Search Results (500)

Floating solar technology has gained significant attention as part of the global expansion of renewable energy due to its potential for installation in underutilized water bodies. Several countries, including the Netherlands, have initiated efforts to extend this technology from inland freshwater applications to open offshore environments, particularly within offshore wind farm areas. This development is motivated by the synergistic benefits of increasing site energy density and leveraging the existing offshore grid infrastructure. The deployment of offshore floating photovoltaic (OFPV) systems involves assembling multiple modular units in a marine environment, introducing operational risks that may give rise to safety concerns. To mitigate these risks, weather windows must be considered prior to the task execution to ensure continuity between weather-sensitive activities, which can also lead to additional time delays and increased costs. Consequently, optimizing marine logistics becomes crucial to achieving the cost reductions necessary for making OFPV technology economically viable. This study employs a simulation-based approach to estimate the installation duration of a 5 MWp OFPV plant at a Dutch offshore wind farm site, started in different months and under three distinct risk management scenarios. Based on 20 years of hindcast wave data, the results reveal the impacts of campaign start months and risk management policies on installation duration. Across all the scenarios, the installation duration during the autumn and winter period is 160% longer than the one in the spring and summer period. The average installation durations, based on results from 12 campaign start months, are 70, 80, and 130 days for the three risk management policies analyzed. The result variation highlights the additional time required to mitigate operational risks arising from potential discontinuity between highly interdependent tasks (e.g., offshore platform assembly and mooring). Additionally, it is found that the weather-induced delays are mainly associated with the campaigns of pre-laying anchors and platform and mooring line installation compared with the other campaigns. In conclusion, this study presents a logistics modeling methodology for OFPV systems, demonstrated through a representative case study based on a state-of-the-art truss-type design. The primary contribution lies in providing a framework to quantify the performance of OFPV installation strategies at an early design stage. The findings of this case study further highlight that marine installation logistics are highly sensitive to local marine conditions and the chosen installation strategy, and should be integrated early in the OFPV design process to help reduce the levelized cost of electricity. Full article

Τhe Greek Seas are one of the most favorable locations for offshore wind energy development in the Mediterranean basin. In 2023, the Hellenic Hydrocarbons & Energy Resources Management Company SA published the draft National Offshore Wind Farm Development Programme (NDP-OWF), including the main pillars for the design, development, siting, installation, and exploitation of offshore wind farms, along with the Strategic Environmental Impact Assessment. The NDP-OWF is under assessment by the relevant authorities and is expected to be finally approved through a Joint Ministerial Decision. In this work, the preliminary offshore wind energy assessment of the Greek Seas is performed using the CERRA wind reanalysis data and in situ measurements from six offshore locations of the Greek Seas. The in situ measurements are used in order to assess the performance of the reanalysis datasets. The results reveal that CERRA is a reliable source for preliminary offshore wind energy assessment studies. Taking into consideration the potential offshore wind farm organized development areas (OWFODA) according to the NDP-OWF, the study of the local wind characteristics is performed. The local wind speed and wind power density are assessed, and the wind energy produced from each OWFODA is estimated based on three different capacity density settings. According to the balanced setting (capacity density of 5.0 MW/km²), the annual energy production will be 17.5 TWh, which is equivalent to 1509.1 ktoe. An analysis of the wind energy correlation, synergy, and complementarity between the OWFODA is also performed, and a high degree of wind energy synergy is identified, with a very low degree of complementarity. Full article

Determining/predicting the environment dominates a variety of marine operations, such as route planning and offshore installation. Significant wave height (Hs) is a critical parameter-defining wave, a dominating marine load. Data-driven machine learning methods have been increasingly applied to Hs prediction, but challenges remain in hyperparameter tuning and spatial generalization. This study explores a novel effective approach for intelligent Hs forecasting for marine operations. Multiple automated machine learning (AutoML) frameworks, namely H2O, PyCaret, AutoGluon, and TPOT, have been systematically evaluated on buoy-based Hs prediction tasks, which reveal their advantages and limitations under various forecast horizons and data quality scenarios. The results indicate that PyCaret achieves superior accuracy in short-term forecasts, while AutoGluon demonstrates better robustness in medium-term and long-term predictions. To address the limitations of single-point prediction models, which often exhibit high dependence on localized data and limited spatial generalization, a multi-point data fusion framework incorporating Principal Component Analysis (PCA) is proposed. The framework utilizes Hs data from two stations near the California coast to predict Hs at another adjacent station. The results indicate that it is possible to realize cross-station predictions based on the data from adjacent (high relevance) stations. Full article

