国内外风电持续迅猛发展

国外统计数据表明,近几年世界风电持续高速发展。据国内统计数据,我国2006年、2007年风电装机容量连续两年翻番。我国内资风电设备制造企业已形成3个梯队,2007年我国内资风电设备制造企业所占市场份额首次超过外资企业。介绍了我国风能资源的最新评估,在假设条件下,全国陆上风能可开发资源量为8×10^8kW,近海为1．5×10^8kw：陆上和近海合计可提供风电电量约为2×10^12kW·h。预计我国风电装机容量2010年和2020年只有分别达到2000×10^4kW和1×10^8kw时才能实现非水电可再生能源发电量的强制性市场份额目标。     

Lifetime and economic analyses of lithium‐ion batteries for balancing wind power forecast error

Wind power plant operators are often faced with extra charges when their power production does not match the forecasted power. Because the accuracy of wind power forecasts is limited, the use of energy storage systems is an attractive alternative even when large‐scale aggregation of wind power is considered. In this paper, the economic feasibility of lithium‐ion batteries for balancing the wind power forecast error is analysed. In order to perform a reliable assessment, an ageing model of lithium‐ion battery was developed considering both cycling and calendar life. The economic analysis considers two different energy management strategies for the storage systems and it is performed for the Danish market. Analyses have shown that the price of the Li‐ion BESS needs to decrease by 6.7 times in order to obtain a positive net present value considering the present prices on the Danish energy market. Moreover, it was found that for total elimination of the wind power forecast error, it is required to have a 25‐MWh Li‐ion battery energy storage system for the considered 2 MW WT. Copyright © 2015 John Wiley & Sons, Ltd.     

中国风电发展目标分析与展望

我国风能资源合理开发利用和风电产业健康发展需要科学合理可行的发展目标做引导。本文提出了制定风电发展目标的基本原则与出发点,对现有的国家风电发展目标做了分析,认为国家规划的风电目标过低,已建和在建风电项目装机容量比到2010年的国家规划目标翻了一番,各省风电规划目标高低不一,总体偏高,且有目标攀比现象。进而采用累加值法、本地增长率法、类比增长率法、GM(1,1)模型法等多方法预测得到我国风电发展的科学目标,即到2010年我国风电装机容量达到1633万kW,到2015年达到4172万kW,到2020年达到10021万kW,占全国同期电力总装机容量的8.35%。预测趋势表明,我国风电正在进入加速发展阶段,到2020年我国风电装机容量将占到全球风电总装机容量的8.15%,风电发展将实现由风能资源大国向风电市场和产业强国的转变。     

Characteristics of day‐ahead wind power forecast errors in Nordic countries and benefits of aggregation

The growing proportion of wind power in the Nordic power system increases day‐ahead forecasting errors, which have a link to the rising need for balancing power. However, having a large interconnected synchronous power system has its benefits, because it enables to aggregate imbalances from large geographical areas. In this paper, day‐ahead forecast errors from four Nordic countries and the impacts of wind power plant dispersion on forecast errors in areas of different sizes are studied. The forecast accuracy in different regions depends on the amount of the total wind power capacity in the region, how dispersed the capacity is and the forecast model applied. Further, there is a saturation effect involved, after which the reduction in the relative forecast error is not very large anymore. The correlations of day‐ahead forecast errors between areas decline rapidly when the distance increases. All error statistics show a strong decreasing trend up to the area sizes of 50,000 km². The average mean absolute error (MAE) in different regions is 5.7% of installed capacity. However, MAE of a smaller area can be over 8% of the capacity, but when all the Nordic regions are aggregated together, the capacity‐normalized MAE decreases to 2.5%. The average of the largest errors for different regions is 39.8% and when looking at the largest forecast errors for smaller areas, the largest errors can exceed 80% of the installed capacity, whereas at the Nordic level, the maximum forecast error is only 13.5% of the installed capacity. Copyright © 2016 John Wiley & Sons, Ltd.     

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.     

Detecting,categorizing and forecasting large ramps in wind farm power output using meteorological observations and WPPT

The Wind Power Prediction Tool (WPPT) has been installed in Australia for the first time, to forecast the power output from the 65MW Roaring 40s Renewable Energy P/L Woolnorth Bluff Point wind farm. This article analyses the general performance of WPPT as well as its performance during large ramps (swings) in power output. In addition to this, detected large ramps are studied in detail and categorized. WPPT combines wind speed and direction forecasts from the Australian Bureau of Meteorology regional numerical weather prediction model, MesoLAPS, with real‐time wind power observations to make hourly forecasts of the wind farm power output. The general performances of MesoLAPS and WPPT are evaluated over 1 year using the root mean square error (RMSE). The errors are significantly lower than for basic benchmark forecasts but higher than for many other WPPT installations, where the site conditions are not as complicated as Woolnorth Bluff Point. Large ramps are considered critical events for a wind power forecast for energy trading as well as managing power system security. A methodology is developed to detect large ramp events in the wind farm power data. Forty‐one large ramp events are detected over 1 year and these are categorized according to their predictability by MesoLAPS, the mechanical behaviour of the wind turbine, the power change observed on the grid and the source weather event. During these events, MesoLAPS and WPPT are found to give an RMSE only roughly equivalent to just predicting the mean (climatology forecast). Copyright © 2007 John Wiley & Sons, Ltd.     

