Variable Wind Research Articles

FLOWERS AEP: An Analytical Model for Wind Farm Layout Optimization

ABSTRACTAnnual energy production (AEP) is commonly used in objective functions for wind farm layout optimization. AEP is proportional to wind farm power production integrated over an annual distribution of free‐stream wind conditions. Physics‐based estimates of wind farm power production typically rely on low‐fidelity engineering wake models that approximate the steady‐state wind farm flow field. AEP estimates are then obtained by performing independent simulations for discrete wind conditions and using rectangular quadrature to account for each condition's expected frequency of occurrence. Depending on the number of simulated discrete wind conditions, this numerical integral could be hampered by poor accuracy or high computational costs. The FLOWERS AEP model instead poses an analytical integral of the engineering wake model over the variable wind conditions, yielding a closed‐form, analytical function for wind farm AEP. This paper derives the analytical functions for FLOWERS AEP and its derivatives with respect to turbine position, which are useful for gradient‐based wind farm layout optimization, in nondimensional form. We then analyze the benefits of the FLOWERS AEP model over conventional reference models, focusing on its low cost, adequate wake loss predictions, and smooth design space. Although the FLOWERS approach is found to predict the exact value of AEP with some error relative to the reference model (within 14% on average), it dramatically reduces computation time by an order of magnitude, produces a qualitatively similar design space at relatively low resolution, and yields comparable optimal layouts. This significant speed improvement is critical in layout optimization applications, where determining an optimal layout in an efficient manner is more important than precise AEP prediction.

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.

A new integrated modelling and control of ternary pumped storage hydro power generation for small signal stability studies

Pumped storage hydro power generation (PSHPG), a reliable renewable energy source with an energy storage feature, can impart efficient damping torque to power system oscillations to improve small signal stability with appropriate modelling and additional compensation through the governor. But ternary PSHPG (T-PSHPG) has the unique feature of hydraulic short circuit (HSC) mode, where both pumping and generating modes operate simultaneously. This study presents a novel fourth-order modified Heffron Phillips (MHP) model of T-PSHPG in coordination with a multi-band Power system stabilizer (MB-PSS) employing 2-way penstock, where primary penstock circulates water from the upper reservoir to the lower reservoir through the turbine and secondary penstock divides primary penstock, thereby creating short-circuit path through the pump. Also, this model includes additional reactive power droop control to maintain the voltage profile. A new state space matrix has been developed for coordinating T-PSHPG in HSC mode with MB-PSS. The control parameters of the proposed coordinated model are set by a new multi-objective differential evolution-improved particle swam optimization (DE-IPSO) algorithm. With variable load, random solar and wind source penetrations, it has been found that the proposed model can provide adequate damping actions. The sensitivity of the proposed model has been observed by varying critical model parameters by ±50 %. For all conditions, the settling time of speed variation is within 2.672 s to 5.951 s and the minimum damping ratio lies within 0.105 to 0.455. The minimum damping ratio is 0.32 and the settling time is found to be 3.1 s for system response subject to sudden solar power variation, which has been observed by shifting system eigenvalues toward the left half of the complex plane. Time and frequency domain parameters with sensitivity analysis predict system oscillations being damped effectively with consistency for the proposed modelling and control strategy. The results are verified in real-time with OPAL-RT real-time digital simulator. It has been justified by the present study that appropriate modelling of T-PSHPG along with coordinated control action provided by MB-PSS can handle critical power system oscillations efficiently and effective damping torque can be imparted in HSC mode.

Optimization of the Small Wind Turbine Design—Performance Analysis

In recent decades, the intensive development of renewable energy technology has been observed as a great alternative to conventional energy sources. Solutions aimed at individual customers, which can be used directly in places where electricity is required, are of particular interest. Small wind turbines pose a special challenge because their design must be adapted to environmental conditions, including low wind speed or variability in its direction. The research study presented in this paper considers the energy efficiency of a small wind turbine with a horizontal axis of rotation. Three key design parameters were analyzed: the shape and inclination of the turbine blades and additional confusor–diffuser shape casings. The tests were carried out for three conceptual variants: a confusor before the turbine, a diffuser after the turbine, and a confusor–diffuser combination. Studies have shown that changing the shape of the blade can increase the analyzed wind turbine power by up to 35%, while changing the blade inclination can cause an increase of up to 16% compared to the initial installation position and a 66% increase in power when comparing the extreme inclination of the blades of the tested turbine. The study has shown that to increase the wind speed, the best solution is to use a confusor–diffuser configuration, which, with increased length, can increase the air velocity by up to 21%.

