Turbine Annual Energy Production Research Articles

Wind Turbine Energy Cost Optimisation Using Various Power Models

In modern times, the worldwide wind turbine installations have developed swiftly resulting in the decrease of green gas emissions. Though wind is a free gift of nature, it is expensive to harness this energy for useful applications like electricity generation. The cost of installation of the wind turbine at a particular station does not depend only on the wind resource, but also on the structure of the turbine and the energy conversion technology. The wind turbine Cost of Energy (CoE) is used to estimate the payback time for the return on the investment made by the wind farm owners for the turbine. Meticulous research is required to optimize the turbine CoE which will make wind a very competent source of energy. In this article, in order to minimize the wind turbine CoE, the wind speed is modelled using three different distributions namely, Dagum, Gamma and Weibull and the evaluation of the turbine Annual Energy Production (AEP) is carried out. Mathematical functions such as linear, quadratic and cubic have been used to model the wind power. For the cost analysis of the turbine, the price model which was established by United States, National Renewable Energy Laboratory (NREL) is employed. The comparative study of the proposed methodology have been done for six different stations. The turbine CoE model is an element of two factors, the rated power Pr of a turbine and the rated wind speed Vr of a turbine. Based on the results obtained, a broad recommendation to reduce the turbine CoE is presented. This study enables us to figure out the minimum turbine CoE among the three discussed mathematical distributions, the finest distribution for wind speed modelling and the optimum mathematical function for wind power modelling. The suitable size of the wind turbine also can be found by optimizing the rotor radius R of the turbine for each data.

High-Reynolds-number wind turbine blade equipped with root spoilers – Part 2: Impact on energy production and turbine lifetime

Abstract. A wind turbine blade equipped with root spoilers is analysed using time domain aeroelastic blade element momentum (BEM) simulations to assess the impact of passive devices on the turbine annual energy production (AEP) and lifetime. Previous 2D computational fluid dynamics (CFD) showed a large unsteadiness in aerodynamic coefficients associated with the spoiler, and such behaviour is captured by the OpenFAST simulations when all degrees of freedom are switched off. Once the turbine is fully flexible, a novel way to account for aerofoil-generated unsteadiness in the fatigue calculation is proposed and detailed. The outcome shows that spoilers, on average, can increase the AEP of the turbine. However, the structural impacts on the turbine can be severe if not accounted for initially in the turbine design.

Minimization of the Cost of Energy of Wind Turbine through Various Power Models

Wind energy is the world’s free form of energy, besides that it has expenses, because of the wind turbine construction and maintenance. The Cost of Energy (CoE) of the wind turbine is used to estimate the amount of time it takes to recover the cost of an investment by the wind farm owners. Hence optimization of the wind turbine CoE will make wind a very competent source of energy. In this article, the wind speed is modified using three alternative distributions in order to reduce the wind turbine CoE, and the turbine Annual Energy Production (AEP) is evaluated. Mathematical functions such as linear, quadratic and cubic have been used to model the wind power. This study enables us to figure out the minimum turbine CoE among the mathematical distributions, along with the optimum mathematical function for wind power modelling and to optimize the rotor radius of turbine.

Method for airborne measurement of the spatial wind speed distribution above complex terrain

Abstract. Wind farm sites in complex terrain are subject to local wind phenomena, which have a relevant impact on a wind turbine's annual energy production. To reduce investment risk, an extensive site evaluation is therefore mandatory. Stationary long-term measurements are supplemented by computational fluid dynamics (CFD) simulations, which are a commonly used tool to analyse and understand the three-dimensional wind flow above complex terrain. Though under intensive research, such simulations still show a high sensitivity to various input parameters like terrain, atmosphere and numerical setup. In this paper, a different approach aims to measure instead of simulate wind speed deviations above complex terrain by using a flexible, airborne measurement system. An unmanned aerial vehicle is equipped with a standard ultrasonic anemometer. The uncertainty in the system is evaluated against stationary anemometer data at different heights and shows very good agreement, especially in mean wind speed (< 0.12 m s−1) and mean direction (< 2.4∘) estimation. A test measurement was conducted above a forested and hilly site to analyse the spatial and temporal variability in the wind situation. A position-dependent difference in wind speed increase of up to 30 % compared to a stationary anemometer is detected.

Rapid Estimate of Wind Turbine Energy Loss Due to Blade Leading Edge Delamination Using Artificial Neural Networks

Abstract Estimating reliably and rapidly the losses of wind turbine annual energy production due to blade surface damage is essential for optimizing maintenance planning and, in the case of leading edge erosion, assessing the need for protective coatings. These requirements prompted the development of the prototype system presented herein, using machine learning, wind turbine engineering codes, and computational fluid dynamics to estimate annual energy production losses due to blade leading edge delamination. The power curve of a turbine with nominal and damaged blade surfaces is determined, respectively, with the open-source FAST and AeroDyn codes of the National Renewable Energy Laboratory, both using the blade element momentum theory for turbine aerodynamics. The loss prediction system is designed to map a given three-dimensional geometry of a damaged blade onto a damaged airfoil database, which, in this study, features 6000+ airfoil geometries, each analyzed with Navier–Stokes computational fluid dynamics over the working range of angles of attack. To avoid lengthy aerodynamic analyses to assess losses due to damages monitored during turbine operation, the airfoil force data of a damaged turbine required by AeroDyn are rapidly obtained using a machine learning method trained using the pre-existing airfoil database. Presented results demonstrate that realistic estimates of the annual energy production loss of a utility-scale offshore turbine due to leading edge delamination are obtained in just a few seconds using a standard desktop computer. This highlights viability and industrial impact of this new technology for managing wind farm energy losses due to blade erosion.