Reliable prediction of tunnel thruster performance under reverse, or off-design, reverse operating direction (ROD) conditions, is crucial for modern vessels that require bidirectional thrust from a single unit—such as yachts and offshore support vessels. Despite the increasing demand for such a capability, there remains limited understanding of the unsteady hydrodynamic behavior and performance implications of ROD operation. This study addresses this gap through a scale-resolving computational fluid dynamics (CFD) investigation of a full-scale, fixed-pitch propeller with a diameter of 0.62, installed in a tunnel geometry representative of yacht-class side thrusters. Using advanced turbulence modeling, we compare the thruster’s performance under both the normal operating direction (NOD) and ROD. The results reveal notable differences: in ROD, the upstream separation zone was more compact and elongated, average thrust increases by approximately 3–4%, and torque and pressure fluctuations rise by 15–30%. These findings demonstrate that a single tunnel thruster can meet bidirectional manoeuvring requirements. However, the significantly elevated unsteady loads during ROD operation offer a plausible explanation for the increased noise and vibration frequently observed in practice. Full article

Simulations are conducted using a microscale model framework to quantify differences in projected Annual Energy Production (AEP), Capacity Factor (CF) and wake losses for large offshore wind farms that arise due to different input datasets, installed capacity density (ICD) and/or wake parameterizations. Differences in CF (and AEP) and wake losses that arise due to the selection of the wake parameterization have the same magnitude as varying the ICD within the likely range of 2–9 MW km⁻². CF simulated with most wake parameterizations have a near-linear relationship with ICD in this range, and the slope of the dependency on ICD is similar to that in mesoscale simulations with the Weather Research and Forecasting (WRF) model. Microscale simulations show that remotely generated wakes can double AEP losses in individual lease areas (LA) within a large LA cluster. Finally, simulations with the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) model are shown to differ in terms of wake-induced AEP reduction from those with the WRF model by up to 5%, but this difference is smaller than differences in CF caused by the wind farm parameterization used in the mesoscale modeling. Enhanced evaluation of mesoscale and microscale wake parameterizations against observations of climatological representative AEP and time-varying power production from wind farm Supervisory Control and Data Acquisition (SCADA) data remains critical to improving the accuracy of predictive AEP modeling for large offshore wind farms. Full article

Helical piles are widely used in geotechnical engineering, and their rapid installation and service reliability have attracted significant interest from the offshore wind industry. These piles are frequently subjected to cyclic loading in complex marine environments. Although the cyclic bearing behavior of helical piles has been studied, most research has focused on soil properties and loading conditions, with a limited systematic analysis of plate parameters. Moreover, the selection of plate parameters is not explicitly defined. As a crucial preliminary step in the capacity calculation, it is vital for the design of helical piles. To address this gap, the present study combines physical modeling tests and finite element simulations to systematically evaluate the influence of plate parameters on their cyclic bearing behavior. The parameters investigated include the plate depth, the plate diameter, plate spacing, and the number of plates. The results indicate that, under the same embedment conditions, cumulative displacement increases with the plate depth, with a critical embedment depth ratio of H_cr/D = 6 under cyclic loading conditions, but decreases with the number of plates. Axial stiffness increases with the plate depth, diameter, and number of plates, with an increase ranging from 0.5 to 3.0. However, the normalized axial stiffness decreases with these parameters, reaching a minimum value of 1.63. The plate spacing has a minimal influence on cyclic bearing behavior. Additionally, this study examines the evolution of displacement and stiffness parameters over repeated cycles in numerical simulations, as well as the post-cyclic pullout capacity of the helical pile foundation, which varies between −5% and +12%. Full article

The increasing deployment of turbines installed offshore is critical for sustainable energy development, yet accurate performance assessment remains challenging due to complex environmental influences, diverse turbine control strategies, and issues with data quality. Traditional performance metrics and power curve models often fail to provide reliable cross-turbine comparisons because they neglect multivariate environmental factors and turbine-specific biases. To address these limitations, this study develops a novel multivariate environmental factor-driven power assessment framework employing segmented long short-term memory (LSTM) models. A hybrid data cleaning method, combining bidirectional quartile analysis with the power curtailment detection, is proposed to effectively identify outliers, including subtle anomalies within typical data ranges. Samples are segmented based on rated wind speed to reflect differences in control strategies, and turbine-specific operational parameters are excluded to ensure unbiased comparisons among turbines. The proposed method achieves substantial improvements in predictive accuracy, with decreases of 9.39% in mean absolute error (MAE) and 11.75% in root mean square error (RMSE), compared to conventional binning approaches. When applied to three 5.5 MW offshore wind turbines, the proposed method reveals significant differences among the units. Turbine A demonstrates the highest performance, while turbines B and C exhibit reductions of 14.35% and 8.29%, respectively. Operational state analysis shows that turbine B experiences substantially longer maintenance durations, indicating severe faults that adversely affect its operational reliability and power output. These findings provide valuable insights for maintenance prioritization and performance benchmarking among wind turbines. Full article

