A Stochastic Approach to Generate Short-Term Feed-in Profiles of Wind Power Plants

Sirkka Porada, Leonard Schulte, A. Moser
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Abstract

The integration of wind turbines into the European power system poses new challenges for grid operations. One reason for this is the volatile feed-in behavior of wind turbines. Due to various meteorological influencing factors, feed-in profiles of wind turbines show not solely fluctuations in a hourly range, but also significant gradients in the timeframe of seconds to a few minutes. These short-term fluctuations of the power feed-in can cause local problems in the power system. Most studies address the generation of synthetic feed-in profiles with of temporal resolution of 15 till 60 minutes. To assess the impact of fluctuations in shorter timeframe, this paper focus on this paper focus on the generation of feed-in profiles with a resolution of 10 seconds. For this purpose, a stochastic method is developed generating feed-in profiles for wind turbines based on a Markov Chain Monte Carlo simulation. The generated feed-in profiles suitably represent the influence of meteorological phenomena in the seconds as well as in the hourly range.
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风电短期馈电曲线的随机生成方法
风力涡轮机与欧洲电力系统的整合对电网运营提出了新的挑战。其中一个原因是风力涡轮机不稳定的馈入行为。由于各种气象因素的影响,风力发电机组的上网廓线不仅在小时范围内出现波动,而且在秒到几分钟的时间范围内也有明显的梯度。这些短期的电力输入波动会引起电力系统的局部问题。大多数研究涉及合成馈入剖面的生成,其时间分辨率为15至60分钟。为了在较短的时间框架内评估波动的影响,本文重点研究了以10秒分辨率生成馈电剖面。为此,提出了一种基于马尔可夫链蒙特卡罗仿真的风电机组馈电剖面随机生成方法。所生成的馈电剖面适当地反映了气象现象在秒和小时范围内的影响。
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