Wind Farm Production Research Articles

Techno-economic benefits deriving from optimal scheduling of a Virtual Power Plant: Pumped hydro combined with wind farms

Wind and solar are renewable sources characterized by a great potential but a variable and unpredictable nature. Characteristics that provoke mismatch between power supply and demand which in turns are the source of network control issues. Problems manageable with the installation of large-scale energy storage like Pumped Hydro Energy Storage (PHES). To this purpose, the paper presents a techno-economic analysis for assessing the benefits deriving from the hybridization of a seawater PHES (sPHES) plant with an in-operation wind farm. Plants work as a Virtual Power Plant (VPP) instead of two separate unit. The hybridization aims to avoid the unbalances of the wind farm, having negative impact on both the grid and the wind farm owner itself. The unbalances cause unpredictable deviations from the predicted production and, hence, power production fluctuations faced by the grid with curtailments and/or calls in operation of fossil-fuelled power plants. A VPP techno-economic optimization on a daily bases is performed using the ASD-PSO algorithm while the optimization aim is the revenue maximization. Results show that a VPP composed by a sPHES and a wind farm and with a single owner, is able to manage the wind farm production, improving the revenue of 6.8% compared to plants acting separately on the market. The VPP approach is also convenient in the case of a joint venture between the plants owners. But, in this case, the revenue is increased only of 0.4% compared to plants working separately. Finally, results also highlight that, in the Italian electricity market, a sPHES plant alone is not able to generate sufficient revenue to guarantee a reasonable payback time while coupling it with a wind farm increases incomes and working days and avoid cost and grid issues in managing the unpredictable wind farm production.

Artificial Neural Networks based wake model for power prediction of wind farm

In the wind industry, power prediction of wind farm is commonly implemented by analytical wake models, which is low-cost but insufficient in accuracy for high-turbulent wake modelling. In this study, a novel machine-learning-based wake model is developed to improve the power prediction of wind farms. The presented model can reproduce the velocity and turbulence fields in turbine wakes commensurate to the high-fidelity Computational Fluid Dynamics (CFD) simulations while achieving good computational efficiency. Driven by massive CFD simulation dataset, the implicit relationship between inflows and wake flows is established using the Artificial Neural Networks (ANN) technique based on back-propagation algorithm. The reduced-order method Actuator Disk Model with Rotation (ADM-R) and modified k−ε turbulence model are implemented into RANS simulations to save the computational costs dramatically in producing the big-data of wake flows. The ANN wake model is deployed in the Horn Rev wind farm, and validated against LES, onsite measurement, and analytical wake models. The conclusions show that the ANN model can appreciably improve the power predictions compared with the existing analytical models and match the LES and measurement data well. The validated model is also adopted to investigate the influence of wind direction and turbine layout on power production of wind farms.

Wind resource evolution in Europe under different scenarios of climate change characterised by the novel Shared Socioeconomic Pathways

Wind energy is a fundamental pillar of the energy mix in Europe – hence the need for understanding the evolution of the wind energy resource under climate change. For this purpose, near-, mid- and long-term wind speed projections from 18 global climate models are considered and a multi-model ensemble is constructed with the ones found to best reproduce past-present conditions. The evolution and temporal variability of wind power is investigated considering different climate change scenarios through the novel Shared Socioeconomic Pathways (SSPs). More specifically, two SSPs are considered, each corresponding to its own socio-economic and political environment and, therefore, its own level of greenhouse emissions: SSP5-8.5 (highest emissions scenario) and SSP2-4.5 (intermediate emissions scenario). Both scenarios lead to a significant reduction (up to 35%) in wind power density in northern Continental Europe and the Central Mediterranean, and an increase of similar magnitude in West Finland. Over the Atlantic Ocean, Ireland and Britain the resource is also projected to decrease significantly. In other regions, however, the general trend (positive or negative) depends on the SSP scenario. This is the case, notably, of Central Europe, with considerable growth in SSP2-4.5 but some reduction in SSP5-8.5. Thus, in the intermediate emissions scenario (SSP2-4.5) both growth and decline in wind power density are forecast, depending on the region. By contrast, in the highest emissions scenario (SSP5-8.5) the forecast is a general decrease, of the order of 15% overall, with an annual rate of change of approximately –0.2% and an increase in seasonal variability. These trends will affect the energy production of wind farms and, therefore, need to be accounted for in assessing wind power projects in Europe.

