Search Results (54)

Long-term energy system planning depends on projections of future wind turbine characteristics, yet existing approaches rely on either costly expert elicitation or deterministic trend extrapolation without formal uncertainty quantification. We present a Bayesian logistic framework that models the temporal evolution of hub height, rotor diameter, and specific power as physically constrained growth and decay processes, producing full posterior predictive distributions via Markov Chain Monte Carlo sampling. The framework is validated across three major onshore wind markets: Austria (534 turbines, 2000–2025), Germany (31,202 turbines, 1988–2026), and the United States (71,457 turbines, 1986–2025); spanning different market structures, regulatory environments, and data availability. Systematic benchmarking against linear, polynomial, and maximum-likelihood alternatives demonstrates superior hindcast performance, particularly for long-range projections where physical saturation constraints become relevant. Prior sensitivity analysis reveals that posteriors are robust for data-rich regions but honestly reflect prior influence for small datasets, identifying where expert knowledge is essential. We extend the framework to climate-aware energy yield estimation by propagating turbine posteriors through synthetic power curves and site-specific wind resource projections under SSP2-4.5 and SSP5-8.5, decomposing the total uncertainty into technology and climate components. When climate uncertainty is measured by scenario spread alone, technology uncertainty dominates. However, accounting for the full inter-model spread across 13 CMIP6 global climate models reveals that climate uncertainty becomes substantial (14–56%) and region-dependent, underscoring that both sources require explicit quantification. The open-source pipeline is designed for direct adoption in energy system planning workflows. Full article

The seismic fragility of reinforced concrete (RC) bridge piers is critical for urban rail transport systems, as severe pier damage may interrupt post-earthquake operation and threaten network safety. Compared with conventional highway bridge piers, urban rail transport RC solid piers usually have lower axial load ratios, larger cross-sections, and stricter serviceability requirements. However, the combined effects of geometric parameters, reinforcement detailing, and material strength on their cyclic behavior, dynamic response, and seismic fragility remain insufficiently understood. To address this issue, seven 1/4-scale RC solid pier specimens were tested under quasi-static cyclic loading to examine the effects of pier height, transverse reinforcement ratio, and longitudinal reinforcement ratio on damage evolution, hysteretic response, skeleton curves, and energy dissipation. A fiber-based OpenSees model considering bond-slip effects was then established, validated against the tests, and extended to a full-scale prototype pier for parametric analysis. The effects of aspect ratio, axial load ratio, longitudinal reinforcement ratio, stirrup ratio, steel yield strength, and concrete strength were evaluated under cyclic loading and nonlinear dynamic time-history excitations. An incremental dynamic analysis-based probabilistic seismic demand model was further developed using 30 near-fault ground motions, with peak ground acceleration as the intensity measure and displacement ductility as the engineering demand parameter. The results showed that increasing the aspect ratio changed the failure mode from flexure-shear-dominated to flexure-dominated behavior, increasing the ultimate displacement from 122 mm to 155 mm while reducing the peak lateral strength from 263 kN to 248 kN. Increasing the longitudinal reinforcement ratio improved both peak strength and ultimate displacement, from 226 kN to 262 kN and from 120 mm to 160 mm, respectively. The numerical results indicated that aspect ratio, axial load ratio, and longitudinal reinforcement ratio had more pronounced effects on seismic demand and fragility than stirrup ratio. Increasing steel yield strength generally reduced seismic fragility, whereas increasing concrete strength enhanced lateral resistance but did not necessarily improve fragility performance. These findings suggest that the seismic performance of urban rail transport RC solid piers should be evaluated by combining cyclic response, dynamic demand, and fragility-based performance, rather than by maximizing any single design parameter. 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

