Variable Speed Wind Research Articles

Predicting the seasonal dynamics of Dalbulus maidis (Hemiptera: Cicadellidae) in corn using artificial neural networks.

This study addresses the challenge of predicting Dalbulus maidis (DeLong & Wolcott) (Hemiptera: Cicadellidae) density in cornfields by developing an artificial neural network (ANN). Over two years, we collected data on meteorological variables (atmospheric pressure, air temperature, dew point, rainfall, relative humidity, solar irradiance, and wind speed), plant age, and density of D. maidis in cornfields located in two Brazilian biomes (Atlantic Forest and Brazilian Tropical Savannah). Out of 1056 ANNs tested, the neural network featuring a 30-day time lag, six neurons, logistic activation, and resilient propagation demonstrated the lowest root mean squared error (0.057) and a high correlation (0.919) with observed D. maidis densities. This ANN exhibited an goodness of fit in low-density (Atlantic Forest) and high-density (Brazilian Tropical Savannah) scenarios for D. maidis. Critical factors influencing D. maidis seasonal dynamics, including corn plant age, rainfall, average air temperature, and relative humidity, were identified. This study highlights the potential of the ANN as a promising tool for precise predictions of pest seasonal dynamics, positioning it as a valuable asset for integrated pest management programs targeting D. maidis.

Gradient Nonwettability Controls Water-Film Flowing Features.

The flow of the water film on solid surface depended on the film Reynolds number and wind speed. Moreover, environmental factors had an impact on the flow process. This study explored how surface wettability impacts the stability and detachment of water film under varying conditions. Superhydrophobic surface played a critical role in speeding up the detachment of water film, especially under conditions of strong wind and specific flow dynamics, such as a wind speed of 19 m/s and a Reynolds number of 83. The efficiency in water removal was due to a combination of forces: capillary action, which pulls the water into smaller areas, reduced surface tension, and decreased adhesion between the liquid and surface. These factors worked together to shrink the area of contact, allowing the film to break and detach more easily. The findings suggested that enhancing nonwettability can improve water removal efficiency, with potential applications in fields like aerospace. Furthermore, additional trials across varying wind speeds and Reynolds numbers consistently supported the superior performance of superhydrophobic surface in driving rapid water-film detachment. To advance this effect, a gradient nonwetting surface was introduced, specifically engineered within the superhydrophobic regime, to amplify the velocity of film separation. This design innovation leveraged variations in surface energy across the gradient, which fostered directional water-film movement and accelerated detachment. A comprehensive analysis of the underlying physical mechanisms further elucidated its potential in enhancing water-shedding applications across a range of environmental conditions.

Power regulation of a wind farm through flexible operation of turbines using predictive control

Wind farms are becoming larger, presenting opportunities to improve their efficiency and effectiveness. Although wind farm control can be used to adjust a wind farm’s power output to meet grid requirements, its development is restricted by the limited flexibility of wind turbine operation. Wind turbines are typically designed to operate at the power output determined by the wind speed, making it difficult to adjust their power output quickly and easily in response to changes in grid demand. A new approach to wind farm control that provides full flexibility for both wind farms and turbines is proposed. This method adjusts the wind farm’s power production using predictive control of each turbine to meet the grid demands. The power generated by each turbine is adjusted to achieve this, keeping fluctuation in the wind farm power output low. A wind farm simulation is conducted under varying wind speeds using the discretized C MEX Matlab/SIMULINKR○ model of the 5 MW Supergen turbine and the wind turbine model from NREL. The results demonstrate that the wind farm power output tracks a reference value that can be set by the grid. The results are also analyzed on the torque/speed plane, and they indicate that the turbines operate within their specified safe operating zones under both normal and gusty wind operating conditions at the same time.