An overview of wind-energy-production prediction bias, losses, and uncertainties

Abstract. The financing of a wind farm directly relates to the preconstruction energy yield assessments which estimate the annual energy production for the farm. The accuracy and the precision of the preconstruction energy estimates can dictate the profitability of the wind project. Historically, the wind industry tended to overpredict the annual energy production of wind farms. Experts have been dedicated to eliminating such prediction errors in the past decade, and recently the reported average energy prediction bias is declining. Herein, we present a literature review of the energy yield assessment errors across the global wind energy industry. We identify a long-term trend of reduction in the overprediction bias, whereas the uncertainty associated with the prediction error is prominent. We also summarize the recent advancements of the wind resource assessment process that justify the bias reduction, including improvements in modeling and measurement techniques. Additionally, because the energy losses and uncertainties substantially influence the prediction error, we document and examine the estimated and observed loss and uncertainty values from the literature, according to the proposed framework in the International Electrotechnical Commission 61400-15 wind resource assessment standard. From our findings, we highlight opportunities for the industry to move forward, such as the validation and reduction of prediction uncertainty and the prevention of energy losses caused by wake effect and environmental events. Overall, this study provides a summary of how the wind energy industry has been quantifying and reducing prediction errors, energy losses, and production uncertainties. Finally, for this work to be as reproducible as possible, we include all of the data used in the analysis in appendices to the article.

Sensitivity and Uncertainty of the FLORIS Model Applied on the Lillgrund Wind Farm

Wind farms experience significant efficiency losses due to the aerodynamic interaction between turbines. A possible control technique to minimize these losses is yaw-based wake steering. This paper investigates the potential for improved performance of the Lillgrund wind farm through a detailed calibration of a low-fidelity engineering model aimed specifically at yaw-based wake steering. The importance of each model parameter is assessed through a sensitivity analysis. This work shows that the model is overparameterized as at least one model parameter can be excluded from the calibration. The performance of the calibrated model is tested through an uncertainty analysis, which showed that the model has a significant bias but low uncertainty when comparing the predicted wake losses with measured wake losses. The model is used to optimize the annual energy production of the Lillgrund wind farm by determining yaw angles for specific inflow conditions. A significant energy gain is found when the optimal yaw angles are calculated deterministically. However, the energy gain decreases drastically when uncertainty in input conditions is included. More robust yaw angles can be obtained when the input uncertainty is taken into account during the optimization, which yields an energy gain of approximately 3.4%.

Optimal Pitch Angle Strategy for Energy Maximization in Offshore Wind Farms Considering Gaussian Wake Model

This paper presents a new approach based on the optimization of the blade pitching strategy of offshore wind turbines in order to maximize the global energy output considering the Gaussian wake model and including the effect of added turbulence. A genetic algorithm is proposed as an optimization tool in the process of finding the optimal setting of the wind turbines, which aims to determine the individual pitch of each turbine so that the overall losses due to the wake effect are minimised. The integration of the Gaussian model, including the added turbulence effect, for the evaluation of the wakes provides a step forward in the development of strategies for optimal operation of offshore wind farms, as it is one of the state-of-the-art analytical wake models that allow the evaluation of the energy output of the project in a more reliable way. The proposed methodology has been tested through the execution of a set of test cases that show the ability of the proposed tool to maximize the energy production of offshore wind farms, as well as highlights the importance of considering the effect of added turbulence in the evaluation of the wake.

