Search Results (796)

This study examines whether green hydrogen production using combined wind and solar energy on Marmara Island can meet the island’s electricity demand and fuel the fuel needs of a hydrogen-powered ferry. A hybrid system consisting of a 10 MW wind farm, a 3 MW solar PV system, and a PEM electrolyzer sized to meet the island’s hydrogen demand was modeled for the island, located in the southwestern Sea of Marmara. The hydrogen production potential, energy flows, and techno-economic performance were evaluated using HOMER-Pro 3.18.4 version. According to the simulation results, the hybrid system generates approximately 62.6 GWh of electricity annually, achieving an 82.8% renewable energy share. A significant portion of the produced energy is transferred to the electrolyzer, producing approximately 729 tons of green hydrogen annually. The economic analysis demonstrates that the system is financially viable, with a net present cost of USD 61.53 million and a levelized energy cost of USD 0.175/kWh. Additionally, the design has the potential to reduce approximately 2637 tons of CO₂ emissions over a 25-year period. The results demonstrate that integrating renewable energy sources with hydrogen production can provide a cost-effective and low-carbon solution for isolated communities such as islands, strengthening energy independence and supporting sustainable transportation options. It has been demonstrated that hydrogen produced by PEM electrolyzers powered by excess energy from the hybrid system could provide a reliable fuel source for hydrogen-fueled ferries operating between Marmara Island and the mainland. Overall, the findings indicate that pairing renewable energy generation with hydrogen production offers a realistic pathway for islands seeking cleaner transportation options and greater energy independence. Full article

The economic dispatch (ED) problem is a major challenge in power system optimization. In this article, an Enhanced Social Group Optimization (ESGO) algorithm is presented for solving the economic dispatch problem with or without wind units, considering various characteristics related to valve-point effects, ramp-rate constraints, prohibited operating zones, and transmission power losses. The Social Group Optimization (SGO) algorithm models the social dynamics of individuals within a group—through mechanisms of collective learning, behavioral adaptation, and information exchange—and leverages these interactions to guide the population efficiently towards optimal solutions. ESGO extends SGO along three complementary directions: redefining the update relations of the original SGO, introducing stochastic operators into the heuristic mechanisms, and dynamically updating the generated solutions. These modifications aim to achieve a more robust balance between exploration and exploitation, enable flexible adaptation of search steps, and rapidly integrate improved-fitness solutions into the evolutionary process. ESGO is evaluated in six distinct cases, covering systems with 6, 40, 110, and 220 units, to demonstrate its ability to produce competitive solutions as well as its performance in terms of stability, convergence, and computational efficiency. The numerical results show that, in the vast majority of the analyzed cases, ESGO outperforms SGO and other known or improved metaheuristic algorithms in terms of cost and stability. It incorporates wind generation results at an operating cost reduction of approximately 10% compared to the thermal-only system, under the adopted linear wind power model. Moreover, relative to the size of the analyzed systems, ESGO exhibits a reduced average execution time and requires a small number of function evaluations to obtain competitive solutions. Full article

Wind-assisted ship propulsion (WASP) has gained considerable interest as a means of reducing fuel consumption and Greenhouse Gas (GHG) emissions, with further benefits when combined with weather-optimized routing. This study employs and extends a National Technical University of Athens (NTUA) weather-routing optimization tool to more realistically assess WASP performance through integrated modeling. The original tool minimized fuel consumption using forecasted weather data and a physics-based performance model. A previous extension to account for the WASP effect introduced a 1-Degree Of Freedom (DOF) model that accounted only for longitudinal hydrodynamic and aerodynamic forces, estimating the reduced main-engine power required to maintain speed in given conditions. The current study incorporates a 3-DOF model that includes side forces and yaw moments, capturing resulting drift and rudder deflection effects. A Kamsarmax bulk carrier equipped with suction sails served as the case study. Initial simulations across various operating and weather conditions compared the two models. The 1-DOF model predicted fuel-saving potential up to 26% for the tested apparent wind speed and the range of possible headings, whereas the 3-DOF model indicated that transverse effects reduce WASP benefits by 2–7%. Differences in Main Engine (ME) power estimates between the two models reached up to 7% Maximum Continuous Rating (MCR) depending on the speed of wind. The study then applied both models within a weather-routing optimization framework to assess whether the optimal routes produced by each model differ and to quantify performance losses. It was found that the revised optimal route derived from the 3-DOF model improved total Fuel Oil Consumption (FOC) savings by 1.25% compared with the route optimized using the 1-DOF model when both were evaluated with the 3-DOF model. Full article

