Multidisciplinary aeroelastic optimization of a 10MW-scale wind turbine rotor targeting to reduced LCoE

Serafeim, Giannis P.; Manolas, Dimitris I.; Riziotis, Vasilis A.; Chaviaropoulos, P.

doi:10.1088/1742-6596/2265/4/042051

Cited by 3 publications

(5 citation statements)

References 15 publications

(36 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The figure provides the optimum distributions of the design parameters in the passive control modifications considered. The optimum ply offset angle obtained by the optimizer is 6.2°, as shown in Figure 12d, being totally aligned with previous studies [11,12,17]. Furthermore, BTC allows for much higher global sectional thickness reduction coefficients as compared to CASES A and B.…”

Section: Combined Application Of Passive Control Methods-cases C D and Esupporting

confidence: 85%

“…The same module calculates beam crosssectional properties in the different components, considering, as input, the material structural properties, thickness and distribution of stresses over every cross-section. -A cost model that determines the cost of the full wind turbine system based on existing data available in the literature [17], as well as on empirical formulae, which specify certain cost parameters as a function of the component dimensions. -An optimization framework based on ready-made functions from the publicly available scipy library in Python [18].…”

Section: Multibody Servo-aero-elastic Analysis Tool Hgastmentioning

confidence: 99%

“…The cost estimation of the generator, the gearbox and the tower is derived through up-scaling [24] the data of a reference wind turbine of nominal power of 1.5 MW, which is described in detail in [23]. For detailed description and verification of the cost model, the reader is directed to [17].…”

Section: Wind Turbine Cost Modelmentioning

confidence: 99%

“…However, it is a choice that ensures that the optimum blade design is directly comparable to the reference blade. A cost model that determines the cost of the full wind turbine system based on existing data available in the literature [17], as well as on empirical formulae, which specify certain cost parameters as a function of the component dimensions. -An optimization framework based on ready-made functions from the publicly available scipy library in Python [18].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Wind Turbine Blade Design Optimization for Reduced LCoE, Focusing on Design-Driving Loads Due to Storm Conditions

Serafeim

Manolas²,

Riziotis³

et al. 2022

Fluids

View full text Add to dashboard Cite

Design modifications of the blade inner structure, targeted at reducing design-driving extreme loads due to storm conditions, are assessed in the present paper. Under survival wind speeds, the lack of sufficient aerodynamic damping in the edgewise direction is responsible for excessive stall-induced vibrations that usually drive wind turbine blade design loads. The modifications considered in the work are (i) a non-symmetric increase in the thickness of the uniaxial and tri-axial material on the suction and pressure side of the blade sections, (ii) a shift in the spar caps in opposite directions and (iii) the ply-angle re-orientation of the laminates on the spar caps. The first two design interventions aim at increasing the damping of the low-damped edgewise modes in the idling rotor, while the third aims at reducing the fatigue and ultimate loads in normal operation. The design parameters in the problem are determined on the basis of a multidisciplinary optimization (MDAO) process, which minimizes the levelized cost of energy (LCoE). The in-house integrated optimization tool employed in the present study combines: (i) a servo-aero-elastic analysis tool for calculating ultimate loads and power yield, (ii) a cross-sectional analysis tool for obtaining structural properties and stress distributions in the modified blades and (iii) a cost model of the overall wind turbine to evaluate the LCoE.

show abstract

Section: Combined Application Of Passive Control Methods-cases C D and Esupporting

confidence: 85%

Section: Multibody Servo-aero-elastic Analysis Tool Hgastmentioning

confidence: 99%

Section: Wind Turbine Cost Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Wind Turbine Blade Design Optimization for Reduced LCoE, Focusing on Design-Driving Loads Due to Storm Conditions

Serafeim

Manolas²,

Riziotis³

et al. 2022

Fluids

View full text Add to dashboard Cite

show abstract

“…In particular, the results obtained from the study of Reference [21] were used to identify the scaling rule tendency. Serafeim et al [23] proposed an MDAO approach to reduce the LCoE of the DTU-10 MW Reference Wind Turbine. A cost model for the entire wind turbine was implemented by combining existing models from the literature with open data.…”

