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This study delves into the challenge of efficiently digitalising wind turbine maintenance data, traditionally hindered by non‐standardised formats necessitating manual, expert intervention. Highlighting the discrepancies in past reliability studies based on different key performance indicators (KPIs), the paper underscores the importance of consistent standards, like RDS‐PP, for maintenance data categorisation. Leveraging on established digitalisation workflows, we investigate the efficacy of text classifiers in automating the categorisation process against conventional manual labelling. Results indicate that while classifiers exhibit high performance for specific datasets, their general applicability across diverse wind farms is limited at the present stage. Furthermore, differences in failure rate KPIs derived from manual versus classifier‐processed data reveal uncertainties in both methods. The study suggests that enhanced clarity in maintenance reporting and refined designation systems can lead to more accurate KPIs. [ABSTRACT FROM AUTHOR]

Eight scenarios where high efficiency reversible solid oxide cells (rSOC) are combined with an offshore wind farm are identified. Thanks to the PyPSA power system modelling tool combined with a sensitivity study, optimized rSOC system capacities, hydrogen storage capacities, and subsea cable connection capacities are investigated under various combinations of rSOC system capital cost, prices paid for hydrogen, and electricity prices, which give indications on the most profitable scenario for offshore hydrogen production from a 600 MW wind farm situated 60 km from shore. Low electricity prices (yearly average 45 £/MWh) combined with mild fluctuations (standard deviation 6 or 13 £/MWh) call for dedicated hydrogen production when the hydrogen price exceeds 4 £/kg. High electricity prices (yearly average 118 or 204 £/MWh), combined with extreme fluctuations (standard deviation between 73 and 110 £/MWh), make a reversible system economically profitable. The amount of hydrogen which is recommended to be reconverted into electricity depends on the price paid for hydrogen. Comparison of the optimized cases to the default case of a wind farm without hydrogen production improved profit by at least 3% and up to 908%. Comparison to the default case of dedicated hydrogen production, showed that in the case of low hydrogen prices, an unprofitable scenario can be made profitable, and improvement of profit in the case of a profitable default case starts at 4% and reaches numbers as high as 324%. [ABSTRACT FROM AUTHOR]

This study explores the optimal balance between grid‐forming (GFM) and grid‐following (GFL) converter capacities within power stations to ensure stable operations. The investigation introduces a novel, generic modelling approach for analysing multiple converter systems in the wind and photovoltaic power plants. The method aims to elucidate the dynamic characteristics of the converters in power plants, particularly focusing on the continuity and existence of the equilibrium manifolds and their impact on system stability. Findings reveal a pronounced difference in the recovery capabilities of GFM and GFL following synchronization losses, highlighting an asymmetry in their abilities. Specifically, GFL converters exhibit more effectiveness in reinstating synchrony after synchronization losses caused by GFM. Conversely, GFM demonstrates a lesser capacity to recover from synchronization losses induced by GFL. Furthermore, analysis indicates that when the capacity ratio of GFL to the system's short‐circuit capacity significantly exceeds that of GFM (exceeding a 1:5 ratio), the system experiences an absence of a stable equilibrium point, thereby affecting the synchronization stability of GFM. These conclusions have been validated through joint controller hardware‐in‐the‐loop testing. [ABSTRACT FROM AUTHOR]

Integration of large‐scale wind farms (WFs) into the grid has to meet the critical constraints set in the national grid code. Wind farm operators (WFOs) are inclined to comply with these constraints and avoid heavy penalty costs for violating such regulations. However, this may result in reduced power sent to the grid. Moreover, the addition of new rules to account for the increased penetration of WFs brings challenges to the profitability of the WFs. A battery energy storage system (BESS), if sized optimally, can be a reliable method to fulfill the grid code requirements without sacrificing profit. This paper provides a techno‐economic model to find the optimal rated capacity and power for a BESS in WFs. This optimization model takes the absolute production and delta production constraints into account. Two approaches are studied for integrating these constraints into the grid code. It is shown that the flexible strategy financially outperforms the strict addition of the new rules. This will be useful, especially to attract investments in wind energy projects despite the abovementioned limitations in the grid code. All the modeling and analysis are done for a potential offshore wind power plant (OWPP) in Turkey. Simulation results show the effectiveness of the optimal BESS in increasing the amount of energy delivered to the grid and improving the profitability of the OWPP. [ABSTRACT FROM AUTHOR]

The rapid development of the wind industry is accompanied by increasing environmental impacts. Currently, there is a lack of research on the impacts of offshore wind farm (OWF) on tropical cyclone (TC) intensity, including the mechanisms involved. This research is carried out by using a coupled and an uncoupled numerical model to investigate the impact of OWF on an autumn TC in the northeastern South China Sea. The results show that the wind speed deficit caused by OWF leads to an increase in surface pressure on the inflow side. This causes the surface pressure in the TC periphery to increase by advection, even if the TC is some distance away from the OWF. The increase in pressure gradient from the periphery to the TC center enhances the TC secondary circulation, thereby intensifying the TC. When the TC enters the OWF, the above mechanisms weaken and the ocean dominates the TC intensification. This is because the reduction in wind speed caused by the OWF results in a weaker sea surface current velocity, which weakens the flow of upstream cold water into the OWF, warming the sea surface temperature (SST) within the OWF. This implies that the horizontal gradient of the local SST is an important factor to be considered in the development of OWF. Sensitivity experiments indicate that OWF can also intensify other types of TC, and that higher cut‐out wind speeds lead to stronger intensification effects. These results also provide a new perspective on TC intensity forecasts. Plain Language Summary: The rapid development of the offshore wind industry has significant impacts on the surrounding environment. There is limited research on the impact of offshore wind farms on the tropical cyclone (TC) intensity. This study indicates that wind farms in the northern South China Sea intensify an autumn TC that makes landfall in China. Wind farms intensify the TC by increasing the surface pressure in the TC periphery when the TC is far from the wind farm. When the TC is close to the wind farm, it is the warming sea water within the wind farm that intensifies the TC. Tropical cyclone can also be slightly intensified when the number of turbines is small. These results help to understand the impact of offshore wind farms on TCs and to improve TC intensity forecasts. Key Points: The tropical cyclone (TC) intensifies before approaching offshore wind farms due to increased peripheral pressure caused by wind farmsThis intensification within wind farms is mainly due to the reduced transport of cold water upstream associated with the wind speed deficitHigher cut‐out wind speeds can lead to a more pronounced intensification of the TC before it enters wind farms [ABSTRACT FROM AUTHOR]

