Stochastic generation, i.e., electrical power production by an uncontrolled primary energy source, is expected to play an important role in future power systems. A new power system structure is created due to the large-scale implementation of this small-scale, distributed, non-di
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Stochastic generation, i.e., electrical power production by an uncontrolled primary energy source, is expected to play an important role in future power systems. A new power system structure is created due to the large-scale implementation of this small-scale, distributed, non-dispatchable generation; the `horizontally-operated¿ system. Modeling methodologies that can deal with the operational uncertainty introduced by these power units should be used for analyzing the impact of this generation to the system. In this contribution, the principles for this modeling are presented, based on the decoupling of the single stochastic generator behavior (marginal distribution-stochastic unit capacity) from the concurrent behavior of the stochastic generators (stochastic dependence structure-stochastic system dispatch). Subsequently, the stochastic bounds methodology is applied to model the extreme power contribution of the stochastic generation to the system, based on two new sampling concepts (comonotonicity¿countermonotonicity). The application of this methodology to the power system leads to the definition of clusters of positively correlated stochastic generators and the combination of different clusters based on the sampling concepts. The stochastic decomposition and clustering concepts presented in this contribution provide the basis for the application of new uncertainty analysis techniques for the modeling of stochastic generation in power systems.
Keywords: Stochastic power generation; Distributed generation; Steady-state analysis; Uncertainty analysis; Monte-Carlo simulation; Risk management@en
