Wind farm layout using biogeography based optimization

Bansal, Jagdish Chand; Farswan, Pushpa

doi:10.1016/j.renene.2017.01.064

Cited by 49 publications

(16 citation statements)

References 15 publications

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“…It was proposed to use machine learning to quickly estimate the potential of the new site. Jagdish Chand Bansal et al [4] proposed a solution to the wind farm layout optimization problem (WFLOP) using BBO (Biogeography Based Optimization), a new optimization algorithm. This paper recommends the maximum number of turbines as well as ensuring the optimum position of the wind turbines for a given wind farm.…”

Section: Of 15mentioning

confidence: 99%

Wind Farm Layout Optimization Using a Metamodel and EA/PSO Algorithm in Korea Offshore

Shin

Baek²,

Rhee

2020

Energies

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This paper examines the solution to the problem of turbine arrangement in offshore wind farms. The two main objectives of offshore wind farm planning are to minimize wake loss and maximize annual energy production (AEP). There is more wind with less turbulence offshore compared with an onshore case, which drives the development of the offshore wind farm worldwide. South Korea’s offshore wind farms, which are deep in water and cannot be installed far off the coast, are affected by land complex terrain. Thus, domestic offshore wind farms should consider the separation distance from the coastline as a major variable depending on the topography and marine environmental characteristics. As a case study, a 60 MW offshore wind farm was optimized for the coast of the Busan Metropolitan City. For the analysis of wind conditions in the candidate site, wind conditions data from the meteorological tower and Ganjeolgot AWS at Gori offshore were used from 2001 to 2018. The optimization procedure is performed by evolutionary algorithm (EA) and particle swarm optimization (PSO) algorithm with the purpose of maximizing the AEP while minimizing the total wake loss. The optimization procedure can be applied to the optimized placement of WTs within a wind farm and can be extended for a variety of wind conditions and wind farm capacity. The results of the optimization were predicted to be 172,437 MWh/year under the Gori offshore wind potential, turbine layout optimization, and an annual utilization rate of 26.5%. This could convert 4.6% of electricity consumption in the Busan Metropolitan City region in 2019 in offshore wind farms.

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Section: Of 15mentioning

confidence: 99%

Wind Farm Layout Optimization Using a Metamodel and EA/PSO Algorithm in Korea Offshore

Shin

Baek²,

Rhee

2020

Energies

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“…In BBO algorithm, there have been lots of advancements done by improving the existing operators and by merging new operators. Some advancements in this BBO algorithm have been proposed in many of the previous researches like modified migration [26][27][28], modified mutation [29][30][31], and introducing new operators [32].…”

Section: Review Of Previous Work On Bbo Algorithmmentioning

confidence: 99%

“…In this study, the convergence speed is improved by modifying mutation operator and exploitation is maintained by keeping original migration [30]. In some of the previous studies like [32,33], BBO has proved itself as a good search algorithm and it has effectively solved complex problems with multiple targets and constraints. Thus the Basic BBO has been applied in handling several optimization problems.…”

Section: Review Of Previous Work On Bbo Algorithmmentioning

confidence: 99%

A Hybrid SCA Inspired BBO for Feature Selection Problems

Sindhu

Ngadiran

Yacob

et al. 2019

Mathematical Problems in Engineering

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Recent trend of research is to hybridize two and more metaheuristics algorithms to obtain superior solution in the field of optimization problems. This paper proposes a newly developed wrapper-based feature selection method based on the hybridization of Biogeography Based Optimization (BBO) and Sine Cosine Algorithm (SCA) for handling feature selection problems. The position update mechanism of SCA algorithm is introduced into the BBO algorithm to enhance the diversity among the habitats. In BBO, the mutation operator is got rid of and instead of it, a position update mechanism of SCA algorithm is applied after the migration operator, to enhance the global search ability of Basic BBO. This mechanism tends to produce the highly fit solutions in the upcoming iterations, which results in the improved diversity of habitats. The performance of this Improved BBO (IBBO) algorithm is investigated using fourteen benchmark datasets. Experimental results of IBBO are compared with eight other search algorithms. The results show that IBBO is able to outperform the other algorithms in majority of the datasets. Furthermore, the strength of IBBO is proved through various numerical experiments like statistical analysis, convergence curves, ranking methods, and test functions. The results of the simulation have revealed that IBBO has produced very competitive and promising results, compared to the other search algorithms.

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“…The optimization of wind farm layout involves identifying the optimal locations for wind turbines in a wind farm for maximizing the total energy output of the farm. One study used a biogeography-based approach to optimize wind farm layout by maximizing energy production and reducing the wake effect [12]. Another study used a geometric pattern-based approach to optimize wind farm layout by maximizing the total power output of a wind farm [13].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of an Offshore Wind Farm by Using Data from the Weather Station, Floating LiDAR, Mast, and MERRA

Yue

Chiu

et al. 2020

Energies

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Offshore wind energy is regarded as a key alternative to fossil fuels in many parts of the world. Its exploitation is based on the sound evaluation of wind resources. This study used data from a meteorological mast, a floating light detection and ranging (LiDAR) device, and the Modern-Era Retrospective Analysis for Research and Applications, a reanalysis data set established by the NASA Center for Climate Simulation, to evaluate wind resources of the Changhua-South Offshore Wind Farm. The average wind speeds evaluated at a height of 105 m in the studied wind farm were 7.97 and 8.02 m/s according to the data obtained from the floating LiDAR device and a mast, respectively. The full-load hours were 3320.5 and 3296.5 h per year when data from the LiDAR device and mast were used, respectively. The estimated annual energy production (AEP) with a probability of 50% (P50) reached 314 GWh/y, whereas the AEPs with a probability of 75% (P75) and with a probability of 90% (P90) were 283 GWh/y and 255 GWh/y, respectively. The estimated AEP of P75 was 90% of the AEP of P50, whereas the estimated AEP of P90 was 81% of the AEP of P50. This difference might need to be considered when assessing the risk of financing a wind project.

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Wind farm layout using biogeography based optimization

Cited by 49 publications

References 15 publications

Wind Farm Layout Optimization Using a Metamodel and EA/PSO Algorithm in Korea Offshore

Wind Farm Layout Optimization Using a Metamodel and EA/PSO Algorithm in Korea Offshore

A Hybrid SCA Inspired BBO for Feature Selection Problems

Evaluation of an Offshore Wind Farm by Using Data from the Weather Station, Floating LiDAR, Mast, and MERRA

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