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.     

Estimating future balancing power requirements in wind–PV power system

This paper presents a general model—based on the Monte Carlo simulation—for the estimation of power system uncertainties and associated reserve and balancing power requirements. The proposed model comprises wind, PV and load uncertainty, together with wind and PV production simulation. In the first stage of the model, wind speed and solar irradiation are simulated, based on the plant disposition and relevant data. The second stage of the model consists of wind speed, PV power and load forecast error simulation, based on the associated statistical parameters. Finally, both wind and PV forecast error are combined with the load forecast error, resulting in the net uncertainty. This net uncertainty, aggregated on a yearly level, presents a dominant component in balancing power requirements. Proposed model presents an efficient solution in planning phase when the actual data on wind and PV production is unavailable.     

Determining wind turbine power curves based on operating conditions

The relation between wind speed and electrical power—the power curve—is essential in the design, management and power forecasting of a wind farm. The power curve is the main characteristic of a wind turbine, and a procedure is presented for its determination, after the wind turbine is installed and in operation. The procedure is based on both computational and statistical techniques, in situ measurements, nacelle anemometry and operational data. This can be an alternative or a complement to procedures fully based on field measurements as in the International Electrotechnical Commission standards, reducing the time and costs of such practices. The impact of a more accurate power curve was measured in terms of the prediction error of a wind power forecasting system over 1 year of operation, whereby the methodology for numerical site calibration was presented and the concepts of ideal power curve and nacelle power curve introduced. The validation was based on data from wind turbines installed at a wind farm in complex topography, in Portugal, providing a real test of the technique presented here. The contribution of the power curve to the wind power forecasting uncertainty was found to be from 10% to 15% of the root mean square error. Copyright © 2013 John Wiley & Sons, Ltd.     

Development modes analysis of renewable energy power generation in North Africa

North African countries generally have strategic demands for energy transformation and sustainable development. Renewable energy development is important to achieve this goal. Considering three typical types of renewable energies— wind, photovoltaic (PV), and concentrating solar power (CSP)—an optimal planning model is established to minimize construction costs and power curtailment losses. The levelized cost of electricity is used as an index for assessing economic feasibility. In this study, wind and PV, wind / PV / CSP, and transnational interconnection modes are designed for Morocco, Egypt, and Tunisia. The installed capacities of renewable energy power generation are planned through the time sequence production simulation method for each country. The results show that renewable energy combined with power generation, including the CSP mode, can improve reliability of the power supply and reduce the power curtailment rate. The transnational interconnection mode can help realize mutual benefits of renewable energy power, while the apportionment of electricity prices and trading mechanisms are very important and are related to economic feasibility; thus, this mode is important for the future development of renewable energy in North Africa.     

Strategic structure matrix: A framework for explaining the impact of superstructure organizations on the diffusion of wind energy infrastructure

Increasing the use of renewables in the global energy mix has become a top priority for policy makers. In this paper, we use a diffusion theory based approach to analyze the impact of government initiatives on the development of wind energy infrastructure focusing on the specific case of wind energy diffusion in India. We propose a new framework—the strategic structure matrix—as a way to characterize the strategic focus and analyze the effectiveness of different initiatives to increase wind power diffusion. We apply the matrix to explain the different pace and paths of wind energy growth observed in five Indian states: Tamil Nadu, Gujarat, Maharashtra, Andhra Pradesh, and Karnataka. Our findings suggest the importance of a comprehensive approach that includes multiple strategies across initiatives, local regulatory measures, and supply-side incentives.     

A compact,closed‐form solution for the optimum,ideal wind turbine

The classical momentum solution for the optimum induced‐flow distribution of a wind turbine in the presence of wake swirl can be found in many textbooks. This standard derivation consists of two momentum balances (one for axial momentum and one for angular momentum), which are combined into a formula for power coefficient in terms of induction factors. Numerical procedures then give the proper induction factors for the optimum inflow distribution at any radial station; and this, in turn, gives the best possible power coefficient for an ideal wind turbine. The present development offers a more straightforward derivation of the optimum turbine. The final formulas give the identical conditions for the ideal wind turbine as do the classical solutions—but with several important differences in the derivation and in the form of the results. First, only one momentum balance is required (the other being redundant). Second, the solution is provided in a compact, closed form for both the induction factors and the minimum power—rather than in terms of a numerical process. Third, the solution eliminates the singularities that are present in current published solutions. Fourth, this new approach also makes possible a closed‐form solution for the optimum chord distribution in the presence of wake rotation. Copyright © 2013 John Wiley & Sons, Ltd.     

