Use of MCMC to incorporate a wind power model for the evaluation of generating capacity adequacy

Abstract

Modeling wind generation for use in reliability assessment requires a large database of historical wind speeds so that the stochastic nature of the wind at a particular site can be accurately captured. The alternative is to use reliable stochastic simulation techniques that can replicate the desired synthetic wind power time series. This paper proposes an assessment framework that uses a Markov chain Monte Carlo (MCMC) method to enable the inclusion of wind farm modeling in conventional techniques for evaluating generation adequacy. The synthetic wind power time series based on the MCMC model has been verified against measured results based on consideration of statistical factors. The model presented in this paper has also been applied on the well-known Roy Billiton Test System (RBTS). As a further demonstration of the effectiveness of the proposed methodology, the reliability indices obtained using the MCMC model have been compared with those produced by the ARMA model, which is often used in reliability studies. The results indicate the effectiveness of the proposed technique for incorporating wind power into generation adequacy evaluation.