Search Results (2,796)

Wind energy is a cornerstone of the global transition to renewable and sustainable energy systems. However, the same meteorological processes that generate this clean energy can also produce extreme wind events that threaten the structural integrity and operational reliability of wind turbines and power grids. Therefore, accurately predicting extreme wind speeds is a critical link between promoting clean energy and ensuring infrastructure resilience. Traditional models often struggle to capture the multimodal characteristics of extreme wind speeds under complex meteorological conditions due to fixed distribution assumptions or unstable training of mixture models, leading to estimation biases that undermine planning reliability and may result in catastrophic turbine failures or overly conservative designs. To address these issues—particularly weight imbalance and overfitting–this study proposes an enhanced regularized extreme value mixture model (ERDC-EVMM). This method integrates elastic network regularization and Kullback–Leibler divergence constraints within a Mixture of Experts framework, and employs K-means initialization and momentum-based training to enhance convergence stability. Validated using daily extreme wind speed sequences from coastal and inland wind farms, the model outperforms standard GEV and mixture models in terms of goodness-of-fit, percentile accuracy, and return period estimates, while achieving a convergence speed that is more than 30% faster (82 iterations). By balancing accuracy and training stability, the ERDC-EVMM model provides a reliable statistical tool for extreme wind speed forecasting, supporting the safe expansion of wind energy infrastructure and the design of climate-resilient communities. Full article

With the increasing penetration of wind power, the participation of wind farms in primary frequency regulation has become important for maintaining power system frequency stability. However, virtual inertia and droop control, while providing frequency support, can increase structural fatigue loads in wind turbines and shorten their service life. To address this issue, this study proposes a coordinated optimization method for wind farm primary frequency control parameters based on fatigue load prediction. First, damage equivalent load (DEL) data under different power disturbances, wind speeds, and control parameter settings are generated through OpenFAST–Simulink co-simulation. Then, a multilayer perceptron (MLP) neural network is developed to establish the mapping from power disturbance, wind speed, and control parameters to turbine DEL. Based on the trained model, an optimization framework is constructed to minimize the total DEL of the wind farm, improve the uniformity of DEL distribution among turbines, and satisfy grid frequency support constraints. Simulation results show that the proposed method effectively reduces the overall fatigue load of the wind farm while ensuring system frequency security and improving load distribution uniformity among turbines. Full article

This study addresses the critical need for accurate sea wind speed predictions to support ocean wind farm operations, equipment maintenance, and maritime navigation safety. To enhance prediction accuracy for any location within target sea areas, we propose a short-to-medium-term wind speed prediction method that effectively explores spatiotemporal correlations in ocean reanalysis grid data. The method involves collecting and reanalyzing data, as well as spatial processing, to reconstruct the historical wind speed sequence at the target point. Finally, a future wind speed time series is generated using an LSTM network and a Transformer encoder. Test results validated against NOAA buoy data demonstrate the effectiveness of our spatiotemporal prediction model, achieving RMSE values of 1.161 m/s, 1.500 m/s, and 1.854 m/s for 1 h, 6 h, and 12 h predictions, respectively, outperforming comparative methods. The conclusions are threefold: (1) The proposed hybrid model effectively captures spatiotemporal dependencies and achieves more accurate spatiotemporal predictions compared to the benchmark model; (2) taking into account seasonal factors and forecasting time periods, the method proposed in this paper maintains good stability; (3) this framework provides a reliable technical approach for generating operational references in maritime navigation and wind power maintenance, with potential applications in wind farm siting and resource assessment. Full article

This paper studies capacity planning for a wind–hydrogen integrated energy system under scenario-based uncertainty in wind generation, hydrogen demand, and electricity prices. The model is formulated as a two-stage stochastic program in which first-stage investment decisions are selected before uncertainty is realized and second-stage hourly operation is optimized for each representative scenario. The main methodological difficulty is that part of the first-stage hydrogen-storage investment cost may be available only through a non-analytical evaluator, such as supplier quotation logic, simulation software, or a data-driven estimator, while the operational recourse model remains linear. To address this setting, a hybrid heuristic–Benders framework, denoted as GSOA-Benders, is developed by coupling the General-Soldiers Optimization Algorithm for derivative-free first-stage search with Benders cuts generated from linear programming subproblems. The framework is not presented as a replacement for commercial solvers on explicit convex or mixed-integer models; rather, it is intended for cases where exact algebraic reformulation of the first-stage cost is unreliable or unavailable. In the black-box case study with 500 scenarios, the method converges in 35.86 s and obtains an investment plan expressed as $\mathbf{x}=[1,0.53,23.23,0]$, corresponding to wind-farm construction, a 0.53 MW electrolyzer, a 23.23 MWh hydrogen tank, and no fuel-cell investment. Additional discussion is provided on stability-gap interpretation, benchmark limitations, component lifetime assumptions, hydrogen losses, and environmental extensions. Full article

