Assessment of the power reduction of wind farms under extreme wind condition by a high resolution simulation model
Highlights
► We develop a model to assess the wind power reduction under extreme wind condition. ► This model takes some physical factors into account under extreme wind condition. ► This model is verified by both qualitative and quantitative ways. ► This model can predict operating reserve requirement due to extreme wind condition. ► This model is suitable for both system planning and operation.
Introduction
With the aim of developing a sustainable and environmentally friendly society, more wind farms are being integrated into power systems for cleaner energy supply and lower CO2 emission. Wind power has been considered as the future technology of choice since it is harvested from nature, clean and free. However, it is also widely accepted that wind power is not a panacea that comes without challenges. One primary concern of the electric utility industry vis-a-vis wind power relates to the stochastic nature of wind. To deal with it, planning has to set aside appropriate reserves during the periods when extreme wind events occur, for examples when wind storm hits or wind does not blow. Planning departments make use of methodologies based on forecasts from meteorological departments and probabilistic analysis.
The impact due to stochastic wind power has been widely addressed by worldwide researchers. Before 2002, it has been concluded from empirical observations that the variation of wind power is reduced if wind turbines are dispersed over a wider geographical area [1]. In contrast, a concentrated wind farm layout results in a higher variation of output power production, especially in the minute-minute time scale [2]. But this study [2] is preliminary since the stochastic characteristic of wind has not been fully considered.
Some up-to-date studies take the stochastic wind power into account. Study [3] uses different probability density functions to estimate the wind energy potential. In [4], [5], ARMA model is used to study the impact of wind farm integration on power systems. By assuming that the wind power generation fits a normal distribution, Refs. [6], [7], [8] study the operating reserve requirement and unit commitment strategy of power systems with significantly high wind penetration. The frequency deviation caused by stochastic power fluctuation is studied in [9], [10] to estimate the highest allowable wind power penetration. Study [20] uses the statistical result of western states of US to quantify the operational reserve requirement by quasi-state analysis technology. From the prospective of reliability, the stochastic wind power is taken into account to arrange the grid operation in eastern states of US [21]. Artificial neural networks is used in study [25] to map the wind speed profile for energy application in Nigeria. The grid operation cost in Central Turkey is estimated by using time-series approach and the economic evaluation of various wind energy conversion systems [26].
The existing studies mostly discuss the impact of wind power under normal wind conditions where the wind variation will result in power variation. In this research area, these studies have contributed a lot to develop some advanced statistical technologies to predict the power output varying stochastically. However, under extreme weather condition, the wind power prediction is still very difficult and lacks accuracy until now. For example, Fig. 7 in Ref. [22] shows that the storm in western Denmark in November 11 2010 is not predicted accurately and the error of power reduction between the measurement and prediction is over 80%. Obviously, the prediction accuracy level is not reliable enough for grid operator and market stakeholders to adjust conventional generation for system balancing in time. Also, the extreme weather does not rarely happen in some specified region. For example, in Zhangjiakou (ZJK) Region, there are over 5 gales recorded from 2005 to 2010, and one recent record is in April 26, 2010 when the recorded maximum wind speed is higher than 30 m/s [23]. The grid operator requires suitable tools to be against the power reduction caused by such extreme wind condition.
Comparing with the urgent demand from industry, however, very few research results are so far reported publicly on the subject of assessing power reduction under extreme wind conditions. This is not only because this topic is new but simulation under extreme weather requires more advanced technologies. When wind speed is higher, power curve of wind turbine is gradually entering into the flat part, so there is much less power variation caused by the wind speed variation. Wind turbine is becoming acting like a conventional generator with a constant rated output in this region. Therefore, the variation of wind farm power output in high wind speed region can be only caused by the wind turbine tripping due to the wind speed entering over the cut-out speed region, if without considering the failure outage. In this extreme wind condition, taking wind storm as an example, it usually causes broader and more severe power reduction. This is because the power reduction caused by wind turbine tripping is obviously higher and faster than the continuous power variation under normal wind condition, which hence requires to be simulated in a higher time resolution. Also, wind turbines are dispersed geographically within a wind farm or a region. Ref. [22] has shown that the magnitude of power reduction from the wind farm and the region are affected significantly by “geography dispersion” under extreme weather from a Danish offshore wind farm study. Therefore under an extreme weather condition, the simulation of wind power reduction requires a model with both higher resolution and consideration of “geography dispersion” at the same time, which cannot be satisfied properly by most of current existing models.
