of offshore wind farm annual energy production to support financial evaluation

Rodrigues

Friis-Møller

Dykes

et al. 2022

J. Phys.: Conf. Ser.

Self Cite

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“…All the simulations were performed in PyWake [12,13] and TOPFARM [14], an open-source tool developed by DTU Wind and that has been used in previous works in the literature [15,16,17,18,19]. The case-study considered in this work is the Borselle wind farm, which has 74 wind turbines with 10MW each.…”

Section: Optimization and Case Studymentioning

confidence: 99%

Multidisciplinary Wind Farm Electrical Infrastructure Optimization

Rodrigues,

Rua

2024

J. Phys.: Conf. Ser.

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During the construction stage of a wind farm, it is paramount to consider which layouts are the most efficient. Recent literature has found that optimization of the annual energy production is not necessarily driven by the same factors as other economic metrics, such as the internal rate of return. One parameter that makes this particularly true is the electrical cable length and layout, which affect both the cash flow of the project and the investments. While placing turbines far from each other is beneficial to mitigate the wake effect, the capital expenditures associated with the longer cables affects economic metrics. On the other hand, even though placing turbines close to each other reduces cable expenses, wind turbine mechanical loads are challenging to determine from wake effects. This work investigates trends between annual energy production, internal rate of return, electrical cable layout, and turbine components degradation due to mechanical loads. Results point towards corroborating recent literature, finding financial opportunities to explore tighter layouts with shorter electrical cables.

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“…For the AEP calculation and the wake modeling involved in the simulations, TOPFARM relies in PyWake (Pedersen et al, 2019), another DTU open source Python library that offers fast AEP evaluation from a range of engineering wake models. Recent works using TOPFARM (Ciavarra et al, 2022;Riva et al, 2020), and PyWake (Rodrigues et al, 2022;Fischereit et al, 2021;Forsting et al, 2021;Pedersen et al, 2021;Quick et al, 2022) can be found in the literature.…”

mentioning

confidence: 99%

Gradient-based Wind Farm Layout Optimization With Inclusion And Exclusion Zones

Risco¹,

Rodrigues²,

Friis-Møller³

et al. 2023

Preprint

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Abstract. Wind farm layout optimization is usually subjected to boundary constraints of irregular shapes. The analytical expressions of these shapes are rarely available, and consequently, it can be challenging to include them in the mathematical formulation of the problem. This paper presents a new methodology to integrate multiple disconnected and irregular domain boundaries in wind farm layout optimization problems. The method relies on the analytical gradients of the distances between wind turbine locations and boundaries, which are represented by polygons. This parameterized representation of boundary locations allows for a continuous optimization formulation. A limitation of the method, if combined with gradient-based solvers, is that wind turbines are placed within the nearest polygons when the optimization is started in order to satisfy the boundary constraints, thus the allocation of wind turbines per polygon is highly dependent on the initial guess. To overcome this and improve the quality of the solutions, two independent strategies are proposed. A study case is presented to demonstrate the applicability of the method and the proposed strategies. In this study, a wind farm layout is optimized in order to maximize the AEP in a non-uniform wind resource site. The problem is constrained by the minimum distance between wind turbines and five irregular polygon boundaries, defined as inclusion zones. Initial guesses are used to instantiate the optimization problem, which is solved following three independent approaches: (1) a baseline approach that uses a gradient-based solver, (2) approach 1 combined with the relaxation of the boundaries, which allows for a better design space exploration, and (3) the application of a heuristic algorithm, smart-start, prior to the gradient-based optimization, improving the allocation of wind turbines within the inclusion polygons based on the potential wind resource and the available area. The results show that the relaxation of boundaries combined with a gradient-based solver achieves on average +10.2 % of AEP over the baseline, whilst the smart-start algorithm, combined with a gradient-based solver, finds on average +20.5 % of AEP with respect to the baseline and +9.4 % of AEP with respect to the relaxation strategy.

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Wind farm optimization with multiple hub heights using gradient-based methods

Cited by 7 publications

References 21 publications

A surrogate model of offshore wind farm annual energy production to support financial evaluation

A surrogate model of offshore wind farm annual energy production to support financial evaluation

Multidisciplinary Wind Farm Electrical Infrastructure Optimization

Gradient-based Wind Farm Layout Optimization With Inclusion And Exclusion Zones

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