Wake‐effect aware optimal online control of wind farms: An explicit solution

Wake effects impose significant aerodynamic interactions among wind turbines. To improve the wind farm operating performance, practical wind farm online control considering wake effects becomes very important. To achieve online optimal wind farm control while responding to grid demands, this paper proposes a novel optimal wind farm supervisory control (SC) model and its explicit solutions. From the controller modelling perspective, the two major wind farm operating modes, the maximum power point tracking mode and the set-point tracking mode, are first analysed and unified in one optimisation model while considering wake effects. In this way, wind farm power production and rotor kinetic energy reserve can be simultaneously considered to conveniently modify the operation mode in response to different grid demands. Aside from controller modelling, the collocation method is first introduced to address the online application problem of such wake-effect aware optimal WF control. Although a few optimisation algorithms have been proposed to find the optimum offline, online optimal control is still challenging because of the computational complexity brought by wake model non-linearity and non-convexity. The proposed collocation method explicitly approximates the optimal solutions to the proposed supervisory control model, through which only a direct algebraic operation is required for online optimal control instead of repeated optimisations. Case studies are carried out on different wind farms under various wind conditions, showing that the wind farm power production potential and releasable power reserve are improved compared to traditional greedy control in both modes. The accuracy of the collocation method is verified. A detailed analysis of the wind farm production capacity under different wind speeds and directions is also provided.

Pоbоlјšаnjе prеdikciје prоizvоdnjе vеtrоеlеktrаnа u Јužnоm Bаnаtu kоmbinоvаnjеm pојеdinаčnih prоgnоzа pоmоću mоdеlа vеštаčkе intеligеnciје

The prediction of day-ahead wind farm production is crucial for optimal unit engagement and minimizing power balancing costs in the power system. Several wind farms which use different models to predict the production have been built in the South Banat region. The basic idea in this paper is to combine the production predictions of each of the wind farms with the production predictions of other wind farms in the region using the artificial intelligence model. This approach has a physical justification given that all wind farms are located in a region with the same wind climatology. Since the total error in estimating production for the day ahead is of interest for planning balancing capacities, this paper analyses the possibility of minimizing cumulative error through the application of artificial intelligence algorithms. The algorithms combine forecasts of individual wind farm models and thus make corrections in estimating the total production of wind farms in this region. The training of the neural network model was performed on the basis of forecasts of individual wind farms for the day ahead and performance. One-year sets of forecasted and realized wind farm productions that were in operation in 2020 were used to train the networks. The developed model of prediction of cumulative wind farm production in South Banat enables the transmission system operator to perform subsequent processing of individual predictions of wind farm production in order to reduce the total error in the assessment of cumulative production.

A Generic Power Converter Sizing Framework for Series-Connected DC Offshore Wind Farms

Wind farms featuring a series-connected dc collection system have been shown to offer advantages in terms of total conversion efficiency and amount of offshore-deployed equipment. To ensure proper exploitation of these benefits, it is necessary to determine the ratings of wind turbine converters. These ratings must be sufficient to cover the expected operating conditions over the life of the wind farm, without unnecessarily oversizing the equipment. As shown in this article, the variable string current results in an interdependence of operating points among wind turbines that has to be considered for sizing these converters. This article proposes a generic sizing framework for such single-string, series-connected dc wind farms. This framework is applied to three wind farm configurations featuring differential power processing, voltage-source converters, and diode-bridge rectifiers and buck converters. Finally, a case study for a 450 MW offshore wind farm demonstrates the implementation of this sizing methodology and quantifies the design tradeoffs between converter ratings and energy production enabled from those converters. In two of the studied wind farm configurations, significant rating reductions are achievable while still avoiding energy curtailment for >99.7% of the energy production of an equivalent ac wind farm.

Effect of low-level jet height on wind farm performance

Low-level jets (LLJs) are the wind maxima in the lowest 50 to 1000 m of atmospheric boundary layers. Due to their significant influence on the power production of wind farms, it is crucial to understand the interaction between LLJs and wind farms. In the presence of a LLJ, there are positive and negative shear regions in the velocity profile. The positive shear regions of LLJs are continuously turbulent, while the negative shear regions have limited turbulence. We present large eddy simulations of wind farms in which the LLJ is above, below, or in the middle of the turbine rotor swept area. We find that the wakes recover relatively quickly when the LLJ is above the turbines. This is due to the high turbulence below the LLJ and the downward vertical entrainment created by the momentum deficit due to the wind farm power production. This harvests the jet's energy and aids wake recovery. However, when the LLJ is below the turbine rotor swept area, the wake recovery is very slow due to the low atmospheric turbulence above the LLJ. The energy budget analysis reveals that the entrainment fluxes are maximum and minimum when the LLJ is above and in the middle of the turbine rotor swept area, respectively. Surprisingly, we find that the negative shear creates a significant entrainment flux upward when the LLJ is below the turbine rotor swept area. This facilitates energy extraction from the jet, which is beneficial for the performance of downwind turbines.

