The effect of timescales on wind farm power variability with nonlinear model predictive control

Model predictive control techniques enable operators to balance multiple objectives in large wind farms, but the controller design depends on modeling effects that propagate at different timescales. This paper uses nonlinear model predictive control to investigate how wind farm power variability can be reduced both by varying ratios of three timescales impacting the system control and by inclusion of a power variability minimization measure in the controller objective function. Tests were conducted to assess how different timescale ratios affect the average farm power and power variability. Power variability measures are shown to be sensitive to the ratio of the incident wind period and the turbine time delay, particularly for cases with dominant incident wind frequencies. The average farm power increases in a series of steps as the controller time horizon increases, which corresponds to time horizon values required for wakes disturbances to propagate to downstream turbines. A second set of tests was conducted in which various measures of power variability were incorporated into the controller objective function and shown to yield significant reductions in farm power variability without significant reductions in farm power output. The controller was found to utilize two different approaches for achieving power variability reduction depending on the formulation of the controller objective function. These results have important implications for the design and operation of wind power plants, including the importance of considering the frequency components of wind during turbine siting and the potential to reduce power variability through the use of farm‐level coordinated control. Copyright © 2017 John Wiley & Sons, Ltd.     

A spatial and temporal correlation analysis of aggregate wind power in an ideally interconnected Europe

Studies have shown that a large geographic spread of installed capacity can reduce wind power variability and smooth production. This could be achieved by using electricity interconnections and storage systems. However, interconnections and storage are not totally flexible, so it is essential to understand the wind power correlation in order to address power system constraints in systems with large and growing wind power penetrations. In this study, the spatial and temporal correlation of wind power generation across several European Union countries was examined to understand how wind ‘travels’ across Europe. Three years of historical hourly wind power generation data from 10 countries were analysed. The results of the analysis were then compared with two other studies focused on the Nordic region and the USA. The findings show that similar general correlation characteristics do exist between European country pairs. This is of particular importance when planning and operating interconnector flows, storage optimization and cross‐border power trading. Copyright © 2017 The Authors Wind Energy Published by John Wiley & Sons Ltd     

Assessment of offshore wind farm characteristics with the cloud resolving storm simulator: A case study in Japan

Wind conditions and output power characteristics of a wind farm in Japan are evaluated with highly resolved weather predictions from the so‐called cloud resolving storm simulator. One year of 30‐hour‐ahead predictions with 2‐km spatial resolution and 1‐hour time resolution are evaluated against 10‐minute averaged measurements (averaged to hourly data) from the wind farm. Also, extremely detailed shorter‐term predictions with 200‐m spatial resolution and 1‐second time resolution are evaluated against 1‐Hz measurements. For the hourly data, wind speeds are predicted with an RMSE of 3.0 to 3.5 m/s, and wind power with about 0.3 per unit. Wind direction is predicted with a standard deviation of errors of 16° to 28° for hourly data, and generally below 10° for the 1‐Hz data. We show that wind power variability—here in terms of increments—can be assessed on the timescale of several hours. The measured and predicted wind spectra are found similar on both short and long timescales.     

Hourly wind power variations in the Nordic countries

Studies of the effects that wind power production imposes on the power system involve assessing the variations of large‐scale wind power production over the whole power system area. Large geographical spreading of wind power will reduce variability, increase predictability and decrease the occasions with near zero or peak output. In this article the patterns and statistical properties of large‐scale wind power production data are studied using the data sets available for the Nordic countries. The existing data from Denmark give the basis against which the data collected from the other Nordic countries are benchmarked. The main goal is to determine the statistical parameters describing the reduction of variability in the time series for the different areas in question. The hourly variations of large‐scale wind power stay 91%–94% of the time within ±5% of installed capacity in one country, and for the whole of the Nordic area 98% of the time. For the Nordic time series studied, the best indicator of reduced variability in the time series was the standard deviation of the hourly variations. According to the Danish data, it is reduced to less than 3% from a single site value of 10% of capacity. Copyright © 2004 John Wiley & Sons, Ltd.     