Managers attempt to minimize spatial use conflicts in siting of offshore wind developments, but they must rely on available data to balance biological, commercial, and recreational needs. Marine spatial planning products are only as good as the data they are built upon and fishing data present major challenges due to their confidentiality and the difficulty in isolating true fishing activity. We present a methodology to increase the spatiotemporal resolution of fishing effort and exposure estimates for Southern New England scallop fishing activity using random decision forests to perform supervised classification on AIS data, with fallback to lower resolution datasets for vessels without AIS coverage. Final predictive accuracy of the tuned random forest AIS model was 97.9%, offering improvements of 24.7, 48.6, and 50% over VTR fishing footprints, and AIS and VMS speed cutoff methods, respectively, to predict whether vessel locations correspond to fishing activity. Comparison of the AIS model with VMS and VTR fallback to the VTR fishing footprints data product demonstrated that the increased precision of the AIS point data delineated as fishing dramatically changed how fishing effort, and therefore exposure in the form of fishery landings values, is distributed spatially in Southern New England wind energy areas. This is due to how the probability of fishing is distributed across location data points in the various products, which has implications for marine spatial planning and mitigation decision-making. Therefore, multiple data products should be considered when evaluating management options, as exposure estimates may differ depending on what inputs are used. The higher resolution AIS product may offer enhanced value in understanding exposure and impacts to individual vessels, especially once wind farms are under construction or operational. [ABSTRACT FROM AUTHOR]

ABSTRACT Accurate ultra‐short‐term wind power prediction techniques are crucial for ensuring the efficient and safe operation of wind farms and power systems. Combined models based on data decomposition‐prediction techniques have shown excellent performance in ultra‐short‐term wind power forecasting. This study introduces a novel ultra‐short‐term multi‐step prediction model for wind power, which integrates the sparrow search algorithm (SSA), variational mode decomposition (VMD), gated recurrent unit (GRU), and support vector regression (SVR). An optimization variational mode decomposition technique is developed by adaptively determining VMD hyperparameters using SSA. The optimization VMD decomposes the original wind power sequence into sub‐modes, and the resulting sequence of decomposed sub‐modes calculates permutation entropy (PE) values. Sub‐modes with similar PE values are combined, reorganized, and categorized into high‐frequency and low‐frequency. High‐frequency sub‐modes data with high complexity and non‐stationarity are predicted by the GRU neural network. Low‐frequency sub‐modes data with low complexity and strong nonlinearity are predicted with SVR. The proposed model was evaluated against seven others using three error metrics: MAE, RMSE, and R2, along with their corresponding enhancement percentages. Experimental results indicate that the proposed model extracts detailed and trend information from the wind power series more effectively and stably than the comparison models. It also demonstrates superior multi‐step prediction performance, offering significant value for practical engineering applications. [ABSTRACT FROM AUTHOR]

This paper proposes a method based on Computational Fluid Dynamics (CFD) and the detection of Wind Energy Extraction Latency for a given wind turbine (WT) designed for ultra-short-term (UST) wind energy forecasting over complex terrain. The core of the suggested modeling approach is the Wind Spatial Extrapolation model (WiSpEx). Measured vertical wind profile data are used as the inlet for stationary CFD simulations to reconstruct the wind flow over a wind farm (WF). This wind field reconstruction helps operators obtain the wind speed and available wind energy at the hub height of the installed WTs, enabling the estimation of their energy production. WT power output is calculated by accounting for the average time it takes for the turbine to adjust its power output in response to changes in wind speed. The proposed method is evaluated with data from two WTs (E40-500, NM 750/48). The wind speed dataset used for this study contains ramp events and wind speeds that range in magnitude from 3 m/s to 18 m/s. The results show that the proposed method can achieve a Symmetric Mean Absolute Percentage Error (SMAPE) of 8.44% for E40-500 and 9.26% for NM 750/48, even with significant simplifications, while the SMAPE of the persistence model is above 15.03% for E40-500 and 16.12% for NM 750/48. Each forecast requires less than two minutes of computational time on a low-cost commercial platform. This performance is comparable to state-of-the-art methods and significantly faster than time-dependent simulations. Such simulations necessitate excessive computational resources, making them impractical for online forecasting. [ABSTRACT FROM AUTHOR]

A comparison and evaluation of the AEP(Annual Energy Production) of a wind farm were conducted in this study with a feasibility study and using the actual operation data from the S wind farm on Jeju Island from January 2020 to December 2022. The free wind speed data were selected from the data measured from a nacelle anemometer, the correlation equation between wind speed and AEP was obtained, and the annual average wind speed for the past 20 years was predicted using the MCP method. As a result, comparing the AEP from the operation data with that estimated in the feasibility study, we found that the AEP was reduced by approximately 2.40% in 2020 and 12.14% in 2021, and increased by 6.76% in 2022. The wind speeds over the 20-year lifetimes of the wind turbines were obtained, and the AEP that could be generated at the S wind farm indicated that it could be used for operation. In the future, the S wind farm will operate at between 25% and 30% availability for the remaining 17 years of operation. If the availability falls below 25%, there will be a need to check the reasons for the deterioration of wind turbine performance and the frequency of failures. [ABSTRACT FROM AUTHOR]

Fonseca, M.; McMahon, A.; Erickson, R.; Kelly, C.; Tiggelaar, J., II, and Graham, B., 2024. Effects of the Block Island Wind Farm on benthic and epifaunal communities. Journal of Coastal Research, 40(6), 1037–1054. Charlotte (North Carolina), ISSN 0749-0208. This study reports on monitoring surveys conducted at three of the five commercially operating turbines in U.S. waters off Block Island, Rhode Island, U.S.A., with an emphasis on the final, fourth year of a Bureau of Ocean Energy Management sampling program. The monitoring focused on changes to sediments and infaunal and epifauna species abundance, richness, and diversity caused by the presence of the turbine structure. As anticipated, based on a comparison with other study results, far-field changes in benthic conditions were not evident. Clear changes to the seabed sediments and faunal composition manifested only in the immediate footprint of the turbine foundations. Aside from a localized and sustained shift in particle size, little evidence of a temporally or spatially progressive pattern (as a function of distance away from the turbines) of change in seabed physical and biological composition, or on the turbine structures themselves, was found. The lack of a systematic pattern of influence suggests that many of the intra- and interannual differences may be attributed to natural fluctuations, especially the epifauna on the turbine structures. Notably, the faunal dynamics suggest a community in constant flux and, as seen in other studies, lacking a trend toward the formation of a climax community, which is characterized by stable faunal composition. For these dynamic communities, future sampling may consider using a fixed station, repeated measures approach, as has been done in similarly dynamic, intertidal communities to manage these scales of habitat variability. [ABSTRACT FROM AUTHOR]

