Wind Farm Production Research Articles

Analysis of Wind Farm Productivity Taking Wake Loss into Account: Case Study

Analysis of Wind Farm Productivity Taking Wake Loss into Account: Case Study

Validation of a methodology for post-construction Energy Yield Assessment of an operational wind farm

The uncertainty associated with the prospective Energy Yield Assessment (EYA) of a wind farm may be reduced by re-estimating the energy yield after it enters normal operation. This study aims to validate a simple methodology for conducting post-construction EYA of an operational wind farm. The proposed methodology derives a linear relationship between a historical source of wind speed data and the observed wind farm production on a monthly basis.In the first stage, the impact of different data sources on the accuracy of the Long-Term energy yield estimate was assessed. Results suggest that the determination coefficient R is a reliable indicator for selecting the most adequate source of historical wind speed data to be used in the Long-Term energy yield estimate.In the second stage, the model was validated from a statistical point of view by testing the premises of the linear regression model, namely the significance of the linear correlation (ANOVA test), and the normally-distributed (Shapiro-Wilk test), non-self-correlated (Durbin-Watson), and homoscedastic (Breusch-Pagan test) residuals. Results show these premises are verified for most test cases, indicating that the model is statistically robust for most test cases.

Wind Farm Power Maximisation via Wake Steering: A Gaussian Process‐Based Yaw‐Dependent Parameter Tuning Approach

ABSTRACTMaximising the power production of wind farms is vital to meet the growing demand for wind energy and reduce its cost. Wake effects, resulting from the aerodynamic interactions between turbines in a wind farm, significantly impact farm efficiency, leading to substantial annual power losses. Wake steering, an influential control strategy, involves mitigating wake effects by strategically yaw misaligning upstream turbines to deflect their wakes. Conventional wake steering approaches typically rely on physics‐based analytical wake models with their parameters often calibrated using higher fidelity data. However, these approaches determine a fixed set of parameters prior to conducting wake steering, neglecting each parameter's dependency on yaw misalignment (i.e. the optimisation variables) exhibited throughout the optimisation process, potentially affecting its accuracy. To address this limitation, this paper introduces a novel data‐driven parameter tuning approach that integrates higher fidelity power measurements using Gaussian processes to continuously adapt parameters in lower fidelity wake models based on the current farm's yaw configuration. The effectiveness of the proposed approach is demonstrated on a wind farm and a layout corresponding to the Horns Rev wind farm, where various wind directions are investigated. The results reveal that the approach can enable a lower fidelity model to capture more complex physics, thereby improving its accuracy in wake steering optimisation, while maintaining robustness and computational efficiency. This method holds promise for real‐time control applications and can be extended to other control strategies and closed‐loop frameworks.

Maximizing wind farm production through pitch control using graph neural networks and hybrid learning methods

AbstractThis article presents a novel methodology to maximize wind farm power generation by integrating graph neural networks (GNN), supervised learning, and reinforcement learning techniques. First, the article introduces a graph‐based representation of the wind farm, capturing wind turbines as vertices and the inter‐turbine wake interactions as edges. The construction of this graph representation integrates the Jensen wake model, which includes insights derived from prior knowledge of wind farm aerodynamics. Subsequently, a detailed description of the GNN model's architecture, incorporating a message passing mechanism, is outlined. This GNN model is trained initially with supervised learning using a dataset of optimal pitch angles generated from the analytical results derived from Jensen wake model. Moreover, to improve the GNN model's accuracy and adaptability, reinforcement learning techniques are employed. The GNN model interacts with a high‐fidelity wind farm simulation environment, receiving feedback in the form of rewards derived from the wind farm's actual power output. Through a policy gradient approach, the GNN parameters undergo iterative updates, enabling the model to learn and adapt to dynamic wind conditions and intricate turbine interactions. The effectiveness and advantages of the proposed methodology are demonstrated through comprehensive case studies across various wind farm layouts.

Wind farm power optimization using system identification

The wake effect reduces the total power production of wind farms. This paper presents a method for wind farm power optimization through wake effect reduction. The proposed method optimizes the yaw angle offsets and de-rating settings of all turbines to maximize total power generation. The optimization approach is gradient-based, with gradients at each iteration obtained through system identification using field test data, eliminating the need for physical models. In system identification, test signal design, model estimation and model validation problems are solved in a systematic manner; in the gradient-based optimization, in order to achieve fast convergence, methods for initial value and initial step-size determination, variable step-size iteration and iteration termination are developed. The method is verified using the FLORIS wind farm model developed by National Renewable Energy Laboratory (NREL), USA. The studied wind farm consists of 80 wind turbines configured similarly to the Horns Rev I offshore wind farm in Denmark. The result of the developed optimization method is highly consistent with those obtained using FLORIS's built-in optimization tool.