Impact of boundary layer parameterizations on simulated seasonal meteorology over North-East India

This study evaluated the accuracy of six planetary boundary layer (PBL) parameterization schemes in simulating two different seasons of pre-monsoon and monsoon in India's North-East region through the Weather Research and Forecasting (WRF) model. Twelve one-month simulations were conducted with the PBL schemes, six each for April (pre-monsoon) and July (monsoon), and the model outputs were compared against observations. Three non-local schemes, Asymmetric Convective Model (ACM2), Yonsei University (YSU), Shin-Hong (HONG), and three local schemes, Quasi Normal Scale Elimination (QNSE), Mellor Yamada Janjic (MYJ) and Mellor Yamada Nakanishi Nino (MYNN3), were tested. The meteorological variables of temperature, relative humidity (RH), wind speed, wind direction, and rainfall were evaluated, and the performance of each scheme for each meteorological variable is reported. The 2m temperature (T2) variable was well simulated by ACM2, MYJ in April, and YSU in July, while MYNN3 best simulated the 2m RH (RH2) during both seasons. 10m wind speed (WS10) and directions (WD10) were better simulated by MYNN3, HONG and YSU. HONG also best-simulated rainfall in April and MYJ in July. April and July being rainfall periods, an analysis of the schemes’ simulated rainfall frequency was also carried out. Moreover, the PBL schemes were also ranked, considering their combined performance with all the above meteorological parameters. While considering both the seasons and all meteorological variables, the scale-aware scheme, HONG, was the best scheme and can be used to simulate both seasons. Additionally, an in-depth analysis of surface and atmospheric parameters was also carried out to reason the simulated meteorology. QNSE expends the highest amount of its surface energy through surface evaporation, leading to the lowest surface skin temperature and T2 predictions. In contrast, MYNN3 produced the lowest mixing, which caused the moistest boundary layer, highest RH, cloud cover, and highly overestimated rainfall. Besides evaluation, which will help to choose a suitable PBL scheme for weather predictions in this region, this study also identifies the characteristics and deficiencies of PBL and surface layer schemes for improvement.

Assesment of wind potential energy of four major cities in Cote d’ivoire using satellite data from 2015 to 2022

In Côte d’Ivoire, the use of renewable energy is becoming a major challenge in the context of climate change and global warming. Therefore, the aim of this study is to characterize the wind profile for potential energy in Abidjan, Man, Bouaké and Korhogo of Côte d’Ivoire using satellite data. Wind speed and direction data from Copernicus Centre from 2015 to 2022 are used for these major cities. The frequency method is used to build wind rose and power density formula to estimate the potential energy for each city, at ground level (10 m) and in particular level at 100 m and 500 m. The results showed a slightly wind speed and direction variation from 2015 to 2022. This suggests that wind has a seasonal (wet and dry) evolution over the year. However, wind speeds increase for each city with the altitude. The higher average wind speed is observed in Abidjan with a power density of 58 W/m2. The lower wind speed and power density are observed in Man, Bouaké and Korhogo. So, the coastline of Côte d’Ivoire has the higher wind energy resource than the inland regions of the country.

Probabilistic modeling and optimization of microgrids with EV parking lots and dispersed generation

Distributed generation sources provide self-governing power during outages, making microgrids and islanded distribution networks vital for service endurance, superior power quality, reliability, and operative efficiency. However, microgrids structure are difficult to control, particularly in islanded mode where no main power source exists if the main grid fails. Fast responses from discrete generation sources using power electronics can undermine the grid during faults or normal operations without proper regulations. The double-fed induction generator (DFIG) has become the preferred wind turbine generator owing to its low cost and flexibility to varying wind speeds. This paper presents a probabilistic scheduling for day-ahead microgrid programming that includes EV parking lots and dispersed generation resources. The microgrid works in both normal and islanded modes depending on main grid conditions. The uncertainty in EV parking lot usage is modeled hourly using the Z-number method, while wind and solar generation, market prices, and loads are modeled using the Monte Carlo method. Scenario-based incidents in the upstream grid that lead to microgrid islanding are considered, focusing on the time and duration of impact. The optimization model accounts for uncertainty, EV charging/discharging, and operational costs under normal and fault conditions. The fault ride-through (FRT) method for maintaining DFIG stability in islanded microgrids are proposed. In this technique stabilizes terminal voltage during faults by employing a resistor in series with the DFIG stator, enhancing voltage stability and FRT capability. Without these methods, the DFIG may lose stability after clearing transient errors, risking generator loss and threatening microgrid stability, particularly in islanded mode. The effectiveness of these control and protection strategies is validated through comprehensive simulations in MATLAB.