A variance analysis of the capacity displaced by wind energy in Europe

Wind energy generation distributed all over Europe is less variable than generation from a single region. To analyse the benefits of distributed generation, the whole electrical generation system of Europe has been modelled including varying penetrations of wind power. The model is chronologically simulating the scheduling of the European power plants to cover the demand at every hour of the year. The wind power generation was modelled using wind speed measurements from 60 meteorological stations for 1 year. The distributed wind power also displaces fossil‐fuelled capacity. However, every assessment of the displaced capacity (or a capacity credit) by means of a chronological model is highly sensitive to single events. Therefore the wind time series was shifted by integer days against the load time series, and the different results were aggregated. The same set of results is shown for two other options, one where the pump storage plants are used more aggressively and the other where all German nuclear plants are shut off. NCEP/NCAR reanalysis data have been used to recreate the same averaged time series from a data set spanning 34 years. Through this it is possible to set the year studied in detail into a longer‐term context. The results are that wind energy can contribute more than 20% of the European demand without significant changes in the system and can replace conventional capacity worth about 10% of the installed wind power capacity. The long‐term reference shows that the analysed year is the worst case for wind power integration. Copyright © 2006 John Wiley &Sons, Ltd.     

Wind farm generation forecast and optimal maintenance schedule model

Wind farms must periodically take their turbines offline in order to perform scheduled maintenance repairs. Given that these interruptions impact energy generation and that under Power Purchase Agreements productions shortfalls must be replaced by energy purchases in the spot market, the optimal time to begin maintenance work in a wind farm is a function of both the expected wind speeds and electricity spot prices. In this article, we develop a model to determine the optimal maintenance schedule of a wind farm based on forecasted wind speeds and energy prices. We analyze a window of opportunity in the most likely period of the year and perform weekly updates of expected wind speeds and energy price forecasts. Wind speeds are forecasted with an ARMAX model, where monthly dummies are used as exogenous variables to capture the seasonality of wind speeds, while spot prices are simulated under a standard dual stochastic programing model. The decision to defer maintenance to a future date is modeled in a probabilistic model and also under the real options approach. We test these models with actual data from a wind farm in the Brazilian Northeast and provide comparisons with current practice in order to determine the benefits of the model. The results suggest that this model may provide advantages over a stopping decision that randomly chooses a week to begin maintenance within the opportunity window and is close to the optimal stopping date considering perfect information on future wind speeds and electricity prices.     

LiDAR‐based characterization of mid‐altitude wind conditions for airborne wind energy systems

Engineers and researchers working on the development of airborne wind energy systems (AWES) still rely on oversimplified wind speed approximations and coarsely sampled reanalysis data because of a lack of high‐resolution wind data at altitudes above 200 m. Ten‐minute average wind speed LiDAR measurements up to an altitude of 1100 m and data from nearby weather stations were investigated with regard to wind energy generation and impact on LiDAR measurements. Data were gathered by a long‐range pulsed Doppler LiDAR device installed on flat terrain. Because of the low overall carrier‐to‐noise ratio, a custom‐filtering technique was applied. Our analyses show that diurnal variation and atmospheric stability significantly affect wind conditions aloft which cause a wide range of wind speeds and a multimodal probability distribution that cannot be represented by a simple Weibull distribution fit. A better representation of the actual wind conditions can be achieved by fitting Weibull distributions separately to stable and unstable conditions. Splitting and clustering the data by simulated surface heat flux reveals substate stratification responsible for the multimodality. We classify different wind conditions based on these substates, which result in different wind energy potential. We assess optimal traction power and optimal operating altitudes statistically as well as for specific days based on a simplified AWES model. Using measured wind speed standard deviation, we estimate average turbulence intensity and show its variation with altitude and time. Selected short‐term data sets illustrate temporal changes in wind conditions and atmospheric stratification with a high temporal and vertical resolution.     

