Search Results (168)

The wind energy resources in the northwestern desert and semi-desert grassland regions of China are abundant. However, the ramifications of large-scale centralized wind farm operations on terrestrial rodents remain incompletely understood. In May and September 2024, we employed a grid sampling method combined with burrow counting and kernel density analysis to investigate the spatial distribution of Alashan ground squirrel (Spermophilus alashanicus) burrows in different wind turbine power zones (control, 750 kW, 1500 kW, 2000 kW, and 2500 kW) at the Taiyangshan wind farm in China. Using generalized additive models and structural equation models, we analysed the relationship between burrow spatial distribution and environmental factors. The results revealed no significant linear correlation between burrow density and turbine layout density, but was significantly positively correlated with turbine power (p < 0.05). The highest burrow density was observed in the 2500 kW zone, with values of 24.43 ± 7.18 burrows/hm² in May and 21.29 ± 3.38 burrows/hm² in September (p < 0.05). The squirrels exhibited a tendency to avoid constructing burrows within the rotor sweeping areas of the turbines. The burrow density distribution exhibited a multinuclear clustering pattern in both May and September, with a northwest–southeast spatial orientation. Turbine power, aspect, and plan convexity had significant positive effects on burrow density, whereas vegetation height had a significant negative effect. Moreover, vegetation height indirectly influenced burrow density through its interactions with turbine power and relief degree. Under the combined influence of turbine power, topography, and vegetation, Alashan ground squirrels preferred habitats in low-density, high-power turbine zones with shorter vegetation, sunny slopes, convex landforms, and minimal disturbance. Full article

This study presents community-specific modeling approaches for simulating power injection from photovoltaic and wind energy systems in a German metropolitan region. Developed within the EMN_SIM project and based on openly accessible datasets, the methods are broadly transferable across Germany. For PV, a cluster-based model groups systems by geographic and technical characteristics, using real weather data to reduce computational effort. Validation against measured specific yields shows strong agreement, confirming energetic accuracy. The wind model operates on a per-turbine basis, integrating technical specifications, land use, and high-resolution wind data. Energetic validation indicates good consistency with Bavarian reference values, while power-based comparisons with selected turbines show reasonable correlation, subject to expected limitations in wind data resolution. The resulting high-resolution generation profiles reveal spatial and temporal patterns valuable for grid planning and targeted policy design. While further validation with additional measurement data could enhance model precision, the current results already offer a robust foundation for urban energy system analyses and future grid integration studies. Full article

In recent times, blades that have the ability to change shape passively or actively have garnered interest due to their ability to optimize blade performance for varying flow conditions. Various versions of morphing exist, from simple chord length changes to full blade morphing with multiple degrees of freedom. These blades can incorporate smart materials or mechanical actuators to modify the blade shape to suit the wind conditions. Morphing blades have shown an ability to improve performance in simulations. These simulations show increased performance in Region 2 (partial load) operating conditions. This study focuses on the effects of the wake for a flexible wind turbine with actively variable twist angle distribution (TAD) to improve the energy production capabilities of morphing structures. These wake effects influence wind farm performance for locally clustered turbines by extracting energy from the free stream. Hence, the development of better wake models is critical for better turbine design and controls. This paper provides an outline of some approaches available for wake modeling. FLORIS (FLow Redirection and Induction Steady-State) is a program used to predict steady-state wake characteristics. Alongside that, the Materials and Methods section shows different modeling environments and their possible integration into FLORIS. The Results and Discussion section analyzes the 20 kW wind turbine with previously acquired data from the National Renewable Energy Laboratory’s (NREL) AeroDyn v13 software. The study employs FLORIS to simulate steady-state non-linear wake interactions for the nine TAD shapes. These TAD shapes are evaluated across Region 2 operating conditions. The previous study used a genetic algorithm to obtain nine TAD shapes that maximized aerodynamic efficiency in Region 2. The Results and Discussion section compares these TAD shapes to the original blade design regarding the wake characteristics. The project aims to enhance the understanding of FLORIS for studying wake characteristics for morphing blades. Full article

