Search Results (175)

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

A standard approach to design begins with scaling up state-of-the-art machines to new target dimensions, moving towards larger rotors with lower specific energy to maximize revenue and enable power production in lower wind speed areas. This trend is particularly crucial in floating offshore wind in the Mediterranean Sea, where the high levelized cost of energy poses significant risks to the sustainability of investments in new projects. In this context, the conventional approach of scaling up machines designed for fixed foundations and strong offshore winds may not be optimal. Additionally, modern large-scale wind turbines for offshore applications face challenges in achieving high aerodynamic performance in thick root regions. This study proposes a holistic optimization framework that combines multi-fidelity analyses and tools to address the new challenges in wind turbine rotor design, accounting for the novel demands of this application. The method is based on a modular optimization framework for the aerodynamic design of a new wind turbine rotor, where the cost function block is defined with the aid of a model reduction strategy. The link between the full-order model required to evaluate the target rotor’s performance, the physical aspects of blade aerodynamics, and the optimization algorithm that needs several evaluations of the cost function is provided by the definition of a surrogate model (SM). An intelligent SM definition strategy is adopted to minimize the computational effort required to build a reliable model of the cost function. The strategy is based on the construction of a self-adaptive, automatic refinement of the training space, while the particular SM is defined by the use of stochastic radial basis functions. The goal of this paper is to describe the new aerodynamic design strategy, its performance, and results, presenting a case study of a 15 MW wind turbine blades optimized for specific deepwater sites in the Mediterranean Sea. Full article

Maximizing the power output of wind farms is critical for improving the economic viability and grid integration of renewable energy. Active wake control (AWC) strategies, such as yaw-based wake steering, offer significant potential for power generation increase but require predictive models that are both accurate and computationally efficient for real-time implementation. This paper proposes a data-driven observer to rapidly estimate the potential power gain achievable through AWC as a function of the ambient wind direction. The approach is rooted in Koopman operator theory, which allows a linear representation of nonlinear dynamics. Specifically, a model is developed using an Input–Output Extended Dynamic Mode Decomposition framework combined with Sparse Identification (IOEDMDSINDy). This method lifts the low-dimensional wind direction input into a high-dimensional space of observable functions and then employs iterative sparse regression to identify a minimal, interpretable linear model in this lifted space. By training on offline simulation data, the resulting observer serves as an ultra-fast surrogate model, capable of providing instantaneous predictions to inform online control decisions. The methodology is demonstrated and its performance is validated using two case studies: a 9-turbine and a 20-turbine wind farm. The results show that the observer accurately captures the complex, nonlinear relationship between wind direction and power gain, significantly outperforming simpler models. This work provides a key enabling technology for advanced, real-time wind farm control systems. Full article

This study investigates the performance of medium-power wind turbines (within kilowatt range) in response to substantial fluctuations in wind speed. The wind turbines utilize induction generators that have a short-circuited rotor and are controlled by a power converter within the stator circuit. This configuration facilitates the adjustment of the stator frequency, thereby allowing the desired rotational speed to be achieved and guaranteeing that the turbine operates at the maximum power point (MPP). Specific mathematical models for the turbine and generator have been developed using technical data from an operational wind turbine. The study demonstrated that utilizing a power converter within the stator circuit enhances the turbine’s operation at its maximum power point. A crucial aspect of effective MPP operation is the accurate determination of the relationship between wind speed and the corresponding optimal angular mechanical speed. Precise understanding and implementation of the interdependence among the primary generator parameters—namely power, frequency, current, and power factor—in relation to wind speed is essential for maximizing power generation and achieving grid stability for wind turbines operating in variable wind speed. Full article

This study presents a multi-objective predictive energy management system (EMS) for optimizing hybrid renewable energy systems (HRES) in autonomous marine vessels. The objective is to minimize fuel consumption and emissions while maximizing renewable energy usage and pure-electric sailing durations. The EMS combines nonlinear model predictive control (NMPC) with metaheuristic optimizers—Grey Wolf Optimization (GWO) and Genetic Algorithm (GA)—and is benchmarked against a conventional rule-based (RB) method. The HRES architecture comprises photovoltaic arrays, vertical-axis wind turbines (VAWTs), diesel engines, generators, and a battery storage system. A ship dynamics model was used to represent propulsion power under realistic sea conditions. Simulations were conducted using real-world operational and environmental datasets, with state prediction enhanced by an Extended Kalman Filter (EKF). Performance is evaluated using marine-relevant indicators—fuel consumption; emissions; battery state of charge (SOC); and emission cost—and validated using standard regression metrics. The NMPC-GWO algorithm consistently outperformed both NMPC-GA and RB approaches, achieving high prediction accuracy and greater energy efficiency. These results confirm the reliability and optimization capability of predictive EMS frameworks in reducing emissions and operational costs in autonomous maritime operations. Full article