Probabilistic maximum‐value wind prediction for offshore environments

High wind speeds can pose a great risk to structures and operations conducted in offshore environments. When forecasting wind speeds, most models focus on the average wind speeds over a given period, but this value alone represents only a small part of the true wind conditions. We present statistical models to predict the full distribution of the maximum‐value wind speeds in a 3 h interval. We take a detailed look at the performance of linear models, generalized additive models and multivariate adaptive regression splines models using meteorological covariates such as gust speed, wind speed, convective available potential energy, Charnock, mean sea‐level pressure and temperature, as given by the European Center for Medium‐Range Weather Forecasts forecasts. The models are trained to predict the mean value of maximum wind speed, and the residuals from training the models are used to develop the full probabilistic distribution of maximum wind speed. Knowledge of the maximum wind speed for an offshore location within a given period can inform decision‐making regarding turbine operations, planned maintenance operations and power grid scheduling in order to improve safety and reliability, and probabilistic forecasts result in greater value to the end‐user. The models outperform traditional baseline forecast methods and achieve low predictive errors on the order of 1–2 m s^?1. We show the results of their predictive accuracy for different lead times and different training methodologies. Copyright © 2014 John Wiley & Sons, Ltd.     

Generating short‐term probabilistic wind power scenarios via nonparametric forecast error density estimators

Forecasts of available wind power are critical in key electric power systems operations planning problems, including economic dispatch and unit commitment. Such forecasts are necessarily uncertain, limiting the reliability and cost‐effectiveness of operations planning models based on a single deterministic or “point” forecast. A common approach to address this limitation involves the use of a number of probabilistic scenarios, each specifying a possible trajectory of wind power production, with associated probability. We present and analyze a novel method for generating probabilistic wind power scenarios, leveraging available historical information in the form of forecasted and corresponding observed wind power time series. We estimate nonparametric forecast error densities, specifically using epi‐spline basis functions, allowing us to capture the skewed and nonparametric nature of error densities observed in real‐world data. We then describe a method to generate probabilistic scenarios from these basis functions that allows users to control for the degree to which extreme errors are captured. We compare the performance of our approach to the current state‐of‐the‐art considering publicly available data associated with the Bonneville Power Administration, analyzing aggregate production of a number of wind farms over a large geographic region. Finally, we discuss the advantages of our approach in the context of specific power systems operations planning problems: stochastic unit commitment and economic dispatch. Our methodology is embodied in the joint Sandia–University of California Davis Prescient software package for assessing and analyzing stochastic operations strategies.     

开发风力发电设备的战略及途径分析

开发利用洁净能源和可再生能源,改善以煤炭为主的能源结构已成为我国能源建设实现可持续发展战略的重要措施。目前,国内外以风能力代表的新能源的开发利用方兴未艾。通过对国内外风电发展现状的调查研究及国内风电市场发展前景的分析与展望,提出发展风电制造业的一些具体措施,加速能源结构的调整。     

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.     

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.     

Characterizing future large,rapid changes in aggregated wind power using Numerical Weather Prediction spatial fields

A critical limiting factor to the successful deployment of a large proportion of wind power in power systems is its predictability. Power system operators play a vital role in maintaining system security, and this task is greatly aided by useful characterizations of future system operations. A wind farm power forecast generally relies on the forecast output from a Numerical Weather Prediction (NWP) model, typically at a single grid point in the model to represent the wind farm's physical location. A key limitation of this approach is the spatial misplacement of weather features often found in NWP forecasts. This paper presents a methodology to display wind forecast information from multiple grid points at hub height around the wind farm location. If the raw forecast wind speeds at hub height at multiple grid points were to be displayed directly, they would be misleading as the NWP outputs take account of the estimated local surface roughness and terrain at each grid point. Hence, the methodology includes a transformation of the wind speed at each grid point to an equivalent value that represents the surface roughness and terrain at the chosen single grid point for the wind farm site. The chosen‐grid‐point‐equivalent wind speeds for the wind farm can then be transformed to available wind farm power. The result is a visually‐based decision support tool which can help the forecast user to assess the possibilities of large, rapid changes in available wind power from wind farms. A number of methods for displaying the field for multiple wind farms are discussed. The chosen‐grid‐point‐equivalent transformation also has other potential applications in wind power forecasting such as assessing deterministic forecast uncertainty and improving downscaling results. Copyright © 2008 John Wiley & Sons, Ltd.     