Integration of alternative fuel production and combined cycle power plant using renewable energy sources

The article explores the integration of a methanol production system with a modern combined cycle power plant utilizing oxy-combustion technology, resulting in enhanced efficiency for both systems (synergy effect). The integration involves various components like a 90 MW wind farm, hydrogen generators, and methanol production units. Notably, CO2 preparation and compression for the methanol reactor within the power plant optimize methanol production and reduce energy consumption in the oxygen separation unit by supplementing oxygen through electrolysis. Utilizing oxygen from electrolysis also decreases energy consumption in the gas turbine's oxidant compressor. Additionally, compressing CO2 to lower pressures reduces energy consumption in the CCU installation. The article presents a methodology for quantifying energy efficiency for both the methanol production unit and the combined cycle power plant. Integration leads to significant efficiency increases, particularly for low-power generation setups and with greater contributions from renewable energy sources. For instance, in scenarios involving a gas turbine with 7.5 MW capacity, efficiency gains range from 0.8 to nearly 6 percentage points with multiple wind farms. This study is the first of its kind to demonstrate the synergistic advantages of directly integrating an oxy-combined cycle power plant with a methanol production facility. The integration results in heightened efficiency for both the power plant and the methanol production setup, contributing significantly to the advancement of hydrogen technology and the green deal initiative. The unique approach of utilizing hydrogen from electrolysis setups and CO2 derived from exhaust gases of power plants to generate liquid methanol, a superior energy carrier, is a significant contribution to the existing body of literature. The demonstrated synergistic outcomes of integrating a methanol production facility with combined cycle power plants stem from augmented unit efficiencies, overall installation improvements, and the amelioration of the ecological attributes of the power plant. The innovative character of this research endeavor is underscored by the fact that no existing installation worldwide currently pairs methanol production with a combined cycle power plant.

Enhanced Wind Farm Maintenance Scheduling Including Wake Effects

With the expansion and dissemination of offshore wind farms, the costs associated with asset maintenance and operation become ever more relevant. Among maintenance activities, preventive interventions represent a significant share of the costs and, as opposed to corrective interventions, may be calendarized according to the farm operator’s strategy. In an offshore wind farm there is often aerodynamic interaction between different machines, namely as a given upstream turbine extracts energy from the wind, it will necessarily reduce the energy available for a turbine that is downstream in the farm. This is broadly referred to as wake effects. The relative proximity of turbines within a farm means such an interaction results in lower energy yield for downstream turbines. Maintenance activities require the shutdown of the turbine, hence the turbine under maintenance effectively produces no wake effect. It follows that, depending on weather conditions, the sequence of wind turbines to be deactivated for large preventive maintenance campaigns may impact the total energy production of an offshore wind farm, especially when the wind direction is considered. The present study investigates the potential of incorporating wake effects in the scheduling of offshore wind farm maintenance activities, considering a reference farm layout and using historical climate data for a real offshore site. Different maintenance scenarios are considered, including distinct maintenance vessels and different intervention criteria. Results show there is a small but relevant potential gain in energy yield when the maintenance scheduling is determined including wake effects, with annual energy production increase of up to 0.18% for the parametric values considered.

Effects of wind turbine rotor tilt on large-scale wind farms

Recent studies have explored the use of rotor tilt adjustments to reduce wake losses in wind farms. While downward wake deflection in aligned wind farms has shown promise for significant power gains, the impact of wind farm layout on the effectiveness of tilt strategies is not yet fully understood. Additionally, the effect on downstream farms remains unclear. Our large eddy simulations reveal that a rotor tilt of 20 degrees significantly reduces wake losses in aligned wind farms. For wind farms with 8 turbine rows, we observe an overall increase in wind farm productivity of up to 11%. However, tilting the rotors may decrease power production in staggered wind farms, where wake losses are inherently lower due to the increased spacing between turbines. Our findings also suggest that a downstream wind farm might benefit from an upstream farm implementing rotor tilt, although this advantage is primarily observed in the first row of the downstream farm.