Development strategies for wind power industry in Jiangsu Province,China: Based on the evaluation of resource capacity

Jiangsu Province is one of the planned strategic areas for wind power development in China, but its current development of wind power industry is not so outstanding. Since, Jiangsu would encounter little market resistance, this paper focuses on the evaluation of resource capacity for wind power development in Jiangsu Province, including the wind energy capacity, land resources capacity and power grid capacity, in order to find their supportive or restrictive effects on the development of wind power industry. The results show that the wind energy resource in Jiangsu Province are sufficient to meet the needs of the development of wind farms; the extensive mudflat in the coastal areas also provides ideal site conditions for wind farms; but the power grid capacity is insufficient for the wind power development in Jiangsu. Therefore, from the aspect of enhancing the capacity of power grid for carrying wind power and from the other aspect of combining the non-grid-connected utilization and the large-scale storage of wind power, this paper suggests some strategies to overcome the constraints of grid capacity and promoting the wind power development in Jiangsu Province.     

Offshore wind energy policy for India—Key factors to be considered

Indian Economy is growing at a healthy pace during the last few years. To sustain this growth, power sector needs to build additional generation capacity. However, continued dependence on fossil fuels to power the growth of electricity generation capacity, is hardly sustainable. Renewable Energy source forms a miniscule portion (25 GW,∼12%) of India's overall power generation today (202 GW). The share of wind energy (17 GW) is 67% of the total renewable energy basket. But the contribution from offshore wind farms is non-existent, as all the wind energy generated in India is only through onshore wind farms. India needs a policy framework to encourage the development of offshore wind farms. Several European countries have effective offshore wind energy policies that have helped them to accelerate the growth of their offshore wind energy sector. This paper does an exhaustive literature survey, to identify 21 building blocks of a successful offshore wind energy policy initiative adopted by select European countries, which have been classified under 5 broad categories—Government support, Fiscal and quota based incentives, Availability of local expertise, Capital for investments and Building an enabling ecosystem, which can be leveraged by India to articulate its own offshore wind energy policy.     

Hindcasting hourly wind power across Scotland based on met station data

This paper presents methods and results from a study where long‐term wind measurements at 10 m above ground level from meteorological stations across Scotland were used to hindcast both average and hour‐by‐hour local wind speeds. For this, Scotland was divided into 21 simulation areas each containing a meteorological station. The Wind Atlas Analysis and Application Program (WAsP) was then used—well outside its specified range for both distance and area slope—to predict the wind climate at 80 m above ground level on a square kilometre basis. With further processing, time series of wind speed were derived for selected locations. Based on wind turbine power curves it was then possible to derive time series of power which were applied in power system analysis and used to study the degree of matching between renewable generation and electricity demand. This paper focuses on the creation of the onshore wind speed and power time series for areas of interest in Scotland. Copyright © 2007 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.     

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.     

Optimizing wind farm siting to reduce power system impacts of wind variability

We introduce and solve two variants of a biobjective optimization model to reduce the negative impact of wind variability on the power system by strategically locating wind farms. The first model variant considers average changes in wind power over time; the second captures extreme fluctuations in wind power. A complementary set of wind sites is selected with the aim of minimizing both residual demand and the variability in residual demand. Because exact optimization is computationally intensive, we develop two heuristics—forward and backward greedy algorithms—to find approximate solutions. The results are compared with the exact optimization results for a well‐selected subset of the data as well as to the results from selecting sites based on average wind alone. The two models are solved using demand data and potential wind sites for the Southwest Power Pool. Though both objectives can be improved by adding more sites, for a fixed number of sites, minimizing residual demand and variability in residual demand are competing objectives. We find an approximate efficient frontier to compare trade‐offs between the two objectives. We also vary the parameter in the heuristic that controls how the two objectives are prioritized. For the case study, the backward greedy algorithm is more effective at reducing the wind power variability than the forward greedy algorithm. Furthermore, using the backward algorithm for the full dataset is more effective than solving the exact optimization on a subset of the data when the results are evaluated using the full dataset.     

Do centre-bodies improve the performance of horizontal-axis,sail-type wind-turbines?

The power harnessing advantages of using hub-fairings—either rotating or stationary, with or without stationary after-bodies—have been measured experimentally. For the flexible sail wind-turbines tested, stationary (rather than rotating) hub-fairings produced greater power augmentations. Employing a stationary after-body is also desirable for the turbine in order to increase the rate of harnessing of wind energy.     