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.

Nonlinear integral backstepping control based on particle swarm optimization for a grid-connected variable wind energy conversion system during voltage dips

Double-fed induction generator (DFIG) wind turbines connected to the grid are particularly subject to grid problems such as voltage dips. Because of this, it might be challenging to maintain system stability and prevent system disconnections when using a Proportional-Integral (PI) Controller to operate this system. This paper applies the Integral Backstepping Control for the wind power plant system connected to the power grid. The Integral Backstepping is a nonlinear and recursive method that employs the Lyapunov theory to ensure the system's stability. The best selection of gain values for the Lyapunov function guarantees improved system control. These gains are frequently adjusted using the trial-and-error strategy, which is time-consuming and inefficient. This technique becomes more complex when many parameters need to be determined. It also limits the system's performance and restricts this nonlinear approach's advantages. The objective is to apply the particle swarm optimization for computing several constant values of the nonlinear approach by minimizing an integral absolute error criterion index. The weighted sum of errors is employed to solve the multiple objective problems. The proposed controller tracks the maximum power point, maintains the voltage of the DC-Link constant, and controls active and reactive power. The robustness of this method is verified in critical conditions, including system parameter variation and asymmetrical and symmetrical grid faults. The simulation findings highlight the effectiveness and robustness of the combination of Integral Backstepping with Particle Swarm Optimization in terms of reducing response time from 10.6 (ms) to 2 (ms), canceling static error, and improving overshoot compared to the vector control based on PI regulator. Besides, the DC-Link voltage ripples during the asymmetrical grid faults are reduced to ±1 (V) using the suggested controller. The latter can be implemented thanks to advancements in Central Processor Unit technology.

Low cloud-SST variability over the summertime subtropical Northeast Pacific: Role of extratropical atmospheric modes

Abstract Over the subtropical Northeast Pacific (NEP), highly reflective low clouds interact with underlying sea-surface temperature (SST) to constitute a local positive feedback. Recent modeling studies showed that, together with wind-evaporation-SST (WES) feedback, the summertime low cloud-SST feedback promotes nonlocal trade wind variations, modulating subsequent evolution of El Niño-Southern Oscillation (ENSO). This study aims to identify drivers of summertime low-cloud variations, using satellite observations and global atmosphere model simulations forced with observed SST. A trans-basin teleconnection is identified, where the North Tropical Atlantic (NTA) warming induced by the North Atlantic Oscillation (NAO) increases precipitation, exciting warm Rossby waves that extend into the NEP. The resultant enhancement of static stability promotes summertime low cloud-SST variability. By regressing out the effects of the preceding ENSO and NTA SST, atmospheric internal variability over the extratropical North Pacific, including the North Pacific Oscillation (NPO), is found to drive the NEP cooling by latent heat loss and subsequent summer low cloud-SST variability. With the help of the background trade winds and WES feedback, the SST anomalies extend southwestward from the low-cloud region, accompanied by ENSO in the following winter. This suggests the nonlocal effects of low clouds identified by recent studies. Analysis of a 500-year climate model simulation corroborates the NTA and NPO forcing of NEP low cloud-SST variability and subsequent ENSO.

The Problem of Power Variations in Wind Turbines Operating under Variable Wind Speeds over Time and the Need for Wind Energy Storage Systems

One of the most important and efficient sources of green electricity is catching air currents through wind turbine technology. Wind power plants are located in areas where the energy potential of the wind is high but it varies. The time variation of the wind generates fluctuations in the power produced by the wind farms that is injected into the grid. This elevates, depending on the intensity, problems of network stability and the need for balancing energy, thus raising both technical and cost issues. The present paper analyzes the behavior of a wind turbine (WT) over time in varying wind speed conditions, highlighting that without automation algorithms, a WT is far from the operation at the maximum power point (MPP). However, even when it is brought to operate at MPP, there are still significant variations in the power injected into the network. These power variations can be compensated if the wind system has energy storage facilities for the captured wind. All of these assumptions are analyzed using improved mathematical models and processed in simulations, with experimental data used as input from a wind turbine with an installed power of 2.5 [MW] in operation on the Romanian Black Sea coastal area. Consequently, the paper demonstrates that during an operation in the optimal area, from an energy perspective, the wind turbine’s maximum power point requires a storage system for the captured wind energy.

