On the use of Markov chain models for the analysis of wind power time-series

Wind energy is becoming a top contributor to the renewable energy mix, which raises potential reliability issues for the grid due to the fluctuating and intermittent nature of its source. This paper explores the use of Markov chain models for the analysis of wind power time-series. The proposed Markov chain model is based on a 2yr dataset collected from a wind turbine located in Portugal. The wind speed, direction and power variables are used to define the states and the transition matrix is determined using a maximum likelihood estimator based on multi-step transition data. The Markov chain model is analyzed by comparing the theoretically derived properties with their empirically determined analogues. Results show that the proposed model is capable of describing the observed statistics, such as wind speed and power probability density as well as the persistence statistics. It is demonstrated how the application of the Markov chain model can be used for the short-term prediction of wind power.

I. Introduction

The EC European Parliament objective to achieve 20% of the consumed energy from the renewable energy sector by 2020, introduced a major challenge to the planning and operation of power systems. Wind energy is becoming a top contributor to the renewable energy mix, which raises potential reliability issues for the grid due to the fluctuating and intermittent nature of its sources. Thus, the understanding of wind speed characteristics and its impact upon the power production is an important task, as shown by the extensive literature addressing wind speed forecast [1] and wind power prediction [2]. Generally speaking, the existent modeling approaches can be classified into: physical, statistical, or a combination of both.