Multivariate time series models for studies on stochastic generators in power systems

Abstract

This paper presents an applied statistics method for the synthesis of multivariate time series of stochastic generation for planning purposes in power systems. It is shown that a suited model should represent satisfactorily the individual univariate stochastic processes and also reproduce the stochastic dependence structure between them. In order to fulfill all requirements, the method proposes the transformation of a set of recorded time series to the multivariate normal domain, the identification of a vector autoregressive model, the synthetization of a multivariate time series with desired length, and subsequently the back-transformation into the initial domain. The method can capture density functions, chronological persistence, diurnal periodicities and dependence structures of and between an arbitrary number of distributed system infeeds. Application can be found in the expansion of short data records into statistically significant synthetic time series for power system studies with distributed and uncertain resources. A simple practical example is given for illustration.