Data-driven optimisation of wind farm layout and wake steering with large-eddy simulations

Abstract. Maximising the power production of large wind farms is key to the transition towards net zero. The overarching goal of this paper is to propose a computational method to maximise the power production of wind farms with two practical design strategies. First, we propose a gradient-free method to optimise the wind farm power production with high-fidelity surrogate models based on large-eddy simulations and a Bayesian framework. Second, we apply the proposed method to maximise wind farm power production by both micro-siting (layout optimisation) and wake steering (yaw angle optimisation). Third, we compare the optimisation results with the optimisation achieved with low-fidelity wake models. Finally, we propose a simple multi-fidelity strategy by combining the inexpensive wake models with the high-fidelity framework. The proposed gradient-free method can effectively maximise wind farm power production. Performance improvements relative to wake-model optimisation strategies can be attained, particularly in scenarios of increased flow complexity, such as in the wake steering problem, in which some of the assumptions in the simplified flow models become less accurate. The optimisation with high-fidelity methods takes into account nonlinear and unsteady fluid mechanical phenomena, which are leveraged by the proposed framework to increase the farm output. This paper opens up opportunities for wind farm optimisation with high-fidelity methods and without adjoint solvers.

Vision of Offshore Energy Hub at Faroe Islands: The Market Equilibrium Impact

This study examines the integration of an offshore wind farm and green hydrogen production as a strategy to enhance the Faroe Islands' energy independence and reduce its carbon footprint. Utilizing the EnergyPLAN tool and market economic simulations, including Levelized Cost of Hydrogen and Net Present Value calculations, the research evaluates the economic viability and environmental impact of transitioning to renewable energy sources by 2030. The analysis explores various scenarios, ranging from dedicated offshore wind for in-turbine hydrogen production to platform-based electricity and hydrogen distribution. Results indicate that integrating offshore wind with green hydrogen production can significantly reduce CO2 emissions and dependency on imported fossil fuels. However, financial viability hinges on supportive mechanisms such as investment subsidies. The study's findings suggest that achieving economic feasibility requires strategic policy frameworks. This research contributes to the discourse on sustainable energy planning by offering insights into the dynamics of system integration, market economic simulations, and the role of support mechanisms in facilitating the green transition.

Evaluation and Improvement of the Accuracy of Reanalysis and Analysis Datasets for Wind Resource Assessment in Sudan

Wind speed datasets are used to evaluate wind resources and energy production of wind farms. In locations where measured data are not available, reanalysis and analysis datasets can be used as an alternative to assess wind resources. This study evaluated the accuracy of wind speed data collected from reanalysis and analysis datasets against mast-measured data between 1975 and 1985 in Sudan, using monthly statistical analyses. Three bias correction methods, based on Measure-Correlate-Predict (MCP) and Linear Adaptation (LA1 and LA2), were applied to determine the original wind speed. The results indicate that LA1 outperformed MCP and LA2. Furthermore, the Weibull distribution function was employed to analyze the wind speed characteristics. In addition, wind power density was calculated using data from different sources. The findings show that although the wind power potential of the chosen locations is not suitable for large wind turbines, wind power can still be exploited with small wind turbines. Consequently, this study introduces a wind energy roadmap to attract investors in clean energy for sustainable development in Sudan, address energy problems, and meet domestic demands. The study also identifies the most important grid datasets for assessing the country's wind potential, enhancing the accuracy of assessments for investors and policymakers.

A Dependability Neural Network Approach for Short-Term Production Estimation of a Wind Power Plant

This paper presents a novel approach to estimating short-term production of wind farms, which are made up of numerous turbine generators. It harnesses the power of big data through a blend of data-driven and model-based methods. Specifically, it combines an Artificial Neural Network (ANN) for immediate future predictions of wind turbine power output with a stochastic model for dependability, using Hybrid Reliability Block Diagrams. A thorough state-of-the-art review has been conducted in order to demonstrate the applicability of an ANN for non-linear stochastic problems of energy or power forecast estimation. The study leverages an innovative cluster analysis to group wind turbines and reduce the computational effort of the ANN, with a dependability model that improves the accuracy of the data-driven output estimation. Therefore, the main novelty is the employment of a hybrid model that combines an ANN with a dependability stochastic model that accounts for the realistic operational scenarios of wind turbines, including their susceptibility to random shutdowns This approach marks a significant advancement in the field, introducing a methodology which can aid the design and the power production forecast. The research has been applied to a case study of a 24 MW wind farm located in the south of Italy, characterized by 28 turbines. The findings demonstrate that the integrated model significantly enhances short-term wind-energy production estimation, achieving a 480% improvement in accuracy over the solo-clustering approach.

