A study on smoothing effect on output fluctuation of distributed wind power generation
ABSTRACT Wind power generation in Japan has shown a marked increase in recent years. This increase has aroused growing concerns about the adverse effects of wind power generation on power system control, operation, and planning, because wind power output cannot be controlled and fluctuates much more than that of the conventional power plants. However, if wind power generators am distributed over a wide area, a "smoothing effect" an output fluctuation of distributed wind turbines could be expected because of the stochastic nature of wind. This paper examines the smoothing effect of output fluctuation of dispersed wind turbines. The remits reveal that the output of a single wind turbine fluctuates almost its rated power in ten minutes and little smoothing effect is expected in a wind farm scale (i.e., within sum kilometers) for a period of 10 minutes. On the other hand, a smoothing effect can be expected for a wider area in 100 minutes.
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ABSTRACT: In this study we examine 12 geographically distributed wind farms in Tohoku area in east Japan to improve our understanding of variable wind output in the point of output fluctuation. Wind power outputs are converted to frequency domain using FFT (Fast Fourier Transform). In frequency domain, we use Power Spectrum Analysis (PSA) to estimate correlation characteristics between wind farms in different frequency domains. Furthermore, we evaluate the degree of smoothing effect of solar power over a certain region and compare with wind power analysis to understand the difference between these two major forces of nature. Our examination of data shows clearly that the smoothing effect of distributed wind farms can contribute largely to its output fluctuation. We also discuss the expected impacts on power system stability, in the event of high level wind power penetration.2013 International Conference on Renewable Energy Research and Applications (ICRERA); 10/2013
Conference Paper: Wind power prediction model considering smoothing effects[Show abstract] [Hide abstract]
ABSTRACT: A novel model of wind farm power output prediction is presented to obtain more accurate results in this paper, in which the smoothing effect and wake effect are integrated into the four-component wind speed model. The speed of wind turbines on different spatial positions can be determined by simulating the influence of wake effect on the speed of wind and considering the delay of wind transmission in the proposed model, and then the smoothing effect of power output is expressed by the overlap of power to shape the power output curve. The simulation shows that the proposed model has high precision and is suitable to evaluate the shock of injected wind power to the power systems, and it is more applicable for power units reserved capacity adjustment and ramping rate correction, and is also good for appropriate reliability assessments of power system with wind farms connected.2013 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC); 12/2013
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ABSTRACT: Large penetration of wind farms in the electricity grid is expected to be an unknown challenge in the near future. The biggest factor is the intermittency of wind turbine output and the difficulty in forecasting it accurately. A number of studies on wind forecasting have shown that the error of statistical methods increases with forecast horizon. However, combined forecasting of geographically dispersed wind farm output, sees a reduction in error. This paper extends this further by analysis of the confidence interval and uncertainty of forecasts when combined forecasting is used. It is observed that both the forecast error as well as uncertainty is reduced when combined forecasting is used for two wind farms in Western Australia. For a short term forecasting horizon of 6 hours, maximum forecasting error is reduced by 6% while uncertainty is reduced by up to 20%. This is expected to improve if more wind farms are considered.Power Electronics and Machines in Wind Applications (PEMWA), 2012 IEEE; 01/2012