Single Wind Farm Research Articles

Time Series Generation and Complex Correlation Assessment for Multiple Wind Farms

Analysis and calculation of power system often requires a large number of medium and long-term wind power output series as data foundation. However, for most wind farms that have not been built or put into operation for a long time, the current output data is limited and it is difficult to support related research. It is necessary to use a limited amount of measured data to generate lots of wind power output time series that are similar to the actual data. Considering the concentrated development mode of wind power, this paper proposes a time series generation method for multiple wind farms based on the Markov Chain and Monte Carlo (MCMC) method and combined with high-dimensianal Markov process. The mixed Copula function fitting model is used to describe the spatial correlation of wind farms accurately. The measured wind power ouput is used to verify the results. It shows that the proposed method can simulate the output characteristics of single wind farm while maintaining the spatial correlation among different wind farms with geographical proximity

Investigating the Wind Power Smoothing Effect Using Set Pair Analysis

This study explores the smoothing effect on the uncertainty of the wind power fluctuations that affect a power system at points of common coupling. Set pair analysis is applied to evaluate the similarity between the power fluctuations of a single wind turbine and those of all aggregated wind turbines, based on which a quantitative index describing the wind power smoothing effect is proposed. The smoothing effect characteristics of a wind farm cluster are investigated for different numbers of wind turbines, different wind speeds, different seasons, and multiple sampling intervals. A significant smoothing effect is usually observed at a shorter sampling interval and a higher wind speed, and the smoothing effect index increases with an increase in the numbers of wind turbines and farms. Additionally, the correlation between the smoothing effect of aggregated wind farms and the forecast accuracy for the corresponding aggregated power output is examined. The experimental results indicate that the wind power forecast accuracy varies with the smoothing effect index, which is influenced by the number of wind farms. Furthermore, the aggregated output from a wind farm cluster with a higher smoothing effect index exhibits better forecasting performance than that from a single wind farm, showing that the trend of the wind power series becomes smoother due to the smoothing effect, thus enabling one-step-ahead wind power forecasting with higher accuracy.

Research on nodal wind power values and optimal accommodation based on locational marginal price

Under the current grid operation mode, reasonable integration of wind power is conducive to the exertion of its value. In this paper, the line flow transmission margin value considering wind power integration is assimilated into the locational marginal price (LMP). Accordingly, the LMP includes four components: energy consumption, network loss, congestion and line flow transmission margin. Based on the LMP difference of load nodes before and after wind power integration and the idea of power flow tracing, the specific values of different grid-connected wind farms at each node are evaluated, namely, the energy value and the network value of wind power. The load nodes that have a critical impact on wind power accommodation for each wind farm are identified. Moreover, the reserve cost caused by the uncertainty of wind power is considered, in which the maximum total value of all wind farms is set as the optimal objective. Finally, this paper analyses the LMP, value and accommodation of a single wind farm in detail through a microgrid system and an IEEE 118-bus system with multiple wind farms to verify the proposed method.

Coupled wind turbine design and layout optimization with nonhomogeneous wind turbines

Abstract. In this study, wind farms were optimized to show the benefit of coupling complete turbine design and layout optimization as well as including two different turbine designs in a fixed 1-to-1 ratio in a single wind farm. For our purposes, the variables in each turbine optimization include hub height, rotor diameter, rated power, tower diameter, tower shell thickness, and implicit blade chord-and-twist distributions. A 32-turbine wind farm and a 60-turbine wind farm were both considered, as well as a variety of turbine spacings and wind shear exponents. Structural constraints as well as turbine costs were considered in the optimization. Results indicate that coupled turbine design and layout optimization is superior to sequentially optimizing turbine design, then turbine layout. Coupled optimization results in an additional 2 %–5 % reduction in the cost of energy compared to optimizing sequentially for wind farms with turbine spacings of 8.5–11 rotor diameters. Smaller wind farms benefit even more from coupled optimization. Furthermore, wind farms with closely spaced wind turbines can greatly benefit from nonuniform turbine design throughout the farm. Some of these wind farms with heterogeneous turbine design have an additional 10 % cost-of-energy reduction compared to wind farms with identical turbines throughout the farm.

