Probabilistic load flow calculation using Monte Carlo techniques for distribution network with wind turbines

Jorgensen, P.; Christensen, Jorgen S.; Tande, John Olav Giæver

doi:10.1109/ichqp.1998.760200

Cited by 73 publications

(37 citation statements)

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“…It is assumed that the load powers at the nodes are stochastic with normal distribution function of probability density. The probability distribution of power generation of wind farm is determined based on the sample data of power values computed according to (5).…”

Section: Modeling Resultsmentioning

confidence: 99%

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Probabilistic assessment of power system mode with a varying degree of wind sources integration

et al. 2017

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Abstract. At present among renewable sources the wind and solar plants have the most significant portion of power generation. Randomly changing and intermittent nature of this power leads to the stochasticity of the power grid mode, estimation of parameters of which requires application of probabilistic modeling. In the paper it is proposed an advanced algorithm of probabilistic load flow based on the development of two-point estimation method, the efficiency of which is confirmed on the basis of computational experiments and comparative analysis of the Monte Carlo simulation results. Calculations and analysis of the modeling results were carried out on standard 14-nodal scheme of IEEE and real electrical network of "Azerenerji" Grid.

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Section: Modeling Resultsmentioning

confidence: 99%

“…Probabilistic methods of calculation and analysis of load flows (LF) in the system are based on two approaches: numerical simulation by Monte Carlo method [3][4][5]9,10], and analytical methods of the transformation in the form of cumulated representations [6], [7], or using of point values of probability distribution functions [8,9].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic assessment of power system mode with a varying degree of wind sources integration

et al. 2017

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“…However, other methods which do not require these simplifications or assumptions in designing the systems/converters or in generating random occurrences can be deployed. They include the Monte Carlo Simulation (MCS) [5,17,18], Unscented Transform (UT) [19,20], Point Estimate Method (PEM) [21], and Univariate Dimension Reduction (UDR) [22,23]. For instance, in Probabilistic Load Flow (PLF) studies, some of these methods have been utilized.…”

Section: Introductionmentioning

confidence: 99%

Predicting Harmonic Distortion of Multiple Converters in a Power System

Ivry

Oke

Thomas

et al. 2017

Journal of Electrical and Computer Engineering

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Various uncertainties arise in the operation and management of power systems containing Renewable Energy Sources (RES) that affect the systems power quality. These uncertainties may arise due to system parameter changes or design parameter choice. In this work, the impact of uncertainties on the prediction of harmonics in a power system containing multiple Voltage Source Converters (VSCs) is investigated. The study focuses on the prediction of harmonic distortion level in multiple VSCs when some system or design parameters are only known within certain constraints. The Univariate Dimension Reduction (UDR) method was utilized in this study as an efficient predictive tool for the level of harmonic distortion of the VSCs measured at the Point of Common Coupling (PCC) to the grid. Two case studies were considered and the UDR technique was also experimentally validated. The obtained results were compared with that of the Monte Carlo Simulation (MCS) results.

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“…In simulation approach, Monte Carlo simulation (MCS) is very famous and had been used in different papers [11], [12], [13]. In this method, a certain probability distribution is assigned to a certain variable.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Power Flow Model to Study Uncertainty in Power System Network Based Upon Point Estimation Method

Li¹

2017

EPES

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Abstract:In this paper, point estimation approach is used to calculate the statistical moment of a random quantity that is a function of m input random variables. In this work, loads of the proposed network is considered as a random variable. Two special cases of point estimation approach are considered such as 2m and 2m+1 concentration schemes. In 2m concentration scheme, skewness is considered, but in 2m+1 concentration scheme, both skewness and kurtosis are taken into account for probability density function. The proposed model is investigated using P. M. Anderson 9-bus test system. As a result, by changing the value of a random variable that follows a predefined distribution, expected bus voltage magnitude and expected line loading are identified. For the comparison purpose, 2m and 2m+1 scheme was compared with deterministic load flow analysis.

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Probabilistic load flow calculation using Monte Carlo techniques for distribution network with wind turbines

Cited by 73 publications

References 1 publication

Probabilistic assessment of power system mode with a varying degree of wind sources integration

Probabilistic assessment of power system mode with a varying degree of wind sources integration

Predicting Harmonic Distortion of Multiple Converters in a Power System

Probabilistic Power Flow Model to Study Uncertainty in Power System Network Based Upon Point Estimation Method

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