Integrating wind output with bulk power operations and wholesale electricity markets

Wind farms have three characteristics that complicate their widespread application as an electricity resource: limited control, unpredictability and variability. Therefore the integration of wind output into bulk power electric systems is qualitatively different from that of other types of generators. The electric system operator must move other generators up or down to offset the time‐varying wind fluctuations. Such movements raise the costs of fuel and maintenance for these other generators. Not only is wind power different, it is new. The operators of bulk power systems have limited experience in integrating wind output into the larger system. As a consequence, market rules that treat wind fairly—neither subsidizing nor penalizing its operation—have not yet been developed. The lack of data and analytical methods encourages wind advocates and sceptics to rely primarily on their biases and beliefs in suggesting how wind should be integrated into bulk power systems. This project helps fill this data and analysis gap. Specifically, it develops and applies a quantitative method for the integration of a wind resource into a large electric system. The method permits wind to bid its output into a short‐term forward market (specifically, an hour‐ahead energy market) or to appear in real time and accept only intrahour and hourly imbalance payments for the unscheduled energy it delivers to the system. Finally, the method analyses the short‐term (minute‐to‐minute) variation in wind output to determine the regulation requirement the wind resource imposes on the electrical system. Copyright © 2002 John Wiley & Sons, Ltd.     

A stochastic power curve for wind turbines with reduced variability using conditional copula

It has been observed that a large variability exists between wind speed and wind power in real metrological conditions. To reduce this substantial variability, this study developed a stochastic wind turbine power curve by incorporating various exogenous factors. Four measurements, namely, wind azimuth, wind elevation, air density and solar radiation are chosen as exogenous influence factors. A recursive formula based on conditional copulas is used to capture the complex dependency structure between wind speed and wind power with reduced variability. A procedure of selecting a proper form for each factor and its corresponding copula models is given. Through a case study on the small wind turbine located in southeast of Edmonton, Alberta, Canada, we demonstrate that the variability can be reduced significantly by incorporating these influence factors. Wind turbine operators can apply the method reported in this study to construct a stochastic power curve for local wind farms and use it to achieve more accurate power forecasting and health condition monitoring of the turbine. Copyright © 2015 John Wiley & Sons, Ltd.     

Impact of hourly wind power variations on the system operation in the Nordic countries

The variations of wind power production will increase the flexibility needed in the system when significant amounts of load are covered by wind power. When studying the incremental effects that varying wind power production imposes on the power system, it is important to study the system as a whole: only the net imbalances have to be balanced by the system. Large geographical spreading of wind power will reduce variability, increase predictability and decrease the occasions with near zero or peak output. The goal of this work was to estimate the increase in hourly load‐following reserve requirements based on real wind power production and synchronous hourly load data in the four Nordic countries. The result is an increasing effect on reserve requirements with increasing wind power penetration. At a 10% penetration level (wind power production of gross demand) this is estimated as 1·5%–4% of installed wind capacity, taking into account that load variations are more predictable than wind power variations. Copyright © 2004 John Wiley & Sons, Ltd.     

Grid support of a wind farm with active stall wind turbines and AC grid connection

One of the main concerns in the grid integration of large wind farms is their ability to behave as active controllable components in the power system. This article presents the design of a new integrated power control system for a wind farm made up exclusively of active stall wind turbines with AC grid connection. The designed control system has the task of enabling such a wind farm to provide the best grid support. It is based on two control levels: a supervisory control level, which controls the power production of the whole farm by sending out reference signals to each individual wind turbine, and a local control level, which ensures that the reference power signals at the wind turbine level are reached. The ability of active stall wind farms with AC grid connection to control the power production to the reference power ordered by the operators is assessed and discussed by means of simulations. Copyright © 2005 John Wiley & Sons, Ltd.     

An integrated approach for optimal coordination of wind power and hydro pumping storage

The increasing wind power penetration in power systems represents a techno‐economic challenge for power producers and system operators. Because of the variability and uncertainty of wind power, system operators require new solutions to increase the controllability of wind farm output. On the other hand, producers that include wind farms in their portfolio need to find new ways to boost their profits in electricity markets. This can be done by optimizing the combination of wind farms and storage so as to make larger profits when selling power (trading) and reduce penalties from imbalances in the operation. The present work describes a new integrated approach for analysing wind‐storage solutions that make use of probabilistic forecasts and optimization techniques to aid decision making on operating such systems. The approach includes a set of three complementary functions suitable for use in current systems. A real‐life system is studied, comprising two wind farms and a large hydro station with pumping capacity. Economic profits and better operational features can be obtained from the proposed cooperation between the wind farms and storage. The revenues are function of the type of hydro storage used and the market characteristics, and several options are compared in this study. The results show that the use of a storage device can lead to a significant increase in revenue, up to 11% (2010 data, Iberian market). Also, the coordinated action improves the operational features of the integrated system. Copyright © 2013 John Wiley & Sons, Ltd.     

