Search Results (970)

High penetration of renewable energy imposes greater demands on the scheduling flexibility of demand-side resources in virtual power plant (VPP) dispatch. Nevertheless, heterogeneous resources exhibit remarkable differences in response reliability, and electric vehicles (EVs) in particular show distinct execution performance between orderly charging and vehicle-to-grid (V2G) modes. To tackle this issue, this paper proposes a two-stage optimal scheduling strategy for multi-agent VPPs incorporating scheduling success probability. A quantitative model for the effective dispatch contribution coefficient is constructed from two dimensions, i.e., relative capacity weight and dispatch execution reliability, with differentiated parameters tailored for EV charging and V2G modes. The two-stage leader–follower game problem is decoupled via backward induction, and the optimal dispatch price is rigorously derived through Karush–Kuhn–Tucker conditions. A 24 h case study covering wind power, photovoltaics, energy storage, EVs, and air-conditioning loads validates the proposed method. Results indicate that the strategy boosts total VPP revenue by 7.43% compared with independent operation, lifts the renewable energy accommodation rate from 88.3% to 94.6%, and reduces the average operating cost by 19 CNY/MWh. Through dual-mode differentiated scheduling, EVs achieve 5.10% revenue growth and serve as a key flexible resource for VPP economic operation. Full article

Accurate ultra-short-term wind power forecasting at the turbine level is important for grid stability and dispatching. To address the time-varying spatial and temporal correlations among multiple turbines in a single wind farm, we build dynamic spatio-temporal graphs to model dynamic spatial dependencies, propose a parallel multi-scale temporal convolutional encoder to combine short-term and long-term dependencies, and propose a graph fusion layer to achieve weight fusion of different graph sources. Experiments demonstrate that GraphFusionGRU achieves lower overall error in short-term forecasting and achieves competitive average performance relative to other baseline models on longer horizons. The results confirm that the model’s robustness and interpretability are enhanced in complex wind-farm environments. Full article

The resilience of electrical grid infrastructures is increasingly challenged by high penetration of renewables, climate-induced stress events, and complex interdependencies between assets and control systems. This paper proposes a structured Resilience Assessment Cycle (RAC) and operationalizes it through a novel LLM-orchestrated Agentic Resilience Assessment System (A-RAS) for quantitative assessment of resilience to extreme weather events. RAC defines a structured assessment process linking disturbance characterization, operational-state evaluation, resilience quantification, and interpretation of the resulting system response. A-RAS implements this process through coordinated numerical engines and agentic components. First, an anomaly detection engine applies a residual-based Exponentially Weighted Moving Average scheme (OpS-EWMA) to identify incipient operational shifts in heterogeneous equipment from SCADA time series. Second, a labeling and diagnostic engine employs a retrieval-augmented RAG-LLM pipeline to generate structured diagnostic explanations and, in a subsequent step, assign operational state labels using a dual-scoring mechanism that combines two independent “votes”: a quantitative score derived from data-driven anomaly severity and a qualitative score derived from LLM-based semantic assessment. Third, a resilience assessment agent integrates (i) a module that detect extreme weather event windows and (ii) a module that computes a dual-output resilience vector—a service-performance deficit and a residual health-state deficit—derived from the temporal evolution of system performance and asset condition over the defined assessment horizon. Finally, a core orchestration agent manages data flow and task delegation, enabling automated, end-to-end resilience assessment. In contrast to existing approaches that represent equipment condition as a binary—functional or failed—the proposed methodology explicitly integrates the heterogeneity and temporal evolution of operating states. By accounting for intermediate health conditions, it addresses a key limitation of prevailing metrics: their limited ability to explain observed system behavior during stress events. The feasibility and practical value of the approach are demonstrated on a real operational wind power plant, showing that resilience trajectories can be traced to residual health deficits and the contributing equipment. As an initial implementation validated on a single site and hazard class, RAC and A-RAS provide a structured and extensible foundation intended to be generalized across additional assets, hazards, and operational contexts in future work. Full article

