• Energy Research

Wind farm layout optimization (WFLO) seeks to alleviate the wake loss and maximize wind farm power output efficiency, and is a crucial process in the design of wind energy projects.Since the optimization algorithms typically require thousands of numerical evaluations of the wake effects, conventional WFLO studies are usually carried out with the low-fidelity analytical wake models.In this paper, we develop an optimization framework for wind farm layout design using CFD-based Kriging model to maximize the annual energy production (AEP) of wind farms. This surrogate-based optimization (SBO) framework uses latin hypercube sampling to generate a group of wind farm layout samples, based on which CFD simulations are carried out to obtain the corresponding AEPs.This wind farm layout dataset is used to train the Kriging model, which is then integrated with an optimizer based on genetic algorithm (GA). As the optimization progresses, the intermediate optimal layout designs are again fed into the dataset.Such adaptive update of wind farm layout dataset continues until the algorithm converges.To evaluate the performance of the proposed SBO framework, we apply it to three representative wind farm cases.Compared to the conventional staggered layout, the optimized wind farm produces significantly higher total AEP.In particular, the SBO framework requires significantly smaller number of CFD calls to yield the optimal layouts that generates almost the same AEP with the direct CFD-GA method.Further analysis on the velocity fields show that the optimization framework attempts to locate the downstream turbines away from the the wakes of upstream ones.The proposed CFD-based surrogate model provides a more accurate and flexible alternative to the conventional analytical-wake-model-based methods in WFLO tasks, and has the potential to be used for designing efficient wind farm projects.

Vertical-axis wind turbines (VAWTs) are gaining attention for urban and offshore applications. However, their development is hindered by suboptimal power performance, primarily attributable to the complex aerodynamic characteristics of the blades. Flow control techniques are expected to regulate the flow on the blade surface and improve blade aerodynamics. In the present study, an effective active flow control technique, multiple boundary layer suction slots (MBLSS), is designed for VAWTs performance improvement. The impact of MBLSS on the aerodynamic performance of VAWTs is examined using high-fidelity computational fluid dynamics simulations. The response surface methodology is employed to identify the relatively optimal configuration of MBLSS. Three key parameters are considered, i.e., number of slots (n), distance between slots (d), and slot length (l), which vary from 2 to 4, 0.025c to 0.125c, and 0.025c to 0.075c, respectively. The results show that MBLSS positively affects the power performance and aerodynamics of VAWTs. Parameter n has the most significant effect on VAWT power performance and the importance of d and l is determined by tip speed ratios (TSRs). Tight and loose slot arrangements are recommended for high and low TSRs, respectively. The relatively optimal configuration (n = 2, d = 0.025c, l = 0.05c) results in a remarkable 31.02% increase in the average net power output of the studied TSRs. The flow control mechanism of MBLSS for VAWT blade boundary layer flow has also been further complemented. MBLSS can prevent the bursting of laminar separation bubbles and avoid the formation of dynamic stall vortices. This increases the blade lift-to-drag ratio and mitigates aerodynamic load fluctuations. The wake profiles of VAWTs with MBLSS are also investigated. This study would add value to the application of active flow control techniques for VAWTs.

The actuator line model is used to study the vertically staggered wind turbine cluster composed of horizontal- and vertical-axis wind turbines (HAWTs and VAWTs) in a tandem layout. We consider three simple configurations, including VAWT upwind of HAWT (V + H), VAWT downwind of HAWT (H + V), and VAWT between the two HAWTs (H + V + H). A VAWT installed upwind of the HAWT can not only generate power by itself but can also enhance the power generation of the HAWT, and the total power increases by about 100 kW. When installed downstream the HAWT, the presence of the VAWT slightly reduces the power generation efficiency of the HAWT. However, the VAWT utilizes the increased wind speed between the HAWT and the ground and generates more power. The total power increases by about 60 kW. When installed between the two HAWTs, the beneficial effects of the VAWT on the downstream HAWT are not manifested. Nevertheless, the wind turbine cluster still generates 50 kW more power than that without the VAWT. Overall, even in the tandem layout where the wake effects are most pronounced, the collocation of VAWTs can still utilize the otherwise wasted wind resources, thus increasing the power generation density of wind farms.