Interannual variability of wind climates and wind turbine annual energy production

Abstract. The interannual variability (IAV) of expected annual energy production (AEP) from proposed wind farms plays a key role in dictating project financing. IAV in preconstruction projected AEP and the difference in 50th and 90th percentile (P50 and P90) AEP derive in part from variability in wind climates. However, the magnitude of IAV in wind speeds at or close to wind turbine hub heights is poorly defined and may be overestimated by assuming annual mean wind speeds are Gaussian distributed with a standard deviation (σ) of 6 %, as is widely applied within the wind energy industry. There is a need for improved understanding of the long-term wind resource and the IAV therein in order to generate more robust predictions of the financial value of a wind energy project. Long-term simulations of wind speeds near typical wind turbine hub heights over the eastern USA indicate median gross capacity factors (computed using 10 min wind speeds close to wind turbine hub heights and the power curve of the most common wind turbine deployed in the region) that are in good agreement with values derived from operational wind farms. The IAV of annual mean wind speeds at or near typical wind turbine hub heights in these simulations and AEP computed using the power curve of the most commonly deployed wind turbine is lower than is implied by assuming σ=6 %. Indeed, rather than 9 out of 10 years exhibiting AEP within 0.9 and 1.1 times the long-term mean AEP as implied by assuming a Gaussian distribution with σ of 6 %, the results presented herein indicate that in over 90 % of the area in the eastern USA that currently has operating wind turbines, simulated AEP lies within 0.94 and 1.06 of the long-term average. Further, the IAV of estimated AEP is not substantially larger than IAV in mean wind speeds. These results indicate it may be appropriate to reduce the IAV applied to preconstruction AEP estimates to account for variability in wind climates, which would decrease the cost of capital for wind farm developments.

A mathematical approach to minimizing the cost of energy for large utility wind turbines

With the aim of reducing green gas emission, wind turbine installations worldwide have grown rapidly in recent years. Wind energy itself is free, but has costs due to the wind turbine infrastructure and maintenance. The installation size of the wind turbine at a specific location is not only determined by the wind statistics at that location, but also by the turbine infrastructure and the maintenance cost. The payback time of the turbine is determined by the turbine cost of energy (COE). In this paper, a mathematical approach is proposed to minimize the turbine cost of energy based on wind statistics. Turbine annual energy production (AEP) is calculated based on turbine output power and annual wind speed distribution. A wind turbine cost model developed by U.S. National Renewable Energy Laboratory (NREL) is used for turbine cost analysis. The turbine cost of energy model includes the turbine rated power and the turbine rated wind speed. Finally a general guideline to minimize the turbine COE is presented. Three case studies are conducted to show the effectiveness of the proposed approach.

Aerodynamic Optimization of a Wind Turbine Blade Designed for Egypt's Saharan Environment Using a Genetic Algorithm

This work aims to optimize the aerodynamic parameters (airfoil chord lengths and twist angles smoothed using Bezier curves) of the NREL 5MW wind turbine and a wind turbine designed for site-specific wind conditions to increase the wind turbine's annual energy production (AEP) under this site conditions. This optimization process is carried out using a Genetic Algorithm (GA) developed in MATLAB and coupled with NREL's FAST Modularization Framework. The results shows that after optimizing the NREL 5MW wind turbine design, the AEP was improved by 5.9% of the baseline design AEP while a site-specific designed wind turbine using Schmitz equations shows 1.2% improvement in AEP. These results shows that optimization of wind turbine blade aerodynamic parameters for site-specific wind conditions leads to improvement in AEP and hence decreasing cost of energy generated by wind turbines.

A novel adaptive blade concept for large-scale wind turbines. Part I: Aeroelastic behaviour

This two-part paper introduces a novel aeroelastic approach to the design of large-scale wind turbine blades. By suitably tailoring the blade's elastic response to aerodynamic pressure, the turbine's Annual Energy Production is shown to increase, while simultaneously alleviating extreme loading conditions due to gusts. In Part I, we use a current blade as the baseline for an aerodynamic analysis aimed at maximising the turbine's yielded power. These results are then used to identify ideal aeroelastic behaviour. In Part II, we exploit material and structural bend-twist couplings in the main spar to induce appropriate differential blade twist, section by section, while bending flap-wise.

Analysis of calibration results from cup and propeller anemometers. Influence on wind turbine Annual Energy Production (AEP) calculations

AbstractThe calibration coefficients of several models of cup and propeller anemometers were analysed. The analysis was based on a series of laboratory calibrations between January 2003 and August 2007. Mean and standard deviation values of calibration coefficients from the anemometers studied were included. Two calibration procedures were used and compared. In the first, recommended by the Measuring Network of Wind Energy Institutes (MEASNET), 13 measurement points were taken over a wind speed range of 4 to 16 m s−1. In the second procedure, 9 measurement points were taken over a wider speed range of 4 to 23 m s−1. Results indicated no significant differences between the two calibration procedures applied to the same anemometer in terms of measured wind speed and wind turbines' Annual Energy Production (AEP). The influence of the cup anemometers' design on the calibration coefficients was also analysed. The results revealed that the slope of the calibration curve, if based on the rotation frequency and not the anemometer's output frequency, seemed to depend on the cup center rotation radius. Copyright © 2010 John Wiley & Sons, Ltd.