Sensitivity analysis of wake steering optimisation for wind farm power maximisation (original) (raw)
Related papers
Wind farm multi-objective wake redirection for optimizing power production and loads
Energy, 2017
Clustering wind turbines as a wind farm to share the infrastructure is an effective strategy to reduce the cost of energy. However, this results in aerodynamic wake interaction among wind turbines. Yawing the upstream wind turbines can mitigate the losses in wind farm power output. Yaw-misalignment also affects the loads, as partial wake overlap can increase fatigue of downstream turbines. This paper studies multi-objective optimization of wind farm wake using yaw-misalignment to increase power production and reduce loads due to partial wake overlap. This is achieved using a computational framework consisting of an aerodynamic model for wind farm wake, a blade-element-momentum model to compute the power and the loads, and a gradient-based optimizer. The results show that yaw-misalignment is capable of increasing the power production of the wind farm, while reducing the loading due to partial wake overlap. A multi-objective optimization is able to further decrease the loads at the expense of a small amount of power production.
A Review of Methodological Approaches for the Design and Optimization of Wind Farms
Energies, 2014
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.
State of the Art of Wind Farm Optimization
In recent years the trend has been to collect wind generators into larger and larger wind farms. As the investments are substantial, the optimization of the wind farm layout plays a major role today. The scope of the present work is to define the state of the art in wind farm optimization. To do so the literature of the last two decades has been analyzed, and the structure of the problem has been defined. The most effective techniques and models used in the past are described. The common pitfalls are listed as well, with the aim to create a blueprint for future development of wind farm optimization tools/softwares. The main findings concern the high dependency of the resulting layout on the objective function chosen, which objective should be as detailed as possible; the energy yield alone has been proven not to be the best function for practical purposes. The need for all-encompassing functions requires the costs to be computed besides the production yield. New strategies have been developed to handle comprehensive objective functions and to reduce long computational times, namely the "two-steps" optimization, which consist of a combination of two algorithms, usually a meta-heuristic and a local search approach. The last point touched by this work highlights the areas where a better understanding is needed and more research should be addressed, like the models for degradation and the solving algorithms used.
Wind-farm layout optimisation using a hybrid Jensen–LES approach
Wind Energy Science Discussions, 2016
Given a wind-farm with known dimensions and number of wind-turbines, we try to find the optimum positioning of wind-turbines that maximises wind-farm energy production. In practise, given that optimisation has to be performed for many wind directions and taking into account the yearly wind distribution, such an optimisation is computationally only feasible using fast engineering wake models such as, e.g., the Jensen model. These models are known to have accuracy issues, in particular since their representation of wake interaction is very simple. In the present work, we propose an optimisation approach that is based on a hybrid combination of Large-Eddy Simulations (LES) and the Jensen model, in which optimisation is mainly performed using the Jensen model, and LES is used at a few points only during optimisation for online tuning of the wake-expansion coefficient in the Jensen model, and for validation of the results. An optimisation case study is considered, in which the placement ...
SADI International Journal of Science, Engineering and Technology, 2024
The increasing demand for sustainable energy solutions has heightened the importance of optimizing wind farm layouts to enhance efficiency and energy output. This study investigates the optimization of wind turbine placement using historical SCADA data, computational fluid dynamics (CFD), and Differential Evolution (DE) algorithms. We analyzed a comprehensive wind turbine dataset, which included wind speed, active power, theoretical power curves, and wind direction data. The analysis revealed a direct relationship between wind speed and power output, with discrepancies observed at both low and high wind speeds due to system inefficiencies and turbine power limits. The Frandsen wake model was employed to account for the wake losses, while the Differential Evolution algorithm was used to optimize the turbine positions to develop an optimal layout for 100 turbines within a minimum area of 21.6 km², aiming to minimize the wake effects and maximize the energy production. The results demonstrate that proper turbine alignment and spacing significantly improve the efficiency, allowing the turbines to operate closer to their theoretical maximum efficiency. The findings highlight the critical role of integrating historical wind data with CFD simulations to optimize turbine placement and performance. This work offers a valuable framework for future windfarm designs, emphasizing the benefits of data-driven approaches in maximizing renewable energy generation and operational efficiency.