This paper presents a method to model and optimize the supply chain processes for floating offshore wind turbines using a network model based on Generalized Multi-Commodity Network Flows (GMCNF). The proposed method represents production bases, base ports, installation sites, component transfer areas, and transportation routes as nodes and arcs within the network. The installation process is modeled using three transport concepts: assembling components at the base port, direct assembly and installation at the installation site, and transferring components to the installation vessel at a nearby port. These processes are expressed as a linear network model, with the objective function set to minimize total transportation and assembly costs. The optimal transportation network is derived by solving the network problem while incorporating constraints such as supply, demand, and transportation capacity. Case studies demonstrate the method’s effectiveness in optimizing the supply chain and evaluating potential new production site locations for floating foundations, considering overall supply chain optimization. Full article

This study identifies the optimal location for an offshore energy island to supply sustainable power to desalination plants along the Red Sea coast. As demand for clean energy in water production grows, integrating renewables into desalination systems becomes increasingly essential. A decision-making framework was developed to assess site feasibility based on renewable energy potential (solar, wind, and wave), marine traffic, site suitability, planned developments, and proximity to desalination facilities. Data was sourced from platforms such as Windguru and RETScreen, and spatial analysis was conducted using Inverse Distance Weighting (IDW) and Multi-Criteria Decision Analysis (MCDA). Results indicate that the central Red Sea region offers the most favorable conditions, combining high renewable resource availability with existing infrastructure. The estimated regional desalination energy demand of 2.1 million kW can be met using available renewable sources. Integrating these sources is expected to reduce local CO₂ emissions by up to 43.17% and global desalination-related emissions by 9.5%. Spatial constraints for offshore installations were also identified, with land-based solar energy proposed as a complementary solution. The study underscores the need for further research into wave energy potential in the Red Sea, due to limited real-time data and the absence of a dedicated wave energy atlas. Full article

As global energy production progressively turns toward a green environment and economy, one of the safety challenges to the maritime industry that has arisen lies within offshore wind farms (OWFs). As renewable sources of energy whose numbers are rapidly expanding, their impact to the safety of navigation of the ships that navigate in their vicinity ought to be examined further. An ever-growing number of OWFs has led to safety concerns that have never been taken into consideration before. This article gives a structured quantitative analysis and an in-depth review of the literature connected to the safety of navigation, collision probability, and risk assessment that OWFs pose to all maritime industry agents. In this article, the main concerns of the impact of OWFs to the safety of navigation are analyzed using a combination of both the PRISMA and PICOC methodologies. Various types of scientific papers such as journal articles, conference proceedings, MSc theses, PhD theses, and online works of research are collated into a detailed bibliometric analysis and categorized by the most relevant parameters providing valuable perspectives on the current state of art in the field. The findings of this research emphasize the need for a further and more thorough analysis on the theoretical installment of OWFs and their inevitable impact on increasing maritime traffic complexity. The results of this article can form a strong basis for further scientific development in the field and can give useful insights to all maritime industry stakeholders dealing with OWFs. Full article

Green hydrogen is a promising solution for decarbonizing emission-intensive sectors, with its production through offshore wind energy offering viable opportunities. This study presents a preliminary assessment of the main environmental factors potentially affected by offshore wind and green hydrogen projects in Uruguay’s Exclusive Economic Zone (EEZ), where such developments pose environmental challenges that require evaluation, particularly given the limited prior research in Uruguay and Latin America. Through a comprehensive review of international literature and national technical data, the study identifies key interactions between project activities and the physical, biotic, and anthropic environmental components during the development, construction, and operational phases. Using cross-reference matrices and impact categorization, the analysis highlights that activities such as foundation installation, submarine cable deployment, and offshore electrolysis could significantly affect the seabed, underwater noise levels, water quality, and marine biodiversity. The biotic and physical environment were found to be the most frequently impacted. To contextualize these findings, technical information specific to Uruguay’s EEZ was reviewed to identify the most vulnerable regional environmental factors. The results offer a science-based foundation to support early-stage environmental assessments and guide sustainable offshore energy development in the region. Full article