Two-phase expansion processes have emerged as a promising technology for enhancing energy efficiency in power generation, refrigeration, waste heat recovery systems (for example, partially evaporated organic Rankine cycle, organic flash cycle, and trilateral flash cycle), oil and gas, and other applications. However, despite their potential, widespread adoption is hindered by inherent challenges, particularly energy losses that reduce operational efficiency. This review systematically evaluates the current state of two-phase expansion technologies, focusing on the root causes, impacts, and mitigation strategies for expansion losses. This work used Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Using the PRISMA framework, 52 relevant publications were identified from Scopus and Web of Science to conduct the systematic review. A preliminary co-occurrence analysis of keywords was also conducted using VOSviewer version 1.6.20. Three clusters were observed in this co-occurrence analysis. However, the results may not be significant. Therefore, the extended work was done through a comprehensive analysis of experimental and simulation studies from the literature. This study identifies critical loss mechanisms in key components of two-phase expanders, such as the nozzle, diffuser, rotor, working chamber, and vaneless space. Also, losses arising from wetness, such as droplet formation, interfacial friction, and non-equilibrium phase transitions, are examined. These phenomena degrade performance by disrupting flow stability, increasing entropy generation, and causing mechanical erosion. Several losses in the turbine and volumetric expanders operating in two-phase conditions are reported. Ejectors, throttling valves, and flashing flow systems that exhibit similar challenges of losses are also discussed. This review discusses the mitigation and the strategy to minimize the two-phase expansion losses. The geometry of the inlet of the two-phase expanders plays an important role, which also needs improvement to minimize losses. The review highlights recent advancements in addressing these challenges and shows optimization opportunities for further research. Full article

Single-phase short-circuit faults are severe asymmetrical fault modes in high renewable energy power systems. They can easily cause large-scale renewable energy to enter the low-voltage ride-through (LVRT) state. When such symmetrical or asymmetrical faults occur in the transmission channels of high-proportion wind power clusters, they may trigger the tripping of thermal power units and a transient voltage drop in most wind turbines in the high-proportion wind power area. This causes an instantaneous active power deficiency and poses a low-frequency oscillation risk. To address the deficiencies of wind turbine units in fault ride-through (FRT) and active frequency regulation capabilities, a power emergency support scheme for wind power clusters based on doubly fed variable-speed pumped storage dynamic excitation is proposed. A dual-channel energy control model for variable-speed pumped storage units is established via AC excitation control. This model provides inertia support and FRT energy simultaneously through AC excitation control of variable-speed pumped storage units. Considering the transient stability of the power network in the wind power cluster transmission system, this scheme prioritizes offering dynamic reactive power to support voltage recovery and suppresses power oscillations caused by power deficiency during LVRT. The electromagnetic torque completed the power regulation within 0.4 s. Finally, the effectiveness of the proposed strategy is verified through modeling and analysis based on the actual power network of a certain region in Northeast China. Full article

Dilemmas related to the development of demand for renewable energy encourage continuous evaluation of such investments in various locations, taking into account market and environmental conditions. The conducted study concerns the analysis of the profitability of investment in a 1.65 MW wind turbine with a hub height of 70 m in various zones in Poland. The analysis was performed using the clustering method (cluster analysis and the Czekanowski diagram). Computer simulation was also used using the Hybrid Optimization of Multiple Energy Resources (HOMER), ver. x64 3.18.4 software. As a result, three zones were distinguished that ensure differentiation in the rates of return on investment in wind energy. The authors positively verified the hypothesis about the spatial differentiation of profitability in relation to the examined factors. The justification for investments in wind farms was demonstrated and factors determining their profitability were indicated. It was emphasized that, in the case of wind farms, energy production is relatively predictable, which shapes the benefits for investors, and facilitates financial planning and long-term return on investment. Full article