New energy power systems with high penetration rates feature complex spatiotemporal coupling relationships among generation, transmission, load, and storage. The random fluctuations in wind and solar power output, combined with load uncertainty, exacerbate system operational risks. Traditional static modeling, single-metric evaluation, and centralized analysis methods struggle to adapt to the dynamic, highly uncertain, and multi-constrained operational scenarios of new power systems. To address this, this paper proposes an Artificial Intelligence-based Comprehensive Evaluation Method for Power System Flexibility and Sustainability (AI-FSEA) under privacy and security constraints. This method first establishes an intelligent fusion module for multi-source, heterogeneous power data, which accurately extracts the system’s multidimensional dynamic features through adaptive wavelet denoising and a temporal self-attention mechanism. Second, it establishes a five-objective coupled evaluation model that balances technical, economic, low-carbon, and reliability considerations, with regulation margin loss, response delay, operating costs, carbon emissions, and power supply instability rate as the core optimization objectives, thereby achieving multi-objective trade-off optimization within the system’s feasible domain; furthermore, a Hierarchical Deep Q-Network-Assisted Multi-Objective Evolutionary Algorithm (HDQN-MOEA) is designed, which leverages the value iteration, composite reward mechanism, and feedback clustering screening mechanism of the deep Q-network to enhance the model’s solution accuracy and convergence efficiency. Results from multiple sets of comparative experiments, ablation studies, and uncertainty generalization experiments conducted using the IEEE standard node system and real-world power grid data from East China indicate that, compared with mainstream optimization algorithms such as NSGA-III and TS-NSGA-II, the proposed HDQN-MOEA algorithm achieves an average improvement of 10.2% in the hypervolume metric and an average reduction of 35.6% in the span metric; the results of the ablation experiments confirm that the absence of the multi-source data fusion module, the hierarchical strategy module, or the feedback clustering screening module would result in a 15.3% and 12.1% decrease in the model’s hypervolume metric, respectively, as well as a slight deterioration in population diversity; under three types of highly uncertain operating conditions—random fluctuations in renewable energy, sudden load spikes, and extreme weather—the algorithm proposed in this paper consistently maintains stable optimization performance, meeting convergence accuracy requirements in as few as 5000 iterations. Without increasing the complexity of existing algorithms, it achieves the synergistic optimization of data privacy and security, evaluation accuracy, and operational efficiency. The proposed method can accurately quantify the dynamic flexibility and long-term sustainability of the new power system, providing reliable intelligent technical support for the planning and dispatch of the new power system, the optimal allocation of resources, and low-carbon, sustainable operation. Full article

High penetration of converter-interfaced photovoltaic and wind generation reduces the effective inertia of islanded renewable energy systems and makes frequency stability more sensitive to load and renewable-power disturbances. This paper proposes a bounded adaptive droop framework for battery energy storage system (BESS) fast frequency response under low-inertia conditions, built around a bounded coordination mechanism. The framework coordinates frequency deviation, filtered rate of change of frequency (RoCoF), renewable-power fluctuation, and state of charge (SOC) to unify frequency-support capability with SOC availability and converter operating constraints. SOC-dependent available power limits, rated converter saturation, a ramp-rate limiter, RoCoF filtering, and measurement delay translate support demand into feasible power commands. A unit-consistent per-unit frequency-response model is combined with kW-level BESS power commands in MATLAB simulations. Compared with Fixed Droop, the proposed method reduces Max |Δf| and RMS Δf by 31.61% and 31.06%, respectively, on average across five deterministic cases, including four main disturbance cases and one charging case. In 30 Monte Carlo validation trials, the mean Max |Δf| decreases from 0.586 Hz to 0.330 Hz (43.66%), and the mean RMS Δf decreases from 0.416 Hz to 0.234 Hz (43.71%), with no SOC violation. Charging-protection tests further show that a fully charged BESS cannot absorb surplus renewable power, indicating the need for coordination with renewable curtailment, dump loads, secondary control, or reserve-SOC management. Full article

