Search Results (5,555)

The growing integration of plug-in electric vehicles (PEVs) into microgrids presents both challenges and opportunities, particularly through vehicle-to-grid (V2G) services. This paper proposes a dynamic horizon optimization (DHO) framework with adaptive pricing for real-time scheduling of PEVs in a renewable-powered microgrid. The system integrates solar and wind energy, V2G capabilities, and time-of-use (ToU) tariffs. The DHO strategy dynamically adjusts control horizons based on forecasted load, generation, and electricity prices, while considering battery health. A PEV-specific pricing scheme couples ToU tariffs with system marginal prices. Case studies on a microgrid with four heterogeneous EV charging stations show that the proposed method reduces peak load by 23.5%, lowers charging cost by 12.6%, and increases average final SoC by 12.5%. Additionally, it achieves a 6.2% reduction in carbon emissions and enables V2G revenue while considering battery longevity. Full article

This study explores the effect of the energy transition on financial stability in the context of 13 OECD countries during the period from 2009 to 2019. In this sense, the soundness of the financial system is expressed through two dimensions: the Zscore and the volume of non-performing loans (NPLs). Using a dynamic panel estimation with the Generalized Method of Moments (GMM), the results highlight the complex effects of the energy transition on financial stability. Switching from fossil to clean energy improves the Zscore and reduces NPLs. In addition, the study reveals heterogeneous impacts depending on the renewable energy source involved. In fact, wind energy makes a positive contribution to both dimensions of financial stability. By linking the dynamics of the energy transition with the resilience of the banking sector, this study provides new insights into how sustainable energy policies can foster long-term financial sustainability. The effects of solar power and hydroelectricity, while positive overall, are not without nuances. Specifically, the former reduces the NPLs but also the Zscore, while the latter has the opposite effect on both aspects of financial stability. At this point, it is crucial to take into account the varying effects of different renewable energy sources when assessing the financial repercussions of the energy transition. Full article

In northern China, the long winter heating period is accompanied by severe wind curtailment. To address this issue, a joint optimization scheduling strategy of electric vehicles (EVs) and electro–olefin–hydrogen electromagnetic energy supply device (EHED) is proposed to promote deep wind–solar integration. Firstly, the feasibility analysis of EVs participating in scheduling is conducted, and the operation models of dispatchable EVs and thermal energy storage EHEDs within the scheduling period are established. Secondly, a control strategy for the joint optimization scheduling of wind–solar farms, EVs, EHEDs, and power grid is constructed. Then, an economic dispatch model for joint optimization of EVs and EHEDs is established to minimize the system operation cost within the scheduling period, and the deep wind–solar integration of the joint optimization model is studied by considering EVs under different demand responses. Finally, the proposed model is solved by CPLEX solver. The simulation results show that the established joint optimization economic dispatch model of EV-EHEDs can improve the enthusiasm of dispatchable EVs to participate in deep wind–solar integration, reduce wind curtailment power, and decrease the overall system operation cost. Full article

The high proportion of renewable energy introduces significant operation risks to the system’s flexibility balance due to its volatility and randomness. Traditional regulation methods struggle to meet the urgent demand for flexible resources. Utilizing wind turbines (WTs) under load shedding operation can provide additional reserve capacity, thereby reducing the risk of insufficient system flexibility. However, since wind speed and turbine output exhibit a cubic relationship, minor fluctuations in wind speed can lead to significant variations in output and reserve capacity. This increases the uncertainty in the supply of flexible resources from WTs, posing challenges to power system flexibility assessment. This paper investigates a method for assessing power system flexibility considering the uncertainty of flexible resources supported by WT under load shedding operation. Firstly, according to the flexibility supply control model of WT under shedding operation, the analytical relationship between output, flexible resources, and wind speed under a specific wind energy conversion coefficient is constructed; secondly, combined with the probabilistic model of wind speed based on the nonparametric kernel density estimation, the wind turbine flexible resource uncertainty model is constructed; thirdly, the Monte Carlo simulation is used to obtain the sampled wind speed data, and the operational flexibility assessment method of the power system considering the flexibility uncertainty of WT under load shedding operation is proposed. Finally, through case studies, the validity of the proposed model and method were verified. The analysis concludes that load shedding operation of WTs can enhance the system’s flexible resources to a certain extent but cannot provide stable bi-directional regulation capabilities. Full article

