TY - JOUR
T1 - Integration of stochastic generation in power systems
AU - Papaefthymiou, G.
AU - Schavemaker, P.H.
AU - Sluis, van der, L.
AU - Kling, W.L.
AU - Kurowicka, D.
AU - Cooke, R.M.
PY - 2006
Y1 - 2006
N2 - 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
AB - 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
U2 - 10.1016/j.ijepes.2006.03.004
DO - 10.1016/j.ijepes.2006.03.004
M3 - Article
SN - 0142-0615
VL - 28
SP - 655
EP - 667
JO - International Journal of Electrical Power and Energy Systems
JF - International Journal of Electrical Power and Energy Systems
IS - 9
ER -