Search Results (88)

Currently, Poland is witnessing a dynamic development of the offshore wind energy sector, which will be a key component of the national energy mix. While many international studies have addressed wind energy deployment, there is a lack of research that compares the energy and economic performance of both onshore and offshore wind farms under Polish climatic and spatial conditions, especially in relation to turbine spacing optimization. This study addresses that gap by performing a computer-based simulation analysis of three onshore spacing variants (3D, 4D, 5D) and four offshore variants (5D, 6D, 7D, 9D), located in central Poland (Stęszew, Okonek, Gostyń) and the Baltic Sea, respectively. The efficiency of wind farms was assessed in both energy and economic terms, using WAsP Bundle software and standard profitability evaluation metrics (NPV, MNPV, IRR). The results show that the highest NPV and MNPV values among onshore configurations were obtained for the 3D spacing variant, where the energy yield leads to nearly double the annual revenue compared to the 5D variant. IRR values indicate project profitability, averaging 14.5% for onshore and 11.9% for offshore wind farms. Offshore turbines demonstrated higher capacity factors (36–53%) compared to onshore (28–39%), with 4–7 times higher annual energy output. The study provides new insight into wind farm layout optimization under Polish conditions and supports spatial planning and investment decision making in line with national energy policy goals. Full article

With the growing global demand for clean energy, the accuracy of wind-power forecasting plays a vital role in ensuring the stable operation of power systems. However, wind-power generation is significantly influenced by meteorological conditions and is characterized by high uncertainty and multiscale fluctuations. Traditional recurrent neural network (RNN) and long short-term memory (LSTM) models, although capable of handling sequential data, struggle with modeling long-term temporal dependencies due to the vanishing gradient problem; thus, they are now rarely used. Recently, Transformer models have made notable progress in sequence modeling compared to RNNs and LSTM models. Nevertheless, when dealing with long wind-power sequences, their quadratic computational complexity (O(L²)) leads to low efficiency, and their global attention mechanism often fails to capture local periodic features accurately, tending to overemphasize redundant information while overlooking key temporal patterns. To address these challenges, this paper proposes a wind-power forecasting method based on dimension-transformed collaborative multidimensional multiscale attention (DTCMMA). This method first employs fast Fourier transform (FFT) to automatically identify the main periodic components in wind-power data, reconstructing the one-dimensional time series as a two-dimensional spatiotemporal representation, thereby explicitly encoding periodic features. Based on this, a collaborative multidimensional multiscale attention (CMMA) mechanism is designed, which hierarchically integrates channel, spatial, and pixel attention to adaptively capture complex spatiotemporal dependencies. Considering the geometric characteristics of the reconstructed data, asymmetric convolution kernels are adopted to enhance feature extraction efficiency. Experiments on multiple wind-farm datasets and energy-related datasets demonstrate that DTCMMA outperforms mainstream methods such as Transformer, iTransformer, and TimeMixer in long-sequence forecasting tasks, achieving improvements in MSE performance by 34.22%, 2.57%, and 0.51%, respectively. The model’s training speed also surpasses that of the fastest baseline by 300%, significantly improving both prediction accuracy and computational efficiency. This provides an efficient and accurate solution for wind-power forecasting and contributes to the further development and application of wind energy in the global energy mix. Full article

This study analyzes the feasibility of energy mixes composed of different shares of various types of power generation units, including photovoltaic (PV) and wind farms, hydropower, fossil fuel-based plants, and nuclear power. The analysis uses the concept of Energy Return on Investment (EROI), which is considered the most reliable indicator for comparing different technologies as it measures the energy required rather than monetary costs needed to build and operate each technology. Literature-based EROI values for individual generation technologies were used, along with the minimum EROI thresholds for the entire energy mix that are necessary to sustain developed societies and a high quality of life. The results show that, depending on the assumed minimum EROI value, which ranges from 10 to 30, the maximum share of intermittent renewable energy sources (IRESs), such as PV and wind farms, in the system cannot exceed 90% or 60%, respectively. It is important to emphasize that this EROI-based analysis does not account for power grid stability, which currently can only be maintained by the inertia of large synchronous generators. Therefore, the scenario with a 90% IRES share should be regarded as purely theoretical. Full article