The variability of interconnected wind plants

We present the first frequency-dependent analyses of the geographic smoothing of wind power’s variability, analyzing the interconnected measured output of 20 wind plants in Texas. Reductions in variability occur at frequencies corresponding to times shorter than ∼24 h and are quantified by measuring the departure from a Kolmogorov spectrum. At a frequency of 2.8×10⁻⁴ Hz (corresponding to 1 h), an 87% reduction of the variability of a single wind plant is obtained by interconnecting 4 wind plants. Interconnecting the remaining 16 wind plants produces only an additional 8% reduction. We use step change analyses and correlation coefficients to compare our results with previous studies, finding that wind power ramps up faster than it ramps down for each of the step change intervals analyzed and that correlation between the power output of wind plants 200 km away is half that of co-located wind plants. To examine variability at very low frequencies, we estimate yearly wind energy production in the Great Plains region of the United States from automated wind observations at airports covering 36 years. The estimated wind power has significant inter-annual variability and the severity of wind drought years is estimated to be about half that observed nationally for hydroelectric power.     

A comparison of methodologies for monthly wind energy estimation

Monthly wind energy estimations obtained by means of three different methodologies are evaluated. Hourly wind and wind power production data measured at five wind farms in the Northeast of Spain within the period spanning from June 1999 to June 2003 were employed for this purpose. One of the approaches is based on the combined contribution of the hourly wind speed frequency distribution and the corresponding power production. Several alternatives to represent the empirical wind power versus wind speed relationship are considered and their impacts on the error of monthly energy estimations assessed. Two more approaches derive monthly energy estimates directly from monthly wind values: one uses the theoretical power curve to obtain interpolated monthly wind power production values and the other consists in a simple linear regression between the observed wind speed and wind power monthly pairs, which serves as an approximation to the global power curve. The three methodologies reproduce reliably the total monthly wind energy. Results also reveal that linearity is a reasonable assumption for the relation between wind speed and power production at monthly timescales. This approach involves a simplification with respect to other standard procedures that require finer temporal resolution data. Copyright © 2009 John Wiley & Sons, Ltd.     

Spatiotemporal variability of the wind power resource in Argentina and Uruguay

The spatiotemporal variability of the wind power resource over Argentina and Uruguay is assessed based on the Modern‐Era Retrospective Analysis for Research and Applications 2 (MERRA2) dataset. Hourly wind speeds were interpolated to 100‐m height, and then, wind power outputs were computed using power curves of three International Electrotechnical Commission (IEC) wind classes. The time series of wind power outputs were filtered using a fast Fourier transform (FFT) to separate regular (annual and daily) from irregular (interannual and synoptic scale) cycles. An empirical orthogonal function analysis was applied to the resulting datasets to obtain the main modes of variability. The results show that the combination of wind power outputs from southern and northern Patagonia broadly follows the average annual electric load. Patagonia exhibits the highest variability on the interannual, annual, and synoptic timescales. On the interannual and synoptic timescales, the variability modes are associated with known and distinct atmospheric circulation modes. The interannual modes of variability are associated with opposite surface level pressure (SLP) anomalies between middle and high latitudes.     

Analysis of the damping contribution of power system stabilizers driving wind power plants

Recently, the concept of wind power plant has been introduced as a result of the increment of wind power penetration in power systems. A wind power plant can be defined as a wind farm, which is expected to behave similar to a conventional power plant in terms of power generation, control and ancillary services. Transmission system operators are requiring wind power generation to help to power system with some ancillary services such as fault ride through or power system stabilizer capability. Therefore, it is important to study the power system stabilizer capability of wind power plants. In this paper, a comparison of various power system stabilizer schemes is presented. The effect of the distance from the tie line to the wind farm on the controller response and the influence of wind power plants proximity to synchronous generators are also evaluated. These studies show that wind power plants have promising power system stabilizer capability even using local input signals. However, the location of the wind power plant on the power system is a critical factor. Copyright © 2012 John Wiley & Sons, Ltd.     

