Wind Energy Utilization Research Articles

Dynamic Demand Modeling Incorporating Renewable Energy Sources Using a Population-Based Optimization Method

Due to the inclusion of distributed generation (DG) in microgrids (MGs), the accelerated growth in demand, and environmental concerns, suitable management and operational strategies are imperative. The utilization of wind and solar energy has rapidly increased in MGs. However, due to the uncertainties these systems present, accurately predicting energy generation remains challenging. This necessitates modeling the system’s random variables (such as renewable resource output and possibly load demand) using appropriate and feasible methods. The primary objective of this article is to determine the optimal setpoints for renewable energy sources (RES) and all elements involved in the MG, minimizing the total operation cost. The system comprises wind turbines (WT), photovoltaic panels (PV), energy storage systems (ESS), and electric vehicles (EVs). Weibull distribution and the Hottel and Liu Jordan equations are employed to determine the potential available capacity of wind and solar energy generation, respectively. ESS is utilized to enhance MG performance. For optimal management, a comprehensive mathematical model with practical constraints for each MG element is extracted. An efficient Population-Based Incremental Learning (PBIL) metaheuristic method is proposed to solve the optimization objective in an MG, demonstrating that this energy management system optimizes and effectively coordinates DG and ESS energy generation considering economic considerations. Finally, PBIL is compared with a commonly used model, Particle Swarm Optimization (PSO), across various scenarios, analyzing and evaluating their outcomes, showcasing a reduction in operation costs.

Root cause localization for wind turbines using physics guided multivariate graphical modeling and fault propagation analysis

Under the background of widespread utilization of wind energy, the improvement of power generation efficiency and the reduction of operation and maintenance costs have increased the demand for anomaly detection and localization of wind turbines through fault propagation analysis, while the large-scale data collected by SCADA systems provide sufficient data support and promising assistance for this purpose. It is an effective and cost-saving solution to this demand to construct a high-precision wind turbine digital twin model under normal operating conditions based on multivariate monitoring data, and to realize root cause localization based on prediction deviation and backtracking analysis. This paper proposes a root cause localization framework for wind turbines based on explicit-implicit knowledge fusion and multivariate time-series graph neural networks, fully utilizing the inter-variable dependencies mining capability of graph structure learning and the temporal prediction advantage of time-series graph neural networks. Five types of explicit knowledge based on explicit rules are constructed to describe the explicit relationships between monitoring variables. The fused knowledge (i.e., graph adjacency matrix) is obtained by fusing the explicit knowledge with the implicit knowledge hidden in the data through a quasi-hard-attention mechanism based on the idea of graph structure learning. A high-precision digital twin model for wind turbines based on fused knowledge is constructed using the multivariate time-series graph neural network. The anomaly degree of monitoring variables’ state is defined based on the prediction deviation, with which the propagation of fault's state over time can be accurately described, and the root cause localization through backtracking analysis can be achieved. Case studies are conducted using field fault data from a wind farm, a sugar factory and a thermal power plant. The results show that the proposed framework can provide a solution for studying the fault propagation process and root cause localization, also demonstrating certain robustness and generalizability.

Solar- and Wind-Energy Utilization in the Kingdom of Saudi Arabia: A Comprehensive Review

Utilizing renewable energy (RE) sources can provide a realistic answer to the problem of many nations’ energy needs. This paper reviews the current status of using RE to produce electricity in the Kingdom of Saudi Arabia (KSA). The primary aim of the review is to identify and analyze the solar- and wind-energy utilization, problems, and future trends in KSA while taking into account all relevant aspects, associated development hurdles, and suggested solutions. All involved parties will receive useful results and conclusions from a thorough examination and analysis of the pertinent literature that can assist them in understanding the potential of solar and wind energy, considering all the linked issues.

