The Significance of Wind Turbines Layout Optimization on the Predicted Farm Energy Yield

Securing energy supply and diversifying the energy sources is one of the main goals of energy strategy for most countries. Due to climate change, wind energy is becoming increasingly important as a method of CO2-free energy generation. In this paper, a wind farm with five turbines located in Jerash, a city in northern Jordan, has been designed and analyzed. Optimization of wind farms is an important factor in the design stage to minimize the cost of wind energy to become more competitive and economically attractive. The analyses have been carried out using the WindFarm software to examine the significance of wind turbines’ layouts (M, straight and arch shapes) and spacing on the final energy yield. In this research, arranging the turbines facing the main wind direction with five times rotor diameter distance between each turbine has been simulated, and has resulted in 22.75, 22.87 and 21.997 GWh/year for the M shape, Straight line and Arch shape, respectively. Whereas, reducing the distance between turbines to 2.5 times of the rotor diameter (D) resulted in a reduction of the wind farm energy yield to 22.68, 21.498 and 21.5463 GWh/year for the M shape, Straight line and Arch shape, respectively. The energetic efficiency gain for the optimized wind turbines compared to the modeled layouts regarding the distances between the wind turbines. The energetic efficiency gain has been in the range between 8.9% for 5D (rotor diameter) straight layout to 15.9% for 2.5D straight layout

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 Review of Methodological Approaches for the Design and Optimization of Wind Farms

This article presents a review of the state of the art of the Wind Farm Design and Optimization (WFDO) problem. The WFDO problem refers to a set of advanced planning actions needed to extremize the performance of wind farms, which may be composed of a few individual Wind Turbines (WTs) up to thousands of WTs. The WFDO problem has been investigated in different scenarios, with substantial differences in main objectives, modelling assumptions, constraints, and numerical solution methods. The aim of this paper is: (1) to present an exhaustive survey of the literature covering the full span of the subject, an analysis of the state-of-the-art models describing the performance of wind farms as well as its extensions, and the numerical approaches used to solve the problem; (2) to provide an overview of the available knowledge and recent progress in the application of such strategies to real onshore and offshore wind farms; and (3) to propose a comprehensive agenda for future research

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 farm layout

U radu je predstavljen metod određivanja optimalnih položaja vetrogeneratora u okviru farme, postavljene na terenu proizvoljne orografije. Optimalni položaji pojedinačnih vetrogeneratora su određeni tako da se postigne njihova maksimalna efikasnost. Metod je zasnovan na genetskom algoritmu kao optimizacionoj tehnici. Aerodinamički proračun vetrogeneratora je izveden na nestacionarnom potencijalnom strujnom polju. Lopatice vetrogeneratora su modelirane kao vrtložne površine, a vrtložni trag je modeliran upotrebom 'freewake' metode. Optimizacioni model je razvijen za dve funkcije cilja. Obe funkcije koriste ukupnu energiju dobijenu iz farme kao jednu od ključnih promenljivih. Druga funkcija cilja uključuje i ukupno ulaganje u svaku pojedinačnu turbinu, tako da optimizacioni proces uključuje i ukupan broj vetrogeneratora kao promenljivu. Metod je testiran na nekoliko proizvoljnih konfiguracija terena, pri čemu je posebna pažnja posvećena izboru parametara genetskog algoritma, kako bi se postigle povoljne performanse optimizacionog procesa.This paper presents a method for determination of optimum positions of single wind turbines within the wind farms installed on arbitrary configured terrains, in order to achieve their maximum production effectiveness. This method is based on use of the genetic algorithm as optimization technique. The wind turbine aerodynamic calculation is unsteady, based on the blade modeled as a vortex lattice and a free-wake type airflow behind the blade. Optimization method is developed for two different fitness functions. Both functions use the total energy obtained from the farm as one of the key variables. The second also involves the total investments in a single wind turbine, so the optimization process can also include the total number of turbines as an additional variable. The method has been tested on several different terrain configurations, with special attention paid to the overall algorithm performance improvements by selecting certain genetic algorithm parameters

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

Considerations for the Design Optimization of Floating Offshore Wind Turbine Blades

Floating offshore wind turbines are an immature technology with relatively high costs and risk associated with deployment. Of the few floating wind turbine prototypes and demonstration projects deployed in real metocean conditions, all have used standard turbines design for onshore or offshore fixed bottom conditions. This neglects the unique unsteady aerodynamics brought on by floating support structure motion. While the floating platform has been designed and optimized for a given rotor, the global system is suboptimal due to the rotor operating in conditions outside of which it was design for. If the potential offered by floating wind turbines is to be realized, offering access to deep water near-shore, costs need to continue to be reduced. This dissertation is the first known design study that considers the optimization of wind turbine rotors specifically for floating conditions. Two design optimization methodologies are presented using different analysis fidelity levels. A relatively computationally efficient, state-state blade element moment optimization of floating wind turbine blades is presented that will be useful for future systems level optimization studies. A higher fidelity methodology is then presented, using time-domain aeroelastic simulations to fully capture the unsteady aerodynamics and dynamic couplings between the rotor and platform motion throughout the optimization process. The principal finding of these studies is that low induction rotors are a promising technology pathway for future FOWT systems, reducing the severity of cyclical loading due to platform motion

Mixed Integer Programming Models and Algorithms for Wind Farm Layout

The aim of the thesis is the optimization of wind farm layout: given a specific wind farm site and wind data for the site, an optimal location of turbines is determined such that the power production is maximized and wake effects and other constraints are taken into account. Several Mixed Integer Linear Programming (MILP) models and ad-hoc heuristics have been proposed, and a new approach for very large-scale instances has been developed. Tests on real data show the effectiveness of our methodope