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

Nada, S.A.; Elsayed, Abdelrahman A.; El‐Sayed, Mostafa A.

doi:10.1002/er.4779

Cited by 4 publications

(4 citation statements)

References 29 publications

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“…Obviously, a feasible solution to the cable layout problem must be at least a spanning tree in which there exists a single path between every pair of nodes (turbines plus transmitter). Therefore, the total number of cables connected the turbines and a transmitter must be at least equal |V| − 1 where |V| = TN + 1 as provided by Equation (9). Equations ( 10) and ( 11) avoid having overlapping cables in each row of N and in each column of N 0 , respectively.…”

Section: Proposed Mixed Integer Programming Modelmentioning

confidence: 99%

“…important problems in the design of a wind farm such as the selection of a right site to construct a wind farm, [1][2][3] layout of turbines in the farm, [4][5][6][7][8][9] installation of electrical infrastructure [10][11][12][13] and connection of turbines and transmitters in the farm through electrical cables. Connection of turbines and transmitters using different cable types to send all the energy production towards one or more transmitters is known as cable layout problem.…”

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confidence: 99%

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Optimization of cable layout designs for large offshore wind farms

Ülkü

Alabaş-Uslu

2020

Int J Energy Res

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Summary Installation of a wind farm exposes several problems such as site selection, placement of wind turbines in the site, and designing of cable infrastructure within the farm. The latter problem, called cable layout design, is the determination of cable connections among turbines and one or more transmitters such that energies generated by turbines will be sent through the cable routes, and eventually gathered at the transmitter(s). This problem is especially important for offshore wind farms where the featured and expensive cables are used. The main objective of the present study is to address the cable layout design problem of offshore wind farms to reduce cable costs in the design using optimization‐based approaches. The problem, firstly, is modelled as a mixed integer linear program (MIP) under a set of real‐life constraints such as different cable and transmitter types and non‐crossing connections between the turbines. Then, a novel mathematical model, which is a modification of the MIP model by imposing several heuristic rules, is proposed to solve the layout problem of large offshore wind farms. Experiments on a set of small‐ and moderate‐sized test instances reveal that the heuristic model, MIP_H, reduces the computer time nearly 55% compared to that of MIP model while the average cable costs generated by the models are close to each other. MIP_H, besides its efficiency, provides more cost‐effective layouts compared to MIP model for large‐sized real‐life examples. Additionally, a comparative study on MIP_H and existing methods in the literature shows that MIP_H is able to solve all instances of the real‐life examples providing less cable costs in average.

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Section: Proposed Mixed Integer Programming Modelmentioning

confidence: 99%

mentioning

confidence: 99%

Optimization of cable layout designs for large offshore wind farms

Ülkü

Alabaş-Uslu

2020

Int J Energy Res

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“…Two important features of the wake are (1) reduced flow velocity and (2) increased turbulence intensity. Notice that, the former reduces wind farm power production while the latter results in higher dynamic loading in downstream turbines and procrastinating wake recovery (Boersma et al, 2018; Nada et al, 2019; Qian and Ishihara, 2021).…”

Section: Introductionmentioning

confidence: 99%

Wind farm supervisory controller design for power optimization in localized areas using adaptive learning game theory (ALGT)

Fazlollahi,

Shirazi,

Taghizadeh

2023

Wind Engineering

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In this paper, a supervisory control concept for wind farms is proposed based on the neighboring wind turbines control functions in localized areas for power optimization considering wake effects. The flow control in wind farms to maximize power production is a challenging problem due to its time-varying nonlinear wake dynamics. Hence, we develop a method that authorizes coordination in a wind farm for a squarely payoff-based scenario where the turbines have access only to measurements from their neighbors via repeated interactions. Therefore, in order to maximize output power in a wind farm, an Adaptive Learning Game Theory (ALGT) method is introduced. This control scheme provides an interaction framework that constructs a series of common control functions. Here, in every iteration, each turbine chooses an independent decision according to a localized control law. The control objective of wind turbine [Formula: see text] determines how each turbine adjusts a decision at each iteration by processing available information.

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“…3 The installation of wind turbines cannot always be as smooth and efficient as it should be, as their installation is highly regulated in some parts of the world. Barriers to the installation of wind turbines include the lack of detailed knowledge on wind resources, 6 geographical restrictions, 7 availability of wind turbine technology, 8 and socio-economic conditions. 9 The basis for wind potential assessments is detailed knowledge of wind resources.…”

Section: Introductionmentioning

confidence: 99%

The role of the power law exponent in wind energy assessment: A global analysis

Jung

Schindler

2021

Int J Energy Res

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The power law is most often applied to extrapolate the near-surface wind speed to the wind turbine hub height. Due to variations of the meteorological conditions, the power law exponent varies over time. Usually, no long-term wind speed measurements from multiple heights are available which would allow time-dependent and spatially explicit power law exponent estimations. Instead, often the mean of the power law exponent or a constant value of 0.14 is assumed. The goal of this study was to quantify the error in wind potential assessments resulting from applying the mean of the power law exponent or a value of 0.14. The data base for this study are the hourly wind speed time series at 10 and 100 m above ground available for the period 2007 to 2018 from the ERA5 reanalysis project at a global 0.25 × 0.25 grid. The errors in the estimation of the wind power density and the capacity factor were calculated. It was found that, onshore, the global median of the absolute percentage error related to the wind power density using the mean of the power law exponent is 7.5%. Assuming a constant value of 0.14, the power law is less accurate (absolute percentage error: 37.1%). For the estimation of the capacity factor the absolute percentage errors are 5.5% and 36.9%. Based on the results of this study, the use of time-dependent and spatially explicit power law exponents is suggested. In the absence of long-term wind speed measurements from multiple heights, the results provide a comprehensive global overview of the errors to be expected from using the mean of the power law exponent or assuming a value of 0.14. In many regions where the wind resource is abundant, using the mean of the power law exponent only leads to minor errors in capacity factor estimation.

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

Cited by 4 publications

References 29 publications

Optimization of cable layout designs for large offshore wind farms

Optimization of cable layout designs for large offshore wind farms

Wind farm supervisory controller design for power optimization in localized areas using adaptive learning game theory (ALGT)

The role of the power law exponent in wind energy assessment: A global analysis

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