Search Results (429)

The growing global demand for electricity, driven by the development of electromobility, data centers, and smart technologies, necessitates innovative approaches to energy generation. Wind power, as a clean and renewable energy source, plays a pivotal role in the global transition towards low-carbon power systems. This paper presents a comprehensive review of generator technologies used in wind turbine applications, ranging from conventional synchronous and asynchronous machines to advanced concepts such as low-speed direct-drive (DD) generators, axial-flux topologies, and superconducting generators utilizing low-temperature superconductors (LTS) and high-temperature superconductors (HTS). The advantages and limitations of each design are discussed in the context of efficiency, weight, reliability, scalability, and suitability for offshore deployment. Special attention is given to HTS-based generator systems, which offer superior power density and reduced losses, along with challenges related to cryogenic cooling and materials engineering. Furthermore, the paper analyzes selected modern generator designs to provide references for enhancing the performance of grid-synchronized hybrid microgrids integrating solar PV, wind, battery energy storage, and HTS-enhanced generators. This review serves as a valuable resource for researchers and engineers developing next-generation wind energy technologies with improved efficiency and integration potential. Full article

This paper addresses the challenge of maximizing power extraction in offshore wind energy systems through the development of an enhanced maximum power point tracking (MPPT) control strategy. Offshore wind energy is inherently intermittent, leading to discrepancies between power generation and electricity demand. To address this issue, we propose three advanced control algorithms to perform a comparative analysis: sliding mode control (SMC), the Integral Backstepping-Based Real-Twisting Algorithm (IBRTA), and Feed-Back Linearization (FBL). These algorithms are designed to handle the nonlinear dynamics and aerodynamic uncertainties associated with offshore wind turbines. Given the practical limitations in acquiring accurate nonlinear terms and aerodynamic forces, our approach focuses on ensuring the adaptability and robustness of the control algorithms under varying operational conditions. The proposed strategies are rigorously evaluated through MATLAB/Simulink 2024 A simulations across multiple wind speed scenarios. Our comparative analysis demonstrates the superior performance of the proposed methods in optimizing power extraction under diverse conditions, contributing to the advancement of MPPT techniques for offshore wind energy systems. Full article

Floating offshore wind turbines (FOWTs) unlock superior wind resources and reduce operational barriers. The dynamics of FOWT platforms present added engineering challenges and opportunities. While the motion of the floating platform due to wind and wave disturbances can worsen power quality and increase structural loading, certain movements of the floating platform can be exploited to improve power capture. Consequently, active FOWT platform control methods using conventional and innovative actuation systems are under investigation. This paper develops a novel framework to design nonlinear control laws for six degrees-of-freedom platform motion. The framework uses simplified rigid-body analytical models of the FOWT. Lyapunov’s direct method is used to develop actuator-agnostic unconstrained control laws for platform translational and rotational control. A model based on the NREL-5MW reference turbine on the OC3-Hywind spar-buoy platform is utilized to test the control framework for an ideal actuation scenario. Possible applications using traditional and novel turbine actuators and future research directions are presented. Full article

This study explores virtual sensing techniques for the Eolink floating offshore wind turbine (FOWT), which features a pyramidal platform and a single-point mooring system that enables weathervaning to maximize power production and reduce structural loads. To address the challenges and costs associated with monitoring submerged components, virtual sensors are investigated as an alternative to physical instrumentation. The main objective is to design a virtual sensor of mooring hawser loads using a reduced set of input features from GPS, anemometer, and inertial measurement unit (IMU) data. A virtual sensor is also proposed to estimate the bending moment at the joint of the pyramid masts. The FOWT is modeled in OrcaFlex, and a range of load cases is simulated for training and testing. Under defined sensor sampling conditions, both supervised and physics-informed machine learning algorithms are evaluated. The models are tested under aligned and misaligned environmental conditions, as well as across operating regimes below- and above-rated conditions. Results show that mooring tensions can be estimated with high accuracy, while bending moment predictions also perform well, though with lower precision. These findings support the use of virtual sensing to reduce instrumentation requirements in critical areas of the floating wind platform. Full article

