Maximal DG indicator to quantify the efficiency of Smart Grid solutions regarding renewable penetration

Alvarez‐Hérault, Marie‐Cécile; Battegay, Archie; Gandioli, Camille; Descloux, J.; Hadjsaïd, Nourédine; Tixador, Pascal

doi:10.1109/ptc.2013.6652440

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…In addition, the maximum PV production rate,

normalP V_{ratio}

, defined in (1), that can be accepted by this network is estimated using a dichotomy process and is connected to the network directly at year 0

normalP V_{ratio} = \frac{\sum_{g = 1}