Optimal Scheduling of Integrated Energy System Considering Hydrogen Blending Gas and Demand Response

In the context of carbon neutrality and carbon peaking, in order to achieve low carbon emissions and promote the efficient utilization of wind energy, hydrogen energy as an important energy carrier is proposed to mix hydrogen and natural gas to form hydrogen-enriched compressed natural gas (HCNG). It is also injected into the natural gas pipeline network to achieve the transmission and utilization of hydrogen energy. At the same time, the participation of demand response is considered, the load’s peak and trough periods are adjusted, and the large-scale consumption of renewable energy and the reduction in carbon emissions are achieved. First of all, a fine model of hydrogen production and hydrogen use equipment is established to analyze the impact of adding hydrogen mixing on the economy and the low-carbon property of the system. With green certificates and demand response, the utilization rate of hydrogen energy is improved to further explore the energy utilization rate and emission reduction capacity of the system. Secondly, on the basis of modeling, the optimal scheduling strategy is proposed with the sum of energy purchase cost, equipment operation cost, carbon emission cost, wind curtailment cost, and green certificate income as the lowest objective function. Considering the constraints such as hydrogen blending ratio and flexible load ratio of the pipeline network, a low-carbon economic scheduling model of hydrogen mixed natural gas was established. The model was linearized and solved by using MATLAB 2021a and CPLEX solver. By comparing different scenarios, the superiority of the model and the effectiveness of the strategy are verified.

Multi-temporal Scale Wind Power Forecasting Based on Lasso-CNN-LSTM-LightGBM

Due to the increasingly severe climate problems, wind energy has received widespread attention as the most abundant energy on Earth. However, due to the uncertainty of wind energy, a large amount of wind energy is wasted, so accurate wind power prediction can greatly improve the utilization of wind energy. To increase the forecast for wind energy accuracy across a range of time scales, this paper presents a multi-time scale wind power prediction by constructing an ICEEMDAN-CNN-LSTM-LightGBM model. Initially, feature selection is performed using Lasso regression to identify the most significant variables affecting the forecast for wind energy across distinct time intervals. Subsequently, the ICEEMDAN is utilized to break down the wind power data into various scales to capture its nonlinear and non-stationary characteristics. Following this, a deep learning model based on CNN and LSTM networks is developed, with the CNN responsible for extracting spatial features from the time series data, and the LSTM designed to capture the temporal relationships. Finally, the outputs of the deep learning model are fed into the LightGBM model to leverage its superior learning capabilities for the ultimate prediction of wind power. Simulation experiments demonstrate that the proposed ICEEMDAN-CNN-LSTM-LightGBM model achieves higher accuracy in multi-time scale wind power prediction, providing more reliable decision assistance with the management and operation of wind farms.

A novel cooperative optimization method of course and speed for wing-diesel hybrid ship based on improved A* algorithm

The wing-diesel hybrid ship, utilizes a wind propulsion system, which is significantly influenced by weather conditions. Therefore, a cooperative optimization method for course and speed is proposed based on an improved A* algorithm, considering weather changes and the ship characteristics, to achieve further improvement in the ship operational energy efficiency. Firstly, a meteorological and hydrological dataset is utilized to establish a dynamic maritime area model with complex weather conditions. Based on this model, the paper considers the actual navigation circumstances to define prohibited and navigable areas. Secondly, the improved A* algorithm incorporates the impact of weather on wing-sails. The improved A* algorithm considers the ship operating position and enhances the evaluation function by variables normalization of fuel consumption and voyage time. This cooperative optimization ensures the maximization of wind energy utilization, thereby achieving the goal of reducing fuel consumption while meeting the overall voyage time requirement. Experimental validation is conducted with the ship “New Aden” in the Indian Ocean region, and the results show that, compared to the original route, the optimized route reduces total fuel consumption by 5.48%, increases the navigation distance by up to 2.18% and increases the navigation time by 5.74%.

Enhancing short-term wind speed prediction based on an outlier-robust ensemble deep random vector functional link network with AOA-optimized VMD

Wind speed prediction is a crucial aspect in the utilization of wind energy. In this paper, a wind speed prediction model based on an outlier-robust ensemble deep random vector functional link network (ORedRVFL) and arithmetic optimization algorithm-optimized variational mode decomposition (AOA-VMD) is designed. First, the penalty factor and the number of mode decompositions of VMD are optimized using the AOA algorithm and the original data are decomposed using the optimized VMD. Then the decomposed data is predicted using the ensemble deep random vector functional link network (edRVFL) model. The edRVFL uses rich intermediate features for the final decision, which can make the final result closer to the real data. In order to strengthen the anti-interference ability to the outliers, this paper robustly improves the edRVFL model, and the improved model is called ORedRVFL. ORedRVFL reduces the impact of outliers by introducing regularization and norm to balance the relationship between training error and weights. The experiments have proved that the model proposed in this paper outperforms other models in terms of anti-interference ability and prediction accuracy.

