A Probabilistic Wind Farm Model for Probabilistic Load Flow Calculation

Li, Xue; Pei, Jianxia; Zhang, Shaohua

doi:10.1109/appeec.2010.5448689

Cited by 11 publications

(4 citation statements)

References 9 publications

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“…It is clear that WT is a nondispatchable power source and thus its output power is supposed to be fixed as forecasted by the models [16]. However, as the result of the variable wind speed behavior, the WT output power can also change.…”

Section: Practical Ed Problem Formulationmentioning

confidence: 99%

Stochastic reactive power planning in distribution systems considering wind turbines electric power variations

Baziareh

Kavousi-Fard

Zare

et al. 2015

Journal of Intelligent &Amp; Fuzzy Systems

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Optimal reactive power planning in the distribution systems is a very significant issue which can reduces the amount of power losses and increase the power quality of the electrical services, effectively. Nevertheless, by the rapid growth of the renewable power sources especially wind turbines (WTs) this problem has been affected greatly. The stochastic nature of WT and its output variable power has become a challenging issue for optimal reactive power planning of the distribution systems such as capacitor placement problem. In this way, this paper suggests a new stochastic framework based on point estimate method (PEM) to solve the capacitor placement problem considering several WTs in the system. The PEM makes use of the first few statistical data of the random variables to find the expected value of the output, here the system total costs. The uncertainties of the forecast error in the WT output power as well as the active and reactive loads are modeled in the proposed framework. Meanwhile, a powerful optimization algorithm called Krill Herd (KH) algorithm is used to solve this problem suitably. The performance of the proposed method is examined on the IEEE distribution test system.

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Section: Practical Ed Problem Formulationmentioning

confidence: 99%

Stochastic reactive power planning in distribution systems considering wind turbines electric power variations

Baziareh

Kavousi-Fard

Zare

et al. 2015

Journal of Intelligent &Amp; Fuzzy Systems

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“…The author compared the results to those obtained by using Gram Charlier expansion, and demonstrated that the Cornish-Fisher expansion is more suitable for non-Gaussian distribution. [4] presented a probabilistic model of wind farm generation that combines the wind turbine and induction wind generator models. In the model, the real power injected and reactive power absorbed by the wind turbine were described as the function of the voltage magnitude, the slip of the induction machine and circuit parameters of the wind turbines.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic load flow analysis for power systems with multi-correlated wind sources

Ghorai

2011

2011 IEEE Power and Energy Society General Meeting

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This paper presents a new probabilistic load flow algorithm which takes consideration of multi-correlated wind sources in the power network. The paper first uses linear approximation to obtain the injected power distribution of wind farm by considering cut-in and cut-out wind speed. Then a 5-point discrete distribution is deduced by the point estimation method. Based on the copula method, a bivariate model which can model the correlation between the wind farms is also developed. The spatiotemporal dependencies of two wind farms are analyzed and discussed. In order to model the high dimensional joint distribution, a combination method, which combines all possible bivariate distributions, is introduced to reduce the dimensional of multivariate distribution for the situation of more than two correlated wind farms. The proposed algorithms have been tested using the IEEE118 bus test system. The results indicate that the proposed algorithm can actually capture the probabilistic characteristics of the power systems with multi-correlated wind sources.

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“…Both wind and solar energies can be harnessed to generate real power based on surrounding environmental factors, while reactive power is consumed (traditional wind sources) or generated (new inverter‐based sources). Several different probability density functions (PDFs) have been proposed in the literature [1–3] to represent such outputs and they show a wide range of uncertainty.…”

Section: Introductionmentioning

confidence: 99%