On the wind power rejection in the islands of Crete and Rhodes

Crete and Rhodes represent the two biggest isolated power systems in Greece. The energy production in both islands is based on thermal power plants. The annual wind energy rejection percentage is calculated for Crete and Rhodes in this paper. The rejected wind energy is defined as the electric energy produced by the wind turbines and not absorbed by the utility network, mainly due to power production system's stability and dynamic security reasons. A parametric calculation of the annual wind energy rejection percentage, in terms of the installed wind power, the power demand and the maximum allowed wind power instant penetration percentage, is accomplished. The methodology takes into account (i) the wind power penetration probability, restricted by the thermal generators technical minima and the maximum allowed wind power instant penetration percentage over the instant power demand; and (ii) the wind power production probability, derived by the islands' wind potential. The present paper indicates that isolated power systems which are based on thermal power plants have a limited wind power installation capacity—in order to achieve and maintain an adequate level of system stability. For a maximum wind power instant penetration percentage of 30% of the power demand, in order to ensure an annual wind energy rejection percentage less than 10%, the total installed wind power should not exceed the 40% of the mean annual power demand. The results of this paper are applicable to medium and great size isolated power systems, with particular features: (i) the power production is based on thermal power plants; (ii) the power demand exhibits intensive seasonal variations and is uncorrelated to the wind data; (iii) the mean annual power demand is greater than 10MW; and (iv) a high wind potential, presenting mean annual wind velocity values greater than 7·5ms^?1, is recorded. Copyright © 2007 John Wiley &Sons, Ltd.     

The effect of spatial dispersion of wind power plants on the curtailment of wind power in the Greek power supply system

To meet the national target of 29% for electricity production from renewable energy sources by 2020 in Greece, effective implementation of massive wind power installed capacity into the power supply system is required. In such a situation, the effective absorption of wind energy production is an important issue in a relatively small and weak power system such as that of Greece, which has limited existing interconnections with neighboring countries. The curtailment of wind power is sometimes necessary in autonomous systems with large wind energy penetration. The absorption or curtailment of wind power is strongly affected by the spatial dispersion of wind power installations. In the present paper, a methodology for estimating this effect is presented and applied for the power supply system of Greece. The method is based on probability theory, and makes use of wind forecasting models to represent the wind energy potential over any candidate area for future wind farm installations in the country. Moreover, technical constraints imposed by the power supply system management, the commitment of power plants and the load dispatch strategies are taken into account to maximize the wind energy penetration levels while ensuring reliable operation of the system. Representative wind power development scenarios are studied and evaluated. Results show that the spatial dispersion of wind power plants contributes beneficially to the wind energy penetration levels that can be accepted by the power system. Copyright © 2009 John Wiley & Sons, Ltd.     

Simulating subhourly variability of wind power output

As installed wind power capacity grows, subhourly variability in wind power output becomes increasingly important for determining the system flexibility needs, operating reserve requirements, and cost associated with wind integration. This paper presents a new methodology for simulating subhourly wind power output based on hourly average time series, which are often produced for system planning analyses, for both existing wind plants and expanded, hypothetical portfolios of wind plants. The subhourly model has an AR(p)‐ARCH(q) structure with exogenous input in the heteroskedasticity term. Model coefficients may be fit directly to high‐pass filtered historical data if it exists; for sets of wind plants containing hypothetical plants for which there are no historical data, this paper presents a method to determine model coefficients based on wind plant capacities, capacity factors, and pairwise distances. Unlike predecessors, the model presented in this paper is independent of wind speed data, captures explicitly the high variability associated with intermediate levels of power output, and captures distance‐dependent correlation between the power output of wind plants across subhourly frequencies. The model is parameterized with 1‐minute 2014 plant‐level wind power data from Electric Reliability Council of Texas (ERCOT) and validated out‐of‐sample against analogous 2015 data. The expanded‐capacity model, fit to 2014 data, produces accurate subhourly time series for the 2015 wind fleet (a 49% capacity expansion) based only on the 2015 system's wind plant capacities, capacity factors, and pairwise distances. This supports its use in simulating subhourly fleet aggregate wind power variability for future high‐wind scenarios.     

