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This work presents a technique to estimate on-line the global momentum of an electric power system. It exploits the footprint of the principal frequency system dynamics. Probing tones are injected by a grid-forming converter-interfaced generator and the speed of its virtual rotor is exploited to extrapolate the footprint. The proposed technique is entirely data driven and thus it does not need any model of the power system. We show that the proposed technique is adequate to accurately estimate the actual global momentum contributed by synchronous machines and provided by the controllers of converter-interfaced generators that emulate the behavior of synchronous machines. The technique is comprehensively tested on a modified version of the IEEE 39-BUS system and a dynamic version of the IEEE 118-BUS system, containing grid forming converter interfaced generators.

The growing deployment of electric mobility calls for power system analyses to investigate to what extent the simultaneous charging of electric vehicles leads to degraded network operation and to validate the efficiency of countermeasures. To reduce complexity and CPU time, a common approach while performing these analyses consists in replacing electric vehicles and their charging stations with constant PQ loads. However, this approach is inaccurate, as the power absorbed by these elements actually depends not only on voltage but also on the state of charge, charging method, cathode chemistry of the battery pack, and converter controls in the electric vehicle and charging station. By considering all these aspects, this article develops a novel static load model and a vector fitting-based dynamic load model for electric vehicles connected to fast charging stations. These computationally efficient representations can replace the standard constant PQ load model of electric vehicles to assess more accurately their impact in static and dynamic grid studies. Simulation results of the IEEE14 system modified by adding fleets of electric vehicles prove the accuracy of the proposed models and highlight the shortcomings of the standard electric vehicle representation as a constant PQ load in some cases.

This letter presents a technique to calculate the variance of algebraic variables of power system models represented as a set of stochastic differential-algebraic equations. The technique utilizes the solution of a Lyapunov equation and requires the calculation of the state matrix of the system. The IEEE 14-bus system serves to demonstrate the accuracy of the proposed technique over a wide range of variances of stochastic processes. The accuracy is evaluated by comparing the results with those obtained with Monte Carlo time domain simulations. Finally, a case study based on a 1479-bus dynamic model of the all-island Irish transmission system shows the computational efficiency of the proposed approach compared to the Monte Carlo method.

This work presents a technique to estimate on-line the inertia of a power system based on ambient measurements. The proposed technique utilizes the covariance matrix of these measurements and solves an optimization problem that fits such measurements to the synchronous machine classical model. We show that the proposed technique is adequate to accurately estimate the actual inertia of synchronous machines and also the virtual inertia provided by the controllers of converter-interfaced generators that emulate the behavior of synchronous machines. We also show that the proposed approach is able to estimate the equivalent damping of the classical synchronous machine model. This feature is exploited to estimate the droop of grid-following converters, which has a similar effect of the swing equation equivalent damping. The technique is comprehensively tested on a modified version of the IEEE 39-bus system as well as on a dynamic 1479-bus model of the all-island Irish transmission system.

The increasing penetration of renewable energy sources has been leading to the progressive phase-out of synchronous generators, which constitute the main source of frequency stability for electric power systems. In the light of these changes, over the past years, some power systems started to exhibit an odd frequency distribution characterised by a bimodal behavior. This results in increased wear and tear of turbine governors and, in general, in degraded frequency performances. This is a cause of concern for grid operators, which have become increasingly interested in understanding the factors shaping frequency distribution. This paper explores the root causes of unwanted frequency distributions. The influence of some main aggregate system parameters on frequency distribution is detailed. The paper also shows that the implementation of the so-called synthetic inertia can lead to a robust unimodal frequency distribution.