Search Results (235)

The deployment of offshore wind farms (OWFs) has increased impressively over the last decade. While a group of frontrunner countries has led early deployment, the offshore wind sector is expanding to new regions; the Thracian Sea represents a promising area for OWFs deployment due to its favorable wind and wave climate. The successful implementation of OWFs projects depends on a comprehensive understanding of local environmental conditions, with particular emphasis on complex wind–wave interactions quantification, as well as on robust and representative power performance evaluation. In the present paper, hourly environmental data spanning 29 years (1993–2021), including wind and wave parameters, are utilized to quantify joint probability distributions at selected four locations in the Thracian Sea. Corresponding environmental contours are derived and presented using a probabilistic model for given return period. The joint probability distributions of wind and wave conditions are estimated and the environmental contour surfaces for 50- and 100-year return periods are calculated and presented for generic use. Furthermore, the power production of an OWF comprising nine IEA 15 MW turbine units arranged in an orthogonal grid layout is assessed through a numerical model developed in an open access computational tool. The model accounts for key physical processes influencing OWF capacity performance, including wake interactions, atmospheric conditions, turbine control strategies, and layout effects. The results indicate a substantial value of annual energy production and capacity factor for different zones within Thracian Sea achieving a value of 526 GWh and 44%, respectively. The presented results provide practical guidance for OWFs development in the Thracian Sea and contributes to reducing uncertainty in early-stage project planning and future engineering studies. Full article

Accurate performance prediction for large offshore wind turbines requires a principled treatment of uncertainty in both the wind resource and the rotor design parameters. In the present work, we develop a surrogate-based, multi-level uncertainty quantification (UQ) framework coupling a physics-based Blade Element Momentum (BEM) solver with a spectral Polynomial Chaos Expansion (PCE) surrogate that replaces the expensive Monte Carlo loop and apply it to the IEA 15 MW offshore reference wind turbine. The framework is completed by Sobol variance-based global sensitivity analysis. The contribution is methodological rather than algorithmic: although each individual ingredient (PCE, Sobol, BEM, and Jensen) is well established, their joint deployment in a single, internally consistent, end-to-end probabilistic workflow that simultaneously delivers (i) aerodynamic–structural UQ with analytical Sobol ranking, (ii) a like-for-like cross-comparison of three reference turbines, (iii) a quantitative leading-edge icing degradation study, and (iv) a farm-level wake-steering optimization on the same IEA 15 MW reference rotor yields a unified probabilistic envelope from which manufacturing tolerances, cold-climate investment thresholds, and farm-layout/control trade-offs can be read off consistently. Five input parameters are treated as random variables: hub-height wind speed (Weibull, k = 2.2, c = 9.8 m/s), air density, blade chord length, twist angle, and rotor speed. A degree-4 sparse PCE is built by non-intrusive spectral projection using N = 5000 Sobol quasi-random realizations, which allows the Sobol indices to be recovered analytically from the expansion coefficients at essentially no extra cost. Three parallel engineering studies complement the core UQ analysis: (A) a head-to-head comparison of the NREL 5 MW, DTU 10 MW, and IEA 15 MW reference turbines; (B) a quantitative assessment of leading-edge ice accretion at four severity levels; and (C) a Jensen-based wake optimization for a 25-turbine offshore array with static wake steering. The main results are as follows: the turbine reaches C_p,max = 0.480 at λ_opt = 8.51, and an annual energy production (AEP) of 71,261 MWh/year (PCE: 70,840 ± 2,140 MWh/year, 95% CI). Wind speed emerges as the dominant driver of C_p variance (S₁ = 0.412), followed by blade twist (0.198) and chord (0.143). Severe icing (30 kg/m) reduces C_p by 18.2% and increases the blade-root Damage Equivalent Load (DEL) by 18.5%. For the array, the optimal spacing (s_x = 8D, s_y = 6D) gives a farm efficiency of 89.6% and 1296 GWh/year, and a 15° wake-steering offset adds a further +3.2% to farm AEP. Compared with plain Monte Carlo, the sparse PCE delivers the same statistics with about 36% fewer model evaluations and a relative error below 0.8%. Full article

The accelerating worldwide shift toward renewable energy sources (RES) has increased the demand for reliable, efficient, and financially sound wind farm implementation. With the ongoing expansion of wind power within contemporary energy systems, the design and analysis of wind farms, frequently referred to as Power Parks, have become progressively intricate. This study employs probabilistic analysis using Monte Carlo simulation (MCS) to analyze the network in terms of losses, energy output, and profit to ensure the network’s economic stability before it is incorporated into the grid. The results indicate that the system will maintain its reliability throughout the year, as demonstrated in the Simulation Results section where the network operates within permissible values. Furthermore, the system is deemed economically viable, as illustrated in the conclusion, which presents the profit and loss figures. This study is significant as it employs effective techniques necessary for analyzing the entire power park in terms of losses, stability, profitability, and energy output over a specified period, taking into account the variability and uncertainty of wind conditions. Full article