Power control of an autonomous wind energy conversion system based on a permanent magnet synchronous generator with integrated pumping storage

Wind energy plays a crucial role as a renewable source for electricity generation, especially in remote or isolated regions without access to the main power grid. The intermittent characteristics of wind energy make it essential to incorporate energy storage solutions to guarantee a consistent power supply. This study introduces the design, modeling, and control mechanisms of a self-sufficient wind energy conversion system (WECS) that utilizes a Permanent magnet synchronous generator (PMSG) in conjunction with a Water pumping storage station (WPS). The system employs Optimal torque control (OTC) to maximize power extraction from the wind turbine, achieving a peak power coefficient (Cp) of 0.43. A vector control strategy is applied to the PMSG, maintaining the DC bus voltage at a regulated 465 V for stable system operation. The integrated WPS operates in both motor and generator modes, depending on the excess or shortfall of generated wind energy relative to load demand. In generator mode, the WPS supplements power when wind speeds are insufficient, while in motor mode, it stores excess energy by pumping water to an upper reservoir. Simulation results, conducted in MATLAB/Simulink, show that the system efficiently tracks maximum power points and regulates key parameters. For instance, the PMSG successfully maintains the reference quadrature current, achieving optimal torque and power output. The system’s response under varying wind speeds, with an average wind speed of 8 m/s, demonstrates that the generator speed closely follows turbine speed without a gearbox, leading to efficient power conversion. The results confirm the flexibility and robustness of the control strategies, ensuring continuous power delivery to the load. This makes the system a feasible solution for isolated, off-grid applications, contributing to advancements in renewable energy technologies and autonomous power generation systems.

Analysis of wind resources in Senegal using 100-meter wind data from ERA5 reanalysis

This study conducts a climatological analysis of wind resources in Senegal, utilizing wind data at 100 m from the ERA5 reanalysis. The results reveal spatio-temporal variations in wind speeds and directions, while also identifying favorable areas for the establishment of wind farms. Wind speeds range from 3 to 9 m/s, with increasing gradients in the north-south and west-east directions. Maximum speeds are observed in winter and spring, especially from December to May. A distinct daily pattern is observed, with the highest speeds at night and lower speeds during the day. On average, nighttime wind speeds are 21 % higher than daytime speeds. Most of the time, the wind blows from optimal directions, ranging from northwest to northeast. The study successfully identifies three particularly suitable zones for wind farm installation: the northwest coast between Saint-Louis and Dakar, the smaller coast between Dakar and Mbour, and the extreme north of the country bordering Mauritania between Saint-Louis and Matam. Furthermore, a long-term analysis highlights evolving wind trends. Trends and anomalies indicate a 7 % increase in winds at 100 m. In conclusion, this study provides a precise assessment of wind resources in Senegal, emphasizing that the most favorable zones are along the coast and in regions near the desert. This information is crucial for the sustainable development of wind energy in the region.

SSO optimized FOFPID regulator design for performance enhancement of doubly fed induction generator based wind turbine system.

A wind turbine system (WTS) is a highly coupled and nonlinear system where the output power depends upon highly uncertain wind speed. Therefore, the quality of produced power becomes a challenging problem for researchers. Direct Vector Control (DVC) is a powerful and widely utilized power control strategy to deal with winds that vary rapidly and randomly. As a result, this article employed the newly developed Social Spider Optimization (SSO) technique to optimize the design parameters of Fractional-Order Fuzzy Proportional-Integral with Derivative (FOFPID) regulator to maintain the output power of the studied DFIG-based WTS at the rated value under dynamic wind conditions. The suggested FOFPID controller integrates the capabilities of the Fuzzy intelligent regulator and the Fractional-Order controller, enhancing DFIG current control while allowing independent control of active and reactive power. The approach is incorporated within the DVC strategy of the DFIG's rotor-side converter (RSC), replacing the conventional Proportional-Integral (PI) regulator in the internal current loops. Extensive performance evaluations are conducted under various operating conditions, including active power reference changes, parameter uncertainties, and rapid wind speed variations. Comparative analyses with SSO-optimized PID and Fuzzy regulators show that the FOFPID regulator performs better in terms of maximum overshoot, extreme undershoot, settling time, Total Harmonic Distortion (THD), and Weighted Total Harmonic Distortion (WTHD). The suggested FOFPID regulator also displays stronger robustness against parameters mismatch and weather change than other regulator architectures.

