Modeling uncertain load and wind power in the electric energy systems

This paper is motivated by the recognition that sources of uncertainties in the electric power systems are multifold and that they may have potentially far-reaching effects. In the past, only system load forecast was considered to be the main challenge. More recently, uncertain price of electricity and hard-to-predict power produced by the renewable resources, such as wind and solar, are making the operating and planning environment much more challenging. It is, therefore, becoming very important to develop modeling methods for predicting uncertain load and wind power, in particular. In this paper we first transform historic time-stamped data into their Fourier Transform (FT) representation. The frequency domain data representation is used to decompose the wind and load power signals and to derive predictive models relevant for short-term and long-term predictions. The short-term results are interpreted next as a Linear Prediction Coding Model (LPC) and its accuracy is analyzed. Next, the Discrete Markov Process (DMP) representation is applied to help assess probabilities of most likely short-, medium- and long-term states and the related multi-temporal risks. Throughout the paper we use publicly available data for the New York Control Area (NYCA).