Integrating renewable energy into modern grids while reducing carbon emissions represents a critical challenge for achieving “dual carbon” objectives. This paper proposes a two-stage stochastic optimization scheduling model for integrated energy systems (IES) that accounts for dynamic carbon emission factors and spatiotemporal uncertainty in wind power. First, a dynamic carbon emission factor model is developed to reflect real-time grid operational status and marginal power generation characteristics, replacing the conventional fixed-factor approach and enabling precise guidance for low-carbon electricity procurement strategies. Second, a Copula-based joint probability distribution model is established to capture complex temporal and spatial correlations in multi-wind-farm clusters, from which representative scenarios are generated and reduced through advanced pruning techniques. The scheduling model minimizes total operating costs and tiered carbon trading costs via mixed-integer quadratic programming (MIQP) and Benders decomposition. Case studies demonstrate that the proposed approach reduces daily operating costs by 6.4% (from 2.069 to 1.936 million yuan) and total carbon emissions by 8.4% (from 1051.8 to 963.2 tonnes) compared to conventional static-factor methods. Further, by accurately characterizing wind power uncertainty, the model achieves wind power absorption rates exceeding 90%, reducing curtailment from 272 kWh to 75 kWh and improving renewable energy utilization from 57.5% to 92%. The results validate that dynamic carbon factors and spatiotemporal correlation modelling effectively enhance both low-carbon performance and economic efficiency in IES dispatch, offering theoretical and practical guidance for achieving carbon-neutral energy system operations. Full article

Wind power forecasting is essential for improving renewable energy exploitation and maintaining power system stability. However, influenced by factors such as the velocity and orientation of the wind and atmospheric pressure, wind power exhibits strong variability and uncertainty. Moreover, raw data often contains missing values, shutdown periods, and anomalies, which can degrade forecasting performance. Aiming at solving these challenges, this study develops a wind power forecasting approach integrating data cleaning with a hybrid prediction model. In the preprocessing stage, correlation analysis is employed to select meteorological variables strongly associated with power output as input features, thereby reducing redundancy and improving model effectiveness. Subsequently, missing values and shutdown records are removed, and an improved DBSCAN method is applied to detect anomalous samples. These outliers are then corrected using least squares regression, enhancing data quality while preserving continuity. In the forecasting stage, a hybrid model integrating TCN, BiLSTM, and the Plant Root Growth Optimization (PRGO) algorithm is developed. Specifically, TCN serves to capture local temporal features, while BiLSTM extracts bidirectional temporal dependencies. The PRGO serves to globally optimize model architecture parameters and key hyperparameters, improving convergence efficiency and generalization performance. Experiments on real wind farm data demonstrate that the proposed TCN-BiLSTM-PRGO model consistently outperforms all baselines (TCN, LSTM, TCN-BiLSTM, TCN-Transformer, and TCN-BiLSTM-WOA) across 12 h, 24 h, and 48 h horizons. At 12 h, it achieves a mean R² of 0.942, NMAE of 6.014%, and NRMSE of 7.539% over five runs, improving R² by 0.008–0.123 and reducing NMAE by 0.37–4.57 percentage points compared to other models. It also attains the highest R² at 24 h (0.791) and 48 h (0.833). Statistical significance (p < 0.05) and chronological split tests (R² = 0.940) further confirm their robustness and generalization. The proposed method offers a reliable solution for high-precision wind power forecasting. Full article

Offshore wind energy plays an increasingly important role in the global energy transition, while its design, layout, and operation involve complex optimization problems with strong nonlinearity, high computational cost, and uncertainty. This paper reviews recent advances (2021–2025) in mathematical optimization methods applied to offshore wind energy systems, focusing on turbine system design, wind farm layout, and control strategy optimization. A systematic and semi-quantitative comparison of optimization methods is conducted, including gradient-based methods, metaheuristic algorithms, surrogate-assisted approaches, and multi-objective optimization techniques. These methods are analyzed in terms of computational efficiency, applicability, global search capability, and engineering relevance, supported by representative results reported in the literature. The review further identifies key methodological patterns, discusses trade-offs among different approaches, and proposes practical guidelines for method selection. Finally, research gaps are highlighted, particularly regarding uncertainty modeling, computational scalability, and integrated optimization frameworks. The findings provide useful insights for both researchers and engineers in offshore wind optimization. Full article

