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This study examines the impact of bias correction on the prediction of offshore wind power ramp events (WPREs) using NOAA's High-Resolution Rapid Refresh (HRRR) model. The analysis focuses on New England's offshore region, using Light Detection and Ranging (LiDAR) wind observations and the power curve derived from the nearby Block Island wind farm as a reference. The study characterizes bias in terms of amplitude and phase, finding a 30-min systematic lag error and a marginal underestimation of high wind speeds. Perfect bias correction treatments including both the amplitude and the phase are applied to HRRR wind speed, and their impact on WPREs is assessed using categorical performance measures. A large discrepancy in the number of cases with HRRR having much fewer cases than the reference is found, which is attributed to the model's temporal and spatial resolution. The results show that the amplitude bias correction has a marginal impact on reducing frequency bias, while the dynamic time warping (DTW) used in the phase bias correction shows promising results but limited performance. The study concludes that phase bias correction can be a feasible option to reduce bias in WPREs in offshore power systems. [ABSTRACT FROM AUTHOR]

Wind farm layout optimization (WFLO) can reduce the turbulence intensity on the wind turbines while maximizing power generation. To accurately compute turbulence intensity distribution in the wind farm, we utilize the innovative three-dimensional cosine-shaped turbulence intensity (3D-COTI) model. By integrating the three-dimensional cosine-shaped linear entrainment (3DCLE) wake model previously developed by the authors, we introduce a new multiple wind turbine wake model (3DCLE-M). This model considers the wake superposition effect, enabling precise calculation of power generation. Building upon the 3D-COTI and 3DCLE-M models, we propose a multi-objective WFLO approach to maximize power generation and minimize streamwise turbulence intensity in the wind-turbine-swept area. The results indicate that under maximized power generation, this approach significantly reduces turbulence intensity and its non-uniform distribution in the wind-turbine-swept area. In two ideal cases, the maximum turbulence intensity in all wind-turbine-swept areas of the optimized layout is 25 % and 3.8 % lower than that of the selected benchmark layout. By optimizing the layout of the Horns Rev offshore wind farm, a new layout can be obtained with increased total power output by 1.58 % and the reduced total duration of high-fatigue-load wind turbines under high turbulence intensity by 25 % compared to the benchmark. [ABSTRACT FROM AUTHOR]

This study evaluates available analytical wake models for flow prediction inside and downstream of wind farms of different sizes and layouts using large-eddy simulation (LES), and introduces an enhanced analytical framework. All the tested analytical wake models, based on the superposition of individual turbine wakes, systematically overestimate the wake recovery both inside and downstream of the wind farms. The results indicate that the overestimation is linked to the assumption of linear or quasilinear wake expansion, which does not hold at large downstream distances. To address this issue, an enhanced analytical framework is proposed based on the extension of a recently developed streamwise scaling model for single wakes that eliminates the need for the linear wake expansion assumption. Since the new framework computes the wake expansion based on the near-wake length and the local turbulence intensity, different methods for their calculation and the superposition of turbulence intensity within wind farms are evaluated against the LES data. The identified best methods are incorporated into the new analytical framework. The proposed framework consistently yields more accurate power estimates and flow predictions inside and downstream of finite-size wind farms with different sizes and configurations. • Available analytical wake models overestimate turbine wake recovery in wind farms. • A new analytical framework is proposed to relax the linear wake growth assumption. • The new model extends the streamwise-scaling method to wind farms. • The new model improves wind speed predictions inside and downstream of wind farms. • Recommendations are given for the prediction of turbulence intensity in wind farms. [ABSTRACT FROM AUTHOR]

This paper examines metocean data from NORA3, a state-of-the-art wind and wave hindcast dataset for Northern Europe. Two offshore Norwegian areas, Utsira Nord (UN) and Sørlige Nordsjø II (SN2), are investigated. Both areas offer significant potential for the offshore wind sector. UN is situated in deep-sea water, suitable for floating offshore wind turbines. In contrast, SN2 lies in intermediate waters and ranks among the North Sea's most promising regions allocated for offshore wind. Data from NORA3, originally on a 3-km resolution grid, are resampled into unstructured grids spanning from 1982 to 2022. This refined dataset offers a climatology time scale with superior spatial and temporal resolution compared to most other hindcast and reanalysis databases. The study examines mean wind speed and direction across seven levels, ranging from 10 m to 750 m above the surface. Analyses of extreme wind and wave conditions have been conducted. Results reveal that UN experiences higher extreme wave heights than SN2 while the extreme wind speeds may be substantially larger at SN2 than UN. Moreover, this study establishes joint distribution models that encompass several parameters, including mean wind speed, significant wave height, wave spectral peak period, and direction difference between wind and waves. Thus, this metocean data is valuable for designing and analyzing floating wind farms over their lifecycles. [Display omitted] • Two sites with high potential for offshore wind farms in Norway are selected. • The climatology of both sites is studied using the NORA3 database. • Key metocean parameters are studied with a focus on temporal and spatial variations. • Extreme conditions and joint distributions of selected metocean parameters are studied. • The potential of the dataset is illustrated for operation of offshore wind farms. [ABSTRACT FROM AUTHOR]