This study aims to conduct a comparative analysis of existing global policies and data for offshore wind (OW) farms (OWFs) by exploring the performance of the United Kingdom (UK), Germany, China, Taiwan and the rest of the world based on chosen quantitative metrics (total installations, energy capacity, bathymetry, wind resources) and qualitative policy drivers (costs, installation regulations, taxation). This research adopts an explorative multi-case study design that involves analyzing quantitative and qualitative metrics of OW energy parameters for the UK, Germany, China, Taiwan and the rest of the world. The quantitative metrics include the total OW energy installations, bathymetric data, wind speed and direction data and OW energy capacity while the qualitative metrics include the policy changes on costs of installations, installation policies and taxation policies. As compared to the United Kingdom and Germany, China reported the highest number of installed OW energy farms between 2019 and 2023. The UK reported a gradual increase in the number of OWFs installed between 2019 and 2023. Taiwan has the lowest number of OWFs and wind energy capacity but ranks almost the same as China and the UK in terms of the bathymetric data and wind speed. Statistically significant correlation, (p ≤ 0.05), between the wind speed and the number of OWFs for all the countries. No statistically significant relationship between the bathymetric characteristics and the number of OW installations and wind energy capacity. Geographical factors, weather patterns and government policies play crucial roles in the successful installation and maintenance of OWFs. Full article

The exploitation of renewable energy is essential for mitigating climate change and reducing fossil fuel emissions. Wind energy, the most mature technology, is highly dependent on wind speed, and the accurate prediction of the latter substantially supports wind power generation. In this work, various artificial neural networks (ANNs) were developed and evaluated for their wind speed prediction ability using the ERA5 historical reanalysis data for four potential Offshore Wind Farm Organized Development Areas in Greece, selected as suitable for floating wind installations. The training period for all the ANNs was 80% of the time series length and the remaining 20% of the dataset was the testing period. Of all the ANNs examined, the hybrid model combining Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks demonstrated superior forecasting performance compared to the individual models, as evaluated by standard statistical metrics, while it also exhibited a very good performance at high wind speeds, i.e., greater than 15 m/s. The hybrid model achieved the lowest root mean square errors across all the sites—0.52 m/s (Crete), 0.59 m/s (Gyaros), 0.49 m/s (Patras), 0.58 m/s (Pilot 1A), and 0.55 m/s (Pilot 1B)—and an average coefficient of determination (R²) of 97%. Its enhanced accuracy is attributed to the integration of the LSTM and GRU components strengths, enabling it to better capture the temporal patterns in the wind speed data. These findings underscore the potential of hybrid neural networks for improving wind speed forecasting accuracy and reliability, contributing to the more effective integration of wind energy into the power grid and the better planning of offshore wind farm energy generation. Full article

Structural health monitoring (SHM) plays a vital role in ensuring the safety, durability, and performance of civil infrastructure. This review delves into the significant advancements in optical fiber sensor (OFS) technologies such as Fiber Bragg Gratings, Distributed Temperature Sensing, and Brillouin-based systems, which have emerged as powerful tools for enhancing SHM capabilities. Offering high sensitivity, resistance to electromagnetic interference, and real-time distributed monitoring, these sensors present a superior alternative to conventional methods. This paper also explores the integration of OFSs with Artificial Intelligence (AI), which enables automated damage detection, intelligent data analysis, and predictive maintenance. Through case studies across key infrastructure domains, including bridges, tunnels, high-rise buildings, pipelines, and offshore structures, the review demonstrates the adaptability and scalability of these sensor systems. Moreover, the role of SHM is examined within the broader context of civil and urban infrastructure, where IoT connectivity, AI-driven analytics, and big data platforms converge to create intelligent and responsive infrastructure. While challenges remain, such as installation complexity, calibration issues, and cost, ongoing innovation in hybrid sensor networks, low-power systems, and edge computing points to a promising future. This paper offers a comprehensive amalgamation of current progress and future directions, outlining a strategic path for next-generation SHM in resilient urban environments. Full article

The growing scale of offshore wind farms and increasing transmission distances has driven the demand for optimized offshore substation (OSS) configurations. This study proposes a comprehensive techno-economic framework to minimize the total lifecycle cost (LCC) of an OSS by determining the optimal number of OSSs and transformers considering wind farm capacity and transmission distance. The methodology incorporates three cost models: capital expenditure (CAPEX), operational expenditure (OPEX), and expected energy not supplied (EENS). CAPEX considers transformer costs, topside structural mass effects, and nonlinear installation costs. OPEX accounts for substation maintenance and vessel operating expenses, and EENS is calculated using transformer failure probability models and redundancy configurations. The optimization is performed through scenario-based simulations and a net present value (NPV)-based comparative analysis to determine the cost-effective configurations. The quantitative analysis demonstrates that for small- to medium-scale wind farms (500–1000 MW), configurations using 1–2 substations and 3–4 transformers achieve minimal LCC regardless of the transmission distance. In contrast, large-scale wind farms (≥1500 MW) require additional substations to mitigate transmission losses and disruption risks, particularly over long distances. These results demonstrate that OSS design should holistically balance initial investment costs, operational reliability, and supply security, providing practical insights for cost-effective planning of next-generation offshore wind projects. Full article