Advanced Markovian wind energy models for smart grid applications
Advanced Markovian wind energy models for smart grid applications
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DOI：
10.1109/isgteurope.2013.6695377
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发表时间：
2013-10
期刊：
影响因子：
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通讯作者：
B. Hayes;S. Djokic
中科院分区：
文献类型：
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作者：
B. Hayes;S. Djokic
马尔科夫链被广泛用于开发电力系统分析应用中的风能资源模型。然而，目前文献中可用的马尔科夫风模型不能捕捉风速/功率输出在短于1小时的时间段内的时间变化。这意味着它们不适合智能电网应用，智能电网应用通常需要短时间步长的仿真，例如分钟或秒的数量级。介绍了一种利用“嵌套马尔可夫链”对风能资源进行建模的新方法。研究表明，该方法能够准确捕捉风能资源的高频变化。该方法使用记录的陆上和海上风能数据集进行了演示。所得到的模型可以很容易地应用于智能电网分析，允许用户用简单而高效的分析模型来替换大型历史风场数据集。
Markov Chains are widely used for developing wind energy resource models for power system analysis applications. However, the Markovian wind models currently available in the literature cannot capture the temporal variations in wind speed/power output over time periods shorter than 1 hour. This means that they are unsuitable for smart grid applications, which typically require simulations with short time steps, e.g. to the order of minutes or seconds. This paper introduces a novel approach to modelling wind energy resources using “Nested Markov Chains”. It is shown in the paper that this method can accurately capture higher-frequency variations in the wind energy resource. The methodology is demonstrated using recorded onshore and offshore wind data sets. The resulting model can be readily applied for smart grid analysis, allowing the user to replace large historical wind data sets with a simple and efficient analytical model.