Offshore Wind Energy and the Romanian Energy Future

The aim of the present work is to assess the electricity production coming from an offshore wind farm that may operate in the northern part of the Romanian coastal area. In the first part, a complete description of the Romanian energy sector is presented considering the t ime interval from January 2008 to December 2018. In general, the elect ricity sold is negat ive (exports exceed imports), with the ment ion that a significant cont ribution comes from hydroelect ric and coal generat ion. It is important to ment ion that , if one of these two sectors willno longer perform on full capacity, the elect ricity balance will be shifted to the elect ricity imports. As for the wind energy, the average values from the vicinity of Sulina site may vary between 5.6 m/s and 8 m/s depending on the season, these results being reported at a wind turbine level (80 m). By using an offshore win d farm which replicates the Greater Gabbard project (504 MW), England, was possible to est imate the annual energy product ion and to indicate the expected impact on the energy sector. For example, a single wind farm may cover 1.7% of the total production, which may be further associated with 9.6% from nuclear, 7.6% from hydroelect ric or 6.4% from coal, respectively.

The control strategy of energy storage externality for reducing wind curtailment from wind farm cluster

Due to its flexible power regulation capability, energy storage is been considered as an efficient technology to reduce wind curtailment. However, since the fluctuation characteristics and low energy density characteristics of wind power, energy storage might be inefficient when reducing the wind curtailment from a single wind farm and the regulation capability of energy storage system (ESS) may not be fully utilized. Based on theoretical analysis the externality effect of energy storage is revealed in this paper, the control strategy of energy storage externality for reducing wind curtailment from wind farm cluster is established. By simulating analysis, the feasibility of the energy storage externality is verified. Moreover, by comparing with the conventional ESS control method, the paper evaluates the efficiency of the control strategy. The simulation results prove it can expand the influence scope of ESS and reduce more wind curtailment from wind farm cluster, the utilization of ESS is also promoted significantly.

Probabilistic short term wind power forecasts using deep neural networks with discrete target classes

Abstract. Usually, neural networks trained on historical feed-in time series of wind turbines deterministically predict power output over the next hours to days. Here, the training goal is to minimise a scalar cost function, often the root mean square error (RMSE) between network output and target values. Yet similar to the analog ensemble (AnEn) method, the training algorithm can also be adapted to analyse the uncertainty of the power output from the spread of possible targets found in the historical data for a certain meteorological situation. In this study, the uncertainty estimate is achieved by discretising the continuous time series of power targets into several bins (classes). For each forecast horizon, a neural network then predicts the probability of power output falling into each of the bins, resulting in an empirical probability distribution. Similiar to the AnEn method, the proposed method avoids the use of costly numerical weather prediction (NWP) ensemble runs, although a selection of several deterministic NWP forecasts as input is helpful. Using state-of-the-art deep learning technology, we applied our method to a large region and a single wind farm. MAE scores of the 50-percentile were on par with or better than comparable deterministic forecasts. The corresponding Continuous Ranked Probability Score (CRPS) was even lower. Future work will investigate the overdispersiveness sometimes observed, and extend the method to solar power forecasts.

Derating a single wind farm turbine for reducing its wake and fatigue

Derating of individual turbines is one of the options to implement wind farm performance optimization, although there are different ways to proceed with such derating at the turbine control level. The present paper develops an option based on the minimal thrust coefficient in accordance with the Cp and Ct contour levels. This strategy is compared for the region below rated wind speed with two other strategies where either the pitch or the tip speed ratio are maintained at their maximum Cp values from normal operation. The study concludes that maintaining the pitch at the optimal value from normal operation produces poorer performance from the thrust and the loads perspective. Practical implementation issues have also been detected.

Observer-based Fault-Tolerant Control of DC-AC Converters in Wind Turbines for Ancillary Service

In this paper, a generic model of DC-AC converters is introduced and an active fault compensation method for the realization of fault tolerant ancillary services of wind power plants is proposed. Ancillary services include the ability to support the grid with reliable and stable power supply which is an increasingly important requirement of single wind turbines and wind farms. The reduced model presents the positive, negative and zero sequence of the three phase system and can therefore take asymmetrical faults of the electrical power system in a wind power plant into account. It is shown that the model-based scheme is able to reconstruct actuator faults and to operate the wind turbine fault tolerant against typical faults in the three-phase power system.