When power system equipment fails due to disasters, resulting in the isolation of parts of the network, the loads within the isolated system cannot be guaranteed a continuous power supply. However, for critical loads—such as hospitals or data centers—continuous power supply is of utmost importance. While distributed energy resources (DERs) within the network can supply power to some loads, outages may lead to compensation and fairness issues regarding the unsupplied loads. In response, this study proposes a methodology to determine the appropriate power contract price for key customers by estimating the unsupplied power demand for critical loads in isolated networks and incorporating both outage compensation costs and voltage stability penalties. The microgrid under consideration comprises DERs—including electric vehicles (EVs), fuel cell electric vehicles (FCEVs), photovoltaic (PV) plants, and wind turbine (WT) plants—as well as controllable resources such as battery energy storage systems (BESS) and hydrogen energy storage systems (HESS). It serves both residential load clusters and critical loads associated with social infrastructure. The proposed methodology is structured in two stages. In normal operating conditions, optimal scheduling is simulated using second-order conic programming (SOCP). In the event of a fault, mixed-integer SOCP (MISOCP) is employed to determine the optimal load shedding strategy. A case study is conducted using the IEEE 123 bus test node system to simulate the outage compensation cost calculation and voltage penalty assessment processes. Based on this analysis, a contract price for key customers that considers both disaster-induced outages and voltage impacts is presented. Full article

Low-emission hydrogen generation systems require large amounts of energy from renewable energy sources. This article characterizes the production of low-emission hydrogen, emphasizing its scale and the necessity for its continuity. For hydrogen production defined in this way, it is possible to select the appropriate renewable energy sources. The research part of the article presents a case study of the continuous production of large amounts of hydrogen. Daily production capacities correspond to the demand for the production of industrial chemicals and artificial fertilizers or for fueling a fleet of hydrogen buses. The production was placed in the Lublin region in Poland, where there is a large demand for low-emission hydrogen and where there are favorable conditions for the production of energy from a photovoltaic–wind mix. Statistical and probabilistic analyses were performed related to the generation of power by a photovoltaic system with a peak power of 3.45 MWp and a wind turbine with an identical maximum power. The conducted research confirmed the complementarity and substitutability relationship between one source and another within the energy mix. Then, unsupervised clustering was applied using the k-Means algorithm to divide the state space generated in the power mix. The clustering results were used to perform an operational analysis of the low-emission hydrogen generation system from a renewable energy sources mix. In the analyzed month of April, 25% of the energy generated in the photovoltaic–wind mix came from the photovoltaic system. The low-emission hydrogen generation process was in states (clusters), ensuring that the operation of the electrolyzer with nominal power amounted to 57% of the total operating time in that month. In May, the share of photovoltaics in the generated power was 45%. The low-emission hydrogen generation process was in states, ensuring that the operation of the electrolyzer with nominal power amounted to 43% of the total time in that month. In the remaining states of the hydrogen generation process, the power must be drawn from the energy storage system. The cluster analysis also showed the functioning of the operating states of the power generation process from the mix, which ensures the charging of the energy storage. The conducted research and analyses can be employed in planning and implementing effective climate and energy transformations in large companies using low-emission hydrogen. Full article

To effectively account for the impact of fluctuations in the power generation efficiency of renewable energy sources such as photovoltaics (PVs) and wind turbines (WTs), as well as the uncertainties in load demand within an integrated energy system (IES), this article develops an IES model incorporating power generation units such as PV, WT, microturbines (MTs), Electrolyzer (EL), and Hydrogen Fuel Cell (HFC), along with energy storage components including batteries and heating storage systems. Furthermore, a demand response (DR) mechanism is introduced to dynamically regulate the energy supply–demand balance. In modeling uncertainties, this article utilizes historical data on PV, WT, and loads, combined with the adjustability of decision variables, to generate a large set of initial scenarios through the Monte Carlo (MC) sampling algorithm. These scenarios are subsequently reduced using a combination of the K-means clustering algorithm and the Simultaneous Backward Reduction (SBR) technique to obtain representative scenarios. To further manage uncertainties, a distributionally robust optimization (DRO) approach is introduced. This method uses 1-norm and ∞-norm constraints to define an ambiguity set of probability distributions, thereby restricting the fluctuation range of probability distributions, mitigating the impact of deviations on optimization results, and achieving a balance between robustness and economic efficiency in the optimization process. Finally, the model is solved using the column and constraint generation algorithm, and its robustness and effectiveness are validated through case studies. The MC sampling method adopted in this article, compared to Latin hypercube sampling followed by clustering-based scenario reduction, achieves a maximum reduction of approximately 17.81% in total system cost. Additionally, the results confirm that as the number of generated scenarios increases, the optimized cost decreases, with a maximum reduction of 1.14%. Furthermore, a comprehensive cost analysis of different uncertainties modeling approaches is conducted, demonstrating that the optimization results lie between those obtained from stochastic optimization (SO) and robust optimization (RO), effectively balancing conservatism and economic efficiency. Full article

