Wind Profiles and Wave Spectra for Potential Wind Farms in South China Sea. Part I: Wind Speed Profile Model

Liu, Yichao; Chen, Daoyi; Qian, Yi; Li, Sunwei

doi:10.3390/en10010125

Cited by 26 publications

(17 citation statements)

References 36 publications

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“…This concept of the scaling property is similar to a power law relationship for a wind profile, although it is not exactly the same because the distributions are the same while the data are not. The power law relationship for the wind profile has been widely used to estimate the wind speed at different heights [78][79][80][81][82][83]. When the wind profile is assumed to follow the power law, the proposed method can be employed for downscaling the distribution of wind speeds near the ground.…”

Section: Discussionmentioning

confidence: 99%

A Novel Statistical Method to Temporally Downscale Wind Speed Weibull Distribution Using Scaling Property

2018

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To improve our capacity to use available wind speed data, it is necessary to develop a new statistical temporal downscaling method that uses one or a few input variables of any temporal scale for mean wind speed data to obtain wind statistics at finer temporal resolution. In the present study, a novel statistical temporal downscaling method for wind speed statistics and probability distribution is proposed. The proposed method uses the temporal structure to downscale the wind speed statistics to a fine temporal scale without the use of additional variables. The Weibull distribution of the hourly and 10-min mean wind speed data is obtained by the downscaled wind speed statistics. The proposed method provides the downscaled Weibull distribution of fine temporal wind speed data using coarse temporal wind statistics. Particularly, the use of sub-daily mean wind speed data in the downscaling of the wind speed Weibull distribution leads to good estimation precision. The Weibull distribution downscaled by the proposed method successfully reproduces the wind power density based on the wind potential energy estimation.

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Section: Discussionmentioning

confidence: 99%

A Novel Statistical Method to Temporally Downscale Wind Speed Weibull Distribution Using Scaling Property

2018

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“…In this study, the Weibull probability density function f i (v) was employed to represent the offshore wind statistics of the installation site, which depended on the Weibull scale and the shape parameters k i and c i that determined the shape and intensity of the wind during one year on a site [27].…”

Section: Weibull Probability Density Distribution Of Offshore Wind Stmentioning

confidence: 99%

“…where, α is the Hellmann exponent, which depends on surface properties of the wind field. The typical value of α is 0.1 over water and 0.14 over land [27]. When the Weibull probability distribution of a single wind turbine in its erected site is mainly determined by the wind characteristics of the wind farm, it is also influenced by the wake effects of the wind farm.…”

Section: Weibull Probability Density Distribution Of Offshore Wind Stmentioning

confidence: 99%

Minimizing the Energy Cost of Offshore Wind Farms by Simultaneously Optimizing Wind Turbines and Their Layout

et al. 2019

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The construction and gradual installation of turbines on wind farms has been hindered by the high cost of the energy production. An effective way to minimize energy costs is via the optimal design of wind turbines and their layout, but relevant and synthetic studies are lacking. This paper proposes a method to minimize the energy cost of offshore wind farms by simultaneously optimizing the rated wind speed, the rotor radius of wind turbines and their layout. Firstly, a new, mixed mathematical formulation of the energy cost is presented, considering the Weibull distribution for wind, the characterizing parameters of wind turbines and the distance between two turbines. Secondly, to obtain the minimum energy cost, a composite optimization algorithm was developed, which consists of an iterative method and an improved particle swarm optimization algorithm. The former was used to search the minimal energy costs that relate to the design parameters of a single wind turbine, while the latter was adopted for optimizing the layout of the wind turbines iteratively. Finally, the proposed method was applied to three case studies with variable wind speed and constant wind direction. Results of the case studies show that the reduced energy cost after optimization has a range of 0–0.001 $/kWh, which confirms the effectiveness of the proposed approach. Meanwhile, the layout of the wind turbines after optimization tends to locate the two wind turbines with the biggest spacing in the wind direction, which justifies the utilization of layout optimization. Furthermore, exploring the optimally designed parameters of wind turbines revealed that the wind farms with a high mean wind speed can have a wider range of turbine capacity than those with a low wind speed, which offers more freedom for the designers when constructing offshore wind farms at wind sites with rich wind resources.

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“…where c 0 is the value of c at reference altitude H 0 , and α is the Hellmann exponent, which depends on serval surface properties of the wind field. The typical value of α is 0.1 over water, and 0.14 over land [39,40]. In this study, the shape parameter k and the annual mean wind speed v m are considered as known parameters, and c 0 is calculated by [41]…”

Section: Weibull Distribution Of the Offshore Wind Statisticsmentioning

confidence: 99%

Optimal Design of Rated Wind Speed and Rotor Radius to Minimizing the Cost of Energy for Offshore Wind Turbines

et al. 2018

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As onshore wind energy has depleted, the utilization of offshore wind energy has gradually played an important role in globally meeting growing green energy demands. However, the cost of energy (COE) for offshore wind energy is very high compared to the onshore one. To minimize the COE, implementing optimal design of offshore turbines is an effective way, but the relevant studies are lacking. This study proposes a method to minimize the COE of offshore wind turbines, in which two design parameters, including the rated wind speed and rotor radius are optimally designed. Through this study, the relation among the COE and the two design parameters is explored. To this end, based on the power-coefficient power curve model, the annual energy production (AEP) model is designed as a function of the rated wind speed and the Weibull distribution parameters. On the other hand, the detailed cost model of offshore turbines developed by the National Renewable Energy Laboratory is formulated as a function of the rated wind speed and the rotor radius. Then, the COE is formulated as the ratio of the total cost and the AEP. Following that, an iterative method is proposed to search the minimal COE which corresponds to the optimal rated wind speed and rotor radius. Finally, the proposed method has been applied to the wind classes of USA, and some useful findings have been obtained.

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Wind Profiles and Wave Spectra for Potential Wind Farms in South China Sea. Part I: Wind Speed Profile Model

Cited by 26 publications

References 36 publications

A Novel Statistical Method to Temporally Downscale Wind Speed Weibull Distribution Using Scaling Property

A Novel Statistical Method to Temporally Downscale Wind Speed Weibull Distribution Using Scaling Property

Minimizing the Energy Cost of Offshore Wind Farms by Simultaneously Optimizing Wind Turbines and Their Layout

Optimal Design of Rated Wind Speed and Rotor Radius to Minimizing the Cost of Energy for Offshore Wind Turbines

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