Elsevier

Energy

Volume 121, 15 February 2017, Pages 341-355
Energy

A novel wake energy reuse method to optimize the layout for Savonius-type vertical axis wind turbines

https://doi.org/10.1016/j.energy.2017.01.004Get rights and content

Highlights

  • A novel wake energy reuse method is proposed to optimize the layout for S-VAWT.

  • Response surface models for Cp and layout positions is created by Kriging Method.

  • Energy distributions of the turbine wake are investigated.

  • The performance of the turbine at optimal position is increased by 22.89%.

  • The optimal position is suitable for multi-turbines layout in a large wind farm.

Abstract

The long wake of a wind turbine has a significant impact on the performance of downstream turbines. Under the inspiration of migrating geese flying in a V or I formation to save energy, a novel wake energy reuse method is proposed to optimize the layout for Savonius-type vertical axis wind turbines (S-VAWT). VAWT wakes include a series of high speed and energy zones. On both sides of the upstream turbine, 7×16 transient two-dimensional numerical simulations are performed with Fluent to investigate wake structure, interaction effect and power coefficients (Cp) of downstream turbines. Based on Kriging Method, a response surface model (Surrogate model) is created to describe the relationship between the optimization objective Cp and layout positions. Finally, particle swarm optimization algorithm is applied to find the optimal relative layout position (5.25 m, −2.18 m) of the downstream turbine. The optimal position is located in the periodic high speed zone of the wake on the advancing blade side. And the optimal position is suitable for multi-turbines in a large wind farm. The optimization results show that Cp of downstream turbines at optimal layout position is significantly increased from 0.2477 to 0.3044 (22.89% higher).

Introduction

The gradually exhausting non-renewable energy resources have stimulated the development of the renewable energy technology. Wind is an important renewable energy source. Recently, a new challenge arises in the design of the wind turbine layout. The wake of the upstream turbine has a detrimental effect in a wind farm. In the turbine wake, wind speed decreases, while the turbulence intensity increases. These effects result in a large disturbance for downstream wind turbines. Previous studies have found that uneven load stress appears on downstream wind turbines, and such fatigue load will reduce the life of turbines [1], [2], [3]. Meanwhile, the unsteady wake will deteriorate the performance of downstream wind turbines, which further reduces the economic efficiency of a wind farm. According to previous work, 10–20% of the total power output of large wind farms will be wasted [2]. Thus, it is quite meaningful to study the wake to wake interaction and the characteristics of the wake. The traditional method for wind turbines layout aims to reduce interaction effects between different turbines and increase power. In other words, the wake of the upstream turbine should not overlap with the downstream turbine [1]. Other researches have shown that increasing the number of turbines could appropriately improve the economic efficiency in equal surfaces [2], [4], [5]. However, there are no public literature relating to the reuse of the wake energy to enhance the performance of downstream turbines. And the relationship between the layout position and the performance of vertical axis wind turbine has not been investigated in detail. Therefore, it is essential to conduct research on wind turbines layout optimization for energy efficiency improvement.

This study takes Savonius-type vertical axis wind turbines (S-VAWT) as an example. S-VAWT is a type of vertical axis wind turbines. Main advantages of S-VAWTs can be described as follows:

  • High starting up and full operation moment [6]

  • Operation in a wide range of wind conditions

  • Simple construction and low noise emission [7], [8]

These advantages make S-VAWTs suitable for small and medium-sized distributed power generation. In general, this power generation is between tens of kW and hundreds of kW. Wind energy utilization efficiency of S-VAWTS is 15–25% in past simulations and experiments [6], [8]. There is still room to improve the relatively low efficiency. For S-VAWTs, after the wind bypasses blades, the incoming flow becomes more complicated. According to previous studies [9], [10], [11], [12], the performance of the downstream turbine is deteriorated by the wake, and therefore the distance between two S-VAWTs should be large enough to reduce the wake effect. The distance for VAWT is usually 15 times larger than the rotor diameter [1]. The distance limits the maximum number of turbines in equal available surfaces. However, a series of high speed and energy zones are also observed in the wake, and so far few investigations were performed to reuse the wake energy of upstream turbines. Thus, a novel wake energy reuse method is proposed for the turbine layout optimization in the current work. Downstream turbines are placed in the high speed and energy zones of the wake to reuse the wake energy. The results show that, compared with the traditional layout, the wind turbine distances are far less, i.e., the number of turbines increases in equal surfaces, and the downstream turbine acquires a higher efficiency.