With the rapid development of offshore wind power, the fractional frequency offshore wind power system based on the modular multilevel matrix converter (M3C) faces severe frequency stability challenges due to the reduced inertia under high wind power penetration. This paper focuses on its frequency control and proposes a set of coordinated strategies. Modified frequency regulation schemes for wind turbines (WTs) under different operating states avoid secondary frequency drop (SFD) and accelerate rotor speed recovery. A coordinated power allocation strategy combining energy storage (ES) and automatic generation control (AGC) suppresses wind-induced power fluctuations, with a reducing pitch angle variation method to extend WTs’ life. Meanwhile, an adaptive virtual inertia control strategy for M3C enhances sustained inertia support. A coordinated frequency control scheme between wind farm, M3C, and ES is further constructed to achieve faster and better frequency stabilization under wind and load variations. Simulation results under a 10.5 MW load disturbance show that, compared with the uncontrolled scheme, the proposed scheme raises the frequency nadir from 49.01 Hz to 49.67 Hz, limits the maximum rate of change of frequency (ROCOF) to 0.583 Hz/s with a 49.8% reduction, fully eliminates SFD, and provides theoretical support for the stable grid integration of fractional frequency offshore wind power. Full article

This paper introduces a policy-search-based reinforcement learning algorithm aimed at generating optimal set-points of wind turbines in wind farms. The proposed approach addresses the problem of multivariable optimization in systems where the objective function is unknown or difficult to model. The algorithm is a model-free framework and relies solely on measured performance of the system. Namely, it does not require gradient information of the objective function or an explicit model of the aerodynamic interaction between wind turbines. The proposed scheme utilizes stochastic policy perturbations to explore the search space and update the policy parameters directly based on the observed reward signal. In this way, the algorithm progressively drives the control variables toward optimal operating conditions. The proposed policy-search reinforcement learning framework is analyzed to establish its connection with gradient-free optimization methods. The proposed method is applied to wind farm power optimization, where multiple turbine control variables must be adjusted in the presence of wake interactions cooperatively. The performance of the proposed approach is evaluated through extensive simulations under both steady-state and time-varying wind conditions. The proposed algorithm is compared with an extremum-seeking control method that was previously suggested for the same problem. The results demonstrate that the proposed approach is able to effectively maximize power production in wind farms while maintaining a simple and model-free optimization structure. Full article

With the increasing power fluctuations and growing pressure on grid stability resulting from the high penetration of renewable energy, the demand for exploring various energy storage technologies with large-scale, long-duration, and low-cost features has become increasingly urgent. This paper proposes a novel single-track gravity energy storage generation system. This system utilizes non-standardized masses (such as natural rocks) operating stably on an inclined track, and combines coordinated feedforward–feedback electromagnetic torque control, multi-station loading scheduling, and synchronous loading/unloading strategies to effectively smooth the power fluctuations of renewable energy sources such as wind power. The core innovations of this system lie in: (1) utilizing non-standardized mass units to achieve gravity energy storage, thereby expanding the application scenarios and design flexibility of solid gravity energy storage systems; and (2) introducing intelligent scheduling strategies and multi-station loading coordination to effectively smooth the power output fluctuations caused by load randomness, rendering the system insensitive to load variations. Simulation results verify that, for power smoothing in a 10 MW-level wind farm, the system can accurately track the target power and maintain a stable output over a long duration. The power fluctuations are controlled to under 0.2%, even when the total load varies by 10% and the instantaneous load fluctuates by 5%. This system demonstrates the theoretical feasibility and scalability of utilizing natural rock resources in mountainous terrains for long-duration energy storage, providing a novel solution for long-duration power smoothing in renewable energy systems. Full article

