On-Line Bayes Estimation of Rotational Inertia for Power Systems with High Penetration of Renewables. Part I: Theoretical Methodology

Abstract

In the last years, large-scale deployment of renewable energy sources has led to the use of significant shares of generation from intermittent sources. Renewable energy sources units are notably inverter-connected wind turbines and photovoltaic that as such do not provide rotational inertia: this has implications for frequency dynamics and power system stability and operation. Frequency dynamics are faster in case of systems with low rotational inertia, thus making frequency control and power system operation more challenging. While the impact of low rotational inertia on power system stability and operation have been widely investigated in the recent literature, in this paper a new Bayesian statistical inference approach is proposed for the on-line estimation of the rotational inertia of a given system. The need for estimation is based upon the observation that the amount of renewable energy affecting the system is a random process. The authors' researches of this paper is devoted to the Bayesian on-line recursive estimation of the amount of the so-called “Renewable Energy Source Share”. The numerical application reported in the companion paper [1] confirms that the proposed estimation technique constitutes a very fast, efficient and, especially, a robust method for ‘tracking’ the above share in view of an efficient assessment of rotational inertia.