The integration of a high proportion of renewable energy has significantly reduced the grid inertia level and markedly increased the risk of transient frequency instability in power systems. Meanwhile, the large-scale integration of diverse heterogeneous resources—such as wind power, photovoltaics, energy storage, and high voltage direct current (HVDC) transmission systems—has considerably enriched the portfolio of frequency regulation assets in modern power grids. However, the marked disparities in the dynamic response characteristics and actuation speeds among these resources introduce significant nonlinearity and high-dimensional complexity into the system’s transient frequency behavior. As a result, conventional methods face considerable challenges in achieving accurate and timely prediction of such responses. However, the substantial differences in the frequency regulation characteristics and response speeds of these resources have led to a highly nonlinear and high-dimensional complex transient frequency response process, which is difficult to accurately and rapidly predict using traditional methods. To address this challenge, this paper proposes an online prediction method for transient frequency response that deeply integrates physical principles with data-driven approaches. First, a frequency dynamic response analysis model incorporating the frequency regulation characteristics of multiple resource types is constructed based on the Single-Machine Equivalent (SME) method, which is used to extract key features of the post-fault transient frequency response. Subsequently, information entropy theory is introduced to quantify the informational contribution of each physical feature, enabling the adaptive weighted fusion of physical frequency response features and Wide-Area Measurement System (WAMS) data. Finally, a physics-guided machine learning framework is proposed, in which the weighted physical features and the complete frequency curve predicted by the physical model are jointly embedded into the prediction process. An MLP-GRU-Attention model is designed as the data-driven predictor for frequency response. A physical consistency constraint is incorporated into the loss function to ensure that predictions strictly adhere to physical laws, thereby enhancing the accuracy and reliability of the transient frequency prediction model. Case studies based on the modified IEEE 39-bus system demonstrate that the proposed method significantly outperforms traditional data-driven approaches in terms of prediction accuracy, generalization capability under small-sample conditions, and noise immunity. This provides a new avenue for online frequency security awareness in renewable-integrated power systems with multiple heterogeneous frequency regulation resources. Full article

Real-time monitoring of offshore wind turbines (OWTs) through satellite remote sensing imagery is considered an essential process for large-scale infrastructure surveillance in ocean engineering. Current detection systems, however, are constrained by persistent technical limitations, including prohibitive deployment costs, insufficient discriminative power for learned features, and susceptibility to environmental interference. To address these challenges, a dual-branch architecture named OWTDNet is proposed, which integrates global contextual modeling via State Space Models (SSMs) with CNN-based local feature extraction for high-resolution OWTs detection. The primary branch utilizes a Mamba-structured encoder with linear computational complexity to establish long-range spatial dependencies, while an auxiliary Blurring-MobileNetv3 (B-Mv3) branch is designed to compensate for the local feature extraction deficiencies inherent in SSMs. Additionally, a novel Feature Alignment Module (FAM) is introduced to systematically coordinate cross-modal feature fusion between Mamba and CNN branches through channel-wise recalibration and position-aware alignment mechanisms. This module not only enables complementary feature integration but also enhances turbine-specific responses through attention-driven feature modulation. Comprehensive experimental validation demonstrated the superiority of the proposed framework, achieving a mean average precision (AP) of 47.1% on 40,000 × 40,000-pixel satellite imagery, while maintaining practical computational efficiency (127.7 s per image processing time). Full article

Cable-driven parallel robots (CDPRs) are increasingly used for load manipulation tasks involving predefined toolpaths with intermediate stops. At each stop, where the platform maintains a fixed pose, and the motors keep the cables under tension, the system must evaluate whether it is safe to proceed by detecting anomalies that could compromise performance (e.g., wind gusts or cable impacts). This paper investigates whether anomalies can be detected using only motor torque data, without additional sensors. It introduces an adaptive unsupervised outlier detection algorithm based on Gaussian Mixture Models (GMMs) to identify anomalies from torque signals. The method starts with a brief calibration period—just a few seconds—during which a GMM is fit on known anomaly-free data. Real-time torque measurements are then evaluated using the Mahalanobis distance from the GMM, with statistically derived thresholds triggering anomaly flags. Model parameters are periodically updated using the latest segments identified as anomaly-free to adapt to changing conditions. Validation includes 14 long-duration test sessions simulating varied wind intensities. The proposed method achieves a 100% true positive rate and 95.4% average true negative rate, with 1-second detection latency. Comparative evaluation against power threshold and non-adaptive GMM methods indicates higher robustness to drift and environmental variation. Full article