In view of the complex operating environments of wind farms and the characteristics of multi-branch mixed collector lines, in order to improve the accuracy of single-phase grounding fault location, the convolutional neural network (CNN), bidirectional long short-term memory network (BiLSTM), and attention mechanism (attention) were combined to construct a single-phase grounding fault location strategy for the CNN–BiLSTM–attention hybrid model. Using a zero-sequence current as the fault information identification method, through the deep fusion of the CNN–BiLSTM–attention hybrid model, the single-phase grounding faults in the collector lines of the wind farm can be located. The simulation modeling was carried out using the MATLAB R2022b software, and the effectiveness of the hybrid model in the single-phase grounding fault location of multi-branch mixed collector lines was studied and verified. The research results show that, compared with the random forest algorithm, decision tree algorithm, CNN, and LSTM neural network, the proposed method significantly improved the location accuracy and is more suitable for the fault distance measurement requirements of collector lines in the complex environments of wind farms. The research conclusions provide technical support and a reference for the actual operation and maintenance of wind farms. Full article

This study investigates aerodynamic degradation and power loss mechanisms in iced wind turbine blades using a hybrid methodology integrating high-fidelity CFD simulations (ANSYS Fluent, FENSAP-ICE, STAR-CCM+ with SST k-ω turbulence model and shallow-water icing theory) with controlled wind tunnel experiments (10–15 m/s). Three ice accretion types, glaze, mixed, and rime, on NACA0012 airfoils are quantified. Glaze ice at the leading edge induces the most severe degradation, reducing lift by 34.9% and increasing drag by 97.2% at 10 m/s. STAR-CCM+ analyses reveal critical pressure anomalies and ice morphology-dependent flow separation patterns. These findings inform the optimization of anti-icing strategies for cold-climate wind farms. Full article

With the rapid development of renewable energy, wind power forecasting has become increasingly important in power system scheduling and management. However, the forecasting of wind power is subject to the complex influence of multiple variable features and their interrelationships, which poses challenges to traditional forecasting methods. As an effective feature extraction technique, representation learning can better capture complex feature relationships and improve forecasting performance. This paper proposes a two-stage forecasting framework based on lightweight representation learning and multivariate feature mixing. In the representation learning stage, the efficient spatial pyramid module is introduced to reconstruct the dilated convolution part of the original TS2Vec representation learning model to fuse multi-scale features and better improve the gridding effect caused by dilated convolution while significantly reducing the number of parameters in the representation learning model. In the feature mixing stage, TSMixer is used as the basic model to extract cross-dimensional interaction features through its multivariate linear mixing mechanism, and the SimAM lightweight attention mechanism is introduced to adaptively focus on the contribution of key time steps and optimize the allocation of forecasting weights. The experimental results conducted on actual wind farm datasets show that the model proposed in this paper significantly improves the accuracy of wind power forecasting, providing new ideas and methods for the field of wind power forecasting. Full article

Floating offshore wind turbines present peculiar characteristics that make them particularly interesting for the implementation of wind farm control strategies such as wake mixing to increase the overall power production. Wake mixing is achieved by generating an unsteady cyclical load on the blades of upwind turbines to decrease the wind deficit on downwind turbines. The possibility of exploiting the yaw motion of a floating offshore wind turbine allows for amplified wake mixing or a reduction in the workload of the control mechanism. To amplify the yaw motion of the system at a selected excitation frequency, a multi-disciplinary optimization framework was developed to modify selected properties of the floating platform and mooring line configuration of the DTU 10 MW turbine on the Triple Spar platform. At the same time, operational and structural constraints were taken into account. A simulation-based approach was chosen to design a floating platform and mooring line configuration that were optimized to integrate with the new control strategy based on wake mixing in floating offshore wind farms. Modifying the floating platform spar arrangement and mooring line properties allowed us to tune the yaw natural frequency of the system in accordance with the excitation frequency of the wake control technique and amplify the yaw motion while controlling the deviations of the operational constraints and costs from the baseline configuration. Full article

Brazil has emerged as one of the global leaders in adopting renewable energy, standing out in the implementation of onshore wind energy and, more recently, in the development of future offshore wind energy projects. Onshore wind energy has experienced exponential growth in the last decade, positioning Brazil as one of the countries with the largest installed capacity in the world by 2023, with 30 GW. Wind farms are mainly concentrated in the northeast region, where winds are constant and powerful, enabling efficient and cost-competitive generation. Although in its early stages, offshore wind energy presents significant potential of 1228 GW due to Brazil’s extensive coastline, which exceeds 7000 km. Offshore wind projects promise greater generating capacity and stability, as offshore winds are more constant than onshore winds. However, their development faces challenges such as high initial costs, environmental impacts on marine ecosystems, and the need for specialized infrastructure. From a sustainability perspective, this article discusses that both types of wind energy are key to Brazil’s energy transition. They reduce dependence on fossil fuels, generate green jobs, and foster technological innovation. However, it is crucial to implement policies that foster synergy with green hydrogen production and minimize socio-environmental impacts, such as impacts on local communities and biodiversity. Finally, the article concludes that by 2050, Brazil is expected to consolidate its leadership in renewable energy by integrating advanced technologies, such as larger, more efficient turbines, energy storage systems, and green hydrogen production. The combination of onshore and offshore wind energy and other renewable sources could position the country as a global model for a clean, sustainable, and resilient energy mix. Full article