Short-term wind power output prediction using hybrid-enhanced seagull optimization algorithm and support vector machine: A high-precision method

ABSTRACT Affected by the uncertainty of external environmental factors, wind power generation has significant characteristics of randomness and non-stationarity. Accurately predicting wind power is a necessary technical means to improve the efficiency of wind energy utilization and reduce wind abandonment. Integrating large-scale wind power into the grid has put higher requirements for wind power prediction accuracy. Hence, this study proposes a combined prediction model based on wavelet denoising (WD), hybrid enhanced seagull optimization algorithm (HESOA) and support vector machine (SVM) to predict short-term wind power. The WD technology obtains effective signals from original wind power data, providing a reasonable basis for prediction research. The HESOA is proposed by introducing the Tent mapping, individual self-learning factor and Gaussian population strategy into the original seagull optimization algorithm to optimize SVM’s prediction ability. The case study results reflect that the prediction method is feasible and universal in suppressing wind power generation volatility in spring and winter. The RMSE of short-term wind power prediction is controlled below 65 kW, and the value of R2 is above 98%. This study improves the accuracy of wind power prediction and helps to strengthen the renewable energy consumption level and reliability of wind power grid-connected generation systems.

Adaptive event-triggering mechanism based Takagi-Sugeno fuzzy automatic generation controller design for offshore wind power system

Interconnected offshore wind power system (OWPS) attracts widespread attention due to its special advantage. With the development of wind power technology, higher requirements are put forward for the reliability of networked OWPS. Considering the nonlinear characteristics of OWPS and limited network bandwidth resource, an adaptive multi-event triggering mechanism based distributed Takagi-Sugeno fuzzy robust H∞ control scheme is investigated in this paper. A modeling method for OWPS integrating communication constraints is proposed. By Lyapunov stability theory, the sufficient condition for the asymptotical stability of OWPS has been derived. Compared with traditional control scheme, the proposed Takagi-Sugeno fuzzy controller provides smoother output power, smaller torque oscillation and higher wind energy utilization. Furthermore, the co-design strategy increases the utilization rate of network bandwidth. The feasibility of adaptive multi-event triggering mechanism based Takagi-Sugeno fuzzy controller is demonstrated through a simulation example.

A novel ensemble system for short-term wind speed forecasting based on hybrid decomposition approach and artificial intelligence models optimized by self-attention mechanism

Accurate wind speed forecasting is crucial for wind energy development and utilization. The non-linearity and non-stationarity of the wind speed leads to difficulties in its prediction. Recent years, plentiful forecasting approaches have been developed to improve the short-term wind speed forecasting. Among them, the ensemble system that consists of several sub-models is highly competitive. However, most of ensemble systems employ traditional data pre-processing method and integrate the outputs of sub-models with fixed weight assignment, which severely constrains the forecasting performance. To address these prevailing problems and further enhance the forecasting accuracy and stability, a novel ensemble system is proposed in this study, which comprising four modules: a data pre-processing module, an optimization module, a multiscale prediction module and a combination module. A hybrid decomposition approach is developed to fully exploit the changing modes of wind speed series in data pre-processing module. The multiscale prediction module employs advanced artificial intelligence models to conduct forecasting with the assistance of the optimization module. Finally, the self-attention mechanism is used to combine the outputs of sub-models. Measured hourly wind speed from four stations are adopted as datasets, and four metrics are used to evaluate the forecasting performance. Three comprehensive experiments are designed to explore the functionality and validate the predictive capability of the proposed ensemble system. The experimental results indicate that the proposed ensemble system significantly outperformed single models. The superiority of the proposed ensemble system is also demonstrated in comparisons with hybrid models and other ensemble systems. Overall, the proposed ensemble system can provide more accurate and reliable forecasting for the scheduling and management of wind power systems than other benchmark models.

High-Performance Coaxial Counter-Rotating Triboelectric Nanogenerator with Lift-Drag Hybrid Blades for Wind Energy Harvesting.