High-resolution assessment of wind energy potential in the Hami region of Northwestern China

Abstract Wind energy plays a pivotal role in the global effort to mitigate climate change, with China emerging as a leader in renewable energy adoption. The Hami region in northwestern China stands out as a crucial area for wind power development, given its substantial wind resources and strategic importance in China’s energy landscape. However, existing studies on wind energy potential vary widely and involve large uncertainties due to sparse measurements and coarse resolution, highlighting the need for more precise assessments to guide policy decisions and optimize energy utilization. This study leverages high-resolution ERA5 reanalysis data and advanced wind turbine technology to assess wind energy potential in the Hami region, taking into account factors including wind speed patterns, turbine heights, and geographical constraints. The comparison with in-situ data demonstrated that high-resolution ERA5 reanalyzed wind speeds enable to capture multi-year wind speed variations in this region. We find substantial potential for wind energy in Hami, with energy densities exceeding 200 W m−2 in the high-potential wind zones. Importantly, this study identifies a new high-potential area in eastern Hami, termed the East Wind Zone. Our high-resolution assessment of wind energy potential at different heights over the past two decades reveals long-term trends and seasonal variations. Increasing the hub height from 95 m to 140 m raises the average wind power generation potential across Hami by 31.3 GWh yr−1. Our findings highlight the importance of strategic wind farm placement to maximize renewable energy output and provide insights for policy and industry, supporting China’s renewable energy goals.

Study on Windage Response Suppression of Large-Span Transmission Tower-Line System Using a Spring-Pendulum Dynamic Vibration Absorber

Insulators and conductors in transmission line systems are susceptible to wind-induced movements, especially when insulators are closely positioned to transmission towers, potentially leading to electrical discharge. To address this issue, this study proposes a spring-pendulum dynamic vibration absorber (SPDVA) for windage suppression in transmission line systems. Using a finite element model based on an actual transmission line project, the study investigates SPDVA’s effectiveness in mitigating windage responses under varying wind speeds. The analysis considers mass and stiffness parameters to elucidate SPDVA’s suppression mechanism. The results indicate that SPDVA outperforms the additional heavy hammer method in suppressing insulator windage responses and also affects the peak acceleration response at the insulator’s bottom. Increasing stiffness initially enhances SPDVA’s suppression effectiveness on both peak and mean square deviation windage angle responses, peaking at 10 kN/m stiffness, while increasing the mass of the mass block also enhances suppression effectiveness.

Application of deep forest algorithm incorporating seasonality and temporal correlation for wind speed prediction in offshore wind farm

Accurate prediction of wind speed is a prerequisite for the safe and accurate operation of wind power generation, however, WRF models typically do not produce sufficiently accurate wind speed predictions. This study proposed a Seasonal and Temporal Correlation - Deep Forest (STC-DF) model for offshore wind speed prediction. Different from traditional methods, the STC-DF model takes the advantages of the deep forest algorithm to automatically learn complex feature interactions without manual feature engineering. The model is designed to capture the seasonal and temporal characteristics of wind speed variations. To test the effectiveness of the proposed method, we applied the trained STC-DF model to an offshore wind farm in Hainan Province, China. Seven days of data from each season were selected for testing. The results show that the STC-DF model can effectively reduce the error caused by WRF forecast. The error index of the corrected wind speed reduced more than 40%, the accuracy of wind speed forecast increased 15%. And the method passed the multi-model comparison test and robustness experiment. These research results show that the STC-DF model has strong versatility and good correction ability, and is suitable for wind speed forecasting in different regions, which is a feasible method to improve the reliability of offshore wind power generation.