Aiming at multi-agent interest demands and environmental benefits, a distributionally robust game-theoretic optimization algorithm based on a green certificate–carbon trading mechanism is proposed for uncertain microgrids. At first, correlated wind–solar scenarios are generated using Kernel Density Estimation and copula theory and the probability distribution ambiguity set is constructed combining 1-norm and $\infty$-norm metrics. Subsequently, with gas turbines, renewable energy power producers, and an energy storage unit as game participants, a two-stage distributionally robust game-theoretic optimization scheduling model is established for microgrids considering wind and solar correlation. The algorithm is constructed by integrating a non-cooperative dynamic game with complete information and distributionally robust optimization. It minimizes a linear objective subject to linear matrix inequality (LMI) constraints and adopts the column and constraint generation (C&CG) algorithm to determine the optimal output for each device within the microgrid to enhance its overall system performance. This method ultimately yields a scheduling solution that achieves both equilibrium among multiple stakeholders’ interests and robustness. The simulation result verifies the effectiveness of the proposed method. Full article

This paper proposes a privacy-preserving framework that couples probabilistic wind power forecasting with decentralized anomaly detection in complex power networks. We first design an adaptive federated learning (FL) scheme to produce probabilistic forecasts for multiple geographically distributed wind farms while keeping their raw data local. In this scheme, an artificial neural network with quantile regression is trained collaboratively across sites to provide calibrated prediction intervals for wind power outputs. These forecasts are then embedded into an alternating direction method of multipliers (ADMM)-based load-side dispatch and anomaly detection model for decentralized power systems with plug-and-play industrial users. Each monitoring node uses local measurements and neighbor communication to solve a distributed economic dispatch problem, detect abnormal load behaviors, and maintain network consistency without a central coordinator. Experiments on the GEFCom 2014 wind power dataset show that the proposed FL-based probabilistic forecasting method outperforms persistence, local training, and standard FL in RMSE and MAE across multiple horizons. Simulations on IEEE 14-bus and 30-bus systems further verify fast convergence, accurate anomaly localization, and robust operation, indicating the effectiveness of the integrated forecasting–dispatch framework for smart industrial grids with high wind penetration. Full article

Global demand for adaptable and rapidly deployable construction solutions in offshore, coastal, and fluvial environments continues to rise, driven by pressing needs to develop energy platforms, improve coastal resilience, and support emergency response in the face of natural disasters. Increased investment in human-made coastal infrastructure, such as piers, support structures for power lines, offshore wind farms, and seawall protection systems, further underscores this trend. This study investigates the development of printable concrete mixtures for underwater environments using seawater as a replacement for freshwater, using a 3D printing syringe-based extrusion system. The effect of seawater addition and the printing medium (in air vs. underwater) was assessed via rheological and mechanical performance characterization. The results indicate rheological properties are favorable for seawater adoption by producing mixtures with higher yield stress and viscosity with the same levels of admixtures used for freshwater. Seawater-based mixtures demonstrated superior dimensional stability compared to freshwater counterparts, maintaining cross-sectional geometry, while compressive strength results showed no statistical differences between in-air and underwater samples. However, flexural strength was significantly influenced by geometry and printing medium. These findings establish critical rheological parameters for printable underwater mixtures and highlight the need for optimized curing strategies and layer bonding techniques to improve interfacial strength in underwater 3D printing applications. Full article