Probabilistic Forecast for Multiple Wind Farms Based on Regular Vine Copulas

The uncertain nature of wind power causes difficulties in power system operation scheduling. Probabilistic descriptions of the uncertainty have been studied for decades. However, probabilistic forecasts designed for the regional multiple wind farms are few. Although the traditional methods for the single wind farm can still be used, they have the limitations in capturing the spatial correlations among wind farms, and they are less robust when multivariate observations are not so complete. To improve the forecast quality in this case, we combine the multivariate distribution modeling and probabilistic forecasts in this paper. An advanced model—the regular vine copula, which can describe the wind farms’ dependence structure precisely and flexibly with various bivariate copulas as blocks, is used in this paper. Enough simulation data can be generated from the model, which can be easily used to form the conditional forecast distributions under multiple forecast conditions. A case of 10 wind farms in East China has been used to compare the proposed method with its competitors. The results showed the method's advantages of providing reliable and sharp forecast intervals, especially in the case with limited observations available.

Strategy for wind power plant contribution to frequency control under variable wind speed

Abstract Current constraints require limiting the power transmitted from wind farms to power grids. Hence, wind turbine systems should be capable of regulating and limiting the generation capacity for a wide range of operating wind speeds. In addition, wind power plants must be endowed with active power and frequency control capabilities. In a single wind farm, wind turbines can be installed at different heights or altitudes, consequently exposing them to variable environmental conditions that can cause variations among their surrounding wind speeds. Thus, we propose a de-loading control strategy that integrates over-speeding and pitch control of wind turbines operating at variable wind speeds. This strategy allows not only storing power to satisfy the constraint demand of the power grid, but also contributing to primary frequency control. In fact, the proposed control strategy can adjust the static frequency difference coefficient of wind turbines, which is based on the proportion of variable-speed wind turbines operating under high wind speeds. Moreover, the proposed control strategy, which supports frequency control of the power grid under power constraints, suitably supports the frequency regulation of wind turbines that work at different wind speeds. Simulation results suggest that the proposed control strategy meets the power grid constraints, enhances the performance of primary frequency control, alleviates the frequency control pressure from thermal power plants, and appropriately generates curtailed wind power under variable wind speeds.

Modelling Correlated Forecast Error for Wind Power in Probabilistic Load Flow

The deepening penetration of wind power has brought about increasing uncertainty in power grid. In system operation, this uncertainty is mainly attributed to forecast uncertainty, which remains a challenging issue in uncertainty analysis. In this paper, a statistical model based on the mixed skewed distribution is developed to provide a perfect fitting for the conditional wind power forecast error in a single wind farm. The dependence structure of forecast error for multiple wind farms is obtained by pair-copula method, which takes mutual dependence of two arbitrary wind farms into account. The case study on a realistic transmission network in China is presented and different modelling schemes are compared to demonstrate the effectiveness of the proposed model in the application of probabilistic load flow.DOI: http://dx.doi.org/10.5755/j01.eie.23.5.19244

Weather dependent estimation of continent-wide wind power generation based on spatio-temporal clustering

Abstract. Europe is facing the challenge of increasing shares of energy from variable renewable sources. Furthermore, it is heading towards a fully integrated electricity market, i.e. a Europe-wide electricity system. The stable operation of this large-scale renewable power system requires detailed information on the amount of electricity being transmitted now and in the future. To estimate the actual amount of electricity, upscaling algorithms are applied. Those algorithms – until now – however, only exist for smaller regions (e.g. transmission zones and single wind farms). The aim of this study is to introduce a new approach to estimate Europe-wide wind power generation based on spatio-temporal clustering. We furthermore show that training the upscaling model for different prevailing weather situations allows to further reduce the number of reference sites without losing accuracy.

