Probabilistic load flow for radial distribution networks with photovoltaic generators

Ruíz-Rodríguez, F.J.; Hernández, Jesús C.; Jurado, Francisco

doi:10.1049/iet-rpg.2010.0180

Cited by 99 publications

(39 citation statements)

References 31 publications

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“…The PV model determines the cumulants of

p_{italicpv, n, italic10 ‐ {min}_{t_{x}}}^{m}

from cumulants of meteorological random variables. Then, series expansion provides the statistical characterisation (PDF, CDF) of the

p_{italicpv, n, italic10 ‐ {min}_{t_{x}}}^{m}

.…”

Section: Probabilistic Modelsmentioning

confidence: 99%

“…From daily typical profiles based on historical statistical data, it is possible to statistically characterise (PDF, CDF) the active or reactive random load for each x th 10‐minute interval of the day ( t x ), time‐frame chosen; m th month; and n th RDS node

p_{l, n, italic10 ‐ {min}_{t_{x}}}^{m}

(

q_{l, n, italic10 ‐ {min}_{t_{x}}}^{m}

) according to Ruiz‐Rodriguez et al Obviously, due to the timedependant nature of loads, each 10‐minute interval of the day has a different normal distribution.…”

Section: Probabilistic Modelsmentioning

confidence: 99%

“…Numerous researchers have assessed the technical impacts of electric vehicles (EVs) on radial distribution systems (RDSs) . In a parallel way, other researchers have investigated the technical impacts of PV systems on RDSs . The assessment of technical impact of PV systems and EVs has recently emerged in literature .…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, probabilistic techniques are the key to a more accurate assessment of the combined impact of uncertainties regarding RDS output variables. Of these techniques, the ones that are most frequently used are the following: probabilistic load flow methods based on Monte Carlo simulation (MCS), analytical techniques, and approximation methods …”

Section: Introductionmentioning

confidence: 99%

“…From the literature review about the impact of PV systems, EVs or the combined interaction on RDSs, major identified shortcomings in studies include: (1) the simulation step is not adjusted to 10 minutess (usually 0.5 or 1 hour is planned); (2) the technical impact is assessed without considering its probability of occurrence. This means that the probability distribution of the RDS output variable is not calculated; the averaged values are usually given and the probability of voltage threshold violation is not computed; (3) the correlation of the input variables is rarely considered.…”

Section: Introductionmentioning

confidence: 99%

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Voltage behaviour in radial distribution systems under the uncertainties of photovoltaic systems and electric vehicle charging loads

Ruíz-Rodríguez

Hernández

Jurado

2017

Int Trans Electr Energ Syst

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Summary This paper presents a probabilistic method that accurately verifies the fulfilment of voltage constraints in radial distribution systems (RDSs) with photovoltaic (PV) systems and electric vehicle (EV) charging loads. This problem has not been comprehensively discussed. The proposed probabilistic method (PPM) involves the calculation of input variable cumulants, the linearization of load‐flow equations, and the application of the cumulant method and the Cornish‐Fisher expansion. Obviously, it is first necessary to model the PV systems and EV charging loads as random variables. In addition, the correlation of the input variables is suitably handled. As part of this research, a Monte Carlo simulation was performed, which confirmed the accuracy of the results obtained with the PPM. As an added value, when compared to the Monte Carlo simulation, there was a significant reduction in computational cost. The results obtained by the PPM demonstrate that the connection of PV systems in the IEEE 33‐node RDS with EVs clearly contributes to keeping voltages within regulatory limits, once the distributions of the RDS output variables are inspected. Nonetheless, the greater dispersion in the distributions in combined PV and EV scenarios means that voltage constraint fulfilment cannot be absolutely guaranteed. This probabilistic approach provides a more accurate assessment than one based on a simple deterministic vision. Thus, the PPM is extremely useful because it provides a better understanding of the combined technical impact under different correlated and uncorrelated scenarios.

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“…The PV model determines the cumulants of

p_{italicpv, n, italic10 ‐ {min}_{t_{x}}}^{m}

from cumulants of meteorological random variables. Then, series expansion provides the statistical characterisation (PDF, CDF) of the

p_{italicpv, n, italic10 ‐ {min}_{t_{x}}}^{m}

.…”

Section: Probabilistic Modelsmentioning

confidence: 99%

p_{l, n, italic10 ‐ {min}_{t_{x}}}^{m}

(

q_{l, n, italic10 ‐ {min}_{t_{x}}}^{m}

) according to Ruiz‐Rodriguez et al Obviously, due to the timedependant nature of loads, each 10‐minute interval of the day has a different normal distribution.…”

Section: Probabilistic Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Voltage behaviour in radial distribution systems under the uncertainties of photovoltaic systems and electric vehicle charging loads

Ruíz-Rodríguez

Hernández

Jurado

2017

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

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Harmonic modelling of PV systems for probabilistic harmonic load flow studies

Ruíz-Rodríguez

Hernández

Jurado

2014

Circuit Theory & Apps

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Summary Probabilistic harmonic load flow (HLF) is currently enjoying renewed popularity. This is not surprising since in the near future, thousands of photovoltaic (PV) systems will be integrated into distribution systems. However, as yet, there is no model capable of explaining PV harmonic current behaviour in probabilistic HLF studies. To fill this gap, the harmonic modelling of PV systems proposed in this paper has three key points. First, it provides an effective model of the relationship between PV harmonic current emission and background harmonic voltages. Second, it statistically characterises PV harmonic currents (relative magnitude and phase angle) at different fundamental‐frequency current output intervals using historical time‐series data. In this statistical characterisation, the first fifth moments of each PV harmonic current are used to accurately approximate the raw probability density function (PDF) by means of the Legendre series. Finally, the third key point of this harmonic modelling is a method capable of determining the distribution functions of PV harmonic currents (absolute magnitude and phase angle), based on the statistical characterisation and a fundamental‐frequency probabilistic PV model. The numerical results obtained confirm the effectiveness of this PV model. Copyright © 2014 John Wiley & Sons, Ltd.

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