Collocating wind data: A case study on the verification of the CERRA dataset

Abstract Wind resource assessment often relies on reanalysis data or meso-scale models due to the challenges and costs associated with on-site measurement campaigns, especially in offshore locations. While these datasets offer extensive spatial coverage, they often provide the desired parameters on a coarse grid only. The spatial averaging over the grid and the low frequency of the outputs hinders the model’s ability to capture the short-term wind speed variability and events with a time scale of a few minutes to a few dozen of minutes. In this paper we investigate the temporal scales of the wind events resolved by the Copernicus European Regional Re-Analysis (CERRA) dataset, ERA5, and an in-house regional down-scaling of ERA5 using the WRF model by comparing them against measurements. We propose a method to collocate the two sources of wind data (measured and modeled), focusing on the verification of CERRA with respect to first and second-order statistics in the North Sea. The results highlight the superiority of CERRA compared to ERA5 in estimating the wind speed distribution, improving the 95th percentile error from -0.85 to -0.19 ms−1 and the average bias from -0.286 to -0.004 m s−1 across all locations. The wind speed change over one hour is underestimated by both CERRA and ERA5 when collocated with 10-minute measurements at the full hour. However, hourly averaged measurements align well with CERRA’s wind speed variation predictions, while an averaging window of 3.5 hours is needed to align with ERA5’s hourly wind speed changes. The results showed that the synthetic wind speeds exhibit optimal correlation with the measurements when the measurements are averaged over 4.5 to 7 hours, depending on the modeled dataset. This time scale corresponds to a length scale of 160-250 km, assuming a mean wind speed of 10 m s−1, which falls within the meso-scale range. The study contributes to the understanding of model validation in offshore wind energy studies, with the potential to enhance wind resource assessment calculations and improve the results of long-term extrapolations.

New genetic gray wolf optimizer with a random selective mutation for wind farm layout optimization

The wake effect is a relevant factor in determining the optimal distribution of wind turbines within the boundaries of a wind farm. This reduces the incident wind speed on downstream wind turbines, which results in a decrease in energy production for the wind farm. This paper proposes a novel approach for optimizing the distribution of wind turbines using a new Genetic Gray Wolf Optimizer (GGWO). The GGWO employs a teamwork model inspired by wolf prey hunting, guided by four leaders: Alpha, Beta, Delta, and Omicron wolves, each with different hierarchical weights. To improve the competitiveness of the wolves, GGWO utilizes genetic algorithm operators such as crossover blending, normal mutation, and a new genetic operator called Random Selective Mutation (RSM), which improves solution search efficiency. The proposed GGWO is compared to other algorithms such as Gray Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Ant Colony Optimization (ACO). The studies examine varying wind speeds in magnitude and direction throughout the year, as well as different wind farm boundaries. The outcomes show that GGWO successfully identifies the ideal locations for wind turbines, scoring better scores in terms of total simulation duration and annual energy generation for the wind farm. It surpasses the performance of GWO, ABC, and PSO algorithms and exhibits comparable competitiveness with more intricate algorithms like ACO.

Evaluation of forecasted wind speed at turbine hub height and wind ramps by five NWP models with observations from 262 wind farms over China

AbstractAccurate wind speed forecasts are essential for optimizing the efficiency of wind energy operations. Currently, there is limited research on nationwide assessment of predictive performance in multiple numerical weather prediction (NWP) models for wind speed at turbine hub height over China, especially concerning wind ramp events. Utilizing observed measurements from 262 wind farms, this study evaluated the performance of five NWP models in forecasting the mean state and spatiotemporal variations of wind speed as well as wind ramps. The results indicated that the European Center for Medium‐Range Weather Forecast Integrated Forecasting System (ECMWF–IFS) performed the best in forecasting climatological wind speed with a temporal correlation coefficient (TCC) of 0.74 and root mean square error (RMSE) of 2.34 m s−1. Although not widely utilized in China, the model from Meteo‐France (MF–ARPEGE) showed promising potential for wind energy forecasting with a TCC of 0.72 and RMSE of 2.45 m s−1. In terms of temporal variations of wind speed, all the models could reasonably predict the seasonal variations of wind speed, whereas only three NWP models were able to capture the characteristics of the observed diurnal variation. An error decomposition analysis further revealed that the primary source of predicted error for wind speed was the sequence error component (SEQU), indicating the model errors were mainly attributed from the temporal inconsistency between forecasts and observations. Furthermore, the occurrences of wind ramps were generally underestimated by NWP models, while this shortcoming can be partly overcome by improving the time resolution of NWP models.

