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In this paper, a novel computationally-efficient optimization scheme is proposed to account for the effect of wind speed variations over non-flat terrain and irregular land plot boundary. In particular, typical analytical wake models are augmented with a wind multiplier (obtained from CFD simulations) to account for the effect of altitude variations, while irregular boundaries are addressed through novel constraint handling method in an unrestricted coordinate optimization formulation. In contrast to the optimization via CFD simulation alone, the augmented wake model provides acceptably accurate results with a reasonable computational burden. The new optimization approach is subsequently applied to two real wind farms with significant altitude variations: the Gokceada Island wind farm and the Grasmere and Albany wind farm. The optimized Gokceada wind farm layout exhibits a significant cost of energy (CoE) reduction when topography is considered (over conventional, flat-terrain optimization). However, the Grasmere and Albany wind farm shows a relatively small decrease in CoE over conventional optimization. This discrepancy can be attributed to the wind characteristics in each location: the Gokceada wind farm has a relatively consistent wind direction and the optimization can use topography to carefully avoid wake interactions. In contrast, the Grasmere and Albany wind farm with less-consistent wind directions leaves no clear method to avoid wakes using topography. These results indicate that the consideration of wind farm topography can yield significant benefits and the wake interactions can be greatly reduced through careful placement of wind turbines in the case where a narrow wind directions is dominant. [ABSTRACT FROM AUTHOR]

The study of wind farm layout optimization considering the decisions of land owners has rarely been reported in literature. In this paper, the common situation of complex land divisions (e.g. unequally-spaced plots) is addressed for the first time. A new constraint handling and fitness evaluation technique is developed to address the more complex wind farm boundaries and integrated into two common wind farm optimization approaches: the grid based method and the unrestricted coordinate method. Enable by the new technique, a numerical optimization study is conducted with the goal of evaluating the impact of the participation of land owners on the economic performance of the wind farm. In particular, two scenarios are considered: 1) the varying land plot scenario, where the land plot availability is included in the decision variables of the optimization, and 2) the sequential land plot scenario, where the land plot availability is fixed prior to optimization. The study reveals that the unrestricted coordinate method under the sequential land plot scenario yields the best optimization results, with the smallest cost of energy and the largest wind farm efficiency. [ABSTRACT FROM AUTHOR]

In this paper, a real wind farm layout optimization is carried out by applying the novel optimized wind farm control strategy (coordinating wind turbine operations by setting different axial induction values) and multiple wind turbine hub height selections. The unrestricted coordinate method (using Cartesian coordinates to determine wind turbine positions) is applied to study the effectiveness of different wind farm control strategies and wind turbine hub heights on the wind farm optimization. Means of handling the irregular boundary constraints and calculating the wind speed over non-flat terrain at different heights are proposed for the real wind farm. The results show that the optimized control strategy can always yield better results than the self-optimum control strategy, achieving a smaller cost of energy production and a larger wind farm efficiency. Quantitatively, the optimized control strategy yields approximately 9 kW more power per turbine than the self-optimum control strategy and reduces the cost of energy by 0.08 million dollars per megawatt with 39 wind turbines installed. The application of different hub height turbines facilitates to place wind turbines more flexibly achieving better optimization results than that with constant hub height turbines. [ABSTRACT FROM AUTHOR]

Abstract The wind farm layout optimization is an effective tool to alleviate the wake power losses caused by wind turbine interactions in a wind farm. It is widely recognized that for a real wind farm site with abundant wind resources, wind speed variation of the wind condition can be approximated by the Weibull distribution, which therefore needs to be incorporated into the power evaluation of a real wind farm design. Current researchers have employed the discretization method by dividing the wind speed region into bins to calculate the power output of wind turbines under Weibull distribution, which has two main drawbacks including: 1) complicated discretization process with large computational cost and 2) dependency of calculation accuracy on the discretization resolution. This paper aims to propose a new Monte Carlo method to evaluate the wind farm power output under Weibull distribution, and verify the effectiveness and efficiency of the new method by comparison to the widely used discretization method. Through reducing the wind speed discretizing interval, it is found that when the discretized wind speed interval is smaller than 0.1 m/s, the improvement of optimization results is negligible while the computational cost significantly increases for the discretization method. By testing different sample numbers, it is found that selecting 100000 samples for the Monte Carlo method calculation is able to achieve accurate results with less computational cost than the discretization method especially when large number of turbines is installed in the wind farm. In conclusion, the Monte Carlo method greatly facilitates the power evaluation of wind farm layout optimization under Weibull distribution, with a balance between the calculation accuracy and the computational cost. Highlights • A novel Monte Carlo method for evaluating wind farm power under Weibull distribution is proposed. • Different wind speed discretizing intervals of discretization method for evaluating total power are tested. • Comparison of effectiveness of discretization method and Monte Carlo method is made. • When discretization is less than 0.1 m/s, improvement is negligible but cost significantly increases. • Monte Carlo method greatly facilitates wind farm layout optimization under Weibull distribution. [ABSTRACT FROM AUTHOR]