Wind farms with type IV wind turbines from various manufacturers are being massively put into operation. These wind turbines comply with the requirements of the grid codes of the countries where they are designed and/or manufactured, but do not factor in the specific features of the distribution networks of other countries to which they are connected. The study at issue involves a comparative analysis of the requirements of grid codes of different countries for the stable operation of wind turbines under standard disturbances. The low voltage ride through (LVRT) characteristic makes it possible to prevent wind turbine shutdowns in case of short-term voltage dips of a given depth and duration. The calculations of transient processes indicate that wind turbines may not meet the requirements of the grid code of a particular country for their stable operation. As a result, standard disturbances will block the reactive current injection and the wind turbine will be switched off. This is often caused by the relay protection devices with a time delay of 1–2 s, which are used in distribution networks and implement the functions of long-range redundancy. Excessive shutdowns of wind turbines lead to emergency rises in the loads for the generating units of conventional power plants, aggravating the post-accident conditions and disconnecting consumers of electricity. This article presents a method for checking the LVRT characteristic settings for compliance with the technical requirements for wind turbines. To prevent wind turbine outages, one should either change the configuration of the LVRT characteristic, upgrade the relay protection devices in the distribution network adjacent to the wind farm, or implement group or individual technical solutions at the wind farm. The performance of the proposed technical solutions is confirmed by the calculations of transient processes. [ABSTRACT FROM AUTHOR]

As the large-scale development of wind farms (WFs) progresses, the connection of WFs to the regional power grid is evolving from the conventional receiving power grid to the sending power grid with a high proportion of wind power (WP). Due to the randomness of WP output, higher requirements are put forward for the voltage stability of each node of the regional power grid, and various reactive power compensation devices (RPCDs) need to be rationally configured to meet the stable operation requirements of the system. This paper proposes an optimal configuration method for multi-type RPCDs in regional power grids with a high proportion of WP. The RPCDs are located according to the proposed static voltage stability index (VSI) and dynamic VSI based on dynamic voltage drop area, and the optimal configuration model of RPCDs is constructed with the lowest construction cost as the objective function to determine the installed capacity of various RPCDs. Finally, the corresponding regional power grid model for intensive access to WFs is constructed on the simulation platform to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

To mitigate the impact of wind power volatility on power system scheduling, this paper adopts the wind-storage combined unit to improve the dispatchability of wind energy. And a three-level optimal scheduling and power allocation strategy is proposed for the system containing the wind-storage combined unit. The strategy takes smoothing power output as the main objectives. The first level is the wind-storage joint scheduling, and the second and third levels carry out the unit combination optimization of thermal power and the power allocation of wind power cluster (WPC), respectively, according to the scheduling power of WPC and ESS obtained from the first level. This can ensure the stability, economy and environmental friendliness of the whole power system. Based on the roles of peak shaving-valley filling and fluctuation smoothing of the energy storage system (ESS), this paper decides the charging and discharging intervals of ESS, so that the energy storage and wind power output can be further coordinated. Considering the prediction error and the output uncertainty of wind power, the planned scheduling output of wind farms (WFs) is first optimized on a long timescale, and then the rolling correction optimization of the scheduling output of WFs is carried out on a short timescale. Finally, the effectiveness of the proposed optimal scheduling and power allocation strategy is verified through case analysis. [ABSTRACT FROM AUTHOR]

In this work, a computer Simulink model of a wind power plant has been developed, which uses a magnetic gearbox instead of a mechanical gearbox and also contains a synchronous permanent magnet generator. A separate Simulink model of a magnetic gearbox built on the basis of a modulated magnetic field in the air gap was developed, which allows to study the stability of its operation both in steady-state and transient modes. Calculations of various dynamic modes of the wind power plant’s operation were carried out, based on the developed model, such as the starting mode, an instantaneous increase in the wind speed acting on the wind turbine, and an increase in the load of the electric generator. According to the results of the calculations, it is shown that in transient modes, when short-term overloads occur, both rotors of the magnetic gearbox can fall out of synchronous motion for a certain period of time and then, depending on the parameters of the gearbox (as well as its other elements), the electromechanical system either reaches a certain operating steady-state mode or loses the ability to transfer mechanical power from the wind turbine to the generator. It has been shown that the use of a more powerful magnetic gearbox, with an increased value of the maximum magnetic torque, allows of a more overload-resistant operation of both: a gearbox and the wind plant as a whole. References 9, figures 10. [ABSTRACT FROM AUTHOR]

Satellite synthetic aperture radar (SAR) provides ocean surface wind fields at 10 m above sea level. The objective is to investigate the capability of SAR satellite StriX observations for mapping offshore wind farm wakes. The focus is on the conditions under which an apparent wind speed‐up is generated, measured in 48% of the 67 images available. The results compare well to Sentinel‐1 observations, showing a 34% wind speed‐up rate during several years based on 1171 images. Three wind speed‐up cases have been studied in detail using the mesoscale Weather, Research, and Forecasting (WRF) model with two wind farm parameterizations. At 10 m above sea level, the SAR‐based observations and WRF model compare for most cases, though only when turbulent kinetic energy (TKE) is included in the wind farm parameterization. The TKE mixes higher momentum downward in a stable atmosphere, causing surface wind speed‐up near the surface. [ABSTRACT FROM AUTHOR]

Recent studies have revealed the large potential of vertical‐axis wind turbines (VAWTs) for high‐energy‐density wind farms due to their favorable wake recovery characteristics. The present study provides an experimental demonstration and proof‐of‐concept for the wake recovery mechanism of the novel X‐Rotor VAWT. The phase‐locked flowfield is measured at several streamwise locations along the X‐Rotor's wake using stereoscopic particle image velocimetry (PIV) with fixed‐pitch offsets applied to the blades. The streamwise vortex system of the upper half of the X‐Rotor is first hypothesized and then experimentally verified. The induced wake deformations of the vortex systems are discussed in comparison with previous studies concerning traditional H‐type VAWTs. The results suggest that positive blade pitch is more favorable for accelerated wake recovery due to the dominant tip‐vortex generated on the upwind windward quadrant of the cycle. Utilizing theoretical blade load variations along the span explains distinct unsteady flow features in the near wake generated at select quadrants of the rotor rotation, shedding light on the potential of the two pitch schemes. [ABSTRACT FROM AUTHOR]

This paper presents a graph‐based dynamic yaw model to predict the dynamic response of the hub‐height velocities and the power of a wind farm to a change in yaw. The model builds on previous work where the turbines define the nodes of the graph and the edges represent the interactions between turbines. Advances associated with the dynamic yaw model include a novel analytical description of the deformation of wind turbine wakes under yaw to represent the velocity deficits and a more accurate representation of the interturbine travel time of wakes. The accuracy of the model is improved by coupling it with time‐ and space‐dependent estimates of the wind farm inflow based on real‐time data from the wind farm. The model is validated both statically and dynamically using large‐eddy simulations. An application of the model is presented that incorporates the model into an optimal control loop to control the farm power output. [ABSTRACT FROM AUTHOR]