This paper presents a hybrid model that combines linguistic data summarization techniques, algorithms for constructing probabilistic trees, and various generative artificial intelligence models for learning and generating linguistic summaries to aid decision-making. The proposal is validated using methodological triangulation techniques that demonstrate high consistency in the knowledge discovered. The proposal also compares different generative artificial intelligence models; among the evaluated models, Gemini achieved the best performance. However, it is evident that, in certain contexts and tasks, small language models can be effective, yielding results comparable to large language models (LLMs) at a lower computational cost. This study applies the algorithms in a case study analyzing oceanographic data from Northern Chile. In the validation scenario, the combination of linguistic data summarization methods with unsupervised learning techniques effectively models human tolerance for imprecision when processing complex data and generated linguistic summaries easily interpretable by human decision-makers with high levels of confidence. Studies of energy capacities in the studied region and their behavior in both winter and summer are presented. Full article

This paper studies Dirichlet problems for one-dimensional Lévy-type nonlocal elliptic equations on the half-line. The equation $L^{\mu }\nu (x)=f(x),x>0,\nu (x)=0,x\le 0$ is transformed into a weighted nonlocal equation associated with a multiplicative jump process. Under basic structural assumptions on the Lévy measure, the transformed generator is realized through a martingale problem, and the associated exponential killing representation gives a probabilistic mild solution with an immediate $L^{\infty }$-estimate. For the one-dimensional fractional Laplacian, the transformed process is exactly multiplicative. This yields a new approach in which solution estimates are derived from the stochastic equation of the transformed process; smooth-data resolvent solutions are estimated in weighted $L_p$-spaces and extended to general data by approximation. For more general Lévy measures, a smooth weighted energy estimate is proved. The key analytic input is a weighted adjoint integral inequality for the transformed generator, verified for subordinate Brownian motions associated with Bernstein functions and for non-unimodal logarithmically perturbed stable-type operators. Full article

Climate change has intensified the frequency and severity of meteorological disasters, posing significant challenges to the resilience and adaptability of port-centric transport networks (PCTNs) and global trade stability. Unlike previous studies that adopt generalised resilience frameworks or treat disaster types uniformly, this study develops a disaster-specific, integrated assessment framework whose novelty lies in coupling three complementary methods, each playing a distinct role: (i) integer programming optimises post-disaster recovery decisions under budgetary constraints by selecting cost-effective measures that maximise re-stored container-handling capacity; (ii) Monte Carlo simulation (10,000 iterations) captures the stochastic nature of meteorological disruptions and quantifies probabilistic resilience under typhoons, storm surges, and heavy fog; and (iii) an Analytic Hierarchy Process–Evidence Reasoning (AHP–ER) hybrid integrates subjective expert judgement with objective field data to evaluate adaptability across a four-level indicator system, thereby reducing the subjectivity of conventional multi-criteria approaches. Applied to Shanghai Port, the framework yields normalised resilience scores on a [0, 1] scale, where 1.0 denotes full operational continuity (network throughput equals demand) and values below 0.80 indicate substantial disruption requiring urgent intervention. Heavy fog produces the lowest score (0.73, ‘moderate-to-severe disruption’), followed by typhoons (0.81, ‘mild disruption’) and storm surges (0.89, ‘near-normal operation’), revealing that low-visibility events—not high-energy storms—pose the dominant operational threat at Shanghai Port. Translating these findings into practice, the study recommends the following: (1) deploying real-time visibility-monitoring (LiDAR) and AI-driven traffic-scheduling systems to mitigate fog-related disruptions; (2) reinforcing gantry-crane anchoring and prepositioning emergency power supplies in typhoon-prone berths; (3) prioritising hinterland-port handling redundancy in Jiangsu and Anhui sub-networks (adaptability scores 0.639 and 0.642); and (4) piloting an integrated Shanghai–Zhejiang cross-regional emergency-response corridor with shared berthing rights and standardised joint drills. These targeted, quantitatively grounded recommendations offer port authorities and policymakers an evidence base for prioritising infrastructure investment and organisational reform to safeguard global supply chains against escalating climatic threats. Full article