Data analysis and simulation of the Lillgrund wind farm

AbstractThe power production of the Lillgrund wind farm has been investigated by means of SCADA data covering almost 10 years of operation, wake models and numerical simulation tools, such as WindSim, OpenFOAM and ORFEUS. The analysis of the SCADA data provided the quantification of wake losses and of blockage effects, results that enable a benchmark to assess the uncertainty of other numerical methods of industrial use. The results have been analysed in terms of global array efficiency for different wind directions, wind velocity and stratification conditions. A comparison of the present results with the Jensen and Ainsle models indicated that the two used wake models are affected by an error of about 8–10% in the estimation of the array efficiency, while the error does not vary significantly when using numerical tools such as ORFEUS, WindSim and OpenFOAM. By comparing wind statistics available before the park construction, an assessment of the blockage of the Lillgrund farm was performed with contrasting results from the other numerical tools, highlighting how challenging is to assess farm blockage with current industrial settings.

Integrated wind farm layout and control optimization

Abstract. The objective of this paper is to investigate the joint optimization of wind farm layout and wind farm control in terms of power production. A successful fulfilment of this goal requires the following: (1) an accurate and fast flow model, (2) selection of the minimum set of design parameters that rules or governs the problem, and (3) selection of an optimization algorithm with good scaling properties. For control of the individual wind farm turbines with the aim of wind farm production optimization, the two most obvious strategies are wake steering based on active wind turbine yaw control and wind turbine derating. The present investigation is limited to wind turbine derating. A high-speed linearized computational fluid dynamics (CFD) Reynolds-averaged Navier–Stokes (RANS) solver models the flow field and the crucial wind turbine wake interactions inside the wind farm. The actuator disc method is used to model the wind turbines, and utilizing an aerodynamic model, the design space of the optimization problem is reduced to only three variables per turbine – two geometric and one carefully selected variable specifying the individual wind turbine derating setting for each mean wind speed and direction. The full design space is spanned by these (2N+NdNsN) parameters, where N is the number of wind farm turbines, Nd is the number of direction bins, and Ns is the number of mean wind speed bins. This design space is decomposed into two subsets, which in turn define a nested set of optimization problems to achieve a significantly faster optimization procedure compared to a direct optimization based on the full design space. Following a simplistic sanity check of the platform functionality regarding wind farm layout and control optimization, the capability of the developed optimization platform is demonstrated on a Swedish offshore wind farm. For this particular wind farm, the analysis demonstrates that the expected annual energy production can be increased by 4 % by integrating the wind farm control into the design of the wind farm layout, which is 1.2 % higher than what is achieved by optimizing the layout only.

Spatiotemporal wind forecasting by learning a hierarchically sparse inverse covariance matrix using wind directions

Given the advances in online data acquisition systems, statistical learning models are increasingly used to forecast wind speed. In electricity markets, wind farm production forecasts are needed for the day-ahead, intra-day, and real-time markets. In this work, we use a spatiotemporal model that leverages wind dynamics to forecast wind speed. Using a priori knowledge of the wind direction, we propose a maximum likelihood estimate of the inverse covariance matrix regularized with a hierarchical sparsity-inducing penalty. The resulting inverse covariance estimate not only exhibits the benefits of a sparse estimator, but also enables meaningful sparse structures by considering wind direction. A proximal method is used to solve the underlying optimization problem. The proposed methodology is used to forecast six-hour-ahead wind speeds in 20-minute time intervals for a case study in Texas. We compare our method with a number of other statistical methods. Prediction performance measures and the Diebold–Mariano test show the potential of the proposed method, specifically when reasonably accurate estimates of the wind directions are available.