Accurate short-term wind-power forecasting supports renewable-energy integration. This study presents a reproducibility and implementation audit of MoRAM, an integrated architecture that combines feature-token attention, a residual Mamba layer, dense two-expert blending, and a one-dimensional convolution (Conv1D)–linear residual output path to forecast 12 h of normalized turbine power from 48 h multivariate histories in Dataset A (200 turbines; 8760 hourly records). Source reconstruction found that the historical PyTorch attention layer used its default sequence-major interface on batch-major data, thereby mixing samples; the dense-window record is therefore retained only as an implementation audit. We corrected the layer to batch-first feature-token semantics and ran an attention-only ablation using all turbines, a 12 h window-start stride, and five paired seeds. Corrected MoRAM achieved mean absolute error (MAE) 0.2428 ± 0.0029 per unit (p.u.) and root-mean-square error (RMSE) 0.3035 ± 0.0059 p.u.; bypassing only attention achieved MAE 0.2298 ± 0.0027 p.u. and RMSE 0.2839 ± 0.0028 p.u. (mean ± sample standard deviation). The attention-free condition had lower overall MAE and RMSE in every seed and, after averaging across seeds, at every forecast horizon. The principal validated contribution is therefore an auditable reconstruction—complete data protocol, source/log mapping, corrected tensor semantics, and a reproducible five-seed negative ablation—rather than evidence that every constituent module or the integrated architecture is superior. Full article

Archimedes Spiral Wind Turbines (ASWTs) are suitable for small-scale urban wind applications, but changing the blade angle can also change the rotor dimensions under different geometric constraints. This study examines these coupled effects by comparing five ASWT configurations with blade angles of 30°, 45°, and 60° under fixed-diameter (Fixed D) and fixed-axial-length (Fixed L) conditions. Steady three-dimensional Reynolds-averaged Navier–Stokes simulations using the Multiple Reference Frame method were conducted to evaluate the power coefficient ($C_p$), torque coefficient ($C_t$), and mid-plane pressure and velocity fields. The numerical setup was assessed through grid-independence and reference-data comparisons. Under the Fixed D constraint, the 60° configuration achieved the highest $C_p$ of 0.2915 at a tip-speed ratio (λ) of 1.9, whereas the 30° configuration reached a maximum $C_p$ of 0.1792 at λ = 1.0. Under the fixed L constraint, the corresponding $C_p$ values were 0.2869 at λ = 2.5 for the 60° configuration and 0.1713 at λ = 0.8 for the 30° configuration. The baseline 45° configuration achieved a maximum $C_p$ of 0.2444 at λ = 1.5. The torque coefficient decreased with increasing λ for all configurations, while the pressure and velocity fields differed between the two constraints at the same blade angle. These results indicate that blade angle should be evaluated together with rotor diameter, axial length, and the installation envelope, because a larger rotor or swept area does not necessarily produce a proportional increase in normalized aerodynamic efficiency. Full article

Toward low-carbon and sustainable power systems, hydro–wind–solar complementarity provides an important pathway for enhancing renewable energy accommodation and operational flexibility, while accurate characterization of uncertainty and cross-energy dependencies is essential for system optimization, operational risk assessment, and sustainable utilization of renewable resources. This study proposes a conditional Wasserstein generative adversarial network with gradient penalty integrating one-dimensional multi-scale channel attention (MSA) and an exponential moving average (EMA) mechanism (MSA-cWGAN-GP) for joint runoff–wind–photovoltaic (PV) scenario generation. The generator employs parallel depthwise 1D convolutions with multiple temporal receptive fields to capture multi-timescale variations, while an EMA shadow generator is used for model validation and scenario generation. Conditional labels are obtained by clustering joint 24 h runoff–wind–PV profiles, enabling generation under typical resource states. Case studies using historical runoff observations from Shuibuya Hydropower Station and wind and PV power series derived from ERA5 reanalysis data show overall absolute errors of the autocorrelation function (ACF) and Kendall coefficient of 0.0113 and 0.0495, respectively. The proposed model achieves the best average performance among the evaluated models in preserving intraday temporal dependence, cross-energy dependencies, and distributional characteristics, providing representative scenarios for uncertainty analysis and subsequent optimization of hydro–wind–solar complementary systems. Full article

Conventional flux-reversal permanent magnet motors have problems such as excessive torque ripple and rich harmonic content in direct drive applications such as oil exploration, which restrict their application in high-precision scenarios. To address this issue, this paper presents a hybrid excitation topology that integrates double-armature windings, stator Halbach hybrid permanent magnet arrays, rotor-staggered unequal-tooth and rotor-hybrid permanent magnets. Two-dimensional finite element analysis was conducted using ANSYS Maxwell 2023 R1 to evaluate electromagnetic performance under rated steady-state conditions, rated power 300 kW, rated speed 83 rpm, rated voltage 660 V, rated phase current 307 A, and axial core length 200 mm. The simulation results show that the proposed topology has an average output torque of 34.5 kN·m at rated conditions compared with the traditional flux-to-reverse permanent magnet motor of the same size, and the torque ripple rate is reduced from 27.5% to 17.4%, a relative reduction of 36.8%. The results are based only on numerical simulation and have not been verified by physical prototype experiments. Dynamic control strategies, multi-load transient responses and experimental verification will be carried out in subsequent work. Full article

