A Case Study: Layout Optimization of Three Gorges Wind Farm Pakistan, Using Genetic Algorithm

Ali, Muhammad Fraz; Ahmad, Zeshan; Alshahrani, Saad; Younis, Muhammad Rizwan; Talib, Irsa; Imran, Muhammad

doi:10.3390/su142416960

Cited by 3 publications

(1 citation statement)

References 36 publications

(54 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Numerous decision variables beyond WT positions are set in the WFLO problem. 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.…”

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

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.

show abstract