A review of grid code technical requirements for wind farms

This paper provides an overview of grid code technical requirements regarding the connection of large wind farms to the electric power systems. The grid codes examined are generally compiled by transmission system operators (TSOs) of countries or regions with high wind penetration and therefore incorporate the accumulated experience after several years of system operation at significant wind penetration levels. The paper focuses on the most important technical requirements for wind farms, included in most grid codes, such as active and reactive power regulation, voltage and frequency operating limits and wind farm behaviour during grid disturbances. The paper also includes a review of modern wind turbine technologies, regarding their capability of satisfying the requirements set by the codes, demonstrating that recent developments in wind turbine technology provide wind farms with stability and regulation capabilities directly comparable to those of conventional generating plants.     

Evaluation of bivariate Archimedean and elliptical copulas to model wind power dependency structures

When modeling wind power from several sources, consideration of the dependency structure of the sources is of critical importance. Failure to appropriately account for the dependency structure can lead to unrealistic models, which may result in erroneous conclusions from wind integration studies and other analyses. The dependency structure is fully described by the multivariate joint distribution function of the wind power. However, few—if any—explicit joint distribution models of wind power exist. Instead, copulas can be used to create joint distribution functions, provided that the selected copula family reasonably approximates the dependency structure. Unfortunately, there is little guidance on which copula family should be used to model wind power. The purpose of this paper is to investigate which copula families are best suited to model wind power dependency structures. Bivariate copulas are considered in particular. The paper focuses on power from wind plants—collections of wind turbines with a common interconnection point—but the methodology can be generally extended to consider power from individual wind turbines or even aggregate wind power from entire systems. Twelve Archimedean and elliptical copulas are evaluated using hourly data from 500 wind plant pairs in the National Renewable Energy Laboratory's Eastern Dataset. The evaluation is based on χ² and Cramér‐von Mises statistics. Application guidelines recommending which copula family to use are developed. It is shown that a default assumption of Gaussian dependence is not justified and that the use of Gumbel copulas can result in improved models. An illustrative example shows the application of the guidelines to model dependence of wind power sources in Monte Carlo simulations. Copyright © 2012 John Wiley & Sons, Ltd.     

Time series modelling of power output for large‐scale wind fleets

Simulations of power systems with high wind penetration need to represent the stochastic output of the wind farms. Many studies use historic wind data directly in the simulation. However, even if historic data are used to drive the realized wind output in scheduling simulations, a model of the wind's statistical properties may be needed to inform the commitment decisions for the dispatchable units. There are very few published studies that fit models to the power output of nation‐sized wind fleets rather than the output at a single location. We fitted a time series model to hourly, time‐averaged, aggregated wind power data from New Zealand, Denmark and Germany, based on univariate, second‐order autoregressive drivers. Our model is designed to reproduce the asymptotic distribution of power output, the diurnal variation and the volatility of power output over timescales up to several hours. For the cases examined here, it was also found to provide a generally good representation of the overall distribution of power output changes and the variation of volatility with power output level, as well as an acceptable representation of the distribution of calm periods. Copyright © 2011 John Wiley & Sons, Ltd.     

Wind power forecast using neural networks: Tuning with optimization techniques and error analysis

The increased integration of wind power into the power system implies many challenges to the network operators, mainly due to the hard to predict and variability of wind power generation. Thus, an accurate wind power forecast is imperative for systems operators, aiming at an efficient and economical wind power operation and integration into the power system. This work addresses the issue of forecasting short‐term wind speed and wind power for 1 hour ahead, combining artificial neural networks (ANNs) with optimization techniques on real historical wind speed and wind power data. Levenberg‐Marquardt (LM) and particle swarm optimization (PSO) are used as training algorithms to update the weights and bias of the ANN applied to wind speed predictions. The forecasting performance produced by the proposed models are compared with each other, as well as with the benchmark persistence model. Test results show higher performance for ANN‐LM wind speed forecasting model, outperforming both ANN‐PSO and persistence. The application of ANN‐LM to wind power forecast revealed also a good performance, with an average improvement of 2.8% in relation to persistence. An innovative analysis of mean absolute percentage error (MAPE) behaviour in time and in typical days is finally offered in the paper.