Virtual synchronous generator control-based permanent magnetic synchronous generators (VSG-PMSGs) have been widely used for their stable operation in a weak grid and strong voltage and frequency support capacity. However, VSG-PMSGs have complex and time-varying dynamics due to control strategy switching, current limiters, and saturations. Additionally, they are prone to transient angle instability during voltage faults. A dynamic analysis method for VSG-PMSGs considering low-voltage ride-through (LVRT) control is proposed in this paper. First, an improved LVRT control strategy based on active power reference reduction and virtual electromagnetic force (EMF) reset is introduced to mitigate the instability risk of VSG-PMSGs. Then, the mechanisms by which initial power and fault voltages influence the dynamic responses are revealed. The dynamics of VSG-PMSGs under different conditions are classified into four types according to the current and EMF limiters’ state. To predict VSG-PMSG dynamics, we propose a method based on fault steady-state power flow for calculating the fault voltage. Using this approach, fault voltage dips in VSG-PMSGs within a wind farm are computed with an error of less than 0.002 p.u., and the dynamic behavior of each unit is accurately predicted within 10 s. To verify the validity of the proposed method, simulations were conducted across diverse scenarios. The results demonstrate that this method enables accurate and computationally efficient prediction of VSG-PMSG fault dynamics. Full article

Morocco is a leading African nation in renewable energy, with growing interest in expanding offshore wind energy. As offshore wind projects have gained momentum worldwide, public acceptance, particularly regarding their environmental and visual impacts, has become a critical consideration. This exploratory study examines the social acceptance of offshore wind farms (OWFs) in Morocco by integrating social acceptance analysis with a visual impact assessment based on three-dimensional (3D) image modeling in an emerging offshore wind context. Social perceptions were first assessed through a small-scale survey, with findings interpreted descriptively and considered alongside results from a public perception survey conducted in Japan, which served as a contextual reference. A hypothetical offshore wind installation along the Moroccan coast was then simulated, followed by a small-scale exploratory perception survey to examine initial reactions to different visual configurations. Given the limited sample size, the findings are indicative rather than generalizable. Nevertheless, they provide preliminary insights into the prominent role of environmental considerations, particularly ecological protection and visual integration, in shaping attitudes toward OWFs. This study highlights the relevance of careful site selection, transparent communication, and early stakeholder engagement as context-sensitive considerations for offshore wind development in Morocco. Full article

Self-contained local forecasting of wind and solar series can improve operational planning of wind farms and photovoltaic (PV) plant day-cycles in addition to numerical models, which are mostly behind time due to high simulation costs. Unstable electricity production requires balancing the availability of renewable energy (RE) with unpredictable user consumption to achieve effective usage. Artificial intelligence (AI) predictive modelling can minimise the intermittent uncertainty in wind and solar resources by trying to eliminate specific problems in RE-detached system reliability and optimal utilisation. The proposed 24 h day-training and prediction scheme comprises the starting detection and the following similarity re-assessment of sampling day-series intervals. Two-point professional weather stations record standard meteorological variables, of which the most relevant are selected as optimal model inputs. Automatic two-layer altitude observation captures key relationships between hill- and lowland-level data, which comply with pattern progress. New biologically inspired differential learning (DfL) is designed and developed to integrate adaptive neurocomputing (evolving node tree components) with customised numerical procedures of operator calculus (OC) based on derivative transforms. DfL enables the representation of uncertain dynamics related to local weather patterns. Angular and frequency data (wind azimuth, temperature, irradiation) are processed together with the amplitudes to solve simple 2-variable partial differential equations (PDEs) in binomial nodes. Differentiated data provide the fruitful information necessary to model upcoming changes in mid-term day horizons. Additional PDE components in periodic form improve the modelling of hidden complex patterns in cycle data. The DfL efficiency was proved in statistical experiments, compared to a variety of elaborated AI techniques, enhanced by selective difference input preprocessing. Successful LSTM-deep and stochastic tree learning shows little inferior model performances, notably in day-ahead estimation of chaotic 24 h wind series, and slightly better approximation of alterative 8 h solar cycles. Free parametric C++ software with the applied archive data is available for additional comparative and reproducible experiments. Full article

High-frequency volatility and extreme tail risks in offshore wind power pose severe challenges to grid stability and economic operation. Conventional storage planning often relies on deterministic profiles or static allocation rules, failing to capture the non-stationary temporal dynamics of marine wind resources. To bridge this gap, this paper proposes a closed-loop framework that integrates ultra-short-term probabilistic forecasting with dynamic hybrid energy storage optimization. A novel Dual-Channel Residual Network is developed to provide well-calibrated predictive uncertainty quantification, which explicitly drives a Prediction-Guided Dynamic Hybrid Storage Optimization Framework. By dynamically coordinating lithium-ion batteries and liquid air energy storage based on evidential predictive variance, the proposed approach achieves superior synergy between short-term power response and long-duration energy shifting. Case studies on an offshore wind farm validate that the framework significantly reduces the Levelized Cost of Energy and loss-of-load risks while enhancing frequency regulation capabilities compared to state-of-the-art benchmarks. Full article