Response of migrating raptors to an increasing number of wind farms

SummaryFlying birds have been documented to respond in different ways to the presence of wind farms. Such responses are species‐ and site‐specific, with wind farm design playing an important role. Between 2009 and 2014, the length of rows of wind turbines within our study area increased from 3·4 km to ∼7·5 km, and the total area occupied by wind farms increased from 6·47 km2to 14·21 km2. This area is located on an important migratory corridor in southern Mexico.We used marine radar and hawk‐watch monitoring stations to collect data during six consecutive autumn seasons from a single wind farm. We analysed the response of migrating raptors to the presence of two new wind farms by comparing the mean bearing of flight trajectories and the number of intersections km−1of trajectories with wind farm areas between 2009–2011 and 2012–2014, representing the pre‐ and post‐construction stages of the new wind farms.Mean raptor count was >600 000 individuals for the six seasons. The most abundant species were Turkey VultureCathartes aura, Swainson's HawkButeo swainsoniand Broad‐winged hawkButeo platypterus. Between 79% and 97% of migration occurred in October. Radar monitoring overlapped with the peak migratory activity each season.We observed significant differences between periods, involving more scattering in flight bearings and less intersections km−1of trajectory in the post‐ than in the pre‐construction period, implying an avoidance of the new wind farms.Synthesis and applications. We show that migrating raptors adjusted their flight trajectories to avoid new wind farms, but also discuss the extent and limitations of our findings. Our results from our hawk‐watch monitoring station, which represent the first published account about the seasonality and intensity of raptor migration in the area, could be used by decision‐makers for careful planning of future wind energy developments in the area. Our results might aid in the conservation of those species of raptors that migrate through the Isthmus of Tehuantepec.

Genetic diversity in migratory bats: Results from RADseq data for three tree bat species at an Ohio windfarm

Genetic analyses can identify the scale at which wildlife species are impacted by human activities, and provide demographic information useful for management. Here, we use thousands of nuclear DNA genetic loci to assess whether genetic structure occurs within Lasiurus cinereus (Hoary Bat), L. borealis (Red Bat), and Lasionycteris noctivagans (Silver-Haired Bat) bats found at a wind turbine site in Ohio, and to also estimate demographic parameters in each of these three groups. Our specific goals are to: 1) demonstrate the feasibility of isolating RADseq loci from these tree bat species, 2) test for genetic structure within each species, including any structure that may be associated with time (migration period), and 3) use coalescent-based modeling approaches to estimate genetically-effective population sizes and patterns of population size changes over evolutionary timescales. Thousands of loci were successfully genotyped for each species, demonstrating the value of RADseq for generating polymorphic loci for population genetic analyses in these bats. There was no evidence for genetic differentiation between groups of samples collected at different times throughout spring and fall migration, suggesting that individuals from each species found at the wind facility are from single panmictic populations. Estimates of present-day effective population sizes varied across species, but were consistently large, on the order of 105–106. All populations show evidence of expansions that date to the Pleistocene. These results, along with recent work also suggesting limited genetic structure in bats across North America, argue that additional biomarker systems such as stable-isotopes or trace elements should be investigated as alternative and/or complementary approaches to genetics for sourcing individuals collected at single wind farm sites.

Time series wind power forecasting based on variant Gaussian Process and TLBO

Due to the variability and stochastic nature of wind power, accurate wind power forecasting plays an important role in developing reliable and economic power system operation and control strategies. As wind variability is stochastic, Gaussian Process regression has recently been introduced to capture the randomness of wind energy. However, the disadvantages of Gaussian Process regression include its computation complexity and incapability to adapt to time varying time-series systems. A variant Gaussian Process for time series forecasting is introduced in this study to address these issues. This new method is shown to be capable of reducing computational complexity and increasing prediction accuracy. The convergence of the forecasting results is also proved. Further, a teaching learning based optimization (TLBO) method is used to train the model and to accelerate the learning rate. The proposed modelling and optimization method is applied to forecast both the wind power generation in Ireland and that from a single wind farm to demonstrate the effectiveness of the proposed method.

