State of the Art in the Optimisation of Wind Turbine Performance Using CFD

Wind energy has received increasing attention in recent years due to its sustainability and geographically wide availability. The efficiency of wind energy utilisation highly depends on the performance of wind turbines, which convert the kinetic energy in wind into electrical energy. In order to optimise wind turbine performance and reduce the cost of next-generation wind turbines, it is crucial to have a view of the state of the art in the key aspects on the performance optimisation of wind turbines using Computational Fluid Dynamics (CFD), which has attracted enormous interest in the development of next-generation wind turbines in recent years. This paper presents a comprehensive review of the state-of-the-art progress on optimisation of wind turbine performance using CFD, reviewing the objective functions to judge the performance of wind turbine, CFD approaches applied in the simulation of wind turbines and optimisation algorithms for wind turbine performance. This paper has been written for both researchers new to this research area by summarising underlying theory whilst presenting a comprehensive review on the up-to-date studies, and experts in the field of study by collecting a comprehensive list of related references where the details of computational methods that have been employed lately can be obtained

A novel hybrid optimization methodology to optimize the total number and placement of wind turbines

Due to increasing penetration of wind energy in the recent times, wind farmers tend to generate increasing amount of energy out of wind farms. In order to achieve the target, many wind farms are operated with a layout design of numerous turbines placed close to each other in a limited land area leading to greater energy losses due to ‘wake effects’. Moreover, these turbines need to satisfy many other constraints such as topological constraints, minimum allowable capacity factors, inter-turbine distances, noise constraints etc. Thus, the problem of placing wind turbines in a farm to maximize the overall produced energy while satisfying all constraints is highly constrained and complex. Existing methods to solve the turbine placement problem typically assume knowledge about the total number of turbines to be placed in the farm. However, in reality, wind farm developers often have little or no information about the best number of turbines to be placed in a farm. This study proposes a novel hybrid optimization methodology to simultaneously determine the optimum total number of turbines to be placed in a wind farm along with their optimal locations. The proposed hybrid methodology is a combination of probabilistic genetic algorithms and deterministic gradient based optimization methods. Application of the proposed method on representative case studies yields higher Annual Energy Production (AEP) than the results found by using two of the existing methods

