Abstract
Large-scale deployment of renewable energy sources in power systems is basically motivated by two universally recognized challenges: the need to reduce as far as possible the environmental impact of the massive increase of energy request and the dependency on fossil-fuel. Renewable energy sources are interfaced to the network by means of interfacing power converters which inherently exhibit zero inertia differently from the conventional synchronous generators. This matter jointly to the high level of time variability of the renewable resources involve dramatically frequency changes, recurrent frequency oscillations and high variability of frequency profile in general. The need of a fast estimation of time variability of the power system inertia arises at the aim of predicting critical conditions. Based on the analysis of some actual data of the Italian Transmission Network, in this paper the authors propose an autoregressive model which is able to describe the dynamic evolution of the power system inertia. More specifically, the inertia is modeled as the sum of a periodic component and a noise stochastic process distributed according a non-Gaussian model. The numerical results reported in the last part of the paper, demonstrating the efficiency and precision of estimation of inertia, allow justifying the assumptions of the above modeling.
Highlights
The containment of frequency deviations within assigned ranges is of vital importance for electrical interconnected systems
On the basis of a deep insight based upon adequate statistical analyses of available real data, the authors realized that a simple dynamic model could be adopted for a feasible and adaptive description of the inertia stochastic process, as a function of renewable source contribution to the total power generation, i.e. on the share of synchronous and renewable – based generators
It has been shown in the paper that the inertia dynamics may be regarded as the sum of a periodic component and a noise stochastic process distributed according a non-Gaussian model, and an autoregressive model with innovations with Logistic underlying distribution has been adopted
Summary
The containment of frequency deviations within assigned ranges is of vital importance for electrical interconnected systems. The contributions in this work are as follows: - the proposal of a new statistical method for the online estimation of the power system inertia based on the recursive observation data of the selected area network; - based on the classical time series theory, the proposed method uses a probabilistic model to characterize the system inertia with high variations caused by the presence of large penetration of RESs; - the spectral analysis is performed with respect to the actual data, allowing the identification of the case of time-varying harmonics which permits to describe the inertia through a simple stochastic model; - the statistical analysis on the actual data allows representing the inertia dynamics as the sum of periodic components and a noise stochastic process distributed according to a non-gaussian model.
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