The Raglan mining site in northern Quebec relies on diesel for electricity and heat generation, resulting in annual emissions of 105,500 tons of CO₂ equivalent. This study investigates the feasibility of decarbonizing the site’s power generation system by integrating a renewable energy network of wind turbines and a pumped hydro storage plant (PHSP). It uniquely integrates PHSP modeling with a dynamic analysis of variable wind speeds and extreme climatic conditions, providing a novel perspective on the feasibility of renewable energy systems in remote northern regions. MATLAB R2024b-based simulations assessed the hybrid system’s technical and economic performance. The proposed system, incorporating a wind farm and PHSP, reduces greenhouse gas (GHG) emissions by 50%, avoiding 68,500 tons of CO₂ equivalent annually, and lowers diesel consumption significantly. The total investment costs are estimated at 2080 CAD/kW for the wind farm and 3720 CAD/kW for the PHSP, with 17.3 CAD/MWh and 72.5 CAD/kW-year operational costs, respectively. The study demonstrates a renewable energy share of 52.2% in the energy mix, with a payback period of approximately 11 years and substantial long-term cost savings. These findings highlight the potential of hybrid renewable energy systems to decarbonize remote, off-grid industrial operations and provide a scalable framework for similar projects globally. Full article

The modern electric power-distribution grid is increasingly integrating various components, including distributed sources of renewable energy, electric vehicles (EVs), and Bitcoin-mining operations. This shift signals a transformation in energy management and consumption. The growing presence of solar and wind energy contributes to a more diversified and sustainable energy mix, while the incorporation of EVs advances the pursuit of sustainable transportation. However, the addition of Bitcoin-mining operations introduces new complexities, raising concerns over energy consumption and grid stability. To address these challenges, this study conducted 24-h load-flow analyses on a power system that integrates intermittent renewable sources, Bitcoin-mining farms, and EVs, considering the variability in power demand. The analysis examined changes in bus voltage and power factor throughout the day using a Matlab/Simulink 2016b program. Simulation results indicate that bus voltages remained relatively stable despite the fluctuations in the generation of renewable energy and load variations. However, as the penetration of distributed generation of renewable energy increased, power factors exhibited a significant decline, dropping as low as 0.59 at certain buses due to increased injection of reactive power. At 13:00, during the period of peak generation of solar energy and high EV demand, voltage levels increased by up to 1.1 p.u., while power factors deteriorated significantly. This study highlights the importance of limiting the production of reactive power from local renewable sources under high-production conditions to sustain power factor stability. The findings emphasize the importance of detecting unfavorable system conditions and implementing safeguards to ensure reliable resource management in the evolving landscape of electric power-distribution grids. Full article

In recent wind power forecasting studies, deep neural networks have shown powerful performance in estimating future power from wind power data. In this paper, a pseudo-twin neural network model of full multi-layer perceptron is proposed for power forecasting in wind farms. In this model, the input wind power data are divided into physical attribute data and historical power data. These two types of input data are processed separately by the feature extraction module of the pseudo-twin structure to obtain physical attribute features and historical power features. To ensure comprehensive integration and establish a connection between the two types of extracted features, a feature mixing module is introduced to cross-mix the features. After mixing, a set of multi-layer perceptrons is used as a power regression module to forecast wind power. In this paper, simulation research is carried out based on measured data. The proposed model is compared with mainstream models such as CNN, RNN, LSTM, GRU, and hybrid neural network. The results show that the MAE and RMSE of the single-step forecasting of the proposed model are reduced by up to 21.88% and 16.85%, respectively. Additionally, the MAE and RMSE of the 1 h rolling forecasting (six steps ahead) are reduced by up to 31.58% and 40.92%, respectively. Full article