In line with the strategic plan for emerging industries in China, renewable energy sources like wind power and photovoltaic power are experiencing vigorous growth, and the number of electric vehicles in use is on a continuous upward trend. Alongside the optimization of the distribution network structure and the extensive application of energy storage technology, the active distribution network has evolved into a more flexible and interactive “source–grid–load–storage” diversified structure. When electric vehicles are plugged into charging piles for charging and discharging, it inevitably exerts a significant impact on the control and operation of the power grid. Therefore, in the context of the extensive integration of electric vehicles, delving into the charging and discharging behaviors of electric vehicle clusters and integrating them into the optimization of the active distribution network holds great significance for ensuring the safe and economic operation of the power grid. This paper adopts the two-stage “constant-current and constant-voltage” charging mode, which has the least impact on battery life, and classifies the electric vehicle cluster into basic EV load and controllable EV load. The controllable EV load is regarded as a special “energy storage” resource, and a corresponding model is established to enable its participation in the coordinated control of the active distribution network. Based on the optimization and control of the output behaviors of gas turbines, flexible loads, energy storage, and electric vehicle clusters, this paper proposes a two-layer coordinated control model for the scheduling layer and network layer of the active distribution network and employs the improved multi-target beetle antennae search optimization algorithm (MTTA) in conjunction with the Cplex solver for solution. Through case analysis, the results demonstrate that the “source–grid–load–storage” coordinated control of the active distribution network can fully tap the potential of resources such as flexible loads on the “load” side, traditional energy storage, and controllable EV clusters; realize the economic operation of the active distribution network; reduce load and voltage fluctuations; and enhance power quality. Full article

Energy generated from wind (in the form of wind farms (WFs)) is expected to help alleviate rising energy demand in Saudi Arabia, driven by industrial development and population growth. However, before implementing wind farms, conducting a comprehensive wind resource assessment (WRA) study is of paramount importance. This paper presents the analysis of the wind resource potential of a site in Yanbu city, which is located on the western coastal area of Saudi Arabia, using a comprehensive study. The hourly data on wind speed and direction over a one-year period was used in the presented analysis. The plant capacity factor (CF) and annual energy production (AEP) are evaluated for more than 100 commercial wind turbines (WTs). The highest AEP was achieved by the ‘Enercon E126/7.5 MW’ turbine, generating 14.49 GWh, with a corresponding CF of 21.82%. In contrast, the lowest AEP was observed for the ‘Northern Power d’ turbine, producing only 0.13 GWh, with a CF of 14.89%. The highest CF was recorded for the ‘Leitwind LTW104/2.0 MW’ turbine at 40.67%, corresponding to an AEP of 7.12 GWh. The results obtained are very valuable for designers in selecting the appropriate WT to obtain the predicted AEP and CF with the appropriate turbine class. Furthermore, this study applied the K-means clustering algorithm to classify WTs into three distinct categories. Building on this classification, synthetic datasets representing tailored WT configurations were generated—a novel methodology that enables the simulation of site-specific designs not yet available in existing market offerings. These datasets equip wind farm developers with the ability to define WT specifications for manufacturers, guided by two key criteria: the site’s wind resource profile and the target performance metrics of the WT. Full article