Wind farm layout optimization (WFLO) is the design of wind turbine layout, subject to various financial and engineering objectives and constraints. The first topic of this thesis focuses on solving two variations of WFLO that have different analytical aerodynamic models, and illustrate deep integration of the wake models into mixed-integer programs and constraint programs. Formulating WFLO as MIP and CP enables more quantitative analysis than previous studies could do with heuristics, and allows the practitioners to use an off-the-shelf optimization solver to tackle the WFLO problem. The second topic focuses on another version of WFLO that has two competing objectives: minimization of noise and maximization of energy. A genetic algorithm (NSGA-II) is used. Under these two objectives, solutions are presented to illustrate the flexibility of this optimization framework in terms of supplying a spectrum of design choices with different numbers of turbines and different levels of noise and energy output. ii I am incredibly fortunate to have Professor Cristina Amon as my supervisor, who has provided unfaltering support and guidance through my MASc journey. I am grateful for her welcoming me into the ATOMS lab and allowing me to pursue my academic interests freely. Her work ethics and ability to discover important research questions are inspirational. Since my first day at ATOMS, Dr. David Romero has been a supportive and resourceful mentor. Without David's confidence in me, I would not have appreciated research so much. David's depth and breadth of knowledge, along with his effective supervision style, are instrumental for my research progress. Sitting beside him brought us many interesting discussions from research to culinary art. Professor Timothy Chan has been an academic and career mentor to me through the many courses and projects that we are both part of. Thank you for fostering my passion for operations research and re-kindling my love for mathematics. Discussions with Professor Chris Beck through various research projects and the CP&LS course honed my critical thinking skills, and brought me one step closer to being a good researcher. have taught me many things in research: work ethics, communication, and research collaboration. Michael Kim, unknowingly, inspired part of my work through his award-winning paper. He probably still does not know if he has not read this paragraph. The Hatch team, especially Michael Morgenroth and Joaquin Moran, brought insights from the industry, without which I would not have been able to write this thesis.
The Modelling of Wind Farm Layout Optimization for the Reduction of Wake Losses
The objective of the present research is to find out the optimized dimensions of the wind farm area and turbines layout to reduce the overall cost per unit power. The velocity deficits caused by the wakes of each turbine were calculated by using Jensen's wake model. The optimal positions of wind turbine placement are evaluated by using genetic algorithm, while sustaining the obligatory space between adjacent turbines for operation safety. The research on the wind farm area dimensions and fully utilization of upstream wind velocity is currently lacking in literature. The logical application of area dimensions and genetic algorithm improved the overall efficiency of the wind farm. It is concluded that proposed duel level optimization method outperforms the existing ones. The total wind farm area (2km ¥ 2km) was divided into 100 identical cells, with each cell having dimensions 200m ¥ 200m. The performance of the proposed method is compared with the results from previous studies. The simulation results showed that power output of the wind farm was increased by using same area with different dimensions. It was observed that by using the same number of wind turbines, the total efficiency of wind farm was increased by 7 %.
Wind Farm Layout Optimization with Wakes from Fluid Dynamics Simulations
2014
Twelve actuator disk CFD simulations of an isolated rotor were carried out, whose setups differ only by a variation of the inflow wind profile and derived quantities. By interpolating the resulting set of velocity deficit fields to arbitrary inflow wind speeds at hub height, a CFD based numerical wake model was obtained. This model was compared to wake models from the literature, using the new wind farm modelling software flapFOAM. It was found that the outcome of a gradient-based wind farm layout optimization depends on the choice of wake model. For the CFD wake model, different wake overlap models were compared to full farm CFD simulations. While the total deficit of a row of turbines is described well, the total deficit between column organized turbines is underestimated by flapFOAM.
The Wind Farm Layout Optimization Problem
2010
An important phase of a wind farm design is solving the Wind Farm Layout Optimization Problem (WFLOP), which consists in optimally positioning the turbines within the wind farm so that the wake effects are minimized and therefore the expected power production maximized. Although this problem has been receiving increasing attention from the scientific community, the existing approaches do not completely respond to the needs of a wind farm developer, mainly because they do not address construction and logistical issues. This chapter describes the WFLOP, gives an overview on the existing work, and discusses the challenges that may be overcome by future research.
Yaw-Misalignment and its Impact on Wind Turbine Loads and Wind Farm Power Output
Journal of Physics: Conference Series, 2016
To make wind energy cost competitive with traditional resources, wind turbines are commonly placed in groups. Aerodynamic interaction between the turbines causes sub-optimal energy production. A control strategy to mitigate these losses is by redirecting the wake by yaw misalignment. This paper aims to assess the influence of load variations of the rotor due to partial wake overlap and presents a combined optimization of the power and loads using wake redirection. For this purpose, we design a computational framework which computes the wind farm power production and the wind turbine rotor loads based on the yaw settings. The simulation results show that partial wake overlap can significantly increase asymmetric loading of the rotor disk and that yaw misalignment is beneficial in situations where the wake can be sufficiently directed away from the downstream turbine.