This paper reviews and discusses the behavioural modelling techniques used to simulate the dynamic behaviour of switching metal-oxide-semiconductor field-effect transistors (MOSFETs) and insulated gate bipolar transistors (IGBTs) employed in power electronic converters. It is necessary to develop efficient, accurate and computationally fast models for simulating the efficiency and power losses of power converters used in modern applications such as electric vehicles (EVs), solar photovoltaic (PV) systems, wind turbines (WTs) and other energy systems. In this context, the paper focuses on the approaches used to estimate the switching losses of these devices. The paper discusses the main differences, advantages, and drawbacks of the behavioural modelling methods presented in the literature including average models, charge-based models, and other physical models. The paper focuses on the Miller Plateau phenomenon in these devices, as it plays a major role in calculating the switching losses of power electronic converters. The paper provides a comprehensive review of the different modelling methods in terms of accuracy, computational effort, execution speed, and feasibility for hardware-in-the-Loop (HiL) systems. The paper also discusses the modern data-driven methods and their potential integration into future power electronic systems. At the end, the review identifies some research gaps and highlights promising directions for future behavioural modelling research. Full article

Wind power interval forecasting remains challenging due to the uncertainty and strong variability of wind generation. To capture temporal dependency and predictive uncertainty, this paper proposes a hybrid interval forecasting framework that integrates an Informer-based point prediction model with a temporal convolutional network (TCN) conditional quantile model. The Informer is used to generate deterministic forecasts, while the TCN models the temporal dependency of prediction residuals and estimates conditional quantiles for interval construction. To further improve interval quality, an improved osprey optimization algorithm (IOOA) is introduced to optimize key TCN hyperparameters. The Coverage–Width Criterion (CWC) on the validation set is adopted as the optimization objective for hyperparameter tuning and adaptive quantile-pair selection. To maintain the nominal 90% confidence level, candidate quantile pairs are constrained to have a fixed quantile span of 0.90. Experiments on real-world wind power datasets demonstrate that, when averaged across the two wind farms, the proposed framework achieves a prediction interval coverage probability (PICP) of 0.910, satisfying the nominal coverage level of 90%, and a mean prediction interval width (MPIW) of 7.48, the lowest among all compared methods. Specifically, it reduces the mean interval width by 8.89–28.35% relative to the benchmark models, indicating that the proposed framework generates sharper prediction intervals without compromising coverage reliability and achieves a better trade-off between reliability and sharpness. Full article

The continuous upscaling of offshore wind turbines exacerbates challenges such as blade flutter, stability degradation, and logistical difficulties. To address these issues, this study proposes a parallel multi-rotor wind turbine and systematically investigates its aerodynamic performance and wake characteristics. Based on a 5 MW reference turbine, validated Unsteady Reynolds-Averaged Navier–Stokes (URANS) simulations incorporating the sliding mesh technique and Shear Stress Transport (SST) k-ω turbulence model are conducted to evaluate twin- and four-rotor configurations under various geometric and rotational layouts. Crucially, this study reveals for the first time that asymmetric rotational configurations induce wind field skewness and modify rotor-edge bypass flow, leading to power imbalance among rotors. Additionally, the twin-rotor configuration enhances total power output with negligible thrust variation. For the four-rotor system, power is highly spacing-dependent: at 1.1 rotor diameter spacing, power increases by approximately 3.0% compared to a single rotor. As spacing increases, rotor coupling weakens, which reduces wake non-uniformity but simultaneously slows wake recovery and decreases power generation. Full article