Construction of a ballistic model of the motion of uncontrolled cargo during its autonomous high-precision drop from a fixed-wing unmanned aerial vehicle

The object of this study is the process related to the fall of an uncontrolled cargo from a height of 400–600 meters relative to sea level in an air environment under the influence of gravity, air drag, and wind in the presence of an initial air speed of about 20 m/s. The study solves the task to build a ballistic model of the movement of an unguided cargo during autonomous high-precision dropping from an unmanned aerial vehicle of aircraft type. A system of equations was derived that explicitly describes the dependence of the cargo's travel speed and coordinates on time and takes into account the effect of gravity, air drag, and the influence of wind. The scope of application of the equations corresponds to drop heights of up to 400 m relative to the surface of the earth and the initial horizontal speed of the cargo up to 20 m/s. The resulting equations were analyzed using an example of a spherical load weighing 10 kg and the largest cross-sectional area of 7.1·10-2 m2 when it falls from heights of 200, 300, and 400 m relative to the earth's surface. In the absence of wind, the horizontal component of the load's speed at the moment of falling is ≈(13–15) m/s, and the vertical component is ≈(50–60) m/s. At the same time, the horizontal displacement of the load under the conditions of a weak crosswind can reach ≈(150–220) m. With a vertical wind speed profile, the equivalent constant wind speed can be determined, resulting in the same effect on the load as a variable speed wind. An algorithm for determining the point of unloading the cargo has been proposed. The cargo delivery error has been evaluated. The most important parameters are the flight time of the cargo and the drop height. In order to achieve a hit accuracy of ±5 m, an error in determining the time of the fall of the load is not more than ≈0.16 s, and an error in determining the height of the drop is not more than ±8 m

Chaotic behavior and multistability of a tree trunk structure driven by self-and parametric hydrodynamic forces

Abstract This study investigates the chaotic behavior of a tree trunk under dynamic wind loads, focusing on control strategies and multistability. We consider time-varying wind speeds and analyze a specific case where hydrodynamic drag forces align with the flow velocity. The stability of the model’s equilibrium points is analyzed theoretically and numerically. Melnikov’s method is employed to identify conditions for homoclinic bifurcation. Numerical simulations employing basin of attraction confirm the analytical predictions. Our findings show a decrease in the threshold for chaos with increasing amplitudes of external excitation, damping coefficient, and parametric damping. The global dynamics are explored numerically using a fourth-order Runge-Kutta method. When solely subjected to external excitation, the system exhibits period doubling bifurcations, multiperiodic oscillations, mixed-mode oscillations, and chaos. Conversely, with self- and parametric drag forces, the system displays reverse periodic bifurcations, periodic bubbling oscillations, antimonotonicity, transient chaos, and chaos. Poincaré maps analyze the geometric structure of chaotic attractors, revealing a strong influence of dimensionless drag parameters. These parameters can be manipulated to control or eliminate chaos. Furthermore, the system exhibits multistability, with coexisting attractors. Beyond its application in protecting infrastructure from wind damage, this research can contribute to ecological balance by improving our understanding of tree wind resistance.

Application of an Optimal Fractional-Order Controller for a Standalone (Wind/Photovoltaic) Microgrid Utilizing Hybrid Storage (Battery/Ultracapacitor) System