This paper presents a comprehensive techno-economic and environmental assessment of offshore wind-powered green hydrogen production systems in Egypt and Oman, two strategically located countries within the MENA region with substantial renewable energy potential. A 120 MW offshore wind farm configuration, employing Vestas 8 MW turbines, was simulated for each country and coupled with an electrolyzer system to evaluate electricity generation, hydrogen yield, system efficiency, and cost-effectiveness. The analysis shows that both Egypt and Oman achieve high annual capacity factors (51% and 49.7%, respectively), resulting in annual green hydrogen production of 11.5 million kg and 11.2 million kg. Despite Egypt’s more stable year-round wind profile and slightly lower Levelized Cost of Hydrogen (LCOH: $3.09/kg vs. $3.17/kg), Oman’s seasonal monsoon (Khareef) offers exceptional summer productivity, with peak capacity factors exceeding 74%. A dual-framework environmental assessment was conducted to quantify CO₂ emissions mitigation. In the first scenario, based on grid substitution, the systems could avoid up to 240,000 and 256,000 tonnes of CO₂ annually in Egypt and Oman, respectively. The second scenario evaluates emissions avoided by replacing conventional gray hydrogen, yielding reductions of 126,500 tCO₂/year and 123,200 tCO₂/year, respectively. These results highlight the flexibility of offshore hydrogen systems in addressing both electricity-sector and hydrogen-market decarbonization goals. Additionally, sensitivity analysis shows that increasing turbine hub height yields only marginal wind speed and cost improvements, suggesting limited economic justification under current site conditions. Overall, the study positions Egypt as a stable, year-round hydrogen producer and Oman as a high-output seasonal exporter, supporting a complementary regional strategy for green hydrogen leadership. Full article

Traditional single-prediction models often exhibit limitations in meeting wind power prediction requirements in complex operational scenarios. Furthermore, the inherent “black-box” nature of deep learning models leads to limited interpretability of predictions, hindering effective support for grid dispatch planning. To address these issues, this study proposes a novel day-ahead wind power prediction method, referred to as SHapley Additive exPlanations (SHAP)–Mixture of Experts (MoE), which integrates SHAP into an MoE framework. Here, SHAP is employed for interpretability purposes. This study innovatively transforms SHAP analysis into prior knowledge to guide the decision-making of the MoE gating network and proposes a two-layer dynamic interpretation mechanism based on the collaborative analysis of gating weights and SHAP values. This approach clarifies key meteorological factors and the model’s advantageous scenarios, while quantifying the uncertainty among multiple expert decisions. Firstly, each expert model was pre-trained, and its parameters were frozen to construct a candidate expert pool. Secondly, the SHAP vectors for each pre-trained expert were computed over all sample features to characterize their decision-making logic under varying scenarios. Thirdly, an augmented feature set was constructed by fusing the original meteorological features with SHAP attribution matrices from all experts; this set was used to train the gating network within the MoE framework. Finally, for new input samples, each frozen expert model generates a prediction along with its corresponding SHAP vector, and the gating network aggregates these predictions to produce the final forecast. The proposed method was validated using operational data from an offshore wind farm located in southeastern China. Compared with the best individual expert model and traditional ensemble forecasting models, the proposed method reduces the Root Mean Square Error (RMSE) by 0.23% to 4.92%. Furthermore, the method elucidates the influence of key features on each expert’s decisions, offering insights into how the gating network adaptively selects experts based on the input features and expert-specific characteristics across different scenarios. Full article

Cheesemaking is an energy-intensive process that relies heavily on heating and cooling operations traditionally powered by fossil fuels and electricity from the national grid. Reducing this dependence and integrating renewable energy sources are essential to align the sector with European decarbonization targets. This study presents the development of a simulation tool for optimizing the energy management of a cheese production facility by integrating solar, wind, and biomass systems. The model evaluates techno-economic and environmental performance under different climatic conditions and operational scenarios. Experimental validation was carried out using a prototype installed at the Polytechnic Institute of Beja (Portugal), achieving a deviation of only 2.3% in renewable energy contribution between simulated and measured data. Results demonstrate that renewable integration can reduce non-renewable energy consumption, achieving weekly profits up to 0.019 €/kg of cheese and carbon emissions as low as 0.0109 kg CO₂e/kg. The proposed approach provides a reliable decision-support tool for small- and medium-scale cheese producers, promoting both environmental sustainability and economic competitiveness in rural regions. Full article