The increasing adoption of Renewable Energy Sources (RESs), due to international energy policies mainly related to the decarbonization of electricity production, raises several operating issues for power systems, which need “flexibility” in order to guarantee reliable and secure operation. RESs can be considered examples of Distributed Energy Resources (DERs), which are typically electric power generators connected to distribution networks, including photovoltaic and wind systems, fuel cells, micro-turbines, etc., as well as energy storage systems. In this case, improved operation of power systems can be achieved through coordinated control of groups of DERs by “aggregators”, who also offer a “flexibility service” to power systems that need to be appropriately remunerated according to market rules. The implementation of the aggregator function requires the development of tools to optimally operate, control, and dispatch the DERs to define their overall flexibility as a “market product” in the form of bids. The contribution of the present paper in this field is to propose a new optimization strategy for flexibility bidding to maximize the profit of the aggregator in flexibility markets. The proposed optimal scheduling procedure accounts for important practical and technical aspects related to the DERs’ operation and their flexibility estimation. A case study is also presented and discussed to demonstrate the validity of the method; the results clearly highlight the efficacy of the proposed approach, showing a profit increase of 10% in comparison with the base case without the use of the proposed methodology. It is evident that quantitatively more significant results can be obtained when larger aggregations (more participants) are considered. Full article

This study presents an advanced hybrid energy management system (EMS) designed for isolated microgrids, aiming to optimize the integration of renewable energy sources with backup systems to enhance energy efficiency and ensure a stable power supply. The proposed EMS incorporates solar photovoltaic (PV) and wind turbine (WT) generation systems, coupled with a battery energy storage system (BESS) for energy storage and management and a microturbine (MT) as a backup solution during low generation or peak demand periods. Maximum power point tracking (MPPT) is implemented for the PV and WT systems, with additional control mechanisms such as pitch angle, tip speed ratio (TSR) for wind power, and a proportional-integral (PI) controller for battery and microturbine management. To optimize EMS operations, a novel hybrid optimization algorithm, the JSO-GJO (Jellyfish Search and Golden Jackal hybrid Optimization), is applied and benchmarked against Particle Swarm Optimization (PSO), Bacterial Foraging Optimization (BFO), Artificial Bee Colony (ABC), Grey Wolf Optimization (GWO), and Whale Optimization Algorithm (WOA). Comparative analysis indicates that the JSO-GJO algorithm achieves the highest energy efficiency of 99.20%, minimizes power losses to 0.116 kW, maximizes annual energy production at 421,847.82 kWh, and reduces total annual costs to USD 50,617,477.51. These findings demonstrate the superiority of the JSO-GJO algorithm, establishing it as a highly effective solution for optimizing hybrid isolated EMS in renewable energy applications. Full article

The reliance on fossil fuels poses significant challenges to the environment and sustainable development. To address the heating requirements of the pyrolysis process in a biomass gasification-based multi-generation system, this study explored the use of low-grade solar energy across the full solar spectrum to supply the necessary energy for biomass pyrolysis while leveraging high-grade solar energy in the short-wavelength spectrum for power generation. The proposed multi-generation system integrates the full solar spectrum, biomass gasification, gas turbine, and waste heat recovery unit to produce power, cooling, and heating. A detailed thermodynamic model of this integrated system was developed, and the energy and exergy efficiencies of each subsystem were evaluated. Furthermore, the system’s performance was assessed on both monthly and annual timescales by employing the hourly weather data for Hohhot in 2023. The results showed that the solar subsystem achieved its highest power output of around 2.5 MWh in July and the lowest of 0.7 MWh in December. The annual electrical output peaked at 10 MWh, occurring around noon in July and August, while the winter peak was typically 2–3 MWh. For the wind power subsystem, the power output was maximized in April at 5.17 MWh and minimized in August at 0.7 MWh. Additionally, considering the overall multi-generation system performance, the highest power output of 14.9 MWh was observed in April, with lower outputs of 10.9, 11.3, and 11.4 MWh from August to October, respectively. Overall, the system demonstrated impressive annual average energy and exergy efficiencies of 74.05% and 52.13%, respectively. Full article