Evaluating winds and vertical wind shear from Weather Research and Forecasting model forecasts using seven planetary boundary layer schemes

The existence of vertical wind shear in the atmosphere close to the ground requires that wind resource assessment and prediction with numerical weather prediction (NWP) models use wind forecasts at levels within the full rotor span of modern large wind turbines. The performance of NWP models regarding wind energy at these levels partly depends on the formulation and implementation of planetary boundary layer (PBL) parameterizations in these models. This study evaluates wind speeds and vertical wind shears simulated by the Weather Research and Forecasting model using seven sets of simulations with different PBL parameterizations at one coastal site over western Denmark. The evaluation focuses on determining which PBL parameterization performs best for wind energy forecasting, and presenting a validation methodology that takes into account wind speed at different heights. Winds speeds at heights ranging from 10 to 160 m, wind shears, temperatures and surface turbulent fluxes from seven sets of hindcasts are evaluated against observations at Høvsøre, Denmark. The ability of these hindcast sets to simulate mean wind speeds, wind shear, and their time variability strongly depends on atmospheric static stability. Wind speed hindcasts using the Yonsei University PBL scheme compared best with observations during unstable atmospheric conditions, whereas the Asymmetric Convective Model version 2 PBL scheme did so during near‐stable and neutral conditions, and the Mellor–Yamada–Janjic PBL scheme prevailed during stable and very stable conditions. The evaluation of the simulated wind speed errors and how these vary with height clearly indicates that for wind power forecasting and wind resource assessment, validation against 10 m wind speeds alone is not sufficient. Copyright © 2012 John Wiley & Sons, Ltd.     

An assessment of the impact of reduced averaging time on small wind turbine power curves,energy capture predictions and turbulence intensity measurements

The effect of varying the averaging time of measured data used to calculate wind turbine power curves is examined. The effects of reducing the averaging time from 10 to 1 min, as recommended for small wind turbines, are investigated using power performance data recorded using a 15 kW wind turbine. Test site data have been processed according to the relevant international standard, IEC 61400‐12‐1, to provide power curves and annual energy yield predictions. A number of issues are explored: the systematic distortion of the power curve that occurs as averaging time is decreased, the errors introduced by the use of 1 min averaged power curves to calculate energy yield and the reduction of turbulence intensity as averaging time is reduced. Recommendations for improved small wind turbine testing and energy yield calculation are given. Copyright © 2012 John Wiley & Sons, Ltd.     

A market‐oriented wind power dispatch strategy using adaptive price thresholds and battery energy storage

In this paper, an adaptive dispatch strategy is presented to maximize the revenue for grid‐tied wind power plant coupled with a battery energy storage system (BESS). The proposed idea is mainly based on time‐varying market‐price thresholds, which are varied according to the proposed algorithm in an adaptive manner. The variable nature of wind power and market price signals leads to the idea of storing energy at low price periods and consequently selling it at high prices. In fact, the wind farm operators can take advantage of the price variability to earn additional income and to maximize the operational profit based on the choice of best price thresholds at each instant of time. This research study proposes an efficient strategy for intermittent power dispatch along with the optimal operation of a BESS in the presence of physical limits and constraints. The strategy is tested and validated with different BESSs, and the percentage improvement of income is calculated. The simulation results, based on actual wind farm and market‐price data, depict the proficiency of the proposed methodology over standard linear programming methods.     

Day-ahead wind speed forecasting using f-ARIMA models

With the integration of wind energy into electricity grids, it is becoming increasingly important to obtain accurate wind speed/power forecasts. Accurate wind speed forecasts are necessary to schedule dispatchable generation and tariffs in the day-ahead electricity market. This paper examines the use of fractional-ARIMA or f-ARIMA models to model, and forecast wind speeds on the day-ahead (24 h) and two-day-ahead (48 h) horizons. The models are applied to wind speed records obtained from four potential wind generation sites in North Dakota. The forecasted wind speeds are used in conjunction with the power curve of an operational (NEG MICON, 750 kW) turbine to obtain corresponding forecasts of wind power production. The forecast errors in wind speed/power are analyzed and compared with the persistence model. Results indicate that significant improvements in forecasting accuracy are obtained with the proposed models compared to the persistence method.     

Trading wind power through physically settled options and short‐term electricity markets

Wind power producers participating in today's electricity markets face significant variability in revenue streams, with potential high losses mostly due to wind's limited predictability and the intermittent nature of the generated electricity. In order to further expand wind power generation despite such challenges, it is important to maximize its market value and move decisively towards economically sustainable and financially viable asset management. In this paper, we introduce a decision‐making framework based on stochastic optimization that allows wind power producers to hedge their position in the market by trading physically settled options in futures markets in conjunction with their participation in the short‐term electricity markets. The proposed framework relies on a series of two‐stage stochastic optimization models that identify a combined trading strategy for wind power producers actively participating in both financial and day‐ahead electricity markets. The proposed models take into consideration penalties from potential deviations between day‐ahead market offers and real‐time operation and incorporates different preferences of risk aversion, enabling a trade‐off between the expected profit and its variability. Empirical analysis based on data from the Nordic region illustrates high efficiency of the stochastic model and reveals increased revenues for both risk neutral and risk averse wind producers opting for combined strategies.