A comprehensive comparative investigation of the Lifting Line Theory and Blade Element Momentum Theory applied to small wind turbines

Small wind turbines are adequate for electricity generation in isolated areas to promote local expansion of commercial activities and social inclusion. Blade element momentum (BEM) method is usually used for performance prediction, but generally produces overestimated predictions since the wake effects are not precisely accounted for. Lifting line theory (LLT) can represent the blade and wake effects more precisely. In the present investigation the two methods are analyzed and their predictions of the aerodynamic performance of small wind turbines are compared. Conducted simulations showed a computational time of about 149.32 s for the Gottingen GO 398 based rotor simulated by the BEM and 1007.7 s for simulation by the LLT. The analysis of the power coefficient showed a maximum difference between the predictions of the two methods of about 4.4% in the case of Gottingen GO 398 airfoil based rotor and 6.3% for simulations of the Joukowski J 0021 airfoil. In the case of the annual energy production a difference of 2.35% is found between the predictions of the two methods. The effects of the blade geometrical variants such as twist angle and chord distributions increase the numerical deviations between the two methods due to the big number of iterations in the case of LLT. The cases analyzed showed deviations between 3.4% and 4.1%. As a whole, the results showed good performance of both methods; however the lifting line theory provides more precise results and more information on the local flow over the rotor blades.

Power Maximization and Turbulence Intensity Management through Axial Induction-Based Optimization and Efficient Static Turbine Deployment

Layout optimization is capable of increasing turbine density and reducing wake effects in wind plants. However, such optimized layouts do not guarantee fixed T-2-T distances in any direction and would be disadvantageous if reduction in computational costs due to turbine set-point updates is also a priority. Regular turbine layouts are considered basic because turbine coordinates can be determined intuitively without the application of any optimization algorithms. However, such layouts can be used to intentionally create directions of large T-2-T distances, hence, achieve the gains of standard/non-optimized operations in these directions, while also having close T-2-T distances in other directions from which the gains of optimized operations can be enjoyed. In this study, a regular hexagonal turbine layout is used to deploy turbines within a fixed area dimension, and a turbulence intensity-constrained axial induction-based plant-wide optimization is carried out using particle swarm, artificial bee colony, and differential evolution optimization techniques. Optimized plant power for three close turbine deployments (4D, 5D, and 6D) are compared to a non-optimized 7D deployment using three mean wind inflows. Results suggest that a plant power increase of up to 37% is possible with a 4D deployment, with this increment decreasing as deployment distance increases and as mean wind inflow increases.

Wind Farm Area Shape Optimization Using Newly Developed Multi-Objective Evolutionary Algorithms

In recent years, wind farm layout optimization (WFLO) has been extendedly developed to address the minimization of turbine wake effects in a wind farm. Considering that increasing the degrees of freedom in the decision space can lead to more efficient solutions in an optimization problem, in this work the WFLO problem that grants total freedom to the wind farm area shape is addressed for the first time. We apply multi-objective optimization with the power output (PO) and the electricity cable length (CL) as objective functions in Horns Rev I (Denmark) via 13 different genetic algorithms: a traditionally used algorithm, a newly developed algorithm, and 11 hybridizations resulted from the two. Turbine wakes and their interactions in the wind farm are computed through the in-house Gaussian wake model. Results show that several of the new algorithms outperform NSGA-II. Length-unconstrained layouts provide up to 5.9% PO improvements against the baseline. When limited to 20 km long, the obtained layouts provide up to 2.4% PO increase and 62% CL decrease. These improvements are respectively 10 and 3 times bigger than previous results obtained with the fixed area. When deriving a localized utility function, the cost of energy is reduced up to 2.7% against the baseline.

The Effect of Wind Energy on Microclimate: Lessons Learnt from a CFD Modelling Approach in the Case Study of Chios Island

During the last three decades, rapid growth of wind energy has led to questions regarding the possible impacts of wind farms on local weather and microclimates. Physically, the increased turbulence due to the wind turbine operation affects the mixing processes, may slightly disturb the pressure and temperature distributions downstream of wind farms and may have an impact on natural ecosystems such as the famous mastic tree population located on the island of Chios in the North Aegean Sea. This study explores the wind farms and their wake effects downstream with a particular focus on the effect on the southern part of the island where the mastic trees cultivation is located. The analysis is carried out with the use of the commercial CFD code ANSYS Fluent. Steady state computations of full 3D Navier–Stokes equations, using the k-ε turbulence closure scheme are carried out. The development of the multiple wake effects of the wind farms and their propagation downstream is examined under low and high turbulence intensities. Results clearly indicate that for both test cases there is no impact to the local microclimate and to the mastic Tree population.