Wind energy holds potential for in-situ powering large-scale distributed wireless sensor nodes (WSNs) in the Internet of Things (IoT) era. To achieve high performance in wind energy harvesting, a coaxial counter-rotating triboelectric nanogenerator with lift-drag hybrid blades, termed CCR-TENG, has been proposed. The CCR-TENG, which can work in non-contact and soft-contact modes, realizes low-speed wind energy harvesting through a combination of counter-clockwise rotating lift-type blades and clockwise rotating drag-type blades. Non-contact CCR-TENG realizes low-speed wind energy harvesting at wind speeds as low as 1 m/s. The output of a CCR-TENG, working in soft-contact mode, achieves 41% promotion with a maximum short-circuit current of 0.11 mA and a peak surface power density of 6.2 W/m2 with two TENGs connected in parallel. Furthermore, the power density per unit of wind speed achieves 746 mW/m3·s/m. Consequently, two fluorescent lamps were successfully illuminated and six temperature sensors were continuously lit by the CCR-TENG. The reported CCR-TENG significantly improves low-speed environmental wind energy utilization and demonstrates broad application prospects for in-situ power supply of distributed wireless transmission devices and sensors in the era of the IoT.

Towards urban wind utilization: The spatial characteristics of wind energy in urban areas

Urban wind energy is gaining increasing recognition for its environmentally friendly nature, particularly in the context of energy shortages. Predicting wind speed in urban environments is challenging due to the varying roughness and drag caused by obstacles on the ground. The complexity of urban morphology significantly disrupts the wind field and hampers the utilization of wind energy. This study aimed to investigate the diverse impacts of highly heterogeneous urban environment on the urban wind energy by innovatively establishing spatially varying power law wind profiles. Firstly, long-term LIDAR observations were conducted to measure the vertical wind profile in the study area. Then, a high-resolution LCZ map was created and integrated into the Weather Research and Forecasting (WRF) model to reproduce the urban wind field, which was validated using the observational data. Finally, the spatial characteristics of the urban wind field and wind energy were analyzed based on 3 representative points and the urban LCZ categories. The results show that the average power law exponents of each LCZ classification ranges from 0.29 to 0.75, with the average power law exponent of the high-volume building area being larger than 0.6. The highest wind power density in the study area can reach 343.3W/m2 at 200m height. Further, significant negative correlation coefficients were found between wind power density and building height, as well as building density, with values of approximately −0.6 and −0.35, respectively. Overall, the high-volume-ratio built-up areas with higher roughness had a notable impact on the wind speed by increasing the power law exponents. These spatially varying wind profiles are expected to provide technical support for the utilization of urban wind energy.

Numerical modeling and dynamic response analysis of an integrated semi-submersible floating wind and aquaculture system

Floating wind turbines (FWT) are facing the challenges of high cost of energy. Integrating floating wind with offshore aquaculture cages presents a mutually beneficial approach to address cost concerns and maximize overall benefits. This study introduces and numerically models an integrated system, encompassing a self-designed semi-submersible FWT coupled and an aquaculture cage (FWT + AC). Comprehensive, fully coupled aero-hydro-servo-elastic-mooring models for this integrated system are established. Subsequently, the numerical model of the FWT is validated against wave basin model tests. The research further discusses the coupled dynamic response characteristics of the integrated system for both the FWT with and without the aquaculture cage. The results highlight that integrating aquaculture cages leads to an increase in the mean surge response by up to 12.6%, while significantly reducing pitch motion by as much as 7.69% under combined wind, wave, and current conditions. Moreover, the mean mooring loads, particularly for line 4 which faces the direction of wind, wave, and current, do not exhibit an increase of more than 15%. This integration not only enhances the stability of wind power generation under different wind degree, but also minimally impacts the average power generation efficiency, showing a variation of only about 2% under rated wind speed. The findings provide beneficial theoretical support for the design of the integrated FWT + AC system, demonstrating the potential of such integrated systems in promoting offshore sustainable development and enhancing wind energy utilization efficiency.