Optimization method is essential in the current work to find the optimal turbine layout. Considering the large number of design schemes and the complexity of the wake, wind tunnel experiments are difficult to implement, while numerical method is efficient and intuitive [4], [6]. To obtain the optimal solution for the turbine layout, a series of transient CFD simulations is performed to investigate different layout characteristics with Fluent. Surrogate model is always used in the optimization process when the evaluation of the objective function is time-consuming, as in the current problem. Kriging Method proposed by Danie G. Krige in 1951 is a widely used surrogate model to establish the response surface [13]. The relationship (response surface model) between optimization objective (energy efficiency) and layout positions could be precisely defined based on Kriging model.

Then, a highly effective optimization method is needed to find the optimal solution based on Kriging model. Particle swarm optimization (PSO) algorithm is an optimization method that is simple in concept, easy to be implemented and computationally inexpensive [14]. Accordingly, the PSO algorithm is adopted to find the optimal relative position of the downstream turbine. The optimal layout position of the downstream turbine is obtained through a series of iterations. Energy distributions, typical velocity distributions, pressure distributions around different blades and interaction effect of multi-turbines are also illustrated and analyzed. Compared with traditional layout, the proposed optimal layout has significant improvements. The power coefficient Cp of downstream turbines is increased by 22.89%. And the distance between the upstream turbine and the downstream turbine is obviously reduced. So in equal surfaces, the number of turbines using the new layout method is about 4 times the number of turbines using past no overlap layout method.

The main contribution of the paper is that a new way to regard the turbine's wake is proposed. The high speed zone in the wake created by the upstream turbine is exploited to improve the performance of downstream turbines. It is a completely new thought to optimize the layout of S-VAWTs. This novel layout method has both theoretical and practical values, and gives a new idea for further development of wind energy utilization.

Section snippets

Geometry configuration

As known to all, migrating geese fly in a V or I formation, as shown in Fig. 1(a). The goose in front produces high speed updrafts in wake, so downstream geese fly in updrafts or use the pressure difference to save energy. Similarly, upstream turbine produces a long wake and a periodic high speed zone on both sides of the wake. As the upstream turbine rotates, high speed rotating vortices appear behind the blades [11]. The periodic high speed zone appears accordingly. The velocity difference

Numerical method

Numerical simulation is a precise and high-efficient investigation approach for the wake flow. A series of two-dimensional representations of the flow field is built. Hence, the flow in the vertical direction is neglected, which is equivalent to 3D Savonius turbines with endplates. It has been already successfully proved in similar studies by using Fluent [6], [17], [18], [19]. Then, the results of the numerical simulation are validated and compared with some previous typical experimental data.

Optimal design method

Generally, particle swarm optimization (PSO) is a method of searching for the optimal value according to a certain known model (Surrogate model). In this paper, the certain known model is established by using Kriging Method. Kriging model describes the relationship between optimization objective Cp and layout positions (x, y). Cp represents the performance of the downstream turbine. PSO could find the maximum value of Cp. Finally, the maximum value of Cp corresponds to the optimal layout

Wake structure

Based on the optimization result in Section 4, the optimal position of the downstream turbine is completely located in the periodic high speed zone, and the optimal layout position on the advancing blade side has a definite advantage. In order to test the accuracy of the layout optimization process, experimental value of Cp is used to verify the correctness of the optimal results. Optimal layout positions, predictive value of Cp, experimental value of Cp, and optimization effect of are compared

Conclusions

The wake effect of Savonius-type vertical axis wind turbines is investigated using CFD method. Based on Kriging Method, the response surface model is established to define the relationship between optimization objective Cp (power coefficient) and layout parameters. PSO algorithm is presented to obtain the optimal position of the downstream turbine. Previous relative essays and results are committed to reducing the interaction effect between different turbines. In other words, the wake of the

Acknowledgement

This research is supported by the National Science Foundation of China (Grant No. 61572404) and the Science and Technology Nova Plan of Shaanxi Province (Grant No. 2016KJXX-57).

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