Site specific optimization of wind turbines energy cost: Iterative approach

Mirghaed, Mohammad Rezaei; Roshandel, Ramin

doi:10.1016/j.enconman.2013.04.016

Cited by 51 publications

(10 citation statements)

References 14 publications

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“…In other instances indirectly as a measure of improvement, the LCoE is employed as the objective function of optimization problems concerning layouts or structural designs. In [7] individual turbines and wind farms are constructively optimized on the basis of reducing the final LCoE. Similarly in [8] the LCoE is employed as the objective function of a bi-level programming approach to also optimize the constructive layout.. And in the particular case of wind energy production-the subject of this paper-Farrell and colleagues' paper offers a thorough review of LCoE estimates existing in the literature [9].…”

Section: Introductionmentioning

confidence: 98%

Dynamic evaluation of the levelized cost of wind power generation

Díaz

Gómez‐Aleixandre

Coto

2015

Energy Conversion and Management

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Section: Introductionmentioning

confidence: 98%

Dynamic evaluation of the levelized cost of wind power generation

Díaz

Gómez‐Aleixandre

Coto

2015

Energy Conversion and Management

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“…Rehman et al [8] presumed OHH to be an intersection where the total cost function of HH and a gradient curve of the AEP function were crossed, but they did not present sufficient rationale that the calculated intersection was theoretically OHH, rather they applied their own method to regions with a few observation data. Maki et al [9] and Mirghaed et al [10] calculated the minimized COE iteratively with regard to all cases by controlling a variety of variables, including generator's revolutions per minute (RPM) and turbine pitch, in addition to HH and RD. This was not only complicated, but also incurred high calculation cost.…”

Section: Introductionmentioning

confidence: 99%

Determining the Optimized Hub Height of Wind Turbine Using the Wind Resource Map of South Korea

et al. 2019

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Although the size of the wind turbine has become larger to improve the economic feasibility of wind power generation, whether increases in rotor diameter and hub height always lead to the optimization of energy cost remains to be seen. This paper proposes an algorithm that calculates the optimized hub height to minimize the cost of energy (COE) using the regional wind profile database. The optimized hub height was determined by identifying the minimum COE after calculating the annual energy production (AEP) and cost increase, according to hub height increase, by using the wind profiles of the wind resource map in South Korea and drawing the COE curve. The optimized hub altitude was calculated as 75~80 m in the inland plain but as 60~70 m in onshore or mountain sites, where the wind profile at the lower layer from the hub height showed relatively strong wind speed than that in inland plain. The AEP loss due to the decrease in hub height was compensated for by increasing the rotor diameter, in which case COE also decreased in the entire region of South Korea. The proposed algorithm of identifying the optimized hub height is expected to serve as a good guideline when determining the hub height according to different geographic regions.

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“…In Reference [22], the authors presented a framework for the site-specific design optimization of a horizontal-axis onshore wind turbine, in which the blade number, rotor diameter, tower height, rotor rotational speed, rated wind speed, and rated power were optimized to match the wind condition described by the Weibull distribution and parameters. Mirghaed et al [23] developed an iterative approach to optimize a single turbine with minimum COE, in which the capital cost was a function of all turbine components, such as rotor diameter, hub height and rated power. In the study, it was found that the onshore turbines with capacities of about 1-2 MW lowered their COE by about 45-75 $/MWh.…”

Section: Introductionmentioning

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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Site specific optimization of wind turbines energy cost: Iterative approach

Cited by 51 publications

References 14 publications

Dynamic evaluation of the levelized cost of wind power generation

Dynamic evaluation of the levelized cost of wind power generation

Determining the Optimized Hub Height of Wind Turbine Using the Wind Resource Map of South Korea

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

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