Optimizing wind farms layouts for maximum energy production using probabilistic inference: Benchmarking reveals superior computational efficiency and scalability

Dhoot, Aditya; Antonini, E.; Romero, David A.; Amón, Cristina H.

doi:10.1016/j.energy.2021.120035

Cited by 9 publications

(8 citation statements)

References 66 publications

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“…In such conditions, the turbine density (turbines per unit area) becomes a dominant parameter, which is also consistent with the theoretical analysis of large wind farm aerodynamics performed by Nishino and Dunstan (53). This is unlikely to be true for a wind farm with few turbines, where optimal layouts have been shown to substantially increase the wind farm power output (21,22,54).…”

Section: Power Density and Wakes As A Function Of Wind Farm Sizementioning

confidence: 99%

Spatial constraints in large-scale expansion of wind power plants

Antonini

Caldeira

2021

Proc. Natl. Acad. Sci. U.S.A.

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When wind turbines are arranged in clusters, their performance is mutually affected, and their energy generation is reduced relative to what it would be if they were widely separated. Land-area power densities of small wind farms can exceed 10 W/m2, and wakes are several rotor diameters in length. In contrast, large-scale wind farms have an upper-limit power density in the order of 1 W/m2 and wakes that can extend several tens of kilometers. Here, we address two important questions: 1) How large can a wind farm be before its generation reaches energy replenishment limits and 2) How far apart must large wind farms be spaced to avoid inter–wind-farm interference? We characterize controls on these spatial and temporal scales by running a set of idealized atmospheric simulations using the Weather and Research Forecasting model. Power generation and wind speed within and over the wind farm show that a timescale inversely proportional to the Coriolis parameter governs such transition, and the corresponding length scale is obtained by multiplying the timescale by the geostrophic wind speed. A geostrophic wind of 8 m/s and a Coriolis parameter of 1.05 × 10−4 rad/s (latitude of ∼46°) would give a transitional scale of about 30 km. Wind farms smaller than this result in greater power densities and shorter wakes. Larger wind farms result instead in power densities that asymptotically reach their minimum and wakes that reach their maximum extent.

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Section: Power Density and Wakes As A Function Of Wind Farm Sizementioning

confidence: 99%

Spatial constraints in large-scale expansion of wind power plants

Antonini

Caldeira

2021

Proc. Natl. Acad. Sci. U.S.A.

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“…As depicted in Equations ( 1)-( 5), the power conversion can be optimized by adjusting the axial factors α j and yaw angle γ j . The interested reader can read a more detailed description of wake deflection in [19].…”

Section: Gaussian-based Wake Model Considering Yaw Anglementioning

confidence: 99%

“…Researchers have recently proposed some control methods for optimizing power conversion in large wind farms without considering wake infection [17,18]. Some kinds of literature [19][20][21] propose novel graph theories. The production of generating an interaction matrix is treated in the same way.…”

Section: Introductionmentioning

confidence: 99%

Modified Beetle Annealing Search (BAS) Optimization Strategy for Maxing Wind Farm Power through an Adaptive Wake Digraph Clustering Approach

2021

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Owing to scale-up and complex wake effects, the centralized control that processes the command from turbines may be unsuitable, as it incurs high communication overhead and computational complexity for a large offshore wind farm (OWF). This paper proposes a novel decentralized non-convex optimization strategy for maxing power conversion of a large OWF based on a modified beetle antennae search (BAS) algorithm. First, an adaptive threshold algorithm which to establish a pruned wake direction graph while preserving the most critical wake propagation relationship among wind turbines are presented. The adaptive graph constraints were used to create wake sub-digraphs that split the wind farm into nearly uncoupled clustering communication subsets. On this basis, a Monte Carlo-based beetle annealing search (MC-BAS) nonlinear optimization strategy was secondly designed to adjust the yaw angles and axial factors for the maximum power conversion of each turbine subgroup. Finally, the simulation results demonstrated that a similar gain could be achieved as a centralized control method at power conversion and reduces the computational cost, allowing it to solve the nonlinear problem and real-time operations of the OWF.

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“…Thus far, geophysical considerations governing site selection of solar installations are mostly solar irradiance and equivalent sun hours (Ara ´n Carrio ´n et al, 2008;Ramachandra and Shruthi, 2007), whereas for wind power plants, the primary geophysical consideration is mean wind power density at the turbine hub height (Becker and Thra ¨n, 2018;Cetinay et al, 2017). For wind power plants, system-level planning is then followed by turbine micro-siting to determine the positions of the individual turbines to minimize wake losses (Antonini et al, 2016(Antonini et al, , 2018a(Antonini et al, , 2018b(Antonini et al, , 2019(Antonini et al, , 2020Dhoot et al, 2021). Methods for optimal siting in a distributed network have been developed to consider additional criteria related to environmental, economic, social, and technical aspects (Georgiou and Skarlatos, 2016;Prakash and Khatod, 2016;Tegou et al, 2010).…”

Section: Introductionmentioning

confidence: 99%