RNN-based linear parameter varying adaptive model predictive control for autonomous driving

Autonomous driving is a complex and highly dynamic process that ensures controlling the coupled longitudinal and lateral vehicle dynamics. Model predictive control, distinguished by its predictive feature, optimal performance, and ability to handle constraints, makes it one of the most promising tools for this type of control application. The content of this article handles the problem of autonomous driving by proposing an adaptive linear parameter varying model predictive controller (LPV-MPC), where the controller's prediction model is adaptive by means of a recurrent neural network. The proposed LPV-MPC is further optimised by a hybrid Genetic and Particle Swarm Optimization Algorithm (GA-PSO). The developed controller is tested and evaluated on a challenging track under variable wind disturbance.

Addressing VAWT Aerodynamic Challenges as the Key to Unlocking Their Potential in the Wind Energy Sector

While the wind turbine industry has been primarily dominated by horizontal-axis wind turbines, the forefront of knowledge of these turbines has revealed significant challenges in various aspects, including manufacturing, structural design, cost, and maintenance. On the other hand, the advantages associated with Darrieus vertical-axis wind turbines (VAWTs) demonstrate significant potential that can address the existing challenges of the wind turbine industry. Current work aims to investigate the practicality of this potential for the wind energy sector. To this end, the benefits of employing Darrieus turbines for domestic and industrial applications, isolated operation, and on/offshore windfarm applications have been explored. It is apparent that Darrieus VAWTs are better suited to a wide range of environments, whether they are deployed in isolation or integrated systems, and whether they are utilized on a small or large scale. Darrieus VAWTs are adaptable to urban unsteady variable wind, are less expensive on large scales, provide higher power density at the windfarm level, and provide stability for offshore platforms. Nevertheless, challenges remain in fully harnessing VAWT potential rooted in their complex aerodynamics. This serves as a primary challenge for VAWTs to address the challenges of the wind turbine industry in line with the 2050 roadmap.

Detection of High-frequency Pulsation in WR 135: Investigation of Stellar Wind Dynamics

We report the detection of high-frequency pulsations in WR 135 from short-cadence (10 minute) optical photometric and spectroscopic time series surveys. The harmonics up to the sixth order are detected from the integrated photometric flux variations, while the comparatively weaker eighth harmonic is detected from the strengths of the emission lines. We investigate the driving source of the stratified winds of WR 135 using the radiative transfer modeling code, CMFGEN, and find the physical conditions that can explain the propagation of such pulsations. From our study, we find that the optically thick subsonic layers of the atmosphere are close to the Eddington limit and are launched by the Fe opacity. The outer optically thin supersonic winds (τ ross = 0.1–0.01) are launched by the He II and C iv opacities. The stratified winds above the sonic point undergo velocity perturbation that can lead to clumps. In the optically thin supersonic winds, dense clumps of smaller size (f VFF = 0.27–0.3, where f VFF is the volume filling factor) pulsate with higher-order harmonics. The larger clumps ( f VFF = 0.2) oscillate with lower-order harmonics of the pulsation and affect the overall wind variability.

A dynamically adaptive Langmuir turbulence parameterization scheme for variable wind wave conditions: Model application

Langmuir circulations and turbulence (LT) are crucial in the upper ocean mixed layer, significantly affecting the air-sea exchange of momentum, heat, and mass. The development of an appropriate LT parameterization scheme is vital for ocean modeling. This study employed the Large-eddy Simulation (LES) and the Physics-informed Neural Network (PINN) to optimize the KC04 Langmuir turbulence scheme by dynamically adjusting E6 as a key parameter determined by winds and waves. The LES simulations under different wind wave states indicated the PINN-inferred values for E6. Modelling results from GOTM in OCSPapa station demonstrated that the optimized scheme outperformed the original KC04 scheme in simulating the vertical eddy diffusivity and temperature, with an ∼6.24% annual reduction in the root mean square error (RMSE) for the temperature and an ∼8.23% reduction in the RMSE during autumn. Furthermore, the optimized scheme resulted in a thicker mixed layer, reaching 4.9 m. This enhanced LT parameterization scheme exhibited the improved robustness for variable spatiotemporal resolutions, significantly improving the modeling accuracy.