Site-specific wake steering strategy for combined power enhancement and fatigue mitigation within wind farms

Wind energy plays a crucial role in the quest for sustainable energy solutions. However, optimizing the efficiency of wind energy utilization remains a significant challenge. Wake steering, a key strategy in the field, offers the potential to address this challenge. This study introduces an innovative site-specific wake steering framework that incorporates a wake superposition model for wake steering, a machine-learning based fatigue and power predictor and a multi-objective optimizer to both enhance total power generation and mitigate fatigue loads within wind farms. The wake superposition model, developed and validated here, successfully replicates secondary wake steering effects and provides a new solution for calculating superimposed transverse velocity. The study comprehensively considers and implements constraints based on physical laws. Analysis of inflow speed and turbulence levels reveals that wake steering can continue to enhance total power output. Power enhancement can reach up to 18% at lower turbulence levels and still achieve significant increases even when inflow speeds exceed rated values, with only marginal increases in fatigue loads. Lower turbulence levels improve optimization results at the expense of heightened structural loads, while higher turbulence levels lead to diminishing power enhancement and additional fatigue loads. Examination of wind turbine spacing shows that smaller intervals yield substantial power enhancement, with improvements of up to 51.7%, although the effect diminishes as intervals increase and wake recovery takes place. In conclusion, the proposed site-specific wake steering framework offers an efficient means of balancing enhanced wind farm power output and structural integrity, representing a significant advancement in wind energy optimization.

A novel data center free radiating system driven by waste heat and wind energy system (DCFRWWs) and its operation performance analysis

Refrigeration equipment for data centers consumes a significant portion of global electricity, resulting in high Power Usage Effectiveness (PUE) values and increased entropy of the data center. To address these challenges, this study proposes a novel data center free radiating system driven by waste heat and wind energy (DCFRWWs). This approach recovers low-grade heat energy from the hot air heated by the central processing units (CPUs) and transform it into work. Unlike conventional cooling systems, the DCFRWWs do not require additional energy to meet CPU cooling demands and can effectively suppress the entropy increase of the data center. The Bernoulli equation was used to establish the mathematical model for DCFRWWs, which explained the underlying mechanisms of pipeline wind speed and the influence of ambient wind speed and pipeline inlet temperature on system operating characteristics. In addition, this study employed pipeline height and diameter as decision variables, while maximizing exergy efficiency served as the optimization objective. To obtain the optimal pipeline size (height and diameter) that is effective under real-world experimental conditions, the optimization calculations were conducted using weather data from Qinhuangdao. To verify the reliability and accuracy of the model, an experimental system was constructed, and the simulation results indicated that the relative error rates for the three key parameters are as follows: pipeline wind speed (33.09%), pipeline inlet temperature (0.59%), and CPU temperature (3.85%). A single factor analysis was performed on 14 different operating conditions to analyze the operating characteristics of the DCFRWWs. Moreover, the applicability verification conducted in three distinct cities across different regions suggest that the system is indeed suitable for diverse settings. The DCFRWW system, utilizing the dual forces of the chimney effect of waste hot air in pipelines and the extraction force generated by unpowered roof ventilator, achieves the objective of free heat dissipation. Furthermore, the DCFRWWs can not only be applied in the field of data center cooling but also serves as a thermodynamic reference for low-grade waste heat recovery in other sectors, thereby further expanding the scope of direct utilization of wind energy.

A GCN-based adaptive generative adversarial network model for short-term wind speed scenario prediction

Wind prediction is of great significance for wind energy utilization due to the stochastic nature of wind. To effectively facilitate various downstream decision-making tasks such as wind turbine control, predictive wind Scenario Generation (SG), which is capable of providing a set of deterministic instantiated wind prediction results, plays a critical role. In this paper, a novel Graph neural networks-based Adaptive Predictive Generative Adversarial Network (GAPGAN) model is proposed for accurate prediction of short-term future scenarios of a wind field. In GAPGAN, the original multivariate time series data are first reconstructed into the form of a graph, and spatiotemporal features are then extracted using Graph Convolutional Networks (GCNs). Next, a predictive generative adversarial network (PGAN) framework is proposed, which could generate different outputs corresponding to given historical observations as conditions. Finally, an adaptive PGAN training mechanism is introduced to stabilize the training process, and the best SG model is selected based on the proposed comprehensive evaluation system. Based on wind speed data collected from 11 wind turbines, computational experiments validate that the GAPGAN outperforms five benchmarking models in terms of point prediction accuracy, shape similarity, uncertainty prediction quality, and prediction scenario diversity.

