Integration of stochastic generation in power systems

Abstract

-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 largescale implementation of this small-scale, distributed, nondispatchable generation; the 'horizontally operated' system. Modelling 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 modelling 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 give the basis for the application of new uncertainty analysis techniques, for the modelling of the stochastic generation in power systems.

From probabilistic forecasts to statistical scenarios of short‐term wind power production

“…More complex interdependence structures could be modeled by using the copula theory, which involves the modeling of the rank correlation of the Y k -variables [15,20]. This would not affect the other parts of the proposed method.…”

“…They should respect the probabilistic forecasts for the next period, and additionally rely on the most recent information about the interdependence structure of the prediction errors. Such kind of scenarios are indeed required by new methodologies specially designed either for optimal integration of stochastic generation into energy systems [15] or for optimal planning in presence of distributed storage devices [16]. The main objective of the present paper is to introduce the general framework for the derivation of such scenarios of short-term wind power production.…”

Dynamic sizing of energy storage for hedging wind power forecast uncertainty

“…Monte-Carlo simulation is further used for the generation of the scenarios. The problem is defined as the sampling from a multidimensional distribution with arbitrary marginals and can be treated using the techniques presented in [22], based on the transformation of the marginals between different domains. First a multivariate Normal distribution is used for generating a set of Gaussian variables realizations that are correlated according to the estimated interdependence structure and then these Gaussian variables realizations are transformed to the desired marginals.…”

Joint data compression and MAC protocol design for smartgrids with renewable energy

“…These parameters and (A, B, C) are determined as in [40]. Finally, the spatial correlation of distributed generation powers can be modeled as in [29]- [31] where the correlation coefficient between 2 distributed generators i and j is ρ i,j = exp (−d i,j /d) where d = 20km is a positive constant and d i,j is the distance between generators i and j, which is randomly chosen in (0, 1km].…”