This paper provides a review of three mainstream technical routes for producing hydrogen from offshore wind power: offshore distributed hydrogen production, offshore centralized hydrogen production, and onshore hydrogen production. Based on global engineering cases, we analyze the characteristics, application scenarios, and current development status of each route, paying particular attention to economic performance, system efficiency, and environmental adaptability. The main challenges identified include the limited adaptability of electrolysis technologies, high full-life-cycle costs, and persistent bottlenecks in storage and transportation. Building on these findings, we summarize technological development trends and propose future directions in areas such as electrolyzer innovation, system efficiency optimization, direct seawater utilization, storage and transport infrastructure. This review aims to provide a reference for advancing research, development, and large-scale applications of offshore wind-to-hydrogen technologies. Full article

Permanent magnet synchronous generators (PMSGs) are suitable for offshore applications due to their high efficiency and power density. Inter-turn short circuits (ITSCs) stand as one of the most critical faults in these machines due to their rapid evolution in phase or ground short circuits. It is therefore necessary to detect ITSCs at an early stage. In the literature, ITSC detection is often based on current signal processing methods. One of the challenges that these methods face is the presence of imperfections in the stator coils, which also affects the three-phase symmetry. Moreover, when the stator coils are connected in parallel, this type of fault becomes important, as circulating currents will flow between the parallel windings. This, in turn, increases the thermal stress on the insulation and the permanent magnets, while also exacerbating the vibrations of the generator. In this study, a finite-element analysis (FEA) model has been developed to simulate a dual-rotor PMSG under conditions of coil asymmetry. To further investigate the impact of this asymmetry, mathematical modeling has been conducted. For fault detection, negative-sequence current (NSC) analysis and torque monitoring have been used to distinguish coil asymmetry from ITSCs. While both methods demonstrate potential for fault identification, NSC induced small amplitudes and the torque analysis was unable to detect ITSCs under low-severity conditions, thereby underscoring the importance of developing advanced strategies for early-stage ITSC detection. The innovative aspect of this work is that, despite these limitations, the combined use of NSC phase-angle tracking and torque harmonic analysis provides, for the first time in a core-less PMSG with parallel-connected coils, a practical way to distinguish ITSC from coil asymmetry, even though both faults produce almost identical signatures in conventional current-based indices. Full article

This study presents the operational assessment of a pilot-scale power-to-gas (PtG) facility located in La Guajira, Colombia, which integrates a 10 kW photovoltaic array and a 5 kW wind turbine to power a system with two anion exchange membrane (AEM) electrolyzer of 4.8 kW in total for green hydrogen production. Unlike most studies that rely on simulations or short-term evaluations, this study analyzes nine months of real operating data to quantify renewable energy availability, system capacity factors, and effective hydrogen output under tropical conditions. The results show that the hybrid system generated 7111 kWh during the monitoring period. The comparison of theoretical models with real-time energy production shows a low correlation between the data. The MBE ranged from 1253 to 2988 for the solar system, from −814 to 1013 for the wind system, and from 338 to 2714 for the hybrid system. The RMSE values obtained for each evaluated month ranged from 3179 to 3811 for the solar system, from 928 to 1910 for the wind system, and from 2310 to 4327 for the hybrid system, suggesting that the theoretical models tend to overestimate the energy production of the hybrid system in general terms. From the renewable energy produced in real conditions, 92 kg of hydrogen was produced at an average rate of 9 kg/month, considering the availability of wind and solar resources. However, approximately 300 kWh/month of renewable electricity remained unused because the removable generation did not meet the operating conditions of the electrolyzers, highlighting the importance of improved energy management and storage strategies. These findings provide a real scenario of power-to-gas system performance under Caribbean climatic conditions in Colombia, demonstrate the challenges of resource intermittency and system underutilization, and underline the importance of design systems that allow these intermittencies to be managed for the more optimal production of hydrogen from renewable sources. The outcomes contribute to the understanding of small-scale PtG systems in developing regions and support decision making for future scaling and replication of hybrid renewable–hydrogen infrastructures. Full article