Excessive reliance on traditional energy sources such as coal, petroleum, and gas leads to a decrease in natural resources and contributes to global warming. Consequently, the adoption of renewable energy sources in power systems is experiencing swift expansion worldwide, especially in offshore areas. Floating solar photovoltaic (FPV) technology is gaining recognition as an innovative renewable energy option, presenting benefits like minimized land requirements, improved cooling effects, and possible collaborations with hydropower. This study aims to assess the levelized cost of electricity (LCOE) associated with floating solar initiatives in offshore and onshore environments. Furthermore, the LCOE is assessed for initiatives that utilize floating solar PV modules within aquaculture farms, as well as for the integration of various renewable energy sources, including wind, wave, and hydropower. The LCOE for FPV technology exhibits considerable variation, ranging from 28.47 EUR/MWh to 1737 EUR/MWh, depending on the technologies utilized within the farm as well as its geographical setting. The implementation of FPV technology in aquaculture farms revealed a notable increase in the LCOE, ranging from 138.74 EUR/MWh to 2306 EUR/MWh. Implementation involving additional renewable energy sources results in a reduction in the LCOE, ranging from 3.6 EUR/MWh to 315.33 EUR/MWh. The integration of floating photovoltaic (FPV) systems into green hydrogen production represents an emerging direction that is relatively little explored but has high potential in reducing costs. The conversion of this energy into hydrogen involves high final costs, with the LCOH ranging from 1.06 EUR/kg to over 26.79 EUR/kg depending on the complexity of the system. 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

Recent studies have shown benefits of using tidal stream and wave energy in the electricity generation mix to improve supply–demand balancing on annual/subannual timeframes. This paper investigates this further by considering the variability of solar photovoltaic, onshore and offshore wind, wave, and tidal stream over multiple years. It also considers their ability to match with electricity demand when combined. Variability of demand and generation can have a significant impact on results. Over the sample of five years considered (2015–2019), demand varied by around 3%, and the availability of each renewable technology differed by up to 9%. This highlights the importance of considering multiple years of input data when assessing power system impacts, instead of relying on an ‘average’ year. It is also key that weather related correlations between renewable resources and with demand can be maintained in the data. Results from an economic dispatch model of Great Britain’s power system in 2030 are even more sensitive to the input data year, with costs and carbon emissions varying by up to 21% and 45%, respectively. Using wave or tidal stream as part of the future energy mix was seen to have a positive impact in all cases considered; 1 GW of wave and tidal (0.57% of total capacity) reduces annual dispatch cost by 0.2–1.3% and annual carbon emissions by 2.3–3.5%. These results lead to recommended best practises for modelling high renewable power systems, and will be of interest to modellers and policy makers. Full article

Collar monopile foundation is a new type of offshore wind power foundation. This paper explores the horizontal bearing performance of collar monopile foundation in saturated cohesive soil through a combination of physical experiments and numerical simulations. After analyzing the deformation characteristics of the pile–soil system under horizontal load through static load tests, horizontal cyclic loading tests were conducted at different cycles to study the cumulative deformation law of the collar monopile. Based on a stiffness degradation model for soft clay, a USDFLD subroutine was developed in Fortran and embedded in ABAQUS. Coupled with the Mohr–Coulomb criterion, it was used to simulate the deformation behavior of the collar monopile under horizontal cyclic loading. The numerical model employed the same geometric dimensions and boundary conditions as the physical test, and the simulated cumulative pile–head displacement under 4000 load cycles showed good agreement with the experimental results, thereby verifying the rationality and reliability of the proposed simulation method. Through numerical simulation, the distribution characteristics of bending moment and the shear force of collar monopile foundation were studied, and the influence of pile shaft and collar on the horizontal bearing capacity of collar monopile foundation at different loading stages was analyzed. The results show that as the horizontal load increases, cracks gradually appear at the bottom of the collar and in the surrounding soil. The soil disturbance caused by the sliding and rotation of the collar will gradually increase, leading to plastic failure of the surrounding soil and reducing the bearing capacity. The excess pore water pressure in shallow soil increases rapidly in the early cycle and then gradually decreases with the formation of drainage channels. Deep soil may experience negative pore pressure, indicating the presence of a suction effect. This paper can provide theoretical support for the design optimization and performance evaluation of collar monopile foundations in offshore wind power engineering applications. Full article