Optimization of Wind Turbine Airfoils/Blades and Wind Farm Layouts

Shape optimization is widely used in the design of wind turbine blades. In this dissertation, a numerical optimization method called Genetic Algorithm (GA) is applied to address the shape optimization of wind turbine airfoils and blades. In recent years, the airfoil sections with blunt trailing edge (called flatback airfoils) have been proposed for the inboard regions of large wind-turbine blades because they provide several structural and aerodynamic performance advantages. The FX, DU and NACA 64 series airfoils are thick airfoils widely used for wind turbine blade application. They have several advantages in meeting the intrinsic requirements for wind turbines in terms of design point, off-design capabilities and structural properties. This research employ both single- and multi-objective genetic algorithms (SOGA and MOGA) for shape optimization of Flatback, FX, DU and NACA 64 series airfoils to achieve maximum lift and/or maximum lift to drag ratio. The commercially available software FLUENT is employed for calculation of the flow field using the Reynolds-Averaged Navier-Stokes (RANS) equations in conjunction with a two-equation Shear Stress Transport (SST) turbulence model and a three equation k-kl-ω turbulence model. The optimization methodology is validated by an optimization study of subsonic and transonic airfoils (NACA0012 and RAE 2822 airfoils). All the optimization results have demonstrated that the GA technique can be employed efficiently and accurately to produce globally optimal airfoils with excellent aerodynamic properties using a desired objective value (minimum Cd and/or maximum Cl /Cd). It is also shown that the multi-objective genetic algorithm based optimization can generate superior airfoils compared to those obtained by using the single objective genetic algorithm. The applications of thick airfoils are extended to the assessment of wind turbine performance. It is well established that the power generated by a Horizontal-Axis Wind Turbine (HAWT) is a function of the number of blades B, the tip speed ratio λ (blade tip speed/wind free stream velocity) and the lift to drag ratio (Cl /Cd) of the airfoil sections of the blade. The airfoil sections used in HAWT are generally thick airfoils such as the S, DU, FX, Flat-back and NACA 6-series of airfoils. These airfoils vary in (Cl /Cd) for a given B and λ, and therefore the power generated by HAWT for different blade airfoil sections will vary. Another goal of this study is to evaluate the effect of different airfoil sections on HAWT performance using the Blade Element Momentum (BEM) theory. In this dissertation, we employ DU 91-W2-250, FX 66-S196-V1, NACA 64421, and Flat-back series of airfoils (FB-3500-0050, FB-3500-0875, and FB-3500-1750) and compare their performance with S809 airfoil used in NREL Phase II and III wind turbines; the lift and drag coefficient data for these airfoils sections are available. The output power of the turbine is calculated using these airfoil section blades for a given B and λ and is compared with the original NREL Phase II and Phase III turbines using S809 airfoil section. It is shown that by a suitable choice of airfoil section of HAWT blade, the power generated by the turbine can be significantly increased. Parametric studies are also conducted by varying the turbine diameter. In addition, a simplified dynamic inflow model is integrated into the BEM theory. It is shown that the improved BEM theory has superior performance in capturing the instantaneous behavior of wind turbines due to the existence of wind turbine wake or temporal variations in wind velocity. The dissertation also considers the Wind Farm layout optimization problem using a genetic algorithm. Both the Horizontal -Axis Wind Turbines (HAWT) and Vertical-Axis Wind Turbines (VAWT) are considered. The goal of the optimization problem is to optimally position the turbines within the wind farm such that the wake effects are minimized and the power production is maximized. The reasonably accurate modeling of the turbine wake is critical in determination of the optimal layout of the turbines and the power generated. For HAWT, two wake models are considered; both are found to give similar answers. For VAWT, a very simple wake model is employed. Finally, some preliminary investigation of shape optimization of 3D wind turbine blades at low Reynolds numbers is conducted. The optimization employs a 3D straight untapered wind turbine blade with cross section of NACA 0012 airfoils as the geometry of baseline blade. The optimization objective is to achieve maximum Cl/Cd as well as maximum Cl. The multi-objective genetic algorithm is employed together with the commercially available software FLUENT for calculation of the flow field using the Reynolds-Averaged Navier-Stokes (RANS) equations in conjunction with a one-equation Sparlart-Allmaras turbulence model. The results show excellent performance of the optimized wind turbine blade and indicate the feasibility of optimization on real wind turbine blades with more complex shapes in the future

Optimal Wind Farm Cabling

Wind farm cable length has a direct impact on the project cost, reliability and electrical losses. The optimum cable layout results in a lower unit cost of generating electricity offshore. This paper explores three cabling structures: the string structure, ring structures and multi-loop structure on a 3D seabed. The newly proposed multi-loop structure increases reliability and proves to be most economic when the failure rate and mean time to repair (MTTR) of cables are relatively high. Particle swarm optimization (PSO) is used to find the optimal substation location that minimizes the overall cable distance

