Advanced Markovian wind energy models for smart grid applications

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.