Optimal Turbine Placement Research Articles

Multi-objective Mathematical Model for Optimal Wind Turbine Placement in Wind Farm under Uncertainty

The main objective of this research is to introduce three energy risk management models grounded in optimization techniques for the strategic placement of wind turbines, considering wake effects and uncertainties in wind speed and direction. For this purpose, wind speed and direction data are gathered, and Monte Carlo simulation is employed to model the uncertainties. Subsequently, the risk management models undergo optimization using Non-Dominated Sorting Genetic Algorithm II (NSGA-II), Pareto envelope-based selection algorithm II (PESA-II), and Multi-Objective Particle Swarm Optimization (MOPSO) algorithms. Findings reveal that the wind farm's maximum power output reaches approximately 5.8 megawatts across all three algorithms and optimal turbine placements. A risk assessment was conducted using a tenth percentile criterion, revealing a significant production risk within the study area, with production falling below 1.8 megawatts in 90% of cases. Regarding the performance evaluation of the algorithms across all three models, superior performance in terms of solution proximity to the ideal solution is exhibited by PESA-II, while enhanced diversity and solution spread compared to the other algorithms are demonstrated by NSGA-II.

On tidal array layout sensitivity to regional and device model representation

In-stream tidal devices are ready to be deployed, yet the largest operational commercial array is limited to 6MW. Upcoming government support should see the size of such arrays increase by orders of magnitude, and thus the optimal placement of turbines within tidal arrays is an emerging challenge for successful commercial integration. Hydrodynamic models are required to predict the power produced by an array and the impact on the surrounding environment. The influence of common model inputs to layout optimisation are investigated herein. This is achieved using a shallow water equation based tidal array modelling framework, Thetis, coupled with a low cost analytical wake model (FLORIS) that allows for rapid assessment of the impact of small changes in hydrodynamic results on array micro-siting. The sensitivity of array optimisation at an intermediate development point (43 turbines) is interrogated through both artificial flow field manipulation and the variation of model input pertinent to the optimisation. A small margin exists in which an optimised array layout will perform efficiently for a deviation in flow prediction accuracy. However, incorrect flow predictions by a range sensitive to model inputs had a reduction of almost 8% on array efficiency relative to a control case. The sensitivity of flow field variance by model input changes, on extractable energy and array layout are substantial. Comparing arrays sited on flow fields using different bathymetry resolution leads to a discrepancy on average of over 2% to average array power. Arrays sited for different mesh resolution and friction representation also see average changes up to and exceeding 0.75%. For array developers and the future of this nascent industry, re-calibration of the model at every stage of data collection coupled with early acquisition of bathymetry data is critical not just for accurate power quantification, but also array efficiency. At a regional scale, quantification of the potential tidal resource must consider the consequences of uncertainty in model input data and site constraints that cannot yet be accounted for.

Advances in Wind Farm Layout Optimization: Wind Direction Robustness and Wake Induced Asymmetric Thrust Load

In this study, we address the wind farm layout optimization (WFLO) problem and tackle the issue of optimal turbine placement by incorporating additional aspects of an economically driven target function. Firstly, we have analyzed the effect of wind direction on a given turbine arrangement. Based on the direction-dependent wake pattern, practitioners sometimes shut down certain turbines on their farms. Our method computes which turbines should be shut down in which wake situation. On this basis, we have developed a method of finding new turbine setups that rarely require shutdowns and are, in a certain sense, “robust” against changes to the wind direction. Secondly, we have presented a partial coverage Jensen wake model in three-dimensional space and have provided the tools for reducing or avoiding wake-induced asymmetric thrust on the rotor disc of the turbine, which leads to reduced energy yield and accelerated wear. This aspect can also be used for finding new turbine setups that take partial coverage into account and avoid it if necessary. Overall, the application of the refinements suggested in this study will result in an increased yearly profit achieved from the produced energy in a wind farm. This is an aspect that decision-makers, such as farm planners/operators, might depend on in a market that typically possesses narrow profit margins. Our methods find entrance into the open-source research framework that comes as the package wflo for the statistical software R.