In high-penetration wind power systems, outage loss uncertainty analysis is fundamental to maintenance scheduling, and its accuracy critically depends on real-time wind power generation, which is dominated by ultra-short-term wind speed fluctuations. Accurate wind speed prediction is therefore essential for reliable outage loss evaluation and subsequent maintenance decision-making. Dense turbine layouts in wind farms lead to strong wake effects, resulting in complex physical attenuation and spatiotemporal correlations in wind speed between upstream and downstream turbines. Leveraging upstream turbine information can therefore enhance the accuracy of downstream wind speed forecasting. However, existing approaches that incorporate neighboring information, such as graph neural networks, rely primarily on data-driven learning and do not explicitly account for the physical mechanisms of wake attenuation, which limits their predictive performance. To address these challenges, a physics-informed ultra-short-term wind speed forecasting method is proposed which integrates an LSTM network for temporal feature extraction with the Jensen wake model through a weighted loss function within a PINN framework. Wake relationships are first identified based on wind direction and turbine layout, and the Jensen wake model is employed to characterize downstream wind speed attenuation. The weighted loss jointly optimizes data-driven and physics-based objectives, enabling the model to coordinate temporal pattern learning with wake-related physical interactions while adhering to wake decay physics. Moreover, the proposed approach accounts for topology-sensitive power flow variations under high-penetration renewable systems, where outage losses are strongly influenced by real-time wind power and wake-effect uncertainties. Case studies demonstrate that, compared with a conventional LSTM model, the proposed method reduces the normalized mean absolute error and the normalized root mean square error by 14.3% and 13.5%, respectively, indicating improved forecasting accuracy and potential for more reliable system outage analysis. Full article

The reduction of carbon dioxide related to the energy sector is one of the greatest challenges of this century. To ensure a proper transition towards a sustainable electric power system, innovative solutions are fundamental for the efficient integration of renewable energy sources. Hybrid Renewable Energy Systems (HRES) play a crucial role in this scenario; they can ensure a stable and reliable electricity supply thanks to the combination of different renewable technologies, particularly thanks to the integration of storage systems. However, the optimal sizing process of such systems is a complex challenge due to the multiple uncertainties that can be present, involving demand fluctuations and electricity zonal price variations. The aim of this work was to develop a Mixed-Integer Linear Programming (MILP) optimization approach for the robust sizing of a HRES under multiple sources of uncertainty. The developed hybrid model consists of a wind farm, a photovoltaic (PV) plant, a Battery Energy Storage System (BESS), and an industrial load with the entire infrastructure for connection to the national power grid. Additionally, the model includes the capability to manage the over-generation of renewable resources through curtailment mechanisms. The objective of the sizing tool is to minimize the Net Present Cost (NPC) of the plant, while ensuring the reliability of the system. The developed tool can represent a useful assistant for the evaluation of different possible configurations, helping the decision-making process during the design of a HRES. The results will show the best trade-off between economic and reliability aspects, highlighting the impact that the uncertainty has on the optimal size of the plant. In particular, the best configuration analyzed is able to reduce the NPC of more than 50% compared to a plant with a single renewable source. Full article

Large-scale wind integration introduces significant harmonic degradation and resonance risks. Traditional strategies primarily targeting Total Harmonic Distortion (THD) often struggle with individual node violations and high investment costs. To address these challenges, this paper proposes a two-stage sequential coordination strategy for Passive Power Filters (PPFs) and Active Power Filters (APFs). First, stochastic harmonic emission and frequency-domain power flow models are developed to characterize wind-induced harmonic propagation. Second, a sequential optimization framework is established to minimize Life Cycle Cost (LCC). In the first stage, PPF siting and sizing are optimized for cost-effective, system-wide mitigation of low-order harmonics while ensuring THD compliance. The second stage utilizes targeted APF deployment to precisely suppress residual high-order violations and localized resonance. Chance-constrained programming is incorporated to manage wind power uncertainty, enhancing the scheme’s robustness. Simulations on an IEEE 17-bus system demonstrate that the proposed method effectively balances harmonic suppression performance with economic efficiency, providing a robust and cost-effective solution for wind farm power quality management. Full article