Direct air-cooling systems use air instead of water as a cooling medium, so they are easily affected by meteorological and hydrological conditions. In this paper, considering the complex geographic conditions around the direct air-cooling units, Gambit is used to model the direct air-cooling system, including the complex mountainous terrain, and the performance simulation of the direct air-cooling system and the complex mountainous terrain near the power plant is realized by combining the CFD method with the MATLAB mathematical model of the power plant. Through the simulation, the operation of the ACC system under various meteorological conditions is obtained, and the influence of environmental factors and complex geographic conditions on the performance of the ACC system is investigated and further analyzed for the special case of the back-furnace wind. On this basis, a clustering analysis algorithm was used to obtain the results of turbine zoning in different wind directions and to analyze the physical field shifts of the units caused by geographic factors. Full article

Accurate recognition of both cold wave occurrence and wind power during cold wave events is critically significant to ensure the stable operation of the power system under extreme meteorological conditions. In view of the above problems, this paper proposes a cold wave event recognition method and a day-ahead wind farm cluster power forecasting model. In order to effectively recognize cold wave events, this paper proposes a cold wave discrimination criterion based on wind turbine operation characteristics and NWP data. The proposed recognition criterion employs seasonal meteorological features processed through a GAN-enhanced multi-modal U-Net architecture, effectively mitigating sample scarcity issues. To improve the forecasting accuracy of wind farm cluster power during cold wave events, a combined forecasting model based on the Ns-Transformer model is constructed by combining NWP data with cold wave recognition results. A wind farm cluster is taken as an example to verify the effectiveness of the proposed method. Compared to that of LSTM, Random Forest, BP, and Transformer models, the RMSE of the proposed method is reduced by 5.65%, 5.58%, 4.81%, and 0.44%, respectively, during cold wave occurrence seasons. The results show that the proposed method is superior to the conventional method in terms of cold wave recognition ability and wind power forecasting accuracy. Full article

Wind energy plays a crucial role in global sustainable development, and accurately estimating the number and spatial distribution of wind turbines is crucial for strategic planning and energy allocation. To address the critical need for wind turbine detection and spatial distribution analysis, this study develops YOLOv5_CDB, an enhanced detection framework based on the YOLOv5 model. The proposed method incorporates two key components: the Convolutional Block Attention Mechanism (CBAM) to improve feature representation and the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for spatial density clustering. The method is applied to 2 m resolution World Imagery data. It detects both tubular and lattice wind turbines by analyzing key features, including turbine towers and shadows. The YOLOv5_CDB demonstrates a substantial enhancement in performance when compared with the YOLOv5s. The F1-score shows an increase of 1.39%, and the mean average precision (mAP) exhibits a 1.5% improvement. Meanwhile, the precision (P) and recall (R) values are recorded at 95.97% and 91.18%, respectively. Furthermore, YOLOv5_CDB evinces consistent performance advantages, outperforming state-of-the-art models including YOLOv8s, YOLOv12s, and RT-DETR by 1.84%, 3.98%, and 1.77% in terms of F1-score and by 3.7%, 4.5%, and 3.0% in terms of mAP, respectively. The YOLOv5_CDB model has been demonstrated to show superior performance in the global wind turbine detection domain, thereby providing a foundation for the management of wind farms and the development of sustainable energy. Full article

With the rapid growth of wind power generation, accurate wind energy prediction has emerged as a critical challenge, particularly due to the highly nonlinear nature of wind speed data. This paper proposes a modularized Echo State Network (MESN) model to improve wind energy forecasting. To enhance generalization, the wind speed data is first decomposed into time series components, and Modes-cluster is employed to extract trend patterns and pre-train the ESN output layer. Furthermore, Turbines-cluster groups wind turbines based on their wind speed and energy characteristics, enabling turbines within the same category to share the ESN output matrix for prediction. An output integration module is then introduced to aggregate the predicted results, while the modular design ensures efficient task allocation across different modules. Comparative experiments with other neural network models demonstrate the effectiveness of the proposed approach, showing that the statistical RMSE of parameter error is reduced by an average factor of 2.08 compared to traditional neural network models. Full article