Conceptual Design of Wind Farms Through Novel Multi-Objective Swarm Optimization

Wind is one of the major sources of clean and renewable energy, and global wind energy has been experiencing a steady annual growth rate of more than 20% over the past decade. In the U.S. energy market, although wind energy is one of the fastest increasing sources of electricity generation (by annual installed capacity addition), and is expected to play an important role in the future energy demographics of this country, it has also been plagued by project underperformance and concept-to-installation delays. There are various factors affecting the quality of a wind energy project, and most of these factors are strongly coupled in their influence on the socio-economic, production, and environmental objectives of a wind energy project. To develop wind farms that are profitable, reliable, and meet community acceptance, it is critical to accomplish balance between these objectives, and therefore a clean understanding of how different design and natural factors jointly impact these objectives is much needed. In this research, a Multi-objective Wind Farm Design (MOWFD) methodology is developed, which analyzes and integrates the impact of various factors on the conceptual design of wind farms. This methodology contributes three major advancements to the wind farm design paradigm: (I) provides a new understanding of the impact of key factors on the wind farm performance under the use of different wake models; (II) explores the crucial tradeoffs between energy production, cost of energy, and the quantitative role of land usage in wind farm layout optimization (WFLO); and (III) makes novel advancements on mixed-discrete particle swarm optimization algorithm through a multi-domain diversity preservation concept, to solve complex multi-objective optimization (MOO) problems. A comprehensive sensitivity analysis of the wind farm power generation is performed to understand and compare the impact of land configuration, installed capacity decisions, incoming wind speed, and ambient turbulence on the performance of conventional array layouts and optimized wind farm layouts. For array-like wind farms, the relative importance of each factor was found to vary significantly with the choice of wake models, i.e., appreciable differences in the sensitivity indices (of up to 70%) were observed across the different wake models. In contrast, for optimized wind farm layouts, the choice of wake models was observed to have no significant impact on the sensitivity indices. The MOWFD methodology is designed to explore the tradeoffs between the concerned performance objectives and simultaneously optimize the location of turbines, the type of turbines, and the land usage. More importantly, it facilitates WFLO without prescribed conditions (e.g., fixed wind farm boundaries and number of turbines), thereby allowing a more flexible exploration of the feasible layout solutions than is possible with other existing WFLO methodologies. In addition, a novel parameterization of the Pareto is performed to quantitatively explore how the best tradeoffs between energy production and land usage vary with the installed capacity decisions. The key to the various complex MO-WFLOs performed here is the unique set of capabilities offered by the new Multi-Objective Mixed-Discrete Particle Swarm Optimization (MO-MDPSO) algorithm, developed, tested and extensively used in this dissertation. The MO-MDPSO algorithm is capable of dealing with a plethora of problem complexities, namely: multiple highly nonlinear objectives, constraints, high design space dimensionality, and a mixture of continuous and discrete design variables. Prior to applying MO-MDPSO to effectively solve complex WFLO problems, this new algorithm was tested on a large and diverse suite of popular benchmark problems; the convergence and Pareto coverage offered by this algorithm was found to be competitive with some of the most popular MOO algorithms (e.g., GAs). The unique potential of the MO-MDPSO algorithm is further established through application to the following complex practical engineering problems: (I) a disc brake design problem, (II) a multi-objective wind farm layout optimization problem, simultaneously optimizing the location of turbines, the selection of turbine types, and the site orientation, and (III) simultaneously minimizing land usage and maximizing capacity factors under varying land plot availability

An optimization framework for wind farm layout design using CFD-based Kriging model

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

Multi-objective evolutionary algorithms and hyper-heuristics for wind farm layout optimisation

Wind farm layout optimisation is a challenging real-world problem which requires the discovery of trade-off solutions considering a variety of conflicting criteria, such as minimisation of the land area usage and maximisation of energy production. However, due to the complexity of handling multiple objectives simultaneously, many approaches proposed in the literature often focus on the optimisation of a single objective when deciding the locations for a set of wind turbines spread across a given region. In this study, we tackle a multi-objective wind farm layout optimisation problem. Different from the previously proposed approaches, we are applying a high-level search method, known as selection hyper-heuristic to solve this problem. Selection hyper-heuristics mix and control a predefined set of low-level (meta)heuristics which operate on solutions. We test nine different selection hyper-heuristics including an online learning hyper-heuristic on a multi-objective wind farm layout optimisation problem. Our hyper-heuristic approaches manage three well-known multi-objective evolutionary algorithms as low-level metaheuristics. The empirical results indicate the success and potential of selection hyper-heuristics for solving this computationally difficult problem. We additionally explore other objectives in wind farm layout optimisation problems to gain a better understanding of the conflicting nature of those objectives