Wind power plays an increasingly important role in the global energy transition. However, its power output exhibits significant uncertainty due to rapid variations in meteorological conditions. Existing forecasting methods still face challenges in large-scale wind farm cluster scenarios. In such cases, spatial heterogeneity and temporal asynchrony among wind farms cannot be fully characterized, which limits the overall prediction accuracy. To address these issues, this study proposes a novel hierarchical and adaptive collaborative forecasting framework for wind farm clusters by integrating meteorology-driven dynamic clustering with deep learning-based prediction. First, a multidimensional feature system is constructed by jointly considering static wind farm attributes and dynamic meteorological variation trends. Based on a sliding time window, real-time meteorological similarity among wind turbines is evaluated, allowing meteorological data to actively drive the formation and continuous evolution of adaptive subcluster structures. Subsequently, a deep learning model is developed to perform short-term power forecasting at the dynamic subcluster level. This approach enables the framework to flexibly capture spatio-temporal heterogeneity while maintaining robust prediction capability under varying cluster structures. Experimental results based on real-world wind farm cluster data demonstrate that the proposed method achieves superior accuracy and robustness compared with conventional whole-farm forecasting and static clustering approaches. The proposed framework enhances forecasting reliability, thereby supporting renewable energy integration and sustainable low-carbon power systems. Full article

The inherent nonlinearity and uncertainty of wind power generation pose significant challenges to the security, stability, and economic operation of power grids. Therefore, accurate and reliable wind power forecasting is crucial for seamless grid integration and effective risk assessment. Existing forecasting models often focus on improving point-prediction accuracy while overlooking effective multivariate dependency modeling and reliable uncertainty quantification, limiting both the informativeness and reliability of their forecasts. This study proposes a Fractional-order Momentum optimized Evidential iTransformer (FoM-EiT) for short-term wind power forecasting from multivariate time series. The proposed model integrates cyclic feature encoding for periodic variables, an inverted Transformer for variable-wise interaction learning, and an evidential output head that jointly produces point forecasts and uncertainty estimates from a shared representation. The proposed fractional-order momentum (FoM) optimization accumulates gradient history over an extended window, thereby smoothing oscillations caused by gradient competition and stabilizing the joint training process. Experiments on four real-world wind farms from different geographical regions show that FoM-EiT achieves competitive point forecasting performance, with $R^2$ values of 0.6342, 0.8211, 0.7844, and 0.9161, and the Wilcoxon signed-rank test indicates that its advantages over the baselines are statistically significant in the vast majority of comparisons. For uncertainty quantification, FoM-EiT achieves Prediction Interval Coverage Probability (PICP) values of 0.9492, 0.9682, 0.9709, and 0.9498, while the Winkler score results further show that its prediction intervals outperform the conformal prediction and quantile regression baselines in terms of overall interval quality. These results indicate that FoM-EiT provides both accurate forecasts and trustworthy uncertainty information, making it a practical tool for dispatch, reserve allocation, and risk-aware short-term power system operation. Full article

To address limitations in existing wind farm energy production calculation and reliability assessment—including simplified wake effects, neglected performance degradation, and over-reliance on large-sample data—this study proposes a belief-reliability modeling framework integrating wake dynamics, performance degradation, and dual-uncertainty analysis. It quantifies wake-induced wind speed deficits, captures the degradation of the wind energy utilization coefficient, and models external and internal uncertainties, with external uncertainty referring to wind speed, which follows a Weibull distribution, and internal uncertainty referring to the degradation of the wind energy utilization coefficient, which falls into the category of epistemic uncertainty. By integrating belief reliability theory, a power generation demand reliability metric is developed to enable accurate assessment for data-limited wind farms, and case studies confirm that the framework improves life cycle energy production and reliability prediction accuracy while supporting wind farm design, maintenance, and energy planning. Full article

Accurate estimation of latent heat flux (LE) and sensible heat flux (H) is essential for determining actual crop evapotranspiration (ET_a) and optimizing irrigation water management. However, uncertainties in characterizing the zero-plane displacement height (d_o) often limit H and LE model accuracy. This study introduces a novel approach to characterize d_o using a dynamic fractional vegetation cover and a new proposed canopy porosity (Φ_dp) term derived from Unmanned Aerial System (UAS) imagery. Field experiments were conducted in 2024 near Greeley, Colorado, USA, at a research farm using fully and deficit-irrigated maize fields. Eddy covariance (EC) systems, handheld multispectral radiometer, and PlanetDove mini-satellite imagery were used in the land surface energy balance (EB). A dynamic heat flux footprint area was implemented based on crop height, atmospheric stability, and wind conditions, to align and integrate those measurements with measured EC heat fluxes. Results indicated that both developed d_o models noticeably outperformed existing d_o methods. The new d_o models reduced the normalized root mean square errors (NRMSE) for H estimation by up to 21.1% in the fully irrigated (FI) field and by 16.9% in the deficit-irrigated (DI) field. Furthermore, a higher index of agreement of up to 0.74 reflected an improved d_o model vs. observation correlation. These findings highlight the potential of incorporating a dynamic canopy porosity and vegetation fractional cover to refine spatially distributed EB-based ET_a modeling and advance agricultural irrigation water management based on remote sensing inputs. 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

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