Dynamic role of clean energy and sustainable economic growth in coastal region: Novel observations from China

China's coastal region is the major geographical unit for the future development of China's industrial sector. The transformation of basic structure to high-class development in China's coastal places is a significant tool for promoting the changes related to quality, power and efficiency in regional economic development. In the 21st century, environmental and energy issues have increased worldwide, and challenges related to environmental pollution, energy crises, and ecological imbalances have emerged. To climate change and energy utilization, the sustainable progress of clean energy is the new route of future energy development. Based on China's non-polluting energy growth process in the last ten years, this article explores China's clean/green energy policies and economic growth development plans. Clean energy utilization is crucial for sustainable development in the context of high-quality economic growth and climate change. However, the monetary evolution and carbon emission are not investigated whole from the clean energy aspects. Using Wind energy sources as the acceptable variable, this paper employs threshold regression and impulse functions to assess the energy consumption and economic growth on carbon emission in 30 Chinese provinces over the 2000 to 2020 period. The Deep Belief Network (DBN) model predicts wind energy utilization and efficiency. The results show that economic development and carbon emissions are connected. Further, growth influences promote the offset of carbon emissions. Green innovation alters the nexus of carbon emissions, and China's economy reduces carbon usage. It provides the decision-making policies for clean energy development.

A novel dynamic spatio-temporal graph convolutional network for wind speed interval prediction

It is crucial to predict the wind speed for the utilization of renewable wind energy and the operation of transmission lines with increased capacity. The intermittency and stochastic fluctuations of wind speed pose a significant challenge for the high-quality wind speed prediction, and a novel wind speed interval prediction (WSIP) model is constructed in this study by employing the residual estimation (RE)-oriented dynamic spatio-temporal graph convolutional network (DSTGCN) approach. Firstly, a dynamic adjacency matrix is designed to obtain time-varying global spatial weight allocations among each wind speed node. Then, the spatio-temporal features are extracted by using gated recurrent units (GRUs) and GCNs to construct the wind speed graph networks. Moreover, the RE-oriented strategy incorporating the pinball loss is designed to provide a guidance the parameter training of the constructed model, thus eliminating the quantile crossings problem. As a result, the deterministic point prediction of the wind speed is expanded to the quantile-based probabilistic interval prediction. Finally, the experimental results are presented to demonstrate the validity and superiority of proposed scheme in both qualitative and quantitative performance.

Optimal Design of Anti-Icing Blunt Trailing-Edge Wind Wheel for H-Type Vertical Axis Wind Turbine Under Operating Conditions

Abstract A novel optimization method is developed for the design of an anti-icing blunt trailing-edge wind wheel of H-type vertical axis wind turbine (VAWT) based on the quasi-steady-state icing. The parametric expression of the airfoil is given using the mean camber and thickness functions, the blunt trailing-edge is constructed by the rotation and zoom of coordinates, and then through the aerodynamic design theory, the geometry control equations of the blunt trailing-edge wind wheel are established. The icing process using Solution and Icing modules is repeated at equal interval azimuths to obtain the ice on the wind wheel per revolution. The optimization model is solved using particle swarm optimization (PSO) algorithm integrated with computational fluid dynamics (CFD) method to maximize the wind energy utilization in both ice-free and icing conditions. Significant improvements are realized for flow and aerodynamic characteristics, confirming that the optimization method provides important guidance for an anti-icing design of VAWT blades.

Active power smoothing control strategy of offshore wind power system based on reduced matrix converter

On account of the fluctuation and randomness of wind speed, the output power of wind turbines fluctuates greatly, which may cause the frequency change of the power grid and increase the difficulty of power system dispatching. To solve this problem, an active power smoothing control (APSC) strategy based on RMC for offshore wind power systems is proposed in this paper. Compared with existing wind power converter systems, RMC adopted in this paper has the advantages of compact structure, small volume and high conversion efficiency. Based on the analysis of the exponential moving average (EMA) algorithm, a method for determining active power based on the fuzzy EMA algorithm is proposed. The method adjusts the cycle number N in real-time according to the wind speed, taking into account both the wind energy utilization rate and the smoothing performance of active power. The control principle of offshore wind power generation system (WPGS) based on RMC is analyzed in detail and the coordinated control strategy of RMC and inverter is proposed. Simulation results demonstrate the validity of the proposed method and control strategy.