A comparison of deterministic refinement techniques for wind farm layout optimization

Nagpal, Shriya; Liu, M. Vivienne; Anderson, C. Lindsay

doi:10.1016/j.renene.2020.12.043

Cited by 9 publications

(1 citation statement)

References 15 publications

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“…Chen et al [17] applied a Greedy algorithm to reduce the wake effect of multiple hub height WTGs on flat and complex terrains. Finally, using the objective function of levelized costs of energy (LCOE), multiple deterministic algorithms have been used to design optimal wind power plant layouts to reduce the LCOE in the presence of wake effects [18].…”

Section: Introductionmentioning

confidence: 99%

Optimal Wind Power Plant Layout Using Ant Colony Optimization

Makhampom,

Waewsak,

Chancham

et al. 2023

ASEAN Sci Tech Rept

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The optimal layout of wind power plants is very important as the arrangement of wind turbine generators (WTGs) profoundly affects the overall energy output of the wind power plant. To address this important issue, this research investigates the best layout for WTGs in wind power plants with different terrain features across three locations in Thailand using the Ant Colony Optimization (ACO) algorithm. The objective functions of maximizing the net annual energy production (AEP) and minimizing the wake losses were used to achieve the optimal wind power plant layout. Using the MERRA-2 database, computational fluid dynamics (CFD) wind flow modeling was performed to create 10x10 km2 microscale wind resource maps of locations characterized by flat, semi-complex, and complex terrains to install wind power plants. The CFD wind flow modeling yielded wind speeds of 5.00 to 5.76, 4.21 to 8.90, and 3.10 to 4.45 m/s for the flat, semi-complex, and complex terrains, respectively, making them feasible for utility-scale wind power plants. WTGs of multiple blade diameters, ranging from 90 to 126 m, with a nominal capacity of 2.5 MW at 100 m above ground-level hub heights, were used in this study. The Gamesa G126-2.5MW WTG with a 126 m blade diameter produces the highest net AEP of 14.3, 76.1, and 38.9 GWh/yr for the three terrains. Hence, this WTG was used to perform an ACO-based optimization to improve the electricity production of the wind power plants. Such studies are important to improve the efficiency of wind power plants, thus extracting the maximum kinetic energy possible from the winds and improving the economic viability of wind power plants.

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Section: Introductionmentioning

confidence: 99%