Wind farm power output optimization using cooperative control methods

AbstractWe study the application of cooperative control and game theoretic approaches to wind farm optimization. The conventional (greedy) wind farm control strategy seeks to individually maximize each turbine power. However, this strategy does not maximize the overall power production of wind farms due to the aerodynamic interactions (wake effect) between the turbines. We formulate the wind farm power optimization problem as an identical interest game which can also be used to solve other cooperative control problems. Two model‐free learning algorithms are developed to obtain the optimal axial induction factors of the turbines and maximize power production. The algorithms are simulated for a four‐turbine wind farm and the Princess Amalia wind farm and are compared to a learning algorithm that uses a game‐theoretic approach. It is shown that the proposed algorithms improve upon benchmark algorithm in terms of both performance and actuation effort.

Hybrid fuzzy decision making approach for wind-powered pumped storage power plant site selection: A case study

Combining wind power with pumped-storage systems is trustworthy for reducing the unreliability of wind energy, caused by the variable nature of the wind for contributing to the grid's peak shaving. Locating suitable sites for constructing both wind parks and pumped-storage systems for peak shaving is a delicate problem-solving. For solving such problems, a wide range of technical, financial, and environmental criteria should be taken into account. A two-step decision framework combining GIS, Analytic Network Process (ANP) and fuzzy VIKOR are proposed in this paper to solve the problem for locating the suitable sites for constructing a wind-powered pump storage plant. In the first step, a GIS database is developed to locate a handful potential sites within the study area. As there are different and even conflicting criteria involved with site selection problem. In the second step, we used an MCDM approach of fuzzy analytic network process (ANP) and fuzzy VIKOR to rank these potential sites. To establish an evaluation system, four major clusters of criteria are introduced (wind farm, water reservoir, water pump and power production) with more addition related sub-criteria. Comprehensive guidelines are formulated through a hierarchical procedure so a data-driven, multi-aspect decision framework could be presented with the goal of improving the benefits of integrated renewable energies. The results show that, the Gilane-Gharb dam is the most promising site within the study area. This site provides a net area of 13.2 km2 for constructing a wind farm with an approximate power capacity of 31 MW. The proposed procedure in this is a framework that are provide a theoretical reference for resolving any multicriteria decision-making problem by introducing the appropriate criteria.

Economic growth and environmental sustainability from renewable energy applications

The use of fossil fuel causes severe environmental problem such as climate change in the global scale and acid rain in the regional level. Along with its nonrenewable property, renewable and clean energy sources must be explored so as to keep the economy growing sustainably. Nowadays many renewable energy alternatives such as bioenergy, solar power, hydro power, and wind power have been employed, and each of them has their own pros and cons. This special issue focuses on the use of urban and rural wastes in several aspects such as (i) the use agricultural residuals in bioenergy production; (ii) treatments of municipal solid waste in pyrolysis and biochar application; (iii) sewer sludge and other organic matters in renewable energy production; and (iv) plastic materials in wind farm construction and production of solar panels, to assist the production of renewable energy to improve the resource management and maintain the environment.

Far off-shore wind energy-based hydrogen production: Technological assessment and market valuation designs

This article provides a techno-economic study on coupled offshore wind farm and green hydrogen production via sea water electrolysis (OWF-H2). Offshore wind energy, wind farms (OWF) and water electrolysis (WE) technologies are described. MHyWind (the tool used to perform simulations and optimisations of such plants) is presented, as well as the models of the main components in the study. Three case studies focus on offshore wind farms, either standalone or connected to the grid via export cables, coupled with a battery and electrolysis systems either offshore or onshore. Exhaustive searches and optimisations performed allowed for rules of thumb to be derived on the sizing of coupled OWF-H2 plants, that minimize costs of hydrogen production (LCoH2, in €/kgH2): Non-connected OWF-H2, coupled to a battery, offers the lowest LCoH2, without the costs of H2 transportation, when compared to cases where the WE is installed onshore and connected to the OWF. Using a simple power distribution heuristic, increasing the number of installed WE allows the system to take advantage of more OWF energy but doesn’t improve plant efficiency, whereas a battery always does. Finally, within the scope of this study, it is observed that power ratios of optimized plant architectures (leading to the lowest LCoH2) are between 0.8-0.9 for PWE/POWF and 0.3-0.35 for PBattery/POWF.