The increasing demand for decarbonized urban environments has intensified interest in integrating renewable energy systems within cities. This review investigates the potential of urban wind energy as a promising technology in the development of Positive Energy Districts, supporting the transition toward climate-neutral urban areas. A systematic analysis of recent literature is presented, covering methodologies for urban wind resource assessment, including Geographic Information Systems (GIS)-based mapping, wind tunnel experiments, and Computational Fluid Dynamics simulations. The study also reviews available small-scale wind technologies, with emphasis on building-integrated wind turbines, and evaluates their contribution to local energy self-sufficiency. The integration of urban wind systems with energy storage, Power-to-Heat solutions, and smart district networks is discussed within the PED framework. Despite technical, economic, and social challenges, such as low wind speeds, turbulence, and public acceptance, urban wind energy offers temporal complementarity to solar power and can enhance district-level energy resilience. The review identifies key technological and methodological gaps and proposes strategic directions for optimizing urban wind deployment in future sustainable city planning. Full article

In this paper, 15-min production data from renewable energy sources (RES), aggregated by technology (onshore wind, offshore wind, solar) and by country (Germany, Austria, Hungary), are analyzed. The concept of a confidence interval is introduced as a parameter for practical use in power-system management. In probabilistic dimensioning of the FRR (Frequency Restoration Reserve; sometimes also called “secondary reserve”), it is necessary to ensure reserve sufficiency for a high percentage of time p, in the order of 99.9%. The confidence interval is specified by upper and lower deviation limits, expressed as percentages of the total installed capacity of the observed RES system, not to be exceeded with a probability greater than $1-p$. The concept of the “regulation multiplier” is also considered, which essentially indicates how many additional megawatts of RES capacity can be installed for each added megawatt of FRR capacity, ceteris paribus. Finally, a previously experimentally developed regulation-multiplier model is verified by replicating the original research on a new dataset used in this paper. Full article

To accommodate the high penetration of intermittent renewable energy sources like wind and solar power into the grid, coal-fired units are required to operate with enhanced deep peak-shaving and variable load capabilities. This study develops a dynamic model of the boiler–turbine coordinated control system (BTCCS) for ultra-supercritical once-through boiler (OTB) coal-fired units operating under wet conditions. A mechanistic model framework is established based on mass and energy conservation. In case of missing steady-state data, this work proposes a mechanism-integrated parameter identification method that determines model parameters using only dynamic running data while incorporating physical constraints. Model validation demonstrates that the proposed approach accurately reproduces the variable-load operation of the BTCCS within the range of 50–350 MW. Mean relative errors of output variables are all less than 7.5%, and root mean square errors of output variables are less than 0.3 MPa, 1.4 kg/s, 0.25 m, and 20.7 MW, respectively. Open-loop simulations further confirm that the model captures the essential dynamic characteristics of the system, making it suitable for simulation studies and control system design aimed at improving operational flexibility and safety of OTB coal-fired units under wet conditions. Full article

The Dvorak technique has represented a fundamental tool for understanding the power of tropical cyclones based on their shape and geometric evolution. However, it should be noted that the Dvorak technique is purely morphological in nature and was developed for wind, not precipitation. The role of shape methods in precipitation prediction remains uncertain, particularly in the context of modern multi-sensor capabilities. This uncertainty forms the motivation for the present study. In an attempt to enrich Dvorak’s technique, this study proposes a novel hypothesis. This study tests the hypothesis that higher precipitation intensity is associated with more organized cloud-system morphology, as captured by simple geometric descriptors and indicative of dynamically coherent convection. A total of 3419 cloud-system objects (after size filter) were utilized to establish geometric relationships in each of them. For the case study of Hurricane Patricia over the Mexican coast in 2015, 3858 geometric shapes were processed. The cloud-system morphology was derived from geostationary imagery (GOES-13) and collocated with satellite precipitation estimates in order to isolate intense-rainfall objects (>50 mm/h). For each object, simple geometric descriptors were computed, and shape variability was summarised via Principal Component Analysis (PCA). The present study sought to evaluate the associations with rain-rate metrics (mean, mode, maximum) using rank correlations and k-means clustering. Furthermore, sensitivity analyses were conducted on the rain threshold and minimum object size. A Shape Descriptor: ratio between perimeter and diameter was identified as a promising tool to enhance early prediction models of extreme rainfall, contributing to enhanced meteorological risk management. The study indicates that cloud shape can serve as a valuable indicator in the classification and forecasting of intense cloud systems. Full article

Fault-tolerant strategies have received increasing attention recently, as reliability requirements have become more stringent. This has drawn significant attention to multiphase machines, due to their inherent fault-tolerance capabilities. Although multiphase machines have been extensively studied as motors since the late 1960s, their use as generators is still in its infancy. Moreover, research on their fault-tolerant capabilities and impact on the power grid remains very limited. With the global expansion of the wind energy sector, the continuous increase in turbine capacities, and the shift in wind energy markets toward offshore wind farms, there is a growing need for studies that investigate the integration of multiphase machines with fault-tolerant strategies and that evaluate their performance and impact on the grid. Therefore, this paper aims to investigate a wind energy conversion system (WECS) based on a five-phase permanent magnet synchronous generator (PMSG) and to evaluate its performance under two fault scenarios: a single-phase open-circuit fault and a double-phase open-circuit fault. A fault-tolerant control strategy is applied in both cases to evaluate its effectiveness under varying wind speeds. The study is carried out using simulation tools developed in MATLAB/Simulink. Full article