A Jaya-based approach to wind turbine placement problem

ABSTRACT Renewable energy resources are natural, clean, economical, and never-ending energy resources. Wind energy is an important clean, cheap, and easy applicable energy sources. On account of this, generation of the energy from wind technology has been raised day by day because of the competition with fossil-fuel power production methods. By depending on increases the number of turbines located in the wind farm, the average power obtains from each wind turbine appreciable reduces due to the existence of wake effects within the wind farm. Therefore, the optimal placement of turbines in a wind farm provides to get optimum wind energy from the wind farm. When the place where the wind turbines are located is considered as NxN grid, a wind turbine can be established to each cell of this grid. Whether a wind turbine is replaced to each cell of the grid or not can be modeled as a binary-based optimization problem. In this study, a Jaya-based binary optimization algorithm is proposed to determine which cells are used for wind turbine replacement. In order to justify the efficiency of the proposed approach, two different test cases are considered, and the solutions produced by the proposed approach are compared with the solutions of the swarm intelligence or evolutionary computation methods. According to the experiments and comparisons the Jaya-based binary approach shows a superior performance than compared approaches in terms of cost and power effectiveness. While the efficiency of the Jaya-based approach is 92.2% with 30 turbines replacement on 10 × 10 grid, the efficiency of the Jaya-based binary method is 95.7% with 43 turbines replacement on 20 × 20 grid.

Bilateral Gaussian Wake Model Formulation for Wind Farms: A Forecasting based approach

Optimal placement of turbines in a wind farm is a major challenge where the wake effect reduces the effective wind power capture. Wind speed prediction is essential from a reliability point of view. In this article, a bilateral wake model which is derived from two benchmark models, namely, Jensen's and Frandsen's variation is used for studying the performance of far-end wakes. A prediction based approach is formulated wherein the inputs to the classical SVR model are based on the two benchmark models and the proposed bilateral Gaussian wake model. Wind speed is predicted for upstream turbines of two wind farm layouts (5-turbine and 15-turbine). Further, to observe the impact of input dimensionality, two techniques: (i) Grey relational analysis (GRA) and (ii) Neighborhood component analysis (NCA), are considered. Results reveal that for a wind site WBZ tower, NCA outperforms GRA by 36.48%, 34.0% and 7.03% for Jensen's, Frandsen's and bilateral wake model respectively. When compared to the two benchmark models for both the techniques (GRA and NCA), the prediction performance of bilateral wake model is superior. Overall, it is observed that the feature selection tools like GRA and NCA improve the wind speed prediction accuracy in the presence of wind wakes.

Layout optimization of a wind farm using geometric pattern-based approach

Abstract Wind farm design deals with the optimal placement of turbines in a wind farm to maximizes the expected power output and increase the overall efficiency of a wind farm. In the present study, a novel procedure known as a geometrical pattern inspired placement methodology is introduced to position wind turbines to maximize the total power output of a wind farm located at the Kutch district at the west cost of the Gujarat province of India. The enhanced passing vehicle search (PVSe) algorithm is applied to rectangular wind farm considering the multi-directional variable wind velocity. The numerical results are compared with the existing placement of turbines at a wind farm. The numerical results indicate that the power output obtained by the geometrical pattern based approach is improved by 4.29 percentages compared to the existing wind farm layout at Kutch district.

A new mathematical programming approach to optimize wind farm layouts

The optimal placement of turbines in a wind farm is critical to the maximization of power production. In this paper, we develop a new mathematical programming approach for wind farm layout optimization. We use Jensen's wake decay model to represent multi-turbine wake effects. We develop mixed integer linear and quadratic optimization formulations and apply them to several example layout cases in the literature. Compared to previous approaches, our models produce layouts that tend to be more symmetric and that generate slightly more power. Our formulations solve quickly, allowing a decision maker to efficiently explore the impact of different turbine densities in a wind farm.

Improved Formulation for the Optimization of Wind Turbine Placement in a Wind Farm

As an alternative to fossil fuels, wind can be considered because it is a renewable and greenhouse gas-free natural resource. When wind power is generated by wind turbines in a wind farm, the optimal placement of turbines is critical because different layouts produce different efficiencies. The objective of the wind turbine placement problem is to maximize the generated power while minimizing the cost in installing the turbines. This study proposes an efficient optimization formulation for the optimal layout of wind turbine placements under the resources (e.g., number of turbines) or budget limit by introducing corresponding constraints. The proposed formulation gave users more conveniences in considering resources and budget bounds. After performing the optimization, results were compared using two different methods (branch and bound method and genetic algorithm) and two different objective functions.