Optimum layout design of onshore wind farms considering stochastic loading

Kallioras, Nikos Ath.; Lagaros, Nikos D.; Karlaftis, Matthew G.; Pachy, Paraskevi

doi:10.1016/j.advengsoft.2015.05.002

Cited by 20 publications

(13 citation statements)

References 39 publications

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“…l), the evaluation value is calculated by Equation (11). When the safe distance condition is failure (d F < l), the evaluation value is very large, such as 10 10 . As the optimization desires the minimum objective value, the layout that does not satisfy the condition will be eliminated during the optimization.…”

Section: Evaluation Function In Greedy Algorithmmentioning

confidence: 99%

Wind turbine layout optimization with multiple hub height wind turbines using greedy algorithm

et al. 2016

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a b s t r a c tWind turbine layout optimization in wind farm is one of the most important technologies to increase the wind power utilization. This paper studies the wind turbine layout optimization with multiple hub heights wind turbines using greedy algorithm. The linear wake model and the particle wake model are used for wake flow calculation over flat terrain and complex terrain, respectively. Three-dimensional greedy algorithm is developed to optimize wind turbine layout with multiple hub heights for minimizing cost per unit power output. The numerical cases over flat terrain and complex terrain are used to validate the effectiveness of the proposed greedy algorithm for the optimization problem. The results reveal that it incurs lower computational costs to obtain better optimized results using the proposed greedy algorithm than the one using genetic algorithm. Compared to the layout with identical hub height wind turbines, the one with multiple hub height wind turbines can increase the total power output and decrease the cost per unit power output remarkably, especially for the wind farm over complex terrain. It is suggested that three-dimensional greedy algorithm is an effective method for more benefits of using wind turbines with multiple hub heights in wind farm design.

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Section: Evaluation Function In Greedy Algorithmmentioning

confidence: 99%

Wind turbine layout optimization with multiple hub height wind turbines using greedy algorithm

et al. 2016

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“…In [13] and [14], the authors have shown that when the wind direction is perpendicular to the lines of the wind farm, the power produced from the wind farm presents more fluctuations. The assumption made in this paper is that the wind speed is the same for all wind turbines positioned in the same line.…”

Section: Wind Distribution Modelling For Wind Farmmentioning

confidence: 99%

“…The comparison criterion is based on an analysis of Fluctuation Harmonic Content (FHC) parameter. The FHC is equal to the Normalized Standard Deviation (NSD) of the power in the time domain described in [14]. FHC parameter can be estimated using 7, and where P0 is the produced power average value for 10 minutes, F presents the frequency interval.…”

Section: Wind Speed Model Evaluationmentioning

confidence: 99%

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An offshore wind farm energy injection mastering using aerodynamic and kinetic control strategies

et al. 2018

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The injection of wind farm production into a grid, needs optimal strategies for energy transfer management. Usually, the power produced by the wind farms does not fulfil all the grid code requirements. The main problem is generally based on the way to reduce the impacts of power production fluctuations on the grid voltage and its frequency. To solve this problem, some authors suggest the use of an interface such as energy storage devices in order to compensate the wind power fluctuations. In fact, the storage devices installed between the wind farm and the grid can improve the power quality in terms of stability but in other hand the size and the cost of the system can be increased. In this paper, two solutions have been proposed in case the power quality produced by the wind farm is out of the grid code requirements. The improvement of the energy quality of an offshore wind farm without storage and connected to the grid is discussed. The proposed solution is to operate the wind turbines with a reserve of power. To distribute this reserve equitably among wind turbines, a proportional distribution algorithms has been developed. The results obtained show clearly the effectiveness of the strategy.

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“…Table 2 presents the main features and limitations of the best-known available commercial software. All the commercial wind farm design tools were specifically built for onshore environments and, therefore, consider some irrelevant design aspects for offshore areas such as visual impact (irrelevant for far offshore), shadow flickering, noise levels [34] and complex terrain elevations [42,43]. Although it is possible to use them to design OWFs, none of them consider some of the important offshore aspects such as number of turbines, collection and transmission systems design, number and location of offshore substations and transmission technology.…”

Section: Commercially Available Softwarementioning

confidence: 99%

A Multi-Objective Optimization Framework for Offshore Wind Farm Layouts and Electric Infrastructures

et al. 2016

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Current offshore wind farms (OWFs) design processes are based on a sequential approach which does not guarantee system optimality because it oversimplifies the problem by discarding important interdependencies between design aspects. This article presents a framework to integrate, automate and optimize the design of OWF layouts and the respective electrical infrastructures. The proposed framework optimizes simultaneously different goals (e.g., annual energy delivered and investment cost) which leads to efficient trade-offs during the design phase, e.g., reduction of wake losses vs collection system length. Furthermore, the proposed framework is independent of economic assumptions, meaning that no a priori values such as the interest rate or energy price, are needed. The proposed framework was applied to the Dutch Borssele areas I and II. A wide range of OWF layouts were obtained through the optimization framework. OWFs with similar energy production and investment cost as layouts designed with standard sequential strategies were obtained through the framework, meaning that the proposed framework has the capability to create different OWF layouts that would have been missed by the designers. In conclusion, the proposed multi-objective optimization framework represents a mind shift in design tools for OWFs which allows cost savings in the design and operation phases.

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Optimum layout design of onshore wind farms considering stochastic loading

Cited by 20 publications

References 39 publications

Wind turbine layout optimization with multiple hub height wind turbines using greedy algorithm

Wind turbine layout optimization with multiple hub height wind turbines using greedy algorithm

An offshore wind farm energy injection mastering using aerodynamic and kinetic control strategies

A Multi-Objective Optimization Framework for Offshore Wind Farm Layouts and Electric Infrastructures

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