Design of Wind Energy Distributed Power Systems: Investigation of Stochastic Bounds using Monte Carlo Simulation

G. Papaefthymiou, P.H. Schavemaker, L. van der Sluis (The Netherlands), and A. Tsanakas (UK)


Distributed generation (DG), steady-state analysis, uncertainty analysis, Monte Carlo simulations, wind turbine generator, risk management.


In this paper, a Monte Carlo based design methodology is presented, for the analysis of wind energy distributed power systems. These are systems with a large-scale penetration of wind turbines connected at the lower voltage levels. In the design algorithm, the uncertainty related to the wind power production and energy consumption is modeled, based on the steady-state uncertainty analysis of the system. This analysis is performed in five distinguished stages: a) definition of the deterministic system model, b) time-conditioning, c) definition of marginal probability distributions, d) modeling of the dependence between the inputs and e) probabilistic processing using Monte Carlo simulations. In this paper, the problem of defining the dependence structures in the system is tackled by modeling the statistically extreme interdependencies in the system inputs using the stochastic bounds methodology.

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