Hysteretic energy, a critical component of seismic input energy, is predominantly dissipated through the hysteretic behavior of structural members in most conventional structures. The motivation is to establish the conditional probabilistic model of normalized hysteretic energy of the structure after determining its displacement, thereby facilitating the estimation of the Park–Ang damage index. This study develops a probabilistic model for normalized hysteretic energy conditional on the ductility ratio. Three macroscopic hysteretic models, representative of the hysteretic behavior of distinct structural types, are employed to quantify the effects of ground motion characteristics (e.g., magnitude, distance, pulse, duration, and site conditions) and structural properties (e.g., post-yield stiffness and damping ratio). The findings reveal that a lognormal distribution effectively characterizes the normalized hysteretic energy. Among the investigated parameters, ground motion duration leads to a significant influence on the distribution of normalized hysteretic energy (maximum difference up to 30%). To facilitate practical applications, a set of predictive expressions is proposed to estimate the mean and standard deviation of normalized hysteretic energy. The resulting conditional distribution reproduces the empirical distribution derived from the original data, with an average error of approximately 5%. Using established expressions, the required ductility capacity under specified performance objectives can be probabilistically determined in seismic design. Moreover, the established distribution can be used to determine the potential hysteretic energy of the structure for assessing its damage state after an earthquake, as demonstrated through a full-scale shaking table test. Full article

Microgrids (MGs) have lately received significant attention from researchers as a contemporary solution to better employ the high penetration of renewable energy sources (RESs) to enhance energy sustainability. They can improve the reliability, resilience, and security of distribution systems. However, a distributed energy management framework is required for the optimal operation of distribution systems with multiple microgrids, given the limited communication between the distribution system operator (DSO) and the microgrid operators. Moreover, distribution systems are unbalanced in nature due to the unbalanced connected loads. Thus, modeling the unbalanced power flow in distributed energy management is essential to ensuring the feasibility of operational decisions. This paper proposes a distributed algorithm based on the alternating direction method of multipliers (ADMM) for optimal operation of distribution systems with multi-microgrids, accounting for uncertainty in demand, RESs, and MG operation modes, as well as unbalanced power flow. A modified IEEE 34-bus distribution system with six microgrids is used to validate the effectiveness of the proposed method. The proposed distributed energy management framework can achieve high solution accuracy with limited information shared among operators, as demonstrated in the case study, providing results comparable to those of the centralized energy management approach, with an insignificant 0.24% error in total operating cost. Moreover, numerical results show that compared with the distribution system and microgrids with forecasted loads and PV outputs under normal operation, the proposed stochastic model yields a 0.56% higher total expected operating cost due to uncertainty in load and PV power outputs. When probabilistic MG islanding operation is considered, the total expected operating cost of the distribution system decreases by 1.03% compared with the stochastic solution under normal operation due to the microgrids’ disconnection from the distribution system during islanding in a few scenarios, hence relieving the distribution system of excessive load. Full article

Compressed air systems (CASs) represent a significant portion of energy consumption in large-scale built environments and manufacturing facilities, suffering from both micro-level physical pipeline leaks and macro-level operational inefficiencies. This paper proposes a unified, dual-action artificial intelligence framework aimed at advancing building decarbonization by systematically integrating acoustic-vision leak quantification with schedule-aware machine learning. Specifically, the framework targets pneumatic pipe connection leaks, fitting leaks, and joint degradation faults within compressed air distribution networks, which are the primary sources of micro-level volumetric energy losses in industrial building systems. First, a probabilistic multimodal fusion algorithm (MPSF) using an ultrasonic camera is developed to detect and geometrically quantify physical leaks, successfully translating pixel areas into physical facility energy loss metrics (estimating 11.0 kW of wasted power from detected severe leaks). Second, to optimize the compressor’s supply matching the actual facility demand without risking data leakage from internal flow sensors, an eXtreme Gradient Boosting (XGBoost) model is proposed. By utilizing only external building environmental conditions and the real-time operational schedules of 13 distinct zones, the model achieves highly accurate dynamic power prediction (R² = 0.9698). Finally, comprehensive simulations based on real-world digital monitoring data from a facility-scale built environment demonstrate that only the concurrent application of both modules ensures stable end-point pressure. The integrated framework achieves a substantial system-wide building energy reduction of over 20% to 40% compared to baseline constant-pressure operations, yielding an estimated annual reduction of 116 tons of CO₂ emissions, thereby providing a direct pathway toward carbon-neutral building operations. Full article

