Wind farm layout optimization under uncertainty

MirHassani, S. A.; Yarahmadi, Aryo

doi:10.1016/j.renene.2017.01.063

Cited by 50 publications

(19 citation statements)

References 6 publications

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“…The wake decay and trust coefficients are considered constant, respectively, in this study but they can be calculated as follows [16]:…”

Section: Wake Modelingmentioning

confidence: 99%

“…Note that this study gives importance only to the concept of using WTs with different hub height neglecting the sensitivity variation of wind speed and wake interactions with tower height. This aspect is then studied by MirHassani et al [16] who developed a mathematical programming model using linearization technique and iterative method to evaluate power production under various conditions of wind speeds and directions. The numeral results showed better arguments compared with the results found in References [13,15].…”

Section: Introductionmentioning

confidence: 99%

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Realistic wind farm design layout optimization with different wind turbines types

Charhouni

Sallaou

Mansouri

2019

Int J Energy Environ Eng

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Seeking for an appropriate design of wind farm (WF) layout constitutes a complex task in a wind energy project. An optimization approach is seriously needed to deal with this complexity, especially with current trend of large WFs area with important number of wind turbines (WTs). The present paper investigates optimization study of realistic offshore WF design layout (horns-rev1). The main objective of the current study is to design WF area that maximizes the extraction of wind power with low cost. In the first step, an optimization model using genetic algorithm with continuous layout representation is developed to look for the optimal design as a function of WTs placement. The effectiveness of such a methodology is validated and compared with the reference and irregular layout of hors-rev1 offshore WF. With the aim to analyze the impact of WTs types on WF objectives, four commercial WTs are considered in the second step. The results showed that designing WF with big WTs gives best design layout. In addition, it demonstrated that selecting WTs based uniquely on rotor diameter size is not always a good idea. It should includes as well the number of WTs that influence significantly the power production and WF cost.Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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“…The wake decay and trust coefficients are considered constant, respectively, in this study but they can be calculated as follows [16]:…”

Section: Wake Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Realistic wind farm design layout optimization with different wind turbines types

Charhouni

Sallaou

Mansouri

2019

Int J Energy Environ Eng

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“…Wind Farm Layout Optimization (WFLO) has been addressed by numerous works, which implemented different optimization methods (see the review by Herbert-Acero et al [15] and references therein). Several of them have used gradient-based WFLO solvers, such as Combinatorial optimization [16], Sequential Linear (SLP) or Quadratic (SQP) Programming [6,[17][18][19] or RANS-based gradients [20]. Most contributions, however, have used gradient-free methods, most of them in the field of Soft Computing [21] (SC).…”

Section: Introductionmentioning

confidence: 99%

“…More recently Parada et al [51] applied an up-to-date GA [52] to the Mosetti et al [44] problem. However, all WFLO contributions mentioned consider at least either a gridded, limited matrix of coordinates for the turbine positions (usually 10 × 10 squared grids [16,17,[23][24][25][29][30][31]35,36,[44][45][46]48,49,51], or either a highly idealized wind climatology [4,6,[18][19][20][26][27][28][32][33][34]37,38,47]. Regarding this last point, it has been shown that the estimate of the overall wind power output in a wind farm does not become reliable until the used wind rose attains resolutions of approximately 3°and 1 m/s [53], and "using few sectors (12 as in the common practice) will lead to impressively high but unreal improvement on power" [54].…”

Section: Introductionmentioning

confidence: 99%

Realistic Wind Farm Layout Optimization through Genetic Algorithms Using a Gaussian Wake Model

Kirchner-Bossi

Porté‐Agel

2018

Energies

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Wind Farm Layout Optimization (WFLO) can be useful to minimize power losses associated with turbine wakes in wind farms. This work presents a new evolutionary WFLO methodology integrated with a recently developed and successfully validated Gaussian wake model (Bastankhah and Porté-Agel model). Two different parametrizations of the evolutionary methodology are implemented, depending on if a baseline layout is considered or not. The proposed scheme is applied to two real wind farms, Horns Rev I (Denmark) and Princess Amalia (the Netherlands), and two different turbine models, V80-2MW and NREL-5MW. For comparison purposes, these four study cases are also optimized under the traditionally used top-hat wake model (Jensen model). A systematic overestimation of the wake losses by the Jensen model is confirmed herein. This allows it to attain bigger power output increases with respect to the baseline layouts (between 0.72% and 1.91%) compared to the solutions attained through the more realistic Gaussian model (0.24–0.95%). The proposed methodology is shown to outperform other recently developed layout optimization methods. Moreover, the electricity cable length needed to interconnect the turbines decreases up to 28.6% compared to the baseline layouts.

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“…Chen et al studied the influence of the wind turbine hub height on the output power of the wind farm and reported the increase of the delivered power and the plant efficiency with the increase of the hub height. Hassan and Yarahmad studied the total output power of a wind farm for different hub heights of the wind turbines. It was reported that the proposed model has high quality and more accurate solution in a shorter time.…”

Section: Introductionmentioning

confidence: 99%

Technoeconomic study for the optimization of turbine size and wind farm layouts

Nada

Elsayed

El‐Sayed³

2019

Int J Energy Res

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Summary A technoeconomic analysis and optimization of wind turbine size and layout are performed using WAsP software. A case study of a 100‐MW wind farm located in Egypt is considered. Wind atlas for Egypt was used as the input data of the WAsP software. Two turbine models of powers 52 and 80 MW are considered for this project. The wind turbine size and distributions are selected based on the technoeconomic optimization, namely minimum wake effect, maximum annual energy production (AEP) rate, optimum cash flow, and payback period. The future worth method is adopted in economic comparison between the two alternatives, and the cash flow diagram provided the payback period and future worth after the lifetime of the plant. The results showed that (1) the AEP dramatically decreases for a wind farm area less than 15 km2; (2) the turbine spacing, spacing‐to‐diameter ratio, and the setback distances decrease and the wind turbine density and wake losses increase with decreasing the wind turbines size; (3) the total net AEP using G52 is lower than that of using G80 by about 16%; (4) the technoeconomic analysis recommended using G80 as it has higher profit than those of G52 by about $20 million.

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Wind farm layout optimization under uncertainty

Cited by 50 publications

References 6 publications

Realistic wind farm design layout optimization with different wind turbines types

Realistic wind farm design layout optimization with different wind turbines types

Realistic Wind Farm Layout Optimization through Genetic Algorithms Using a Gaussian Wake Model

Technoeconomic study for the optimization of turbine size and wind farm layouts

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