DeepFore: A Deep Reinforcement Learning Approach for Power Forecasting in Renewable Energy Systems

An open network known as the “energy internet” links every component of the whole energy supply chains, from the generations. Due to their ability to mimic regional flow dynamics that have an impact on wind farm production, regional meteorological models are increasingly being used as a general tool for wind resource forecasting. In this study, higher vertical and horizontal resolutions WRF (weather research and forecasting) paradigm simulation are used to anticipate and validate production for a genuine onshore wind farm. This paper proposed a DeepFore which is a power forecasting system for hybrid renewable energy systems. Initially, the dataset is generated by the hybrid system. This data is preprocessed to improve the quality of the data by incorporating, filtering and outlier detection techniques. Then, this enriched data is fed into K++ means clustering algorithm to separate the normal data from faulty data. With the normal and original data, Teaching-Learning based optimization algorithm attempts to realize the optimal features which are important for forecasting. Finally, Deep SARSA which is deep reinforcement learning algorithm is incorporated to determine the power generated by the hybrid system. Better winds energy prediction estimations enable more efficient utilization of the produced electricity, according to computational models.

Aerodynamic interference caused by wake effects of repowered wind farms on the annual energy production in neighboring wind farms

Worldwide, wind farms are updated due to technological advances and the end of life from different aspects: social, environmental, technical, and economical. In Brazil, by 2030, around 50 wind farms will reach the age of 20 years of operation, representing more than 600 wind turbines and a total power of around 1,000 MW. This study aims to develop a technical and economic analysis of the unaddressed topic of interference of repowering in neighboring wind farms by analyzing four wind farms in Brazil. Technical analysis of the wake is based on NOJ and BastanKhah models using the software PyWake, which is also employed to calculate the energy production of wind farms. Three wind turbine models with different hub heights and rated power were also considered. Results showed that the repowered wind turbine characteristics, micrositing, and distance between wind farms greatly influence the energy production of neighboring wind farms. The energy deficit in the existing wind farms varied from 433 MWh to 5391 MWh, indicating that, although repowering is a viable solution for wind farms reaching their end-of-life, careful technical analysis regarding the micrositing and wind turbine model should be considered to mitigate energy losses in neighboring existing wind farms.

Techno-economic analysis of the effect of a novel price-based control system on the hydrogen production for an offshore 1.5 GW wind-hydrogen system

The cost of green hydrogen production is very dependent on the price of electricity. A control system that can schedule hydrogen production based on forecast wind speed and electricity price should therefore be advantageous for large-scale wind-hydrogen systems. This work presents a novel price-based control system integrated in a techno-economic analysis of hydrogen production from offshore wind. A polynomial regression model that predicts wind power production from wind speed input was developed and tested with real-world datasets from a 2.3 MW floating offshore wind turbine. This was combined with a mathematical model of a PEM electrolyzer and used to simulate hydrogen production. A novel price-based control system was developed to decide when the system should produce hydrogen and when it should sell electricity to the grid. The model and control system can be used in real-world wind-hydrogen systems and require only the forecast wind speed, electricity price and selling price of hydrogen as inputs. 11 test scenarios based on 10 years of real-world wind speed and electricity price data are proposed and used to evaluate the effect the price-based control system has on the levelized cost of hydrogen (LCOH). Both current and future (2050) costs and technologies are used, and the results show that the novel control system lowered the LCOH in all scenarios by 10–46%. The lowest LCOH achieved with current technology and costs was 6.04 $/kg H2. Using the most optimistic forecasts for technology improvements and cost reductions in 2050, the model estimated a LCOH of 0.96 $/kg H2 for a grid-connected offshore wind farm and onshore hydrogen production, 0.82 $/kg H2 using grid electricity (onshore) and 4.96 $/kg H2 with an off-grid offshore wind-hydrogen system. When the electricity price from the period 2013–2022 was used on the 2050 scenarios, the resulting LCOH was approximately twice as high.

An optimization framework for wind farm layout design using CFD-based Kriging model

Wind farm layout optimization (WFLO) seeks to alleviate the wake loss and maximize wind farm power output efficiency, and is a crucial process in the design and planning of wind energy projects. Since the optimization algorithms typically require thousands of numerical evaluations of the wake effects, conventional WFLO studies are usually carried out with the low-fidelity analytical wake models, while the higher-fidelity computational-fluid-dynamics-based (CFD-based) methods are seldom used due to the excessive computational cost. In this paper, we develop a self-adaptive optimization framework for wind farm layout design using CFD-based Kriging model to maximize the annual energy production (AEP) of wind farms. This surrogate-based optimization (SBO) framework uses latin hypercube sampling to generate a group of wind farm layout samples, based on which CFD simulations with the turbines modeled as actuator disks are carried out to obtain the corresponding AEPs. This initial wind farm layout dataset is used to train the Kriging model, which is then integrated with an optimizer based on genetic algorithm (GA). As the optimization progresses, the intermediate optimal layout designs are again fed into the dataset to re-train the Kriging model. Such adaptive update of wind farm layout dataset continues until the algorithm converges to the optimal layout design. To evaluate the performance of the proposed SBO framework, we apply it to four representative wind farm cases. Compared to the conventional staggered layout along the dominant wind direction, the optimized wind farm produces significantly higher total AEP, which is more evenly distributed among the turbines. In particular, the SBO framework requires significantly smaller number of CFD calls to yield the optimal layouts that generates almost the same AEP with the direct CFD-GA method. Further analysis on the velocity fields show that the optimization framework always attempts to locate the downstream turbines away from the wakes of upstream ones along the dominant wind directions. The proposed CFD-based surrogate model provides a more accurate and flexible alternative to the conventional analytical-wake-model-based methods in WFLO tasks, and has the potential to be used for designing efficient wind farm projects.