Fast and accurate large‐eddy simulation (LES) of the atmospheric boundary layer plays a crucial role in advancing wind energy research. Long‐duration wind farm studies at turbine‐resolving scales have become increasingly important to understand the intricate interactions between large wind farms and the atmospheric boundary layer. However, the prohibitive computational cost of these turbulence‐ and turbine‐resolving simulations has precluded such modeling to be exercised on a regular basis. To that end, we implement and validate the generalized actuator disk (GAD) model in the computationally efficient, graphics processing unit (GPU)–resident, LES model FastEddy. We perform single‐turbine simulations under three atmospheric stabilities (neutral, unstable, and stable) and compare them against observations from the Scaled Wind Farm Technology (SWiFT) facility and other LES codes from the recent Wakebench turbine wake model benchmark. Our idealized LES results agree well with observed wake velocity deficit and downstream recovery across stability regimes. Turbine response in terms of rotational speed, generated power, torque, and thrust coefficient are well predicted across stability regimes and are consistent with the LES results from the benchmark. The FastEddy simulations are found to be at least two orders of magnitude more efficient than the traditional CPU‐based LES models, opening the door for realistic LES simulations of full wind plants as a viable standard practice. [ABSTRACT FROM AUTHOR]

The power performance and the wind velocity field of an onshore wind farm are predicted with machine learning models and the pseudo‐2D RANS model, then assessed against SCADA data. The wind farm under investigation is one of the sites involved with the American WAKE experimeNt (AWAKEN). The performed simulations enable predictions of the power capture at the farm and turbine levels while providing insights into the effects on power capture associated with wake interactions that operating upstream turbines induce, as well as the variability caused by atmospheric stability. The machine learning models show improved accuracy compared to the pseudo‐2D RANS model in the predictions of turbine power capture and farm power capture with roughly half the normalized error. The machine learning models also entail lower computational costs upon training. Further, the machine learning models provide predictions of the wind turbulence intensity at the turbine level for different wind and atmospheric conditions with very good accuracy, which is difficult to achieve through RANS modeling. Additionally, farm‐to‐farm interactions are noted, with adverse impacts on power predictions from both models. [ABSTRACT FROM AUTHOR]

Wind farms experience significant power losses due to wake interactions between turbines. Research shows that wake steering can alleviate these losses by redirecting the flow through the farm. However, dynamic closed‐loop implementations of wake steering are rarely presented. We present a model‐free closed‐loop control method using reinforcement learning methodology known as policy gradients in combination with recursive least squares to perform real‐time wake steering in a wind farm. We present dynamic simulations of a four‐turbine wind farm row using HAWC2Farm, implementing the reinforcement learning control method for various inflow conditions and controller configurations. By controlling the three most upstream turbines, mean power gains of 11.6±3.0% and 1.4±0.5% (95% confidence interval) are observed in partial wake and full wake conditions respectively at 7.5% turbulence intensity. The study helps to bridge the gap between theoretical wind farm control and real‐world wind farm systems. [ABSTRACT FROM AUTHOR]

The development of new wind farm control strategies can benefit from combined analysis of flow dynamics in the farm and the behavior of individual turbines within one simulation environment. In this work, we present such an environment by developing a new coupling between the large‐eddy simulation (LES) code GRASP and the multiphysics wind turbine simulation tool OpenFAST via an actuator line model (ALM). In addition, the implementation of the recently proposed filtered actuator line model (FALM) within the coupling is described. The new ALM implementation is cross‐verified with results from four other commonly used research LES codes. The results for the blade loads and the near wake obtained with the new coupling are consistent with the other codes. Deviations are observed in the far wake. The results further indicate that the FALM is able to reduce the lift and power overprediction from which the traditional ALM suffers on coarse LES grids. This new simulation environment paves the way for future wind farm simulations under realistic weather conditions by leveraging GRASP's ability to impose data from large‐scale meteorological models as boundary conditions. [ABSTRACT FROM AUTHOR]

The wind farm layout optimization problem (WFLOP) is a significant problem in the field of renewable energy. In this development of wind farm, solving the WFLOP is a crucial task, which entails placing the turbines in the wind farm in the best locations to reduce wake effects and enhance predicted power generation. In recent decades, the WFLOP problem has been solved mathematically. Meanwhile, the growing load demand led to an increase in the complexity of WFLOP. The results obtained by mathematical methods were not accurate enough to suit complexity of WFLOP. Nowadays, researchers from a variety of fields are developing nature‐inspired algorithms (NIAs) to solve difficult real‐world problems. This study is an attempt to review the most important innovations in the field of NIAs to solve the WFLOP problem. The classification of the reviewed literature is based on different applied approaches and models. In addition to specific proposals, the advantages and disadvantages of certain domains are also discussed. This study provides a future direction to the fellow researchers who are working in the field of WFLOP. [ABSTRACT FROM AUTHOR]

This work assesses a chemical plant for the conversion of biogas into negative emission "emerald hydrogen" via electrified reforming and CO 2 separation. Electrification of the reformer allows for enhanced syngas production, compact reactor designs and flexible operation, thanks to the avoidance of combustion and heat transfer through pressure walls. The integration of the process with solar and wind power generation has been assessed by part-load process simulations and plant sizing and operation optimization through yearly simulations with hourly discretization. Different European locations with different wind and solar availabilities were assessed considering (i) short- and long-term cost scenarios for renewables and battery technologies and (ii) different plant size (from 390 to 3900 Nm3/h of biogas capacity). The overarching scope of the paper is to calculate the cost of the produced hydrogen and the economic value of flexibility for plants installed in different locations, under different cost scenarios. At design load, the assessed process consumes 17.7 kWh of electricity per kg H2 and retains 96% of the biogas chemical energy in the produced hydrogen. Additionally, 76% of the biogenic carbon is recovered as high-purity liquid CO 2 , achieving up to −9 kg CO2 /kg H2 negative emissions. When powered with 95% of renewable energy, hydrogen production cost ranges from 2.5 to 2.9 €/kg for a long-term REN cost scenario and large-scale flexible plant to 5.9–7.1 €/kg for a short-term REN cost scenario and small-scale inflexible plants. For small-scale plants, flexibility allows to reduce the hydrogen production cost by 11–16% with respect to the inflexible plant in the short-term renewables cost scenario and by 1–4% in the long-term cost scenario. For large-scale plants, the adoption of a flexible plant leads to a reduction of 17–23% of the hydrogen cost in the short-term scenario and of 6–22% in the long-term scenario. Operational flexibility of electrified reforming allows reducing the cost of negative-emission bio-hydrogen by 1–23%, depending on location and cost scenario. [Display omitted] • Process engineering study with off-design model. • Economic optimization of flexible process with PV, wind, BESS and gas storages. • 17.7 kWh/kg H2 of electric consumption and up to −9 kg CO2 /kg H2 negative emissions. • H 2 cost of 2.5–6.2 €/kg depending on location, REN cost scenario and plant capacity. • Flexibility reduces H 2 cost by 1–23% depending on location and REN cost scenario. [ABSTRACT FROM AUTHOR]