Wind energy is paramount to the European Union’s decarbonization and electrification goals. As wind farms expand with larger turbines and more powerful generators, conventional ‘greedy’ control strategies become insufficient. Coordinated control approaches are increasingly needed to optimize not only power output but also structural loads, supporting longer asset lifetimes and enhanced profitability. Despite recent progress, the effective implementation of multi-objective wind farm control strategies—especially those involving yaw-based wake steering—remains limited and fragmented. This study addresses this gap through a structured review of recent developments that consider both power maximization and fatigue load mitigation. Key concepts are introduced to support interdisciplinary understanding. A comparative analysis of recent studies is conducted, highlighting optimization strategies, modelling approaches, and fidelity levels. The review identifies a shift towards surrogate-based optimization frameworks that balance computational cost and physical realism. The reported benefits include power gains of up to 12.5% and blade root fatigue load reductions exceeding 30% under specific scenarios. However, challenges in model validation, generalizability, and real-world deployment remain. AI emerges as a key enabler in strategy optimization and fatigue damage prediction. The findings underscore the need for integrated approaches that combine physics-based models, AI techniques, and instrumentation to fully leverage the potential of wind farm control. Full article

This study explores the potential of wind energy to address electricity shortages in South Africa, focusing on the Ekuphumleni community in Whittlesea. Given the challenges of expanding the national grid to these areas, wind energy is considered to be a feasible alternative to provide clean, renewable energy and reduce fossil fuel dependence in this community. This research evaluates wind potential utilizing the two-parameter Weibull distribution, with scale and shape parameters estimated by five traditional numerical methods and one metaheuristic optimization technique: whale optimization algorithm (WOA). Goodness-of-fit tests, such as the coefficient of determination (R²) and wind power density error (WPDE), were utilized to determine the best method for accurately estimating Weibull scale and shape parameters. Furthermore, net fitness, which combines R² and WPDE, was employed to provide a holistic assessment of overall performance. Whittlesea showed moderate wind speeds, averaging 3.88 m/s at 10 m above ground level (AGL), with the highest speeds in winter (4.87 m/s) and optimum in July. The WOA method outperformed all five numerical methods in this study in accurately estimating Weibull distribution parameters. Interestingly, the openwind method (OWM), a numerical technique based on iterative methods, and the Brent method showed comparable performance to WOA. The wind power density was 67.29 W/m², categorizing Whittlesea’s potential as poor and suitable for small-scale wind turbines. The east wind patterns favor efficient turbine placement. The study recommends using augmented wind turbines for the site to maximize energy capture at moderate speeds. Full article

Wind energy is a vital renewable resource with substantial economic and environmental benefits, yet its spatial variability poses significant optimization challenges. This study advances wind farm layout optimization by employing a systematic genetic algorithm (GA) tuning approach using the design of experiments (DOE) method. Specifically, a full factorial 2² DOE was utilized to optimize crossover and mutation coefficients, enhancing convergence speed and overall algorithm performance. The methodology was applied to a hypothetical wind farm with unidirectional wind flow and spatial constraints, using a fitness function that incorporates wake effects and maximizes energy production. The results demonstrated a 4.50% increase in power generation and a 4.87% improvement in fitness value compared to prior studies. Additionally, the optimized GA parameters enabled the placement of additional turbines, enhancing site utilization while maintaining cost-effectiveness. ANOVA and response surface analysis confirmed the significant interaction effects between GA parameters, highlighting the importance of systematic tuning over conventional trial-and-error approaches. This study establishes a foundation for real-world applications, including smart grid integration and adaptive renewable energy systems, by providing a robust, data-driven framework for wind farm optimization. The findings reinforce the crucial role of systematic parameter tuning in improving wind farm efficiency, energy output, and economic feasibility. Full article

This work analyzes high-power wind turbines (WTs) from the Oravita region, Romania. These WTs are based on slip ring induction generator with wound rotor and we propose a modified architecture with two power converters on both the stator and on the rotor, functioning at variable wind speeds spanning a large interval. Investigations developed around a realistic WT model with doubly fed induction generator show how WT control enables variable wind speed operations at optimal mechanical angular speed (MAS), guaranteeing maximal power point (MPP), but only up to a critical wind speed value, after which the electrical power must saturate for reliable operation. In this reliable operating region, blade pitch angle control must be enforced. Variable wind speed acts as a time-varying parameter disturbance but also imposes the MPP operation setpoint in one of the two analyzed regions. To achieve null tracking errors, a double integrator must appear within the MAS controller when the wind speed disturbance is realistically modeled as a ramp-like input; however, inspecting the linearized model reveals several difficulties as described in the paper, together with the proposed solution tradeoff. The study developed around the Fuhrlander-FL-MD-70 1.5[MW] WT model shows that several competitive controllers are designed and tested in the identified operating regions of interest, as they validate the reliable and performant functioning specifications. Full article

