Optimal placement of wind turbines in a wind park using Monte Carlo simulation

Marmidis, Grigorios E.; Lazarou, Stavros; Pyrgioti, E.

doi:10.1016/j.renene.2007.09.004

Cited by 294 publications

(149 citation statements)

References 6 publications

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“…To guarantee the minimal distance between any turbines, the most convenient way is to partition a wind farm into square cells of predefined width and to only allow turbines to be placed in the center of appropriate cells (Grady et al, 2005;Marmidis et al, 2008;Mosetti et al, 1994), as illustrated in Figure 3. The square meshing is simple and intuitive, and easy to implement.…”

Section: Equilateral-triangle Meshmentioning

confidence: 99%

“…As recommended in Troen & Petersen (1989), for a flat farm with unidirectional wind, turbines should be place about 3 ∼ 5 times of rotor diameter apart in columns and about 5 ∼ 9 times in rows. In this paper, we follow Mosetti et al (1994), Grady et al (2005) and Marmidis et al (2008), and set the side length of the triangle as five times of the turbine rotor diameter. Figure 5.…”

Section: Equilateral-triangle Meshmentioning

confidence: 99%

“…Under this definition, the orientation of the square meshes used in Mosetti et al (1994), Grady et al (2005), and Marmidis et al (2008) were 45 • , which can be denoted as ETM-45 • in short.…”

Section: Definition 2 (Sm Orientation) Pick Up Any Square In the Mesmentioning

confidence: 99%

See 2 more Smart Citations

Genetic Optimal Micrositing of Wind Farms by Equilateral-Triangle Mesh

Wang¹,

Li²,

Zhang³

2011

Wind Turbines

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Section: Equilateral-triangle Meshmentioning

confidence: 99%

Section: Equilateral-triangle Meshmentioning

confidence: 99%

See 1 more Smart Citation

Genetic Optimal Micrositing of Wind Farms by Equilateral-Triangle Mesh

Wang¹,

Li²,

Zhang³

2011

Wind Turbines

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“…In light of the above considerations, many scholars have conducted further research on DER system optimization with regard uncertainty. With regard to stochastic uncertain optimization, the Monte Carlo simulation is one of the most common methods to deal with models [45][46][47]. The principle is that the uncertain parameters of the models are stochastically generated with several groups of values according to probability distributions.…”

Section: Introductionmentioning

confidence: 99%

An MILP Method for Design of Distributed Energy Resource System Considering Stochastic Energy Supply and Demand

Duan

Yan

et al. 2017

Energies

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A distributed energy resource (DER) system, which can be defined as a medium or small energy conversion and utilization system with various functions for meeting multiple targets, is directly oriented towards users and achieves on-site production and energy supply according to users' demands. Optimization research on system construction has recently become an important issue. In this paper, simple stochastic mathematical equations were used to interpret the optimal design problem of a DER system, and based on this, a novel method for solving the optimization problem, which has multi-dimensional stochastic uncertainties (involving the price of input-energy and energy supply and demand), was put forward. A mixed-integer linear programming (MILP) model was established for the optimal design of the DER system by combining the ideas of mean value and variance, aiming to minimize the total costs, including facility costs, energy purchase costs, and loss caused by energy supply shortage, and considering the energy balance and facility performance constraints. In the end, a DER system design for an office building district in Xuzhou, China, was taken as an example to verify the model. The influences of uncertainty on the selection of system facilities and the economic evaluation were analyzed. The result indicated that uncertainty of energy demand played a significant role in optimal design, whereas energy price played a negligible role. With respect to economy, if uncertainties are not considered in system design, it will result in a short supply, and therefore the total cost will increase considerably. The calculation convergence was compared with previous work. The implementation results showed the practicality and efficiency of the proposed method.

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“…[2][3][4][5] Such calculations aim to place the turbines such that wake effects are limited with respect to the main incoming wind direction at the site under consideration. There are also academic studies that use wake models to optimize the placement of a limited number of turbines on a given land area using Monte Carlo simulations, 6 genetic algorithms 7 or evolutionary algorithms. 8,9 For an overview on this, we refer to the review by Herbert-Acero et al 10 The typical wind turbine spacing that is used in actual wind farms nowadays is 6 10D, where D is the turbine diameter.…”

Section: Introductionmentioning

confidence: 99%

Combining economic and fluid dynamic models to determine the optimal spacing in very large wind farms

et al. 2016

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Wind turbine spacing is an important design parameter for wind farms. Placing turbines too close together reduces their power extraction because of wake effects and increases maintenance costs because of unsteady loading. Conversely, placing them further apart increases land and cabling costs, as well as electrical resistance losses. The asymptotic limit of very large wind farms in which the flow conditions can be considered 'fully developed' provides a useful framework for studying general trends in optimal layouts as a function of dimensionless cost parameters. Earlier analytical work by Meyers and Meneveau (Wind Energy 15, 305-317 (2012)) revealed that in the limit of very large wind farms, the optimal turbine spacing accounting for the turbine and land costs is significantly larger than the value found in typical existing wind farms. Here, we generalize the analysis to include effects of cable and maintenance costs upon optimal wind turbine spacing in very large wind farms under various economic criteria. For marginally profitable wind farms, minimum cost and maximum profit turbine spacings coincide. Assuming linear-based and area-based costs that are representative of either offshore or onshore sites we obtain for very large wind farms spacings that tend to be appreciably greater than occurring in actual farms confirming earlier results but now including cabling costs. However, we show later that if wind farms are highly profitable then optimization of the profit per unit area leads to tighter optimal spacings than would be implied by cost minimization. In addition, we investigate the influence of the type of wind farm layout.

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Optimal placement of wind turbines in a wind park using Monte Carlo simulation

Cited by 294 publications

References 6 publications

Genetic Optimal Micrositing of Wind Farms by Equilateral-Triangle Mesh

Genetic Optimal Micrositing of Wind Farms by Equilateral-Triangle Mesh

An MILP Method for Design of Distributed Energy Resource System Considering Stochastic Energy Supply and Demand

Combining economic and fluid dynamic models to determine the optimal spacing in very large wind farms

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