Wind farms are still developing dynamically worldwide, with promising prospects for further growth. Therefore, the assessment of their impact on animals has been carried out. So far, few studies have been conducted on game mammals, and their results are divergent. Previous studies on the impact of wind farms on game species were typically based on regional research covering one or, at most, several wind farms. In this study, we aimed to verify the effect of wind farms on the density of game mammals through a large-scale analysis at the country level, using lowland Poland as an example. The study was based on hunting bag data from open-field hunting districts. It covered seven game species: roe deer (Capreolus capreolus), wild boar (Sus scrofa), red fox (Vulpes vulpes), raccoon dog (Nyctereutes procyonoides), European badger (Meles meles), European polecat (Mustela putorius), and European hare (Lepus europaeus). We used Corine Land Cover to account for differences in land cover and the area covered by wind farms in generalized linear mixed models. The study showed that in agricultural landscapes, mainly herbivorous species of game mammals were related to land cover types. These species tend to exhibit higher densities in agricultural areas containing more natural landscape features. Conversely, mesocarnivores are primarily driven by the abundance of prey with little to no observable effects from land cover types. Only roe deer and wild boar presented lower densities with an increase in the area covered by wind farms (for roe deer: estimate: − 0.05, 95% CI: − 0.1–0.0; for wild boar: estimate: − 0.03, 95% CI: − 0.11–0.05), while no effect was observed for mesocarnivores or European hare. The underlying reasons for these relationships remain unclear and require more specific studies. The uncertainty regarding the cause of the observed effects did not allow for a large-scale assessment of the impact of further wind energy development on the studied game mammals. [ABSTRACT FROM AUTHOR]

Wind power DC collection system, as a crucial component of wind farms, plays a vital role in ensuring the safe and stable operation of the entire wind farm. This paper proposes a reliability assessment method for wind power DC collection systems based on MLFTA-SMC. Firstly, it analyzes the topology and key equipment of wind power DC collection systems. Secondly, based on the topology of different wind power DC collection systems, it constructs multi-level fault tree models to calculate the comprehensive importance of different events, thus providing data support for subsequent reliability assessment. Then, it utilizes the MLFTA-SMC method to assess and analyze the reliability of different wind power DC collection system topologies. Finally, taking a 100 MW wind farm in Northwest China as an example, the proposed reliability assessment method is verified through simulation. The results indicate that this method exhibits good effectiveness and superiority. [ABSTRACT FROM AUTHOR]

The short-term fluctuation of wind power can affect its prediction accuracy. Thus, a short-term segmentation prediction method of wind power based on ramp segment division is proposed. A time-series trend extraction method based on moving average iteration is proposed on the full-time period to analyze the real-time change characteristics of power time-series initially; secondly, a ramp segment extraction method based on its definition and identification technique is proposed based on the results of the trend extraction; and a segmentation prediction scheme is proposed to lean the power prediction under different time-series: the LightGBM-LSTM is proposed for the non-ramping segment using point prediction, and the CNN-BiGRU-KDE is proposed for probabilistic prediction of ramp segments. From the results, this ramp segment definition and identification technique can effectively identify the ramp process of wind power, which makes up for the misidentification and omission of the classical climbing event definition; meanwhile, the segment prediction scheme not only meets the prediction accuracy requirements of the non-ramping segment, but also provides the effective robust information for the prediction of the ramping period, which offers reliable reference information for the actual wind farms. In particular, it is well adapted to wind power prediction under extreme working conditions caused by ramping weather, which is a useful addition to short-term wind power prediction research. [ABSTRACT FROM AUTHOR]

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To improve the forecast accuracy of wind power, diffusion model based on prior knowledge (DMPK) is proposed. Different from the traditional diffusion model (DM), where the noise perturbation in the diffusion or generation process is random, the noise added in DMPK is modified aiming to the characteristics of wind power signals. The distribution of wind power forecast errors is not a standard Gaussian. Wind power forecast errors are related to forecast methods, weather conditions, and other factors, containing both random signals and certain regularity. This paper adapts the Gaussian distribution to fit the historical forecast error to represent the prior knowledge of wind power. Then, the sampling distribution is derived from its relationship with the fitted prior distribution to replace the standard Gaussian in DM. Taking the prior knowledge into account during the process of noise sampling, the data in the forward process of DMPK can be guided by the distribution of historical errors for diffusion, while the generated result by the reverse process is more consistent with the actual wind power signal. Finally, the superiority of the proposed method is verified by using the wind power data from two real‐world wind farms. [ABSTRACT FROM AUTHOR]

The threat of long‐term low wind output processes (LWOP) on the supply ability of the power system is escalating with the increasing integration of wind power. Accurate prediction of LWOP is crucial for maintaining the stable operation of the power system. However, the occurrence probability of LWOP is low and the available samples are lacking, limiting the high‐accuracy predictive modeling of LWOP. Therefore, a novel prediction method for LWOP under very few samples based on improved Wasserstein deep convolutional generative adversarial networks (W‐DCGAN) is proposed here. Firstly, a multi‐dimensional identification method is proposed to accurately identify historical LWOP. Then, an LWOP sample generation model based on improved W‐DCGAN is established. The model integrates a long short‐term memory layer into the deconvolutional layer of the generator to enhance the temporal characteristics of generated samples. Finally, three prediction algorithms are used to construct LWOP prediction models based on both generated and actual samples, respectively. The wind power operation data from a province in China is taken as an example to verify the effectiveness of the proposed method. The results show that the prediction accuracy of LWOP can be improved by 14.36%–55.85%. [ABSTRACT FROM AUTHOR]