The major challenges in designing a Hybrid Renewable Energy System (HRES) include selecting appropriate renewable energy sources and storage systems, accurately sizing each component, and defining suitable optimization criteria. This study addresses these challenges by employing Particle Swarm Optimization (PSO) within a multi-criteria optimization framework to design an HRES in Kern County, USA. The proposed system integrates wind turbines (WTS), photovoltaic (PV) panels, Biomass Gasifiers (BMGs), batteries, electrolyzers (ELs), and fuel cells (FCs), aiming to minimize Annual System Cost (ASC), minimize Loss of Power Supply Probability (LPSP), and maximize renewable energy fraction (REF). Results demonstrate that the PSO-optimized system achieves an ASC of USD6,336,303, an LPSP of 0.01%, and a REF of 90.01%, all of which are reached after 25 iterations. When compared to the Genetic Algorithm (GA) and hybrid GA-PSO, PSO improved cost-effectiveness by 3.4% over GA and reduced ASC by 1.09% compared to GAPSO. In terms of REF, PSO outperformed GA by 1.22% and GAPSO by 0.99%. The PSO-optimized configuration includes WT (4669 kW), solar PV (10,623 kW), BMG (2174 kW), battery (8000 kWh), FC (2305 kW), and EL (6806 kW). Sensitivity analysis highlights the flexibility of the optimization framework under varying weight distributions. These results highlight the dependability, cost-effectiveness, and sustainability for the proposed system, offering valuable insights for policymakers and practitioners transitioning to renewable energy systems. Full article

This study presents a micro-scale hybrid wind turbine that integrates a Savonius rotor with a Helical Darrieus rotor, aiming to enhance energy conversion efficiency and adaptability for decentralized renewable energy generation. The hybrid design leverages the high torque generation of the Savonius rotor and the aerodynamic efficiency of the Helical Darrieus rotor. Computational analyses using CFD simulations and experimental validation with a 3D-printed prototype in a closed wind tunnel were conducted at speeds ranging from 3 to 8 m/s. The results demonstrate that the hybrid turbine achieves a power coefficient of 0.26 at an optimal tip-speed ratio of 2.7, marking a 180% improvement over standalone Savonius rotors. The hybridization process mitigates the low-speed inefficiencies of the Savonius rotor. It compensates for the high-speed limitations of the Darrieus rotor, resulting in a turbine capable of operating efficiently over a wider range of wind speeds. This balanced integration maximizes energy harvesting and improves adaptability to varying wind conditions, achieving balanced and synergistic performance. Full article

Fossil fuel depletion, environmental concerns, and energy efficiency initiatives drive the rapid growth in the use of electric vehicles. However, lengthy battery charging times significantly hinder their widespread use. One proposed solution is implementing battery swapping stations, where depleted electric vehicle batteries are quickly exchanged for fully charged ones in a short time. This paper evaluates the techno-economic feasibility and optimal design of a grid-connected hybrid wind–photovoltaic power system for electric vehicle battery swapping stations. The aim is to evaluate the viability of this hybrid power supply system as an alternative energy source, focusing on its cost-effectiveness. An optimal control model is developed to minimize the total life cycle cost of the proposed system while reducing the reliance on the utility grid and maximizing system reliability, measured by loss of power supply probability. This model is solved using mixed-integer linear programming to determine key decision variables such as the power drawn from the utility grid and the number of wind turbines and solar photovoltaic panels. A case study validates the effectiveness of this approach. The simulation results indicate that the optimal configuration comprises 64 wind turbines and 402 solar panels, with a total life cycle cost of ZAR 1,963,520.12. These results lead to an estimated energy cost savings of 41.58%. A life cycle cost analysis, incorporating initial investment, maintenance, and operational expenses, estimates a payback period of 5 years and 6 months. These findings confirm that the proposed hybrid power supply system is technically and economically viable for electric vehicle battery swapping stations. Full article