Large-eddy simulations of turbulent flows in arrays of helical- and straight-bladed vertical-axis wind turbines

Effects of helical-shaped blades on the flow characteristics and power production of finite-length wind farms composed of vertical-axis wind turbines (VAWTs) are studied numerically using large-eddy simulation (LES). Two helical-bladed VAWTs (with opposite blade twist angles) are studied against one straight-bladed VAWT in different array configurations with coarse, intermediate, and tight spacings. Statistical analysis of the LES data shows that the helical-bladed VAWTs can improve the mean power production in the fully developed region of the array by about 4.94%–7.33% compared with the corresponding straight-bladed VAWT cases. The helical-bladed VAWTs also cover the azimuth angle more smoothly during the rotation, resulting in about 47.6%–60.1% reduction in the temporal fluctuation of the VAWT power output. Using the helical-bladed VAWTs also reduces the fatigue load on the structure by significantly reducing the spanwise bending moment (relative to the bottom base), which may improve the longevity of the VAWT system to reduce the long-term maintenance cost.

New developments in heavy plates for offshore structures

AbstractWind energy is one of the most important pillars in the urgently needed transformation to a more renewable and sustainable world. Electricity demands as well as hydrogen demands are growing and therefore more and more wind turbines will be installed on‐ as well as offshore. To the success of wind energy steel is indispensable and especially heavy plates play a crucial role in the production and installation of offshore wind farms, e.g. for the towers, the foundations of bottom‐fixed turbines or for the strongly growing demand of floating offshore wind turbines. New developments in heavy plate production can therefore help fulfilling the demands for new and larger turbines as well as to bring overall construction cost down by improving the fabrication of these structures. These developments can be related to plate dimensions, tougher materials and steel grades with higher strength or the weldability of the steel in use. This paper will include some of these developments for offshore wind turbines as well as offshore wind business in general and will give an overview on their benefits.

Research on a random search algorithm for wind turbine layout optimization

Wind turbine layout design has an important impact on the energy production and economic benefits of wind farms. The wind resource grid data include the realistic wind distributions of the wind farm. Combined with the Jensen wake model, it can be used to calculate the net production considering the wake effect of turbines. Based on the wind resource grid data and taking net energy production as the objective function, this paper proposes a random search algorithm for wind turbine layout optimization. The algorithm couples the random function with multiple optimization parameters and optimizes the wind turbine layout by considering restriction conditions of area and minimum turbine spacings. According to the results of the case study in an actual wind farm, the optimization processes using the proposed algorithm have high calculation efficiency and stability. The sensitivity analysis of parameters indicates that the effect of optimization calculation can be effectively improved by appropriately increasing the turbine coordinate searching range or the number of random operations within one single search.

Assist in GHG Abatement of Offshore Ships: Design and Economic Analysis of an Integrated Utilization Model of hydrogen-powered Ship and Offshore Wind Power

As a hopeful solution to help the shipping sector achieve the greenhouse gas (GHG) abatement goal of the IMO by 2050, the application of hydrogen-powered ships has attracted more and more attention. To solve the problem of hydrogen supplement for offshore polymer electrolyte membrane fuel cell (PEMFC) ships, this paper presents a scenario design for the integrated application of coastal hydrogen-powered ships and offshore wind power, and analyzes its techno-economic feasibility. This model concept considers the problems in offshore hydrogen transportation and offshore wind power development together. It provides a feasible integrated scheme and operation mode for the combination of offshore wind power and hydrogen energy storage. A transformation scheme is also provided for the marine ranch ships, and these PEMFC-powered ships are combined with the hydrogen production of offshore wind farms. The analysis results show that the integrated utilization model is economically feasible and with a significant effect on decarbonization. It also shows great potential for further expansion to other coastal areas.