Surge in Power and Gas Demand in the Southeast Atlantic—Reasons and Solutions

The Southeast Atlantic region (much of it in Virginia and the Carolinas) is anticipating an extraordinary growth in electric demand over the next few years, with a resulting surge in natural gas demand. Utilities in all three states, anticipating rapid growth in electricity demand through the 2030s, have ambitious plans for renewables. However, these states are acknowledging that solar and offshore wind will need to be backed up by a significant increase in natural gas‐fired generation to join energy storage in providing load‐following dispatchable power, to compensate for the variability of wind and solar generation. There is also a recognition that load is growing too fast to rely solely on new wind and solar for baseload generation. Further, a significant amount of remaining coal‐fired generation is expected to phase out in the three states, creating the need for more generation unrelated to load growth.

Scenario-based stochastic optimization on the variability of solar and wind for component sizing of integrated energy systems

The inherent intermittency and variability of renewable energy sources present significant challenges to the optimal design and implementation of integrated energy systems (IES). This paper introduces a novel stochastic optimization model that integrates advanced scenario generation and clustering algorithm for renewable energy sources within a multi-objective, bi-level optimization framework. Specifically, the clearness index is employed to represent the stochastic distribution of solar radiation intensity by beta distribution, while wind speed uncertainty is modeled seasonally using the Weibull distribution. Monte Carlo sampling with synchronous back substitution is applied for scenario generation and reduction of solar radiation and wind speed. To address the multi-objective evaluation, the analytic hierarchy process is utilized, and the joint optimization is achieved by combining a region contraction algorithm with stochastic programming. The proposed methodology is validated on an IES featuring various heating devices, incorporating uncertainties in both wind and solar energy. The results indicate that the absorption heat pump-based scheme achieves superior energy-saving performance, achieving an energy rate of 0.4724. Additionally, the compression heat pump-based scheme exhibits excellent economic efficiency and environmental sustainability, with a cost of energy of 0.3639 and a renewable fraction of 0.5536.

Protective benefits of HDPE board sand fences in an environment with variable wind directions on Gobi surfaces: wind tunnel study

Protective benefits of HDPE board sand fences in an environment with variable wind directions on Gobi surfaces: wind tunnel study

Methods for addressing variable turbine operations during wind farm noise compliance monitoring

Post-construction wind farm noise measurements at dwelling locations are commonly required in Australia to verify compliance with noise limits. These are often required as soon as the wind farm begins operating. However, commissioning issues and electricity network restrictions are a common occurrence during initial stages of operation. As such, curtailed modes of operation and shutdowns are a regular occurrence, and large sections of a wind farm may only be operating intermittently. This presents challenges to obtaining noise measurement data that is representative of normal operation of the wind farm, and may ultimately limit the conclusions that are able to be reached from measurements during the early stages of operation. To address these challenges, it is necessary to identify and account for any periods when operational noise levels may be lower as a result of turbines being curtailed or shut down. Several methods have been developed for this purpose, and range from simple procedures that remove any data affected by curtailment or shutdown, through to more complex assessments of the magnitude and implications of the uncertainty introduced by variable wind turbine operations. These methods are presented and assessed in this paper with case studies of their application to operational projects.

Effectiveness of natural ventilation in controlling the smoke spread within a refuge floor of a high-rise building

Refuge floors in high-rise buildings ensure a haven for occupants to stay in an emergencyprimarily during a fire breakout. Provisions for natural ventilation of the refuge floor are mandatory in all fire codes as they help release smoke and heat build-up. However, the effectiveness of such provisions has been questioned in many studies. This research employs large eddy simulations based three-dimensional computational fluid dynamics modelling on a real-world high-rise building to investigate the efficacy of natural ventilation in a refuge floor during a fire. Consequently, the performance was assessed by evaluating the temporal and spatial distributions of temperature, visibility, smoke and carbon monoxide concentration. Emphasis was placed on determining the adequacy of window placement and openings. Analysis was started with a code-compliant window configuration. Subsequently the window size and the layout were iteratively modified using simulation results. Seasonal wind variations were also considered. Results showed that a well-engineered natural ventilation strategy can considerably improve the refuge floor habitability. Simulations also revealed limitations of natural ventilation under certain wind conditions, necessitating a backup mechanical system. This also underlined that combining fire analysis with cutting-edge modelling allows tailored natural ventilation for refuge floors, maximising safe occupancy during fires.