The determinantal noise pollution from wind turbines has constricted the acceptance of wind energy, posing health and environmental concerns. Existing solutions often compromise wind turbine efficiency or wind farm output, limiting the full utilization of wind energy. To address this challenge, we propose a novel method of effectively employing acoustic metamaterials (AMMs) inside a wind farm that leverages phase cancellation for noise suppression and energy enhancement. Through a combined phase retrieval, noise propagation model, and genetic algorithm, we determine the optimal layout and structural design of these AMMs inside a wind farm to minimize noise. We present two AMM designs, full-walled and segmented, and demonstrate their effectiveness for both single-turbine and multiple-turbine layout scenarios by achieving 91% (in Pa) and 10%–68% (in Pa) noise reduction, respectively, compared to reference layouts. Furthermore, we exhibit the AMM's impact in enhancing the energy throughput of wind farms by installing an additional wind turbine in a noise-restricted area of an existing AMM-equipped wind farm while maintaining a 70% (in Pa) reduction in noise levels. This approach paves the way for constructing wind farms near urban-suburban areas, complying with landscaping and visual government policies, and securing community acceptance towards sustainable power generation systems. [ABSTRACT FROM AUTHOR]

In existing wind farms, the overall power output can be increased through yaw control. However, the cooperative control of start/stop, yaw and turbines positions is often overlooked, leading to wake superposition to downstream wind turbines and suboptimal power output. This paper proposes a synchronized optimized method that considers start/stop, yaw and turbines positions control based on a three-dimensional wake model and yaw flow superposition model. The objective function of the proposed strategy is to maximize the power output of the Chapman Ranch (CR) wind farm. Four cases are considered: start-stop, yaw control, start-stop & yaw control and start-stop & yaw & turbines positions control. The particle swarm algorithm is introduced to optimize the wind farm layout. According to the results, considering start-stop, yaw and turbines positions optimization can not only increase the annual power output of the wind farm by 8.85 %, but also avoid the colliding wake in the CR wind farm. However, the other three cases will cause colliding wake in some fields of the CR wind farm. This study provides important guidance on improving the overall power output of existing wind farms. [ABSTRACT FROM AUTHOR]

This work studies the power density (PD) optimization in wind farms, and its sensitivity to the available area size. A novel genetic algorithm (PDGA) is introduced, which optimizes PD and the turbine layout, by self-adapting to the PD and to the solutions diversity. PDGA uses the levelized cost of energy (LCOE) as cost function, which in turn employs the EPFL analytical wake model to derive the power output. For the baseline area size, PDGA reduces 2.25% the original LCOE, 2.6 times more than optimizing with constant PD. PDGA-driven solutions provide 11% and 6% LCOE reductions against the default layout for the smallest (6.4 km2) and largest (386 km2) scaled wind farm areas, respectively. Specially relevant for the industry, PDGA solutions depict convex fronts for area vs. LCOE or vs. PD, which allows determining the required area or turbine number given a target LCOE. Unlike default layouts, optimized ones reveal a linear relationship between LCOE and PD. The mean turbine spacing tends to 8-9D for very large areas. The economics-optimized PDs are below the estimated PD available in the atmosphere. This work is limited to a simplified, offshore wind climatology, a specific wind turbine model, and the LCOE specifications used herein. • Power density optimization vs. the wind farm area size is tackled for the first time. • A new algorithm especially oriented to power density (PD) optimization is introduced. • A 2.6 times larger LCOE reduction is attained compared to a PD-fixed optimization. • PD-optimized wind farms reveal a linear relationship between PD and LCOE. • Obtained PD's tend to 8-9D turbine distances, and are below the atmospheric PD limit. [ABSTRACT FROM AUTHOR]