Predicting wind farm wake interaction with RANS: an investigation of the Coriolis force

A Reynolds-averaged Navier-Stokes code is used to simulate the interaction of two neighboring wind farms. The influence of the Coriolis force is investigated by modeling the atmospheric surface/boundary layer with three different methodologies. The results show that the Coriolis force is negligible for a single wind turbine, small for a single wind farm, but important for simulations of wind farm wake interaction.

Towards a cumulative collision risk assessment of local and migrating birds in North Sea offshore wind farms

Bird collision assessments are generally made at the scale of a single wind farm. While especially in offshore situations such assessments already hold several assumptions, even bigger challenges exist on estimating the cumulative impact of multiple wind farms and the impacts at population level. In this paper, the number of collision victims at Belgian offshore wind farms was estimated with a (theoretical) collision risk model based on technical turbine specifications, bird-related parameters and bird density data of both local seabirds and passerine migrants. Bird density data were gathered by visual censuses and radar registrations. The outcome of the model was extrapolated to future development scenarios in the Belgian part of the North Sea and in the entire North Sea, and then further used for a preliminary assessment of the impact at population level for the species at risk. The results indicate that the cumulative impact of a realistic scenario of 10,000 turbines in the North Sea might have a significant negative effect at population level for lesser and great black-backed gull. We further show that during a single night of intense songbird migration, the number of collision victims among passerine migrants might be in the order of magnitude of several thousands in the entire North Sea. We argue that it is of great importance to further develop methods to quantify the uncertainties and to minimise the assumptions, in order to assure more reliable cumulative impact assessments.

Analysis on the Fluctuation of Wind Power

This paper is aimed at exploring the characteristic fluctuation of wind power based on samples from a certain wind farm. First, the paper is to analyze fluctuations of wind power at different time scales. According to a sliding difference algorithm to build wind power fluctuations evaluation. Wind power fluctuation index for different time scales are used to fit probability distributions, indicating that the best form of distribution of wind power fluctuations is t location scale distribution. Secondly, considering the wind power has the characteristics of non-linear, non-stationary signal of the data, it fully meets the wavelet neural network analysis of the characteristics of the data. Therefore, select wavelet neural network training and testing so as to make predictions about the future of the total power of wind farm. It points out the differences between different regions covered by the index from the fluctuation characteristics of wind power, thus further understanding the fluctuation characteristics of wind power: Influenced by the time and space distribution and other factors, there is a big difference between the output power fluctuation characteristics of single wind generator and wind farm, which is because of the different wind machine in the field by the wind energy differences, and the wake effect of organic groups, making frequent fluctuations in power distribution; the fluctuation of wind is gentle, i.e. with increasing spatial distribution scale, so gentle effect occurs to wind power fluctuations. Finally, through the analysis of the fluctuation characteristics of power, power factor and analyses the influence of the characteristics of fluctuation, the paper draws a conclusion of the following improvement programs to overcome the adverse effects of wind power fluctuation of power grid operation: the rational allocation of energy storage devices, expanding the coverage area of a wind farm, or improving the design of the windmill, which will make wind farms adapt to different wind directions, thus eliminating the impact of fluctuations on the power grid from the wind farm power output by the energy storage device, and covering the area of large wind farms can adapt to different wind directions, and with power complementary, it has achieved the amount of stable power transmission into the grid.

Large-eddy simulation of multiple wakes in offshore wind farms

In this study, high-resolution large-eddy simulations of two German offshore wind farms are performed with the LES model PALM in which the turbines are parameterized with an actuator disk approach. The simulation of a single wind farm is realized by a stationary model domain with a turbulent inflow. Different atmospheric stratifications from slightly stable to unstable have been simulated and their effect on the wake characteristics is investigated. The results show a clear development of the flow within the wind farms with large differences between single, double and triple wakes. The wake deficit is increasing up to the second wake for the neutral and stable boundary layer and up to the third wake in the convective case before reaching a nearly constant value. This is explained with the turbulence intensity being much higher behind the second and subsequent turbines compared to the wake of the first turbine. With increasing atmospheric stability the wake deficits are shown to be stronger due to reduced turbulence.