This paper describes a compact low-cost telemetry system featuring ready-made sensors and an acquisition unit based on the ESP32, which makes use of the LoRa/Wi-Fi wireless standard for communication, and autonomous fallback logging to guarantee data recovery during communication loss. Ensuring safe atmospheric re-entry requires reliable onboard monitoring of capsule conditions during descent. The system is intended for sub-orbital, low-cost educational capsules and experimental atmospheric descent missions rather than full orbital re-entry at hypersonic speeds, where the environmental loads and communication constraints differ significantly. The novelty of this work is the development of a fully self-contained telemetry system that ensures continuous monitoring and fallback logging without external infrastructure, bridging the gap in compact solutions for CubeSat-scale capsules. In contrast to existing approaches built around UAVs or radar, the proposed design is entirely self-contained, lightweight, and tailored to CubeSat-class and academic missions, where costs and infrastructure are limited. Ground test validation consisted of vertical drop tests, wind tunnel runs, and hardware-in-the-loop simulations. In addition, high-temperature thermal cycling tests were performed to assess system reliability under rapid temperature transitions between −20 °C and +110 °C, confirming stable operation and data integrity under thermal stress. Results showed over 95% real-time packet success with full data recovery in blackout events, while acceleration profiling confirmed resilience to peak decelerations of ~9 g. To complement telemetry, the TeleCapsNet dataset was introduced, facilitating a CNN recognition of descent states via 87% mean Average Precision, and an F1-score of 0.82, which attests to feasibility under constrained computational power. The novelty of this work is twofold: having reliable dual-path telemetry in real-time with full post-mission recovery and producing a scalable platform that explicitly addresses the lack of compact, infrastructure-independent proposals found in the existing literature. Results show an independent and cost-effective system for small re-entry capsule experimenters with reliable data integrity (without external infrastructure). Future work will explore AI systems deployment as a means to prolong the onboard autonomy, as well as to broaden the applicability of the presented approach into academic and low-resource re- entry investigations. Full article

Accurate wind power prediction is of great significance for grid stability and renewable energy integration. Addressing the challenge of effectively integrating physical mechanisms with data-driven methods in wind power prediction, this paper innovatively proposes a two-stage deep learning prediction framework incorporating physics-guided fusion and distribution constraints, aiming to improve the prediction accuracy and physical authenticity of individual wind turbines. In the first stage, we construct a baseline model based on multi-branch multilayer perceptrons (MLP) that eschews traditional attempts to accurately reconstruct complex three-dimensional spatiotemporal wind fields, instead directly learning the power conversion characteristics of wind turbines under specific meteorological conditions from historical operational data, namely the power coefficient (Cp). This data-driven Cp fitting method provides a physically interpretable and robust benchmark for power prediction. In the second stage, targeting the prediction residuals from the baseline model, we design a bidirectional long short-term memory network (BiLSTM) for refined correction. The core innovation of this stage lies in introducing Maximum Mean Discrepancy (MMD) as a regularization term to constrain the predicted wind speed-power joint probability distribution. This constraint enforces the model-generated power predictions to remain statistically consistent with historical real data distributions, effectively preventing the model from producing predictions that deviate from physical reality, significantly enhancing the model’s generalization capability and reliability. Experimental results demonstrate that compared to traditional methods, the proposed method achieves significant improvements in Mean Absolute Error, Root Mean Square Error, and other metrics, validating the effectiveness of physical constraints in improving prediction accuracy. Full article

Accurate short- and medium-term forecasting of photovoltaic (PV) power generation is vital for grid stability and renewable energy integration. This study presents a comparative scenario-based approach using Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Gated Recurrent Unit (GRU) models trained with one year of real-time meteorological and production data from a 250 kWp grid-connected PV system located at Dicle University in Diyarbakır, Southeastern Anatolia, Turkey. The dataset includes hourly measurements of solar irradiance (average annual GHI 5.4 kWh/m²/day), ambient temperature, humidity, and wind speed, with missing data below 2% after preprocessing. Six forecasting scenarios were designed for different horizons (6 h to 1 month). Results indicate that the LSTM model achieved the best performance in short-term scenarios, reaching R² values above 0.90 and lower MAE and RMSE compared to CNN and GRU. The GRU model showed similar accuracy with faster training time, while CNN produced higher errors due to the dominant temporal nature of PV output. These results align with recent studies that emphasize selecting suitable deep learning architectures for time-series energy forecasting. This work highlights the benefit of integrating real local meteorological data with deep learning models in a scenario-based design and provides practical insights for regional grid operators and energy planners to reduce production uncertainty. Future studies can improve forecast reliability by testing hybrid models and implementing real-time adaptive training strategies to better handle extreme weather fluctuations. Full article