Pulsed wind-driven control of phytoplankton biomass at a groundwater-enriched nearshore environment

Along some Mediterranean coastal areas and other world regions, nutrient and chlorophyll concentrations often show gradient increases of up to one order of magnitude perpendicular to the coast. This nearshore stripe, extending a few hundred meters from the coast, is enriched by submarine groundwater discharges (SGD) containing elevated nutrient concentrations that may eventually sustain high biomass phytoplankton blooms. During a survey carried out in the summer of 2018, we examined the short-term (hours) variability of the phytoplankton biomass (measured as chlorophyll; Chl) in response to environmental changes associated with SGD and wind forcing in the nearshore waters of Palma Beach (Mediterranean Sea). Continuous CTD records revealed a general salinity decline indicative of SGD along the shoreline. Large and pulsed salinity fluctuations (i.e. 2–3 psu variations, 1–4 h) were observed each day that were consistent with offshore advection episodes of the lower salinity water retained in the nearshore (peak crosshore velocity 5–6 cm s−1). Chl near the shoreline was markedly higher than offshore (3.55 ± 1.29 and 0.68 ± 0.27 mg m−3 respectively) but recurrently fluctuated in the afternoon to up to >7 mg m−3. Primary production estimations showed that despite the higher production in the nearshore (50.29 ± 10.98 μmol O2 L−1 d−1, 4-fold offshore values) productivity per unit chlorophyll did not significantly vary (p > 0.01) therefore suggesting that, at this time scales, high biomass episodes in the nearshore are driven by an accumulation mechanism. Statistical analysis (CCA) demonstrates that Chl variability is largely explained (93 %) by variations in wind and current velocity. Our results provide evidence that the dynamics of this nearshore environment are modulated by the interplay between the shoreward wind-induced flow and the offshore directed density flow. This mechanism could explain the occurrence and episodic nature of high biomass blooms in the nearshore, as well as be an important factor influencing the microbial community structure at the coastal zone.

Recurrent Coronal Mass Ejections and Their Geomagnetic Storms Association on 2012 January 19: Solar Surface to Upper Earth’s Atmosphere Analyses

In this study, we have conducted an analysis of space weather disruptions that occurred on 19 January 2012. Our analysis identified three coronal mass ejections (CMEs), CME1, CME2, and CME3—which were ejected at 09:48:05 universal time (UT), 14:36:05 UT, and 16:12:06 UT, respectively. Nonrecurrent disturbances in space weather, such as geomagnetic storms, result from CMEs originating from the Sun and traveling toward Earth. We assess the contribution of CME–CME interactions on 2012 January 19 and the volume emission rate of nitric oxide (NO) near the Earth's upper atmosphere in prolonging the geomagnetic disturbances observed on 2012 January 23. The findings suggest an increase in intensity at the interacting boundaries of CME1 and CME2, indicating an increase in pressure and density, leading to the compression of the magnetosphere. The 3D reconstructions of the CMEs provide evidence of unequal expansion and rotations within coronagraphic frames attributed to structural variability in the background solar wind during the eruptions. Furthermore, highlights from the in situ observations suggest that the impact of the recurrent CMEs on the geomagnetic disturbance was more pronounced within the auroral region synchronizing with a significant increase in NO volume emission rate on 2012 January 23, near the upper Earth's atmosphere. Our focus is on exploring the interactions between these CMEs to understand their potential contribution to the extended duration of the observed geomagnetic disturbance.

Crop yield prediction with environmental and chemical variables using optimized ensemble predictive model in machine learning

Abstract Enhanced crop yield prediction is necessary for agronomists to make dynamic premonsoon decisions. The input variables precipitation, temperature, evaporation, wind speed, and chemical use influence crop yield estimations. In this study, we analyzed the correlation between crop yield and input features, and scaled up the prediction power of the crop yield model using optimized ensemble learning for machine learning. The proposed model is expected to deal with the limitations of existing models by minimizing effort and data requirements. It achieved better performance than the other approaches with a MSE (Mean Squared Error) of 42963, MAE (Mean Absolute Error) of 87, and R 2 (Coefficient of Determination) of 0.96. The findings of this study have important suggestions for agricultural management and policy-making. The proposed model offers possible applications for enhancing crop yield prediction across various perspectives, thereby assisting more informed decision-making in agriculture.