This paper presents an integrated techno–economic framework for coordinated grid-to-vehicle and vehicle-to-grid (G2V–V2G) operation in unbalanced distribution networks. A hardware-compatible bidirectional charger with nested AC/DC and DC/DC control loops, together with a rule-based energy management system (EMS), enables seamless mode transitions while enforcing state-of-charge (SoC) and network constraints. A probabilistic Monte Carlo study on the IEEE 13-bus feeder shows that uncoordinated G2V charging induces adverse grid impacts such as voltage stress, line-ampacity violations, and transformer overloading, whereas EMS-driven V2G support improves voltage by 2–4%, reduces line loading by 15–25%, and lowers transformer stress by up to 10%. To align these technical benefits with economic incentives, a bi-level Stackelberg model is formulated where the utility updates locational energy prices based on combined voltage, line ampacity, transformer loading stress indices and EVs choose profit-maximizing nodes, modes and power levels. The interaction converges to a Stackelberg equilibrium with a clear win–win situation; the feeder’s average locational energy price falls entirely within the win–win region, yielding positive per-session profits for both the EV (≈$0.80) and the utility (≈$0.48) while reducing feeder stress. These results demonstrate that stress-aware locational pricing, combined with detailed converter-level control provides a technically robust and economically sustainable pathway for large-scale EV integration. Full article

The increasing penetration of wind generation requires performance evaluation methods that extend beyond average annual energy production. Temporal delivery characteristics, such as monthly dispersion and exposure to low-production periods, can influence both technical robustness and economic sensitivity. Building upon a previously developed probabilistic and entropy-based assessment framework, this study evaluates the robustness of delivery-oriented performance metrics for onshore and offshore wind units under parametric and economic uncertainty. Using high-resolution operational data from four wind units (three onshore and one offshore), the analysis incorporates percentile sensitivity, threshold variation in low-production exposure, bootstrap-based uncertainty intervals, and Monte Carlo simulation of economic inputs including CAPEX, operation and maintenance costs, and discount rate. The results indicate that variations in percentile definitions and stochastic economic assumptions modify absolute performance values but do not substantially alter the relative positioning between offshore and onshore units. Averaged over 2022–2024, the analyzed offshore unit exhibited a lower monthly energy dispersion coefficient ($C{V}_E=0.255$) than the analyzed onshore units ($C{V}_E=0.368$), corresponding to an approximate 30% reduction in relative variability. The offshore unit also showed lower mean low-production exposure ($LPE=0.526$ versus $0.581$ for onshore units) and consistently lower amplification of robustness-adjusted LCOE under conservative delivery assumptions. These results indicate that the analyzed offshore unit retains stronger delivery robustness and lower economic sensitivity across the tested parameter ranges. The proposed robustness-validation framework complements conventional yield-based assessments and provides additional insight for risk-aware evaluation of wind generation assets in renewable-dominated power systems. Full article

This study quantifies how confinement changes the orientational phase space of proteins by comparing hanging-drop (HD) with Langmuir–Blodgett (LB) conditions within a unified probabilistic framework grounded in structural data from the Protein Data Bank (PDB). For each protein, principal moments of inertia are computed from atomic coordinates, trace-normalized, and used to define a geometry-based benchmark for the probability of occupying a predefined productive-orientation set. In parallel, a Hamiltonian-weighted probability is obtained within a classical statistical–mechanical treatment by reconstructing the orientational distribution over the polar–azimuthal domain under a fixed global confinement protocol. The analysis is carried out on a ten-protein panel spanning diverse sizes and anisotropies, and the HD→LB contrast is characterized through probability gains, distributional distances, and an energy-basin decomposition that distinguishes basin depth from basin measure. Under identical parameterization, LB globally produces higher productive-orientation probabilities than HD across all proteins, establishing a uniform direction of the confinement effect while preserving protein-dependent magnitudes. The inertia-based benchmark exhibits broader dispersion in LB/HD amplification, whereas the Hamiltonian construction yields a more regular cross-protein gain, consistent with LB acting as a global reweighting of orientational phase space rather than a protein-specific re-tuning. By integrating PDB-derived structural descriptors with a statistical–mechanical operator, the framework provides a transparent bridge between molecular geometry and confinement-driven ordering and offers a compact basis for comparing crystallization-relevant confinement protocols across structurally heterogeneous proteins. Full article