A more detailed model is hence used in this paper to assess the power reduction when a wind farm or a regional wind farm cluster suffers an extreme weather condition as a compliment of existing studies. Though the model has been developed by a joint research between Risø Laboratory, Denmark [11], [12], [13], [14] and Tsinghua University, China [15], [16], [17], the originality of this paper is on the study of the applicability of this model under an extreme wind condition. This model provides a second-by-second simulation resolution and uses a coherence matrix in frequency domain to describe the coherences of power reduction among wind turbines, which are suitable to assess power reduction under extreme weather. Also, for a regional wind farm cluster, an additional advantage of this model is to provide a reasonable estimation of wind power reduction under extreme wind condition without using extensive history data of all the wind farms of the whole region. Therefore department of grid planning and operation can obtain the estimated power reduction under extreme weather by this model before all the wind farms of whole region are constructed and operated for a relatively long term. To demonstrate the above, this paper addresses an industry case study of Zhangjiakou (ZJK) region, China. IN this case study, the model is extended for application in a regional wind farm cluster by combining with an numerical weather system (NWS). The simulation result gives a reasonable estimation on the reserve requirement of ZJK region under extreme wind condition. Meanwhile, the case study demonstrates that the proposed methodology is applicable for both power system planning and operating phases. In system planning phase, the assessment methodology estimates the future reserve requirement of the whole ZJK region before all the wind farms are constructed. For system operation, the grid operator uses the estimation result as an alternative while there is lack of history data from wind farm running under extreme weather to help operating power systems with high wind penetration.
This paper is organized as follows: Section 2 introduces the stochastic wind power fluctuation model based on frequency domain, and then Section 3 gives a wind farm case study to verify this model under extreme wind condition using recorded wind power profile. In Section 4, this model is extended for a power system region in China to explore the applicability in actual projects. Conclusions are finally presented in Section 5.
Section snippets
The wind power fluctuation model in frequency domain
This section presents a model in frequency domain to perform the modeling of stochastic wind speed, dynamics of wind turbine and dispersion effects of wind farm. Related research has been published in [14], [15], but in this section all pieces of sub-models are well organized together and corresponding illustrations are given to help readers without the background knowledge of aerodynamics to understand the entire frequency domain model. Some essential comments are also added to explain why
Validation of model under extreme wind condition
One wind farm that is located within the Zhangbei (ZB) county of ZJK region, China, is selected as a case study example to verify the applicability of the proposed model under extreme wind condition. ZB (Zhangbei) County is one of the windiest regions in northern China that has been planned as one of the GW-level wind bases of China [19]. During the winter, the wind speed sometimes is over 22 m/s, which is lower than the cut-out speed of 25 m/s. However, there are several records showing that the
The case study of ZJK region
This section shows a good case study oriented from an actual project to use the tools of “Lowest power curve” and “Reserve requirement curve” to extend the model in an extended power system region −ZJK region, China. ZJK region is rich with wind resources and the government plans to integrate more wind power into ZJK regional grid to provide green energy for industrial loads.
Two main wind farm clusters have been planned and are currently under construction located within ZB (Zhangbei) county
Conclusion
This paper proposes a model to assess the power reduction of wind farms under extreme wind condition. This model takes some critical physical factors into account, such as the stochastic nature of wind and the geography dispersion, so it is capable of simulating power reduction due to some extreme wind events. Also, by building up the sub-models at high frequencies, this model facilitates a high resolution simulation although the model input is from a low sampling rate measurement or NWS.
Acknowledgement
This work is supported by National High-Technology Research and Development Program (“863” Program) of China (SQ2010AA0521075001) and Chinese National Natural Science Funds (50823001, 50977050).
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