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In this paper, a risk-based security assessment methodology is presented, which allows the assessment of operational security of a power system’s future state under uncertainty deriving from varying topology scenarios (contingencies) and forecast errors (loads and renewable infeeds). The methodology models input uncertaintywith a copula function-based Monte–Carlo (MC) framework. Furthermore, it provides the highest level of accuracy on initiating causes of failures through an AC power flow (AC PF) framework. Finally, it achieves speed in solution by the combination of twomeasures of risk. A fast screening tool, based on severity functions, allows us to quickly screen the system for the most severe states. A detailed analysis tool, based on an AC optimal power flow (AC OPF) framework and the notion of lost load, provides additional valuable information, including remedial actions to steer away from severe system states. This paper presents results from the application of the methodology proving the necessity of such a framework. It is shown that not taking into account stochastic dependence through a proper MC setup seriously underestimates system risk and that an AC framework is needed, as voltage deviations are shown to often be initiators of system collapse., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Numerical Analysis, Intelligent Electrical Power Grids

Current and future developments in the power system industry demand fast power flow solvers for larger power flow problems. The established methods are no longer viable for such problems, as they are not scalable in the problem size. In this paper, the use of Newton-Krylov power flow methods is proposed, and a multitude of preconditioning techniques for such methods are discussed and compared. It is shown that incomplete factorizations can perform very well as preconditioner, resulting in a solver that scales in the problem size. It is further shown that using a preconditioned inner-outer Krylov method has no significant advantage over applying the preconditioner directly to the outer iterations. Finally, Algebraic Multigrid is demonstrated as a preconditioner for Newton-Krylov power flow and argued to be the method of choice in some scenarios

The increasing penetration of renewable generation in power systems necessitates the modeling of this stochastic system infeed in operation and planning studies. The system analysis leads to multivariate uncertainty analysis problems, involving non-Normal correlated random variables. In this context, the modeling of stochastic dependence is paramount for obtaining accurate results; it corresponds to the concurrent behavior of the random variables, having a major impact to the aggregate uncertainty (in problems where the random variables correspond to spatially spread stochastic infeeds) or their evolution in time (in problems where the random variables correspond to infeeds over specific time-periods). In order to investigate, measure and model stochastic dependence, one should transform all different random variables to a common domain, the rank/uniform domain, by applying the cumulative distribution function transformation. In this domain, special functions, copulae, can be used for modeling dependence. In this contribution the basic theory concerning the use of these functions for dependence modeling is presented and focus is given on a basic function, the Normal copula. The case study shows the application of the technique for the study of the large-scale integration of wind power in the Netherlands., Electrical Sustainable Energy, Electrical Engineering, Mathematics and Computer Science

This paper contributes a Markov chain Monte Carlo (MCMC) method for the direct generation of synthetic time series of wind power output. It is shown that obtaining a stochastic model directly in the wind power domain leads to reduced number of states and to lower order of the Markov chain at equal power data resolution. The estimation quality of the stochastic model is positively influenced since in the power domain, a lower number of independent parameters is estimated from a given amount of recorded data. The simulation results prove that this method offers excellent fit for both the probability density function and the autocorrelation function of the generated wind power time series. The method is a first step toward simple stochastic black-box models for wind generation., Electrical Sustainable Energy, Electrical Engineering, Mathematics and Computer Science

The incorporation of wind power generation to the power system leads to an increase in the variability of the system power flows. The assessment of this variability is necessary for the planning of the necessary system reinforcements. For the assessment of this variability, the uncertainty in the system inputs should be modeled, comprising of the time-dependent stochasticity of the system loads and the correlated wind resources. In this contribution, a unified Monte-Carlo simulation methodology is presented that addresses both issues. The application of the method for the analysis of the wind power integration in the New England test system is presented., Electrical Engineering, Mathematics and Computer Science

Electrical Engineering, Mathematics and Computer Science

Stochastic Generation is the electrical power production by the use of an uncontrollable prime energy mover, corresponding mainly to renewable energy sources. For the large-scale integration of stochastic generation in power systems, methods are necessary for the modeling of power generation uncertainty. In this thesis, new tools for the uncertainty analysis of such power systems are developed., Electrical Engineering, Mathematics and Computer Science