Optimization for Variable Height Wind Farm Layout Model

Xu, Bin; Zhu, Jianming; Wen, Junzhe; Lin, Shanshan; Zhao, Yunkai; Qi, Jin; Xue, Yu; Qin, Sichong

doi:10.32604/iasc.2021.018338

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…Ali et al [37] used a GA to maximize the output powers of two wind farms in Pakistan by considering the hub height and WT spacing as the decision variable. Authors in [38][39][40] utilized the WT hub height as a key decision variable in optimizing wind farm layouts. Their research outcomes demonstrate that adjusting the hub height can reduce the wake effect of a wind farm.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Non-Uniform Onshore Wind Farm Layout Using Modified Electric Charged Particles Optimization Algorithm Considering Different Terrain Characteristics

Hidayat,

Ramli,

Alqahtani

2024

Sustainability

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Designing an onshore wind farm layout poses several challenges, including the effects of terrain and landscape characteristics. An accurate model should be developed to obtain the optimal wind farm layout. This study introduces a novel metaheuristic algorithm called Modified Electric Charged Particles Optimization (MECPO) to maximize wind farms’ annual energy production (AEP) by considering the different terrain and landscape characteristics of the sites. Some non-uniform scenarios are applied to the optimization process to find the best combination of decision variables in the wind farm design. The study was initiated by a uniform wind farm layout optimization employing identical wind turbine hub heights and diameters. Following this, these parameters underwent further optimization based on some non-uniform scenarios, with the optimal layout from the initial uniform wind farm serving as the reference design. Three real onshore sites located in South Sulawesi, Indonesia, were selected to validate the performance of the proposed algorithm. The wind characteristics for each site were derived from WAsP CFD, accounting for the terrain and landscape effects. The results show that the non-uniform wind farm performs better than its uniform counterpart only when using varying hub heights. Considering the impacts of the terrain and landscape characteristics, it is observed that sites with a higher elevation, slope index, and roughness length exhibit a lower wake effect than those with lower ones. Moreover, the proposed algorithm, MECPO, consistently outperforms other algorithms, achieving the highest AEP across all simulations, with a 100% success rate in all eight instances. These results underscore the algorithm’s robustness and effectiveness in optimizing wind farm layouts, offering a promising avenue for advancing sustainable wind energy practices.

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Section: Introductionmentioning

confidence: 99%

Optimization of Non-Uniform Onshore Wind Farm Layout Using Modified Electric Charged Particles Optimization Algorithm Considering Different Terrain Characteristics

Hidayat,

Ramli,

Alqahtani

2024

Sustainability

View full text Add to dashboard Cite

show abstract

“…The result showed that the WT selection decreased the LCOE, which are set as an objective function. Moreover, in a recent study (2021), Xu et al [30] used differential evolution and greedy algorithm to design wind farm layout with multiple hub heights of WTs. The results show that by using multi hub height, it can reduce the LCOE by 13.96%, 12.54%, 8.22%, 6.14%, and 7.77% for the number of WT of 5, 10, 30, and 50, respectively.…”

Section: Introductionmentioning

confidence: 99%

Design of 3D Wind Farm Layout Using an Improved Electric Charge Particles Optimization With Hub-Height Variety

et al. 2022

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The limited resources of land and wind have increased the requirement for better designing of wind farm layouts in the wind industry. A three-dimensional layout of wind turbines (WTs) is proposed in this paper to optimize the horizontal and vertical layouts of wind farms. The issue of optimization is a highly challenging task as it involves many variables and requires handling conflicting criteria. Classical optimization algorithms cannot handle this problem due to discontinuity and nonlinear behavior. Considering this, a metaheuristic algorithm called electric charged particle optimization (ECPO) is developed and implemented in four different shapes and cases studies. All the scenarios implemented have the same wind distribution and obstacles. The result shows that ECPO achieves better performance in the case study when the maximum number of a wind turbine is the same as the number of grids when compared to three wellknown metaheuristic algorithms, which are binary particle swarm optimization (BPSO), genetic algorithm (GA), and artificial bee colony (ABC). By implementing the three-dimensional WFLO, the levelized cost of energy (LCOE) will increase by 7% in the case of the optimal number of WT and 3% in the case of the fixed number of WT.

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The Optimization Method of the Layout of Integrated Passenger Transport Terminals in Beijing-Tianjin- Hebei Urban Agglomeration

Sun

Duan

Tian

et al. 2022

Advances in Artificial Intelligence and Security

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Optimization for Variable Height Wind Farm Layout Model

Cited by 6 publications

References 17 publications

Optimization of Non-Uniform Onshore Wind Farm Layout Using Modified Electric Charged Particles Optimization Algorithm Considering Different Terrain Characteristics

Optimization of Non-Uniform Onshore Wind Farm Layout Using Modified Electric Charged Particles Optimization Algorithm Considering Different Terrain Characteristics

Design of 3D Wind Farm Layout Using an Improved Electric Charge Particles Optimization With Hub-Height Variety

The Optimization Method of the Layout of Integrated Passenger Transport Terminals in Beijing-Tianjin- Hebei Urban Agglomeration

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