The maritime industry is under increasing pressure to reduce greenhouse gas emissions, especially in countries such as Saudi Arabia that are actively working to transition to cleaner energy. In this paper, a new hybrid shipboard power system, which incorporates wind turbines, solar photovoltaic (PV) panels, proton-exchange membrane fuel cells (PEMFCs), and a battery energy storage system (BESS) together for propulsion and hotel load services, is proposed. A multi-loop Energy Management System (EMS) based on proportional–integral control (PI) is developed to coordinate the interconnections of the power sources in real time. In contrast to the widely reported model predictive or artificial intelligence optimization schemes, the PI-derived EMS achieves similar power stability and hydrogen utilization efficiency with significantly reduced computational overhead and full marine suitability. By taking advantage of the high solar irradiance and coastal wind resources in Saudi Arabia, the proposed configuration provides continuous near-zero-emission operation. Simulation results show that the PEMFC accounts for about 90% of the total energy demand, the BESS (±0.4 MW, 2 MWh) accounts for about 3%, and the stationary renewables account for about 7%, which reduces the demand for hydro-gas to about 160 kg. The DC-bus voltage is kept within ±5% of its nominal value of 750 V, and the battery state of charge (SOC) is kept within 20% to 80%. Sensitivity analyses show that by varying renewable input by ±20%, diesel consumption is ±5%. These results demonstrate the system’s ability to meet International Maritime Organization (IMO) emission targets by delivering stable near-zero-emission operation, while achieving high hydrogen efficiency and grid stability with minimal computational cost. Consequently, the proposed system presents a realistic, certifiable, and regionally optimized roadmap for next-generation hybrid PEMFC–battery–renewable marine power systems in Saudi Arabian coastal operations. Full article

The rapid use of renewables like wind power (WP) and photovoltaic (PV) power is essential for a sustainable energy future, yet their volatility poses a threat to grid stability. Electric vehicles (EVs) contribute to the solution by providing storage, while biomass energy (BE) ensures a reliable and sustainable power supply, solidifying its critical role in the stable operation and sustainable development of the power system. Therefore, a dynamic economic emission dispatch (DEED) model based on WP–PV–BE–EVs (DEED_WPBEV) is proposed. The DEED_WPBEV model is designed to simultaneously minimize operating costs and environmental emissions. The model formulation incorporates several practical constraints, such as those related to power balance, the travel needs of EV owners, and spinning reserve. To obtain a satisfactory dispatch solution, an adaptive improved multi-objective evolutionary algorithm based on decomposition with differential evolution (IMOEA/D-DE) is further proposed. In IMOEA/D-DE, the initialization of the population is achieved through an iterative chaotic map with infinite collapses, and the differential evolution mutation operator is adaptively adjusted. Finally, the feasibility and effectiveness of the proposed model and algorithm are verified on the ten-units system. The experimental results show that the proposed model and algorithm can effectively mitigate renewable energy uncertainty, reduce system costs, and lessen environmental impact. Full article

Integrated energy systems (IES) are central to sustainable energy transitions because sector coupling can raise renewable utilization and cut greenhouse gas emissions. Yet, traditional optimizers often become trapped in local optima and struggle with multi-objective trade-offs between economic and environmental goals. This study applies the hippopotamus optimization algorithm (HOA) to the sustainability-oriented, multi-objective operation of an electricity–gas–heat IES that incorporates power-to-gas (P2G), photovoltaic generation, and wind power. We jointly minimize operating cost and carbon emissions while improving renewable energy utilization. In comparative tests against pigeon-inspired optimization (PIO) and particle swarm optimization (PSO), HOA achieves superior Pareto performance, lowering operating costs by ~1.5%, increasing energy utilization by 16.3%, and reducing greenhouse gas emissions by 23%. These gains stem from HOA’s stronger exploration–exploitation balance and the flexibility introduced by P2G, which converts surplus electricity into storable gas to support heat and power demands. The results confirm that HOA provides an effective decision tool for sustainable IES operation, enabling deeper variable-renewable integration, lower system-wide emissions, and improved economic outcomes, thereby offering practical guidance for utilities and planners pursuing cost-effective decarbonization. Full article