Wind power ramp events (abrupt swings in output driven by frontal passages, sea-breeze transitions, and turbulence) are among the hardest problems for operators integrating renewables. Forecasting models are usually judged by aggregate error metrics (MAE, RMSE, overall directional accuracy) that average stable and ramp periods together, masking how a model behaves during the ramps that actually stress the grid. We address this on two fronts. First, we propose a regime-stratified evaluation that reports ramp event directional accuracy (ramp-DA) separately from stable-period accuracy and argue that ramp-DA should be a primary metric for grid-integration forecasting. Second, we build an ensemble of five regime-specialised sub-models trained with a direction-focused loss that penalises sign errors in the forecast power change, using only on-site SCADA wind speed and power. On 33,411 held-out samples from three onshore Greek farms, the ensemble reaches 76.5% ramp-DA, against 70.1% for a two-layer LSTM (+6.4 pp) and 74.3% and 74.1% for the PatchTST and iTransformer baselines. A strict leave-one-farm-out test retains 77.4% ramp-DA on a fully unseen farm. Overall directional accuracy rises 3.4 points, evidence that aggregate metrics understate the ramp-focused gain, while mean absolute error falls 19% compared to the LSTM (Diebold–Mariano p < 0.001). Full article

Virtual synchronous generator (VSG) control is widely used to improve the frequency stability of low-inertia microgrids. However, most existing adaptive VSG strategies tune the virtual inertia and damping coefficient mainly according to local frequency deviations of the energy storage converter, while the effect of supplementary wind turbine frequency support on the admissible VSG parameter range is rarely considered. To address this limitation, this paper proposes a wind–storage coordinated frequency control strategy that combines an energy storage fuzzy VSG with active-power-frequency droop support from a doubly fed induction generator (DFIG). The scientific contribution of this study is that the DFIG droop support term is incorporated into a reduced-order wind–storage small-signal model, and an admissible scheduling region for the virtual inertia and damping coefficient is constructed according to prescribed damping ratio and natural angular frequency constraints. This region is used to constrain the online fuzzy parameter scheduling of the energy storage VSG. In addition, bell-shaped membership functions are introduced to obtain smoother parameter variation and are compared with triangular membership functions under the same operating conditions. MATLAB/Simulink simulations are conducted under grid-connected/islanded transition, load switching, and renewable-power fluctuation conditions. Compared with the benchmark strategies, the proposed method reduces the maximum and average frequency deviations to 0.181 Hz and 0.016 Hz, respectively. The maximum discharge power, RMS power, and cumulative energy throughput of the energy storage system are reduced to 236.853 kW, 155.822 kW, and 5.751 kWh, respectively. These results indicate that the proposed coordinated strategy improves frequency regulation while reducing the transient regulation burden of the energy storage system within the investigated operating conditions. Full article

The blades directly affect the safety and power generation efficiency of the wind turbines. With the blade size increases, the reliable modal identification becomes important for vibration-based health monitoring. Although operational modal analysis (OMA) technique has been used in condition monitoring for the wind turbine blades, most existing studies focus on investigating a specific single method or under ideal excitation conditions. To overcome this limitation, this study takes the IEA-15MW large wind turbine blade as the research object and compares three OMA methods through numerical simulations, namely covariance-driven stochastic subspace identification (SSI-COV), frequency domain decomposition (FDD), and poly-reference least squares complex frequency domain (PolyMAX). The performance of the modal parameter identification methods is evaluated with respect to different sensor layouts, blade–tower coupling conditions, and environmental excitations. The results indicate that sparse sensor deployment cannot reliably identify the damage-sensitive high-order and complex modes. A nine-channel layout concentrated near second-order deformation regions significantly improves the identification of second-order flapwise frequencies and controls the average error of the first six modes within 3%. PolyMAX shows the best identification stability under different numbers and layouts of the sensors. Blade–tower coupling changes the blade modal characteristics and increases identification difficulty. Under this condition, FDD can still identify both low-order and high-order modes with good stability. Under different real wind conditions, the increasing wind speed causes the aerodynamic load to deviate from the white noise assumption, generally leading to fluctuations in the identification errors, with relatively large local errors occurring at certain medium and high wind speeds. Overall, the three OMA methods show different advantages under different identification conditions. PolyMAX shows the best stability under different sensor layouts and performs best when wind speed increases in the coupled wind turbine model, indicating that it is the most suitable for the actual complex coupling effects and environmental conditions. This research hopefully provides a basis for the subsequent engineering application of vibration-based modal identification of large offshore blades. Full article