This study presents a novel approach in the field of renewable energy, focusing on the power generation capabilities of floating offshore wind turbines (FOWTs). The study addresses the challenges of designing and assessing the power generation of FOWTs due to their multidisciplinary nature involving aerodynamics, hydrodynamics, structural dynamics, and control systems. A hybrid deep learning model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks is proposed to predict the performance of FOWTs accurately and more efficiently than traditional numerical models. This model addresses computational complexity and lengthy processing times of conventional models, offering adaptability, scalability, and efficient handling of nonlinear dynamics. The results for predicting the generator power of a spar-type floating offshore wind turbine (FOWT) in a multivariable parallel time-series dataset using the Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model showed promising outcomes, offering valuable insights into the model’s performance and potential applications. Its ability to capture a comprehensive range of load case scenarios—from mild to severe—through the integration of multiple relevant features significantly enhances the model’s robustness and applicability in realistic offshore environments. The research demonstrates the potential of deep learning methods in advancing renewable energy technology, specifically in optimizing turbine efficiency, anticipating maintenance needs, and integrating wind power into energy grids. Full article

Wake steering has emerged as a promising strategy to mitigate turbine wake losses, with existing research largely focusing on the aerodynamic optimization of yaw angles. However, many prior approaches rely on static look-up tables (LUTs), offering limited adaptability to real-world wind variability and leading to non-optimal results. More importantly, these energy-focused strategies overlook the mechanical implications of frequent yaw activities in pursuit of the maximum power output, which may lead to premature exhaustion of the yaw system’s design life, thereby accelerating structural degradation. This study proposes a supervisory control framework that balances energy capture with structural reliability through three key innovations: (1) upstream-based inflow sensing for real-time capture of free-stream wind, (2) fatigue-responsive optimization constrained by a dynamic actuation quota system with adaptive yaw activation, and (3) a bidirectional threshold adjustment mechanism that redistributes unused actuation allowances and compensates for transient quota overruns. A case study at an offshore wind farm shows that the framework improves energy yield by 3.94%, which is only 0.29% below conventional optimization, while reducing yaw duration and activation frequency by 48.5% and 74.6%, respectively. These findings demonstrate the framework’s potential as a fatigue-aware control paradigm that balances energy efficiency with system longevity. Full article

The series-connected DRU-MMC hybrid converter, with its compact size and cost-effectiveness, presents an attractive solution for long-distance offshore wind power transmission. However, its application is limited by the DRU’s unidirectional power flow and the voltage mismatch between the auxiliary MMC and the onshore MMC during black-start operations. To overcome these challenges, a four-stage black-start strategy utilizing an auxiliary step-down transformer connected to the onshore MMC is proposed. The proposed strategy operates as follows: The onshore MMC first lowers its valve-side voltage via an auxiliary transformer, enabling reduced DC-side voltage. With the DRU bypassed, the offshore MMC draws startup power through the DC link, then switches to V/f mode with wind turbine curtailment to reduce DC current below the DRU bypass threshold. After stable, low-power operation, the DRU is integrated. The onshore MMC then restores rated DC voltage and disconnects the transformer, allowing gradual wind turbine reconnection to complete black-start. The simulation results confirm the approach’s feasibility under conditions where all wind turbines operate in grid-following mode. Full article

Offshore synthetic fuel production and marine carbon dioxide removal can be enabled by direct ocean capture, which extracts carbon dioxide from the ocean that then can be used as a feedstock for fuel production or sequestered underground. To maximize carbon capture, plants require a variety of low-carbon energy sources to operate, such as variable renewable energy. However, the impacts of variable power on direct ocean capture have not yet been thoroughly investigated. To facilitate future deployments, a generalizable model for electrodialysis-based direct ocean capture plants is created to evaluate plant performance and electricity costs under intermittent power availability. This open-source Python-based model captures key aspects of the electrochemistry, ocean chemistry, post-processing, and operation scenarios under various conditions. To incorporate realistic energy supply dynamics and cost estimates, the model is coupled with the National Renewable Energy Laboratory’s H2Integrate tool, which simulates hybrid energy system performance profiles and costs. This integrated framework is designed to provide system-level insights while maintaining computational efficiency and flexibility for scenario exploration. Initial evaluations show similar results to those predicted by the industry, and demonstrate how a given plant could function with variable power in different deployment locations, such as with wind energy off the coast of Texas and with wind and wave energy off the coast of Oregon. The results suggest that electrochemical systems with greater tolerances for power variability and low minimum power requirements may offer operational advantages in variable-energy contexts. However, further research is needed to quantify these benefits and evaluate their implications across different deployment scenarios. Full article