Chile stands out for its renewable energy resources and its commitment to developing green hydrogen. However, achieving cost parity with gray hydrogen remains an obstacle, mainly due to high capital costs and sensitivity to scale. This study assesses the technical and economic feasibility of green hydrogen production, using five different plants located in the Magallanes region in the south of the country as a reference. The model integrates a detailed framework of wind generation, PEM electrolysis, compression, and high-pressure storage subsystems, as well as a stochastic economic layer that combines the CAPEX, NPV, and LCOH assessments using Monte Carlo simulations. It also incorporates real-world capacity distributions and probabilistic fluctuations in systems. A sensitivity analysis confirms production scale as the main factor affecting profitability, with a break-even threshold of 0.5 MW. The results show that the LCOH decreases from 7.1 USD to 3.4 USD/kgH₂ as capacity increases. The analysis reveals that only 23.88% of small-scale configurations yield positive NPV, underscoring the need for scaling to achieve economic viability. Full article

Precise Estimated Time of Arrival (ETA) prediction in Terminal Maneuvering Areas (TMA) constitutes a prerequisite for efficient arrival sequencing and airspace capacity management. While data-driven approaches outperform kinematic models, conventional Recurrent Neural Networks (RNNs) exhibit limitations in modeling complex multi-aircraft spatial interactions and lack the capability to quantify predictive uncertainty. Conversely, Spiking Neural Networks (SNNs) enable energy-efficient event-driven computation, yet their applicability to continuous trajectory regression is hindered by “input starvation,” where normalized state vectors fail to induce sufficient neural firing rates. This study proposes a Probabilistic Spiking Transformer (PST) architecture to integrate neuromorphic sparsity with global attention mechanisms. An Adaptive Spiking Temporal Encoding mechanism incorporating learnable linear projections is introduced to resolve the regression-spiking incompatibility, facilitating the autonomous mapping of continuous trajectory dynamics into sparse spike trains without heuristic scaling. Concurrently, a Distance-Biased Multi-Aircraft Cross-Attention (MACA) module models air traffic conflicts by weighting spatial interactions according to physical proximity, thereby embedding separation constraints into the feature extraction process. Evaluation on large-scale real-world ADS-B datasets demonstrates that the PST yields a Mean Absolute Error (MAE) of 49.27 s, representing a 60% error reduction relative to standard LSTM baselines. Furthermore, the model generates well-calibrated probabilistic distributions (Prediction Interval Coverage Probability > 94%), offering quantifiable uncertainty metrics for risk-based decision support while ensuring real-time inference suitable for operational deployment. Full article

The transition from centralized power systems to decentralized infrastructures with a high share of renewable energy sources calls for reliable settlement in P2P electricity trading across “smart” regions. Blockchain platforms can enhance transparency and facilitate automated settlement; however, double-spend attacks still pose a threat to transaction finality and, consequently, undermine trust in the payment layer. This paper quantifies this risk through a probabilistic analysis of classical double-spend scenarios for Proof-of-Work (PoW) and Proof-of-Stake (PoS) blockchains augmented with periodic checkpoints, which render the chain history prior to the latest checkpoint effectively irreversible. We develop attack models for both consensus mechanisms and derive explicit formulas for the attacker’s success probability as a function of the adversarial share, the spacing between checkpoints, and the number of confirmation blocks. On this basis, we compute the minimum confirmation depth needed to satisfy a predefined risk threshold. Numerical evaluation using the derived expressions shows that checkpoints consistently reduce double-spend probability relative to checkpoint-free baselines; in the evaluated settings, the reduction reaches up to 44% and becomes more pronounced as the adversarial share increases. Finally, the analysis yields practical guidance for energy trading applications: accept a payment after the computed number of confirmations when it fits within a single checkpoint interval; otherwise, treat finality as reaching the next checkpoint. Full article