Nowadays, standalone microgrids that make use of renewable energy sources have gained great interest. They provide a viable solution for rural electrification and decrease the burden on the utility grid. However, because standalone microgrids are nonlinear and time-varying, controlling and managing their energy can be difficult. A fractional-order proportional integral (FOPI) controller was proposed in this study to enhance a standalone microgrid’s energy management and performance. An ultra-capacitor (UC) and a battery, called a hybrid energy storage scheme, were employed as the microgrid’s energy storage system. The microgrid was primarily powered by solar and wind power. To achieve optimal performance, the FOPI’s parameters were ideally generated using the gorilla troop optimization (GTO) technique. The FOPI controller’s performance was contrasted with a conventional PI controller in terms of variations in load power, wind speed, and solar insolation. The microgrid was modeled and simulated using MATLAB/Simulink software R2023a 23.1. The results indicate that, in comparison to the traditional PI controller, the proposed FOPI controller significantly improved the microgrid’s transient performance. The load voltage and frequency were maintained constant against the least amount of disturbance despite variations in wind speed, photovoltaic intensity, and load power. In contrast, the storage battery precisely stores and releases energy to counteract variations in wind and photovoltaic power. The outcomes validate that in the presence of the UC, the microgrid performance is improved. However, the improvement is very close to that gained when using the proposed controller without UC. Hence, the proposed controller can reduce the cost, weight, and space of the system. Moreover, a Hardware-in-the-Loop (HIL) emulator was implemented using a C2000™ microcontroller LaunchPad™ TMS320F28379D kit (Texas Instruments, Dallas, TX, USA) to evaluate the proposed system and validate the simulation results.

High-Resolution Wind Speed Estimates for the Eastern Mediterranean Basin: A Statistical Comparison Against Coastal Meteorological Observations

Wind speed (and direction) estimated from numerical weather prediction (NWP) models is essential to wind energy applications, especially in the absence of reliable fine scale spatio-temporal wind information. This study evaluates four high-resolution wind speed numerical datasets (UERRA MESCAN-SURFEX, CERRA, COSMO-REA6, and NEWA) against in situ observations from coastal meteorological stations in the eastern Mediterranean basin. The evaluation is based on statistical comparisons of long-term wind speed data from 2009 to 2018 and involves an in-depth statistical comparison as well as a preliminary wind power density assessment at or near the meteorological station locations. The results show that while all datasets provide valuable insights into regional wind variability, there are notable differences in model performance. COSMO-REA6 and UERRA exhibit higher variability in wind speed but tend to underestimate extreme values, particularly in the southern coastal areas, whereas CERRA and NEWA provided closer fits to observed wind speeds, with CERRA showing the highest correlation at most stations. NEWA data, where available, overestimate average wind speeds but capture extreme values well. The comparison reveals that while all datasets provide valuable insights into the spatial and temporal variability of wind resources, their performance varies by location and season, emphasizing the need for the careful selection and potential calibration of these models for accurate wind energy assessments. The study provides essential groundwork for leveraging these datasets in planning and optimizing offshore wind energy projects, contributing to the region’s transition to renewable energy sources.

Complementary Analysis and Performance Improvement of a Hydro-Wind Hybrid Power System

Hydropower as a flexible regulation resource is a rare choice to suppress the ever-increasing penetration of wind power in electrical power systems. The complementary characteristics and performance improvement of a hydro–wind hybrid power system based on a mathematical model of the hybrid power system is studied in this paper. This established model takes into account the stochastic variation in wind speeds in the wind power subsystem and the hydraulic–mechanical–electrical coupling characteristics of the hydropower subsystem. The complementary analysis is conducted based on the evaluation variables outputted by the established model, such as the wind power, hydro-regulation power, hydraulic power, and frequency. To make full use of the regulation capability of the hydropower system, the optimization of parameter settings is also carried out to improve complementary performances of the hybrid power system. The results from the complementary analysis show the detailed characteristics of hydro–wind coordinated operation under different types of real wind speeds. Here, 95% of installed hydro-capacity is used to complement the power shortage of the intermittent wind energy under the low wind speed. Alternatively, only around 66% of the installed hydro-capacity can be utilized to cope with the fluctuation in wind power under the medium and high wind speeds before the optimization of parameter settings. The recommended values and change rules of the control, hydraulic, and electrical parameters for the hydropower system are subsequently revealed from the analysis of parameter settings to contribute to a stable and safe hybrid power system. The results show that the optimized parameter can increase the maximal regulating capacity of the hydropower system by nearly 9 MW, approximately a sixth of the total installed hydropower capacity. The method and results obtained in this paper provide theoretical and technical guidance for the safe and economical operation of power stations.