Accurate estimation of wind power is essential for predicting and maintaining the power balance in the power system. This paper proposes a novel approach to enhance the accuracy of wind power estimation through a hybrid model integrating neural networks and error discrimination‐correction techniques. In order to improve the accuracy of estimation, a bidirectional gating recurrent unit is developed, forming an initial wind power estimation curve through training. Additionally, a sequential model‐based algorithmic configuration optimizes bidirectional gating recurrent unit's network hyperparameters. To tackle estimation errors, a multi‐layer perceptron combined with sequential model‐based algorithmic configuration is employed to create a classification model that automatically discerns the quality of estimates. Subsequently, an innovative correction model, based on grey relevancy degree and relevancy errors, is devised to rectify erroneous estimates. The final estimates result from a summation of the initial estimates and the values derived from error corrections. By analysing the real data from a wind farm in northwest China, a simulation test validates the proposed hybrid model. Experimental results demonstrate a substantial improvement in modelling accuracy when compared to the initial model. [ABSTRACT FROM AUTHOR]

The transient voltage stability of modern power systems is challenged by the increasing uncertainty on source‐load dual sides. To cope with it, a robust optimal configuration method for dynamic VAR compensators (DVCs) is proposed. First, a series of power‐flow scenarios are generated with uncertainty and correlation considered, where nonparametric kernel density estimation is used to predict the marginal distribution of wind speeds and combined Copula function is employed to characterize correlations between nearby wind farms. Then, the variation‐of‐information indicator is introduced to assess the similarity among power‐flow (PF) scenarios, and representative PF (RPF) scenarios are screened with the help of Spectral clustering algorithm. Finally, locating and sizing of DVCs for RPF scenarios are achieved. By synthesizing the compensation schemes for RPF scenarios, the robust configuration scheme is suggested. Simulation studies in the modified New England 10‐machine 39‐bus system show the proposed method can ensure transient voltage security of the studied power system under source‐load uncertainty. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study offers a modular isolated grid-connected DC/DC medium-voltage DC aggregation converter to support offshore full DC wind farms' need for lightweight and highly efficient power aggregation and transmission. The converter can simultaneously have a smaller transformer size and lower switching frequency during operation through the dual-voltage stabilization three-loop control strategy and phase-shift modulation strategy, which greatly reduces the space occupied by the converter and lowers the switching loss, Additionally, the use of a two-level structure at a lower switching frequency has lower loss, which effectively reduces the cost of the power device compared with the commonly used three-level converter. The input series output series connection between the converter sub-modules effectively lowers the voltage stress on each power switching device and facilitates expansion into a multi-module structure, expanding its application in high-voltage and large-capacity environments. This study analyzes the two working modes of the DC/DC converter and its control approach, in addition to providing a detailed introduction to the application scenarios of this converter. Ultimately, the efficacy and practicability of the suggested topology and control scheme are confirmed by simulations and experiments. [ABSTRACT FROM AUTHOR]

Due to real-time fluctuations in wind farm output, large-scale renewable energy (RE) generation poses significant challenges to power system stability. To address this issue, this paper proposes a deep reinforcement learning (DRL)-based electric hydrogen hybrid storage (EHHS) strategy to mitigate wind power fluctuations (WPFs). First, a wavelet packet power decomposition algorithm based on variable frequency entropy improvement is proposed. This algorithm characterizes the energy characteristics of the original wind power in different frequency bands. Second, to minimize WPF and the comprehensive operating cost of EHHS, an optimization model for suppressing wind power in the integrated power and hydrogen system (IPHS) is constructed. Next, considering the real-time and stochastic characteristics of wind power, the wind power smoothing model is transformed into a Markov decision process. A modified proximal policy optimization (MPPO) based on wind power deviation is proposed for training and solving. Based on the DRL agent's real-time perception of wind power energy characteristics and the IPHS operation status, a WPF smoothing strategy is formulated. Finally, a numerical analysis based on a specific wind farm is conducted. The simulation results based on MATLAB R2021b show that the proposed strategy effectively suppresses WPF and demonstrates excellent convergence stability. The comprehensive performance of the MPPO is improved by 21.25% compared with the proximal policy optimization (PPO) and 42.52% compared with MPPO. [ABSTRACT FROM AUTHOR]

Accurate ultra-short-term power prediction for wind farms is challenging under rapid wind speed fluctuations, complicating production planning and power balancing. This paper proposes a new method considering spatial and temporal correlations of wind fluctuations among adjacent wind farms. The method first calculates the time difference between power fluctuations based on wind speed, direction, and relative positions, determining the prior information period. The variational Bayesian model is then used to extract implicit relationships between power fluctuations of adjacent wind farms, enabling power prediction during the prior information period. Finally, the non-prior information period is predicted to complete the ultra-short-term power prediction. Using measured data from three wind farms in Fujian Province, compared to other models, the method demonstrates improved accuracy by effectively leveraging the power fluctuation characteristics of adjacent wind farms, and it has a certain amount of generalizability. [ABSTRACT FROM AUTHOR]

Assessing market power in the presence of different production technologies such as renewable energies, including wind and solar power, is crucial for electric market analysis and operation. This paper investigates structural market power by incorporating wind farms and solar generation over a short-term period. The study examines the issue of market concentration boundaries to assess structural market power by calculating the minimum and maximum market concentration index values in the day-ahead market. It models the technical specifications of power plants, such as the maximum and minimum production limits, ramp-up and ramp-down rates, and minimum required up and down times. By extracting the spatiotemporal correlation of wind power generation from real data, the uncertainty of renewable power generation is represented through a set of scenarios. The analysis explores the correlation effects of wind farms, solar generation, and wind penetration levels under different ownership structures. Simulation results using a modified PJM five-bus system illustrate the effectiveness of the developed method. Our results indicate that integrating renewable energy can reduce the Herfindahl–Hirschman Index (HHI) by up to 30% as wind penetration levels rise from 0% to 40%, fostering a more competitive market structure. However, the correlation between wind farms also increases market volatility, with the standard deviation of the HHI rising by about 25% during peak load periods. These findings demonstrate the practical applicability of the developed methodology for assessing market dynamics in the presence of renewable energy sources. [ABSTRACT FROM AUTHOR]

Misinformation campaigns target wind farms, but levels of agreement with this misinformation among the broader public are unclear. Across six nationally quota-based samples in the United States, United Kingdom, and Australia (total N = 6008), over a quarter of respondents agree with half or more of contrarian claims about wind farms. Agreement with diverse claims is highly correlated, suggesting an underlying belief system directed at wind farm rejection. Consistent with this, agreement is best predicted (positively) by a conspiracist worldview (i.e., the general tendency to believe in conspiracy theories; explained variance ΔR² = 0.11–0.20) and (negatively) by a pro-ecological worldview (ΔR² = 0.04–0.13). Exploratory analyses show that agreement with contrarian claims is associated with lower support for pro-wind policies and greater intentions to protest against wind farms. We conclude that wind farm contrarianism is a mainstream phenomenon, rooted in people's worldviews and that poses a challenge for communicators and institutions committed to accelerating the energy transition. Six surveys show substantial public agreement with misinformation about wind farms. Agreement with diverse contrarian claims is best predicted by participants' worldviews, most notably the tendency to believe conspiracy theories. [ABSTRACT FROM AUTHOR]

The long-term decline of the black grouse population throughout Europe is influenced by many factors that affect populations differently depending on the distribution range, from Central Europe to the Scandinavian countries. Therefore, we analyzed available literature sources to describe the main reasons for the population decline of this species and to suggest conservation measures. In total, 228 pieces of literature from 1955 to 2024 were analyzed for this study. Based on the literature analysis, it is clear that the drivers of population decline differ across the distributional range. In Central Europe, where the population is declining rapidly, habitat loss and forest fragmentation are crucial factors, as is the negative impact of tourism. In Scandinavia, where the population is gradually declining, decreasing breeding success and increasing chick mortality rates are generally considered the main negative factors. However, these factors also affect black grouse populations in Central Europe. It is crucial to acknowledge that a significant proportion of the contributing factors, such as predation and habitat loss, can be attributed to human activities. Therefore, it is necessary to emphasize that environmental protection should work hand in hand with wildlife managers to improve the situation. [ABSTRACT FROM AUTHOR]

The mechanical multiplier gearbox is one of the most important parts for wind power generation efficiency. Modern epicyclic gearboxes are compact, lightweight builds capable of high power ratings with coaxial input and output shafts. To achieve this, it is very important to select the proper internal gearbox layout and other relevant parameters in the early design stages as the wrong choices will result in a suboptimal solution. Parametric optimization was applied to select the optimal gearbox solution for a wind turbine application, while taking into account both two-carrier and three-carrier solutions. The large number of possible solutions has resulted in the development of the 2-SPEED software to conduct systematic analysis and comparison. The best five two-carrier solutions and the one best three-carrier solution have been selected from the solution pool, with the selection being based on the criteria of maximum efficiency, minimum weight, and minimal greater-ring diameter size. One optimal two-carrier solution was then selected from the five and compared to the three-carrier solution. Recommendations for the selection of either two-carrier and three-carrier gear train solutions according to the application demands have been deducted and provided. This will result in lighter, more efficient designs with smaller radial dimensions. [ABSTRACT FROM AUTHOR]

Wind farms are a clean and efficient source of renewable energy. However, they cause negative impacts on raptors. Here, we present a review of the existing scientific literature on the effects of wind farms on raptors' ecology with a particular interest in the potential solutions. After collecting 216 studies, we found a consensus in the literature that raptors exhibit avoidance behaviors, and that the abundance of raptors decreases after wind farm installation, although it might recover over time. The position of wind farms on mountaintop ridges poses a particular danger to large soaring raptors, as they rely on orographic uplift to gain altitude. Adult mortality significantly affects population dynamics, particularly in endangered species, but young inexperienced individuals show a higher collision risk. The combination of different methods including field monitoring, GPS telemetry and systematic search for carcasses is an adequate approach to further investigate the problem and solutions. Shutdowns on demand, the installation of deterrents, turbine micro‐sitting and the repowering of wind farms have been suggested as potential solutions, although results are contradictory and case‐specific. Furthermore, it is essential to report the potential occurrence of conflicts of interest in scientific papers, as they can influence the interpretation of the results. Finally, from a future perspective, it is crucial to assess the effectiveness of solutions to mitigate the negative effects of wind farms to promote raptor conservation. This becomes increasingly relevant in the context of renewable energy development and increasing energy demand worldwide. [ABSTRACT FROM AUTHOR]

Wind farm can obtain the maximize profit by optimizing micro‐locations and cables. The factors that affect profit include the power output of wind turbines, cost and et al., where power output is affected by wake effect, cable cost is related to the length and type of collector cable. The profit is calculated on the premise that the costs and power loss of collector cable are determined. Obviously, there is a hierarchical relationship between the above problems. Therefore, a bi‐level optimization model with constraints is constructed in this paper, where the upper‐level objective function is the maximum profit, and the lower‐level objective functions are consists of minimum the cable cost and the power loss of collector cable; Moreover, an improved algorithm (IDEDA), based on differential evolution and Dijkstra, is used to optimize above model; Finally, simulation experiments are carried out for IDEDA and four algorithms for two different wind conditions, and the results show that IDEDA performs better compared to the other four algorithms in terms of profit and cable cost. [ABSTRACT FROM AUTHOR]

The ruling of the Superior Court of Justice of Asturias on June 10, 2024 resolves an administrative dispute regarding the denial of authorization for the Teixo Wind Farm in Taramundi. The appealing company challenges the decision based on a negative urban planning report, arguing that the installation of wind energy is of higher public interest. The Court determines that the municipal report is only binding on urban planning aspects and that the completion of the procedure for urban planning reasons is legitimate. The contested resolution is annulled and the procedure is ordered to be reverted to address deficiencies and consider the presumption of higher public interest for renewable energies. [Extracted from the article]

This paper presents an evaluation method for the entropy-weighting of wind power clusters that comprehensively evaluates the allocation problems of wind power clusters by considering the correlation between indicators and the dynamic performance of weight changes. A dynamic layered sorting allocation method is also proposed. The proposed evaluation method considers the power-limiting degree of the last cycle, the adjustment margin, and volatility. It uses the theory of weight variation to update the entropy weight coefficients of each indicator in real time, and then performs a fuzzy evaluation based on the membership function to obtain intuitive comprehensive evaluation results. A case study of a large-scale wind power base in Northwest China was conducted. The proposed evaluation method is compared with fixed-weight entropy and principal component analysis methods. The results show that the three scoring trends are the same, and that the proposed evaluation method is closer to the average level of the latter two, demonstrating higher accuracy. The proposed allocation method can reduce the number of adjustments made to wind farms, which is significant for the allocation and evaluation of wind power clusters. [ABSTRACT FROM AUTHOR]

In this work, we numerically investigated the effects of yaw angle on the wake and power characteristics of two National Renewable Energy Laboratory (NREL) 5 MW wind turbines based on actuator line method (ALM) and large eddy simulation (LES) under a neutral atmospheric boundary layer (ABL) with specified offshore surface roughness. The turbines are placed in tandem, with a spacing of seven rotor diameters, and the yaw angles range from 0° to 30°. The results indicate that under coordinated yaw conditions, the wakes of the two turbines significantly shift with increasing yaw angles, encroaching on the trailing edge of the turbines. The expansion of the wakes also gradually weakens, leading to a reduction in width. The superposition of the wake generated by the downstream turbine diminishes, leading to both turbines exhibiting approximately comparable physical characteristics within their respective wakes. As the wake of the upstream turbine propagates downstream, a secondary low-speed region emerges between the primary low-speed zone of the wake of downstream turbine and the surrounding atmosphere. With the increase in yaw angle, this secondary low-speed region significantly enhances the rate of wake recovery while also inducing a more pronounced deflection of the wake, thereby demonstrating a stronger entrainment effect. Regarding load characteristics, the time history of power characteristics and the power spectral density (PSD) spectra indicate a good turbine response to the inflow. The power characteristics of the upstream turbine exhibit a scaling law is closely related to the yaw angle. The quantitative relationship is established between yaw angle and the power distribution of the turbines, alongside a proposed correlation between the yaw angle and the cos 2 (γ) scaled power curve. The power of upstream turbine decreases and the power of downstream turbine gradually increases with the increase in yaw angle. It is further found that the downstream turbine demonstrates optimal performance at a yaw angle of 20°due to the influence of the yawed upstream turbine. These analyses provide insights into the characteristics of wind turbine arrays under yaw conditions from the perspective of unsteady wake features, interactions, and aerodynamic performance, which can aid in wind farm unit planning and control strategies. [ABSTRACT FROM AUTHOR]

The rapid expansion of wind power worldwide underscores the critical significance of engineering-focused analytical wake models in both the design and operation of wind farms. These theoretically derived analytical wake models have limited predictive capabilities, particularly in the near-wake region close to the turbine rotor, due to assumptions that do not hold. Knowledge discovery methods can bridge these gaps by extracting insights, adjusting for theoretical assumptions, and developing accurate models for physical processes. In this study, we introduce a genetic symbolic regression (SR) algorithm to discover an interpretable mathematical expression for the mean velocity deficit throughout the wake, a previously unavailable insight. By incorporating a double Gaussian distribution into the SR algorithm as domain knowledge and designing a hierarchical equation structure, the search space is reduced, thus efficiently finding a concise, physically informed, and robust wake model. The proposed mathematical expression (equation) can predict the wake velocity deficit at any location in the full-wake region with high precision and stability. The model's effectiveness and practicality are validated through experimental data and high-fidelity numerical simulations. [ABSTRACT FROM AUTHOR]

The speed of using renewable resources is expanding day by day. Renewable energy systems have many benefits for energy supply that do not include diesel, natural gas, or coal. Despite the many advantages, the use of renewable resources also includes basic challenges. With the presence of these sources, many technical issues must be considered in the network, the most important of which are voltage quality and network losses. The presence of these power plants can reduce fossil fuel costs and help reduce emissions. However, the high‐capacity connection of these types of power plants in the transmission networks despite the uncertainty may cause the congestion of transmission lines, increase losses and decrease voltage quality. Therefore, to reduce the need to build transmission lines, energy storage devices can be installed and energy can be stored and returned to the network in certain hours. The purpose of this paper is to build the maximum capacity of wind power plants in the transmission network in such a way that its profitability is guaranteed. For this purpose, in addition to considering the costs related to the power plant, the costs of storage devices and the construction of possible new lines have been considered. Also, improving the technical conditions of the network and reducing the maximum emission after installing these units is considered as a multiobjective function. The problem tested on the standard IEEE test transmission network and the results show that it is possible to determine the maximum profitable capacity of wind power plants. [ABSTRACT FROM AUTHOR]

The integration of large-scale intermittent renewable energy generation into the power grid imposes challenges to the secure and economic operation of the system, and energy storage (ES) can effectively mitigate this problem as a flexible resource. However, the conventional ES allocation is mostly planned to meet the regulation demands of individual entities, which is likely to result in low utilization of ES and difficult to recover the investment cost. Therefore, a two-stage stochastic optimal allocation model for grid-side independent ES (IES) considering ES participating in the operation of multi-market trading, such as peak-valley arbitrage, frequency regulation, and leasing, is proposed in this paper to improve the comprehensive benefits and utilization rate of ES. The first stage aims to allocate IES and develop a systematic scheduling plan based on the forecast of wind power output and load demand, while the second stage responds to the uncertainty of wind power output by re-dispatching generating units and invoking ES power leased by wind farms. Then, a two-layer loop iterative solution algorithm based on the Benders decomposition is formed to effectively solve the proposed model. Finally, the approach developed in this paper is applied to a modified IEEE RTS-79 test system, and the results verify that it is both feasible and effective. [ABSTRACT FROM AUTHOR]

The high demand for generation and development in wind electrical power competitiveness has gained significant popularity in wind energy and speed forecasting models. It is also an essential method for planning the wind power plant system. Several models were created in the past to forest the speed and energy of the Wind. However, results have very low prediction accuracy due to their nonlinear and irregular characteristics. Therefore, a novel Modular Red Deer Neural System (MRDNS) was developed in this research to forecast wind speed and energy effectively. Primarily, the system accepted the data from the wind turbine SCADA database and preprocessed it to remove the training flaws. Further, the relevant features are extracted. The complexity of the prediction process was reduced by processing the relevant features. By analysing these features, the wind speed and energy were predicted in accordance with the fitness function of the MRDNS. The model obtained higher prediction accuracy. The recommended strategy was checked in the Python platform and the robustness metrics including RMSE, MSE, and precision were computed. The model scored 99.99% prediction accuracy; also gained 0.0017 MSE value, 0.0422 RMSE value for wind power forecasting and 0.0003 MSE, 0.0174 RMSE for wind speed forecasting. [ABSTRACT FROM AUTHOR]

Wind power fluctuation significantly impacts the safe and stable operation of the wind farm power grid. As the installed capacity of grid‐connected wind power expands to a certain threshold, these fluctuations can detrimentally affect the wind farm's operations. Consequently, wind power prediction emerges as a critical technology for ensuring safe, stable and efficient wind power generation. To optimize power grid dispatching and enhance wind farm operation and maintenance, precise wind power prediction is essential. In this context, we introduce a joint deep learning model that integrates a compact pyramid structure with a residual attention encoder, aiming to bolster wind farm operational safety and reliability. The model employs a compact pyramid architecture to extract multi‐time scale features from the input sequence, facilitating effective information exchange across different scales and enhancing the capture of long‐term sequence dependencies. To mitigate vanishing gradients, the residual transformer encoder is applied, augmenting the original attention mechanism with a global dot product attention pathway. This approach improves the gradient descent process, making it more accessible without introducing additional hyperparameters. The model's efficacy is validated using a dataset from an actual wind farm in China. Experimental outcomes reveal a notable enhancement in wind power prediction accuracy, thereby contributing to the operational safety of wind farms. [ABSTRACT FROM AUTHOR]