Review of control strategy of large horizontal‐axis wind turbines yaw system

AbstractIn order to meet the increasing demand of wind energy utilization, wind turbines (WTs) are developing toward the trend of large size and large capacity. In such a trend, various advanced yaw control strategies have been proposed to improve large WTs' comprehensive performance, but the analysis and summary of these strategies are still lacking. Therefore, it is necessary to have a review of yaw control, which not only enables readers to understand the current status of yaw control research but also promotes the development of wind energy technology. This paper presents a review of the current situation of yaw control for WTs, focusing on the mechanical/aerodynamic parts. The mechanical part is concerned with the WT yaw system and its effect on the fatigue load of the WT, and the aerodynamic part involves the wind energy capture and wake redirection to reduce the impact on adjacent WTs. In this review, the existing yaw control methods are classified in term of three control objectives: (1) increasing the wind energy capture of a single WT, (2) reducing the fatigue load of a single WT, and (3) maximizing the total power production of the whole wind farm and optimizing the wind farm fatigue load. On this basis, the control mechanism, the control algorithm, and the results are presented and analyzed in detail. Meanwhile, the advantages and disadvantages of the existing achievements are discussed. In addition, in a conclusion of the review, the future research direction has been identified.

The effects of Open Cellular Convection on Wind Farm Operation and Wakes

Majority of the severe variability in power production of an offshore wind farm occurs when open cellular convection (OCC) is observed. With a diameter of 10-80 km, the open cells are essentially the main drivers of hour-scale wind fluctuations passing through the wind farm. Here we aim to quantify the impact of the OCC on Horns Rev-I offshore wind farm located in the North Sea, in terms of variance in the power production and turbulence intensity. Using mesoscale simulations, met-mast measurements and high frequency (1 Hz) SCADA data from all the operating turbines, the behaviour of power deficit and added turbulence intensity is explored comparatively with and without presence of open cells. The investigation is a case study performed on a ‘day-to-day’ basis with an in depth analysis of the in-farm effects, such as the wake behaviour and smaller scale atmospheric structures. For the investigated event, the study shows striking difference in wind farm operation under the open cell structures and underlines the importance of taking local mesoscale phenomena into account for wind farm operation monitoring and control, short-term wake estimation, forecasting and market participation.

Energy harvesting via co-locating horizontal- and vertical-axis wind turbines

Co-locating horizontal- and vertical-axis wind turbines has been recently proposed as a possible approach to enhance the land-area power density of wind farms. In this work, we aim to study the benefits associated with such a co-location using large-eddy simulation (LES) and analytical wake models. In this regard, small-scale vertical-axis wind turbines (VAWTs) in triangular clusters are deployed within a finite-size wind farm consisting of horizontal-axis wind turbines (HAWTs). Wake flow within the wind farm and the effect of VAWTs on the overall wind-farm efficiency are investigated and quantified. The results show that the optimal deployment of small-scale VAWTs has a negligible impact on the performance of HAWT arrays while increasing the total power production. For the particular cases considered here, the power output of the co-located wind farm increases up to 21% compared to the baseline case in which only the HAWTs are present. Also, by comparing to the LES results, it is shown that the analytical framework proposed here is able to accurately predict the power production of wind farms including both HAWTs and VAWTs. Finally, as a real-world application, potential benefits of deploying small-scale VAWTs inside the Horns Rev 1 wind farm are explored for various wind directions using the calibrated wake model. The results show potential for about an 18% increase in the wind-farm power production, averaged over all wind directions, for a particular VAWT layout investigated in this study. The levelized cost of energy (LCoE) for the co-located wind farm is also assessed. The simulations finds that meanwhile the installation of VAWTs increases the annual energy production (AEP) of the wind farm, it also increases the LCoE, which is caused by a) lack of operational data, and b) a low TRL (Technology Readiness Levels) for VAWTs and floating foundations.