Section: Introductionmentioning

confidence: 99%

Blade Design and Aerodynamic Performance Analysis of a 20 MW Wind Turbine for LCoE Reduction

Jang

Ryu

2023

Energies

View full text Add to dashboard Cite

The aim of this study is to develop a blade mass model that incorporates a low-induction rotor (LIR) and a low-specific power concept to reduce aerodynamic loads and lower the Levelized Cost of Energy (LCoE). This blade mass model replaces the traditional simple scaling rule and incorporates the concept of LCoE reduction, presenting not only the mass distribution in the blade span direction but also the stiffness distribution. In order to achieve the desired reduction in LCoE, we developed a mathematical model that expresses blade mass as a function of the axial induction factor, which influences the aerodynamic load on the blade. We used this model to determine geometries of various low-induction rotors for 20 MW class horizontal axis wind turbine, and to identify the axial induction factor that correlates with the lowest blade mass. The chord length and twist angle in the spanwise direction of the blade were determined using PROPID’s reverse design process, based on the specified axial induction factor. Since the low-induction concept is not aerodynamically optimal, a low-specific power design approach was also adopted. This involved increasing the blade length and shifting the power curve to the left. By doing so, the AEP is increased, directly contributing to a reduction in the LCoE. Mass per unit length of the blade was presented, reflecting the distribution of airfoil type, blade geometry, and shapes of internal structures such as spars and webs.

show abstract

Drivers for optimum sizing of wind turbines for offshore wind farms

Mehta,

Zaaijer,

von Terzi

2024

Wind Energ. Sci.

View full text Add to dashboard Cite

Abstract. Large-scale exploitation of offshore wind energy is deemed essential to provide its expected share to electricity needs of the future. To achieve the same, turbine and farm-level optimizations play a significant role. Over the past few years, the growth in the size of turbines has massively contributed to the reduction in costs. However, growing turbine sizes come with challenges in rotor design, turbine installation, supply chain, etc. It is, therefore, important to understand how to size wind turbines when minimizing the levelized cost of electricity (LCoE) of an offshore wind farm. Hence, this study looks at how the rated power and rotor diameter of a turbine affect various turbine and farm-level metrics and uses this information in order to identify the key design drivers and how their impact changes with setup. A multi-disciplinary design optimization and analysis (MDAO) framework is used to perform the analysis. The framework uses low-fidelity models that capture the core dependencies of the outputs on the design variables while also including the trade-offs between various disciplines of the offshore wind farm. The framework is used, not to estimate the LCoE or the optimum turbine size accurately, but to provide insights into various design drivers and trends. A baseline case, for a typical setup in the North Sea, is defined where LCoE is minimized for a given farm power and area constraint with the International Energy Agency 15 MW reference turbine as a starting point. It is found that the global optimum design, for this baseline case, is a turbine with a rated power of 16 MW and a rotor diameter of 236 m. This is already close to the state-of-the-art designs observed in the industry and close enough to the starting design to justify the applied scaling. A sensitivity study is also performed that identifies the design drivers and quantifies the impact of model uncertainties, technology/cost developments, varying farm design conditions, and different farm constraints on the optimum turbine design. To give an example, certain scenarios, like a change in the wind regime or the removal of farm power constraint, result in a significant shift in the scale of the optimum design and/or the specific power of the optimum design. Redesigning the turbine for these scenarios is found to result in an LCoE benefit of the order of 1 %–2 % over the already optimized baseline. The work presented shows how a simplified approach can be applied to a complex turbine sizing problem, which can also be extended to metrics beyond LCoE. It also gives insights into designers, project developers, and policy makers as to how their decision may impact the optimum turbine scale.

show abstract

Multidisciplinary aeroelastic optimization of a 10MW-scale wind turbine rotor targeting to reduced LCoE

Cited by 3 publications

References 15 publications

Wind Turbine Blade Design Optimization for Reduced LCoE, Focusing on Design-Driving Loads Due to Storm Conditions

Wind Turbine Blade Design Optimization for Reduced LCoE, Focusing on Design-Driving Loads Due to Storm Conditions

Blade Design and Aerodynamic Performance Analysis of a 20 MW Wind Turbine for LCoE Reduction

Drivers for optimum sizing of wind turbines for offshore wind farms

Contact Info

Product

Resources

About