The problem of ice accretion on wind turbine blades seriously affects the safe operation and efficiency of wind farms. In this paper, FENSAP-ICE software is adopted to conduct research on this issue. The mechanism of ice accretion on wind turbine blades is analyzed, including the formation process of ice accretion, as well as three types of ice accretion, namely glaze ice, rime ice, and mixed ice, and their occurrence conditions. A prediction method for ice accretion on the blades is elaborated. A numerical calculation method is employed, and the accuracy of the numerical model is verified through the design of multiple groups of numerical simulation calculations for ice accretion on the NACA0012 airfoil. Using this model, the laws governing how environmental temperature, incoming flow rate, liquid water content, and droplet diameter influence ice accretion on wind turbine blades are studied. It is found that reducing the environmental temperature and increasing the incoming flow rate and the liquid–liquid water content will increase the ice accretion mass and area. Increasing the droplet diameter will increase the ice accretion mass, but the ice-covered area will decrease and will concentrate towards the leading edge. Full article

Wind-turbine blades pose significant disposal challenges in the wind-energy sector due to the increasing demand for wind farms. Therefore, this study researched the revaluation of Raw-Crushed Wind-Turbine Blade (RCWTB), obtained through a non-selective blade crushing process, as a partial substitute for aggregates in Self-Compacting Concrete (SCC). The aim was to determine the most adequate water/cement (w/c) ratio and amount of superplasticizing admixtures required to achieve adequate flowability and 7-day compressive strength in SCC for increasing proportions of RCWTB, through the production of more than 40 SCC mixes. The results reported that increasing RCWTB additions decreased the slump flow of SCC by 6.58% per 1% RCWTB on average, as well as the compressive strength, although a minimum value of 25 MPa was always reached. Following a multi-criteria decision-making analysis, a w/c ratio of 0.45 and a superplasticizer content of 2.8% of the cement mass were optimum to produce SCC with up to 2% RCWTB. A w/c ratio of 0.50 and an amount of superplasticizers of 4.0% and 4.6% were optimum to produce SCC with 3% and 4% RCWTB, respectively. Concrete mixes containing 5% RCWTB did not achieve self-compacting properties under any design condition. All modifications of the SCC mix design showed statistically significant effects according to an analysis of variance at a confidence level of 95%. Overall, this study confirms that the incorporation of RCWTB into SCC through a careful mix design is feasible in terms of flowability and compressive strength, opening a new research avenue for the recycling of wind-turbine blades as an SCC component. Full article

As the penetration of renewable energy sources (RESs), particularly wind power, continues to rise, the uncertainty in power systems increases. This challenges traditional optimal power flow (OPF) methods. This paper proposes a Calibrated Safety Constraints Optimal Power Flow (CSCOPF) model that uses the Improved Acceleration Coefficient-Based Bee Swarm algorithm (IACBS) in combination with the equivalent current injection (ECI) model. The proposed method addresses key challenges in wind-integrated power systems by ensuring preventive safety scheduling and enabling effective power incident safety analysis (PISA). This improves system reliability and stability. This method incorporates mixed-integer programming, with continuous and discrete variables representing power outputs and control mechanisms. Detailed numerical simulations were conducted on the IEEE 30-bus test system, and the feasibility of the proposed method was further validated on the IEEE 118-bus test system. The results show that the IACBS algorithm outperforms the existing methods in both computational efficiency and robustness. It achieves lower generation costs and faster convergence times. Additionally, the CSCOPF model effectively prevents power grid disruptions during critical incidents, ensuring that wind farms remain operational within predefined safety limits, even in fault scenarios. These findings suggest that the CSCOPF model provides a reliable solution for optimizing power flow in renewable energy-integrated systems, significantly contributing to grid stability and operational safety. Full article

A common retrofitting method for wind farms is the replacement of low-power turbines with high-power ones. The determination of the optimal replacement sequence for the purpose of maximizing revenue is a significant challenge. This paper employs a combination of FLORIS and a sequencing algorithm to simulate the power output resulting from the replacement of 1.5 MW small turbines with 5 MW large turbines. This study demonstrates that the optimal strategy for maximizing the overall power output is to replace the turbines in the first column. When the turbines situated in the first column have already undergone replacement or are unable to be replaced due to the characteristics of the terrain, it would be prudent to prioritize those in the final column. In the case of staggered arrangements, priority should be given to diagonal points that do not have turbines situated behind them. In the case of replacing the same number of large wind turbines, the preferred replacement option has a minimal impact on the power output of the existing small wind turbines, with an estimated reduction of 0.67%. This effectively enhances the economic efficiency of wind farm renovation. Full article