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 STEP toward understanding wind power development policy barriers in advanced economies

A widely accepted premise regarding wind power development policy is that implementation of economic policy instruments, which are designed to close the cost gap between wind power and entrenched fossil fuel power generation technologies, will significantly catalyze enhanced levels of wind power development activity. This paper contests this premise by arguing that non-economic barriers to wind power development have the capacity to significantly inhibit wind power development in industrialized nations despite the implementation of economic policy instruments. Forces which deter wind power development in four economically advanced economies that exhibit phlegmatic progress in wind power development – Australia, Canada, Japan and Taiwan – are identified and amalgamated into a STEP framework describing social, technical, economic and political forces that inhibit wind power development. The conclusions of this analysis are twofold. First, failure to mitigate these STEP forces may undermine the efficacy of any given economic policy instrument that aims to close the cost gap between wind power and entrenched generation technologies. Second, attempts to mitigate these impediments might represent a way to achieve better policy results with less government financial commitment.     

基于熵权TOPSIS模型的风电光伏发展潜力评估

当前中国新能源（特别是风电与光伏）发展存在宏观引导与长期规划不足的现象。如何准确地评估我国各地区的风电光伏发展潜力,从而科学地指导新能源健康有序发展是亟待解决的问题。为此,提出一种基于熵权TOPSIS的风电光伏发展潜力评估模型。首先,在考虑中国风电光伏能源发展特点与主要矛盾的基础上,构建风电光伏发展潜力的评估指标体系,并提出基于熵权法TOPSIS的风电光伏发展潜力评估模型;然后,使用该模型对中国31个地区的风电光伏发展潜力进行评估与分析;最后,根据各地区的评估分析结果与风电光伏发展特点,提出针对性的发展建议。     

大规模海上风电集中送出建设模式研究

  目的  当前海上风电已成为全球风电发展的研究热点。我国海上风电尚处于起步阶段,而当前的运营模式存在不利于海上风电大规模集中送出等缺点,无法适应新形势下的长远发展。另一方面,欧洲各国对海上风电的补贴政策与我国当前的发展思路有所冲突,可借鉴性不强,且欧洲模式本身仍存在弊端。因此亟需探索适合我国近海深水区海上风电发展的新模式。  方法  首先分析了欧洲各国海上风电发展现状,并对海上风电输电模式进行总结;其次给出了四种海上风电输电技术应用场景;最后对海上风电商业模式进行了可行性分析。  结果  结果表明:在拆分海上发、输电侧环节后,广东省预计“十四五”期间能够实现平价上网;相比于分散式开发,深水区海上风电统筹集约式开发从经济性及环境集约利用层面更具有优势。  结论  形成的研究结论可以为后续大规模海上风电集中送出项目的方案设计和建设模式提供技术支持,具有很好的示范应用前景。     

风电发展影响因素及节能减排效益分析

从风电功率预测、低电压穿越能力以及我国的电价政策3方面分析了影响风电发展的因素,指出现阶段由于风电功率预测难、风电机组低电压穿越能力不足限制了风电的发展,而我国现阶段实行的风电标杆上网电价极大地促进了我国风电的发展。还研究了风电的经济效益和环境效益,结呆表明：从长远来发展来说,风电场的经济效益与环境效益都将优于传统火力发电,随着风力发电技术的成熟与发电规模的日益壮大,风电对火电置换的节能减排效益将日益显著。     

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。     

我国非并网风电开发与发展建议

我国风电产业虽然起步晚,但发展快,2012年累计装机容量已达7532×104kW,处于全球领先水平。然而跟随式发展思维导致风电开发应用模式单一,加之我国电源结构中适合调峰的灵活电源比重小,导致难以并网的风电数量逐年增加,限电"弃风"问题日益突出,影响了风电产业的可持续发展。必须打破跟随式发展思维的束缚,通过风电发展模式多元化,有效提高风电消纳能力,实现风电发展的社会效益、环境效益和经济效益"三个统一"。"非并网风电"理论突破传统观念,研究风电在不上网情况下如何高效、低成本开发利用,目前已在非并网风电直接应用于某些高耗能产业方面取得重大进展,进入科技创新的中后期。由于单台兆瓦级风机造价都在数百万元以上,建设大规模"非并网风电"应用产业化示范工程投入巨大,存在一定风险,需要多措并举:以政产学研合作为基础,打造大规模"非并网风电"应用产业化联盟;转变重生产、轻消费的传统思维模式,将财政补贴重心从生产端转向消费端,鼓励高效、低成本、大规模直接消纳风电;加大政策扶持力度,降低应用非并网风电的行业准入门槛。