Probabilistic power-flow techniques extended and applied to operational decision making

Allan, R.N.; Grigg, C.; Newey, D.A.; Simmons, Rebecca

doi:10.1049/piee.1976.0264

Cited by 49 publications

(16 citation statements)

References 2 publications

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“…The PPF was first introduced in 1976 [11]. In [12], a convolution based technique was applied to consider the interdependent demands. In [13], a linearized set of load-flow equations were introduced to reduce the complexity of the problem.…”

Section: B Literature Reviewmentioning

confidence: 99%

A Probabilistic Modeling of Photo Voltaic Modules and Wind Power Generation Impact on Distribution Networks

Soroudi

Aien

Ehsan

2012

IEEE Systems Journal

217

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Section: B Literature Reviewmentioning

confidence: 99%

A Probabilistic Modeling of Photo Voltaic Modules and Wind Power Generation Impact on Distribution Networks

Soroudi

Aien

Ehsan

2012

IEEE Systems Journal

217

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“…It was first proposed by Borkowska in 1974 [1], and widely developed by Allan [2][3][4][5][6][7][8][9][10][11][12]. It has been applied to management, and also to short-term and long-term electrical network planning.…”

Section: Probabilistic Load Flowmentioning

confidence: 99%

“…The correlation between injected powers, loads and also between injected power and loads due to meteorological or social factors has also been considered [10,11,13].…”

Section: Probabilistic Load Flowmentioning

confidence: 99%

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Probabilistic Load Flow Considering Correlation between Generation, Loads and Wind Power

Villanueva¹,

Feijóo²,

Pazos³

2011

SGRE

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In this paper a procedure is established for solving the Probabilistic Load Flow in an electrical power network, considering correlation between power generated by power plants, loads demanded on each bus and power injected by wind farms. The method proposed is based on the generation of correlated series of power values, which can be used in aMonteCarlo simulation, to obtain the probability density function of the power through branches of an electrical network.

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“…In cases of non-correlated inputs, the analytical methods perform very well. The analytical methods proposed in the literature for the treatment of correlated inputs are restricted to linear [13], [14], [15] or second order dependence, i.e. the statistical dependence among the variables is assumed to be captured in their second order moments [16], [17].…”

Section: Probabilistic Power System Analysismentioning

confidence: 99%

Integration of stochastic generation in power systems

Papaefthymiou

Schavemaker

Sluis

et al. 2006

International Journal of Electrical Power & Energy Systems

148

103

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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 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.

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Probabilistic power-flow techniques extended and applied to operational decision making

Cited by 49 publications

References 2 publications

A Probabilistic Modeling of Photo Voltaic Modules and Wind Power Generation Impact on Distribution Networks

A Probabilistic Modeling of Photo Voltaic Modules and Wind Power Generation Impact on Distribution Networks

Probabilistic Load Flow Considering Correlation between Generation, Loads and Wind Power

Integration of stochastic generation in power systems

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