Integrating offshore renewable energy (ORE) into power systems is vital for sustainable energy transitions. This paper examines the challenges and opportunities in integrating ORE, focusing on offshore wind and floating solar, into grid systems. A simulation was conducted using a 5 MW offshore wind turbine and a 2 MW floating PV (FPV) system, complemented by a 10 MWh battery energy storage system (BESS). The simulation utilized the typical load profile of Belgium and actual 2023 electricity price data, along with realistic wind and solar generation patterns for a location at the sea border of Belgium and the Netherlands. The use of real operational and market data ensures the practical relevance of the results. This study highlights the importance of BESS, targeting a significant revenue by participating in system imbalance and providing ancillary services (aFRR and mFRR). Key findings emphasize the need for grid infrastructure transformation to support ORE’s growing investments and deployment. This research underscores the essential role of technological innovation and strategic planning in optimizing the potential of ORE sources. Full article

Against the backdrop of the global energy transition, offshore wind power has undergone rapid development. As a vital component of offshore wind power infrastructure, dry shrinkage cracking in concrete piles poses a significant threat to the safe and stable operation of offshore wind power systems. However, the fundamental mechanism of concrete pile cracking during dry shrinkage—particularly the coupled effects of moisture diffusion, meso-structural heterogeneity, and stress evolution—remains poorly understood, lacking a unified theoretical framework. This knowledge gap hinders the development of targeted anti-cracking strategies for offshore concrete structures. Hence, investigating the mechanism of dry shrinkage cracking is of substantial importance. This paper employs numerical simulation to explore the patterns and influencing factors of dry shrinkage cracking in concrete piles for offshore wind turbines, aiming to provide theoretical support for enhancing pile performance. A meshless numerical simulation method based on the smoothed particle hydrodynamics (SPH) framework is developed, which generates concrete meso-structures via a specific algorithm, discretizes the moisture diffusion equation, defines dry shrinkage stress terms, and introduces a fracture coefficient to characterize particle failure, enabling the simulation of concrete dry shrinkage cracking processes. Simulation schemes are designed for varying aggregate percentages, aggregate particle sizes, dry shrinkage coefficients, and moisture diffusion coefficients, using a 100 mm-diameter circular concrete model. Qualitative results reveal the following: Increased aggregate percentages lead to more uniform moisture diffusion, with dry shrinkage crack number and length first increasing and then decreasing; larger aggregate particle sizes exacerbate moisture diffusion non-uniformity and intensify dry shrinkage cracking; higher dry shrinkage coefficients correlate with increased crack number and length; elevated moisture diffusion coefficients accelerate surface water loss, with cracking severity first increasing and then decreasing. The proposed SPH-based meshless method effectively simulates dry shrinkage cracking in offshore wind turbine concrete piles, demonstrating the significant impact of different factors on moisture diffusion and cracking patterns. This study offers insights for applying the SPH method in related fields, deepens the understanding of concrete dry shrinkage cracking mechanisms, and provides a theoretical foundation for the design and optimization of offshore wind power concrete piles. Full article

The configuration of energy storage systems in offshore wind farms can effectively suppress fluctuations in wind power and enhance the stability of the power grid. However, the economic balance between the cost of energy storage systems and the fluctuations in wind power remains an urgent challenge to be addressed, especially against the backdrop of widespread spot trading in the electricity market. How to achieve effective wind power stabilization at the lowest cost has become a key issue. This paper proposes three different energy storage configuration strategies and adopts the non-dominated sorting genetic algorithm (NSGA-II) to conduct multi-objective optimization of the system. NSGA-II performed stably in dual-objective scenarios and effectively balanced the relationship between the investment cost of the energy storage system and power fluctuations through the explicit elite strategy. Furthermore, this study analyzed the correlation between the rated power and rated capacity of the energy storage system and the battery life, and corrected the battery life of the Pareto frontier solution obtained by NSGA-II. The research results show that when only considering the investment cost of the energy storage, the optimal configuration was a rated power of 4 MW and a rated capacity of 28 MWh, which could better balance the investment economy and power fluctuation. When further considering the participation of energy storage systems in the electricity spot market, the economic efficiency of the energy storage systems could be significantly improved through the fixed-period electricity price arbitrage method. At this point, the optimal configuration was a rated power of 8 MW and a rated capacity of 37 MWh. The corresponding project investment cost was CNY 242.77 million, and the annual fluctuation rate of the wind power output decreased to 17.84%. Full article