The rapid expansion of renewable energy has raised the demand for accurate, long-term wind power forecasting. However, wind power series are strongly affected by meteorological factors and exhibit pronounced volatility, making long-term prediction challenging. To model these characteristics more comprehensively, we propose STFNet, a dual-branch neural architecture that integrates time-domain and frequency-domain modeling. STFNet contains two key modules: (1) an MLFE module, which explicitly captures lag effects and non-stationary transitions through parallel multi-scale convolutions and a difference-convolution branch and further enhances multivariate dependency learning via cross-variable interaction modeling, and (2) an FGFE module, which applies DCT to capture long-cycle trends and uses a learnable low-pass filter for noise suppression. Experiments on two real-world wind farm datasets (LY and HG) show that STFNet consistently outperforms strong baselines, achieving average MSE reductions of 15.9–26.6% while maintaining a high computational efficiency. Ablation studies further confirm the effectiveness of each module, indicating the strong practical potential of STFNet for wind farm operation and management. Full article

The transition toward renewable energy sources has positioned wind energy as a critical technology for achieving global carbon neutrality targets. While large-scale wind farms dominate current installations, micro-scale horizontal-axis wind turbines present significant potential for distributed energy generation in remote and rural areas. This study presents a comprehensive methodology for designing micro-scale wind turbine blades through comparative analysis of three computational approaches: classical blade element momentum theory (BEMT), QBlade 2.0.9.6 software, and Computational Fluid Dynamics (CFD) simulations, with the design methodology selected based on a trade-off between accuracy and computational cost. A numerical campaign for airfoil assessment was conducted to identify optimal blade geometries, with performance evaluated based on power coefficient distribution, peak power output, and cut-in wind speed. The investigation reveals that steady CFD simulations predict peak power coefficients 23.34% higher than those predicted by BEMT and 22.46% higher than those predicted by QBlade due to three-dimensional effects, including rotational stall delay. Considering unsteady effects, the CFD simulations show a decrease of 4.08% with respect to steady simulations. The addition of endplates to the optimized blade design demonstrates significant performance improvements. This multi-fidelity approach provides a robust framework for micro-scale wind turbine design, balancing computational efficiency with accuracy requirements, and examines the impact of adding endplates. Full article

Given the energy-intensive operations and considerable carbon emissions of offshore oil and gas platforms (OOGPs) in deep-sea regions, adopting floating offshore wind farms (FOWFs) as power sources offers substantial benefits. However, the expenses associated with dynamic submarine cables constitute a substantial portion of the capital expenditure (CAPEX) for this hybrid system, highlighting the crucial need for optimization in the power collection system design. In this study, we present a mixed-integer quadratic programming (MIQP) model designed to reduce both the costs of investment and power losses associated with dynamic submarine cables, taking into account the influence of the wake effect in local wind conditions. Due to the complexity of this problem, we employ the Benders’ decomposition method to reformulate it into a master problem and a slave problem. Additionally, two valid inequalities are specifically incorporated into the master problem to accelerate the solution process. These constraints are derived from a heuristic combination of various cable connection configurations and a greedy-based spanning tree structure. Through multiple case studies, we first demonstrate the accuracy and rapid convergence of our method. Furthermore, we reveal that as the wind farm grows in size, the influence of the wake effect becomes increasingly pronounced. Full article

The increasing deployment of dense offshore wind turbine monopile foundations pose significant challenges for accurately simulating tidal-flow modification and energy transport at the array scale. Balancing physical realism with computational efficiency remains a key challenge in hydrodynamic modelling of offshore wind farms. In this study, an established drag-based source-term approach is implemented through a dedicated module developed within the TELEMAC-3D framework to represent the momentum-blocking effects of offshore wind-farm arrays. A representative dense 8 × 10 wind turbine monopile array configuration is constructed in a typical tidal channel to systematically examine array-induced tidal-flow responses. The results indicate that the drag-based source-term approach preserves the regional-scale tidal flow structure while effectively capturing array-induced local velocity adjustments and pronounced downstream wake attenuation and recovery. Detailed analyses further reveal distinct spatial and temporal characteristics of the velocity response, including the decay and recovery of velocity deviations downstream of the array. In addition, the monopile array induces a clear modulation of flow kinetic energy, characterized by enhanced energy dissipation and a finite array-scale redistribution of kinetic energy. These findings demonstrate that this approach efficiently simulates the array-scale hydrodynamic and energetic impacts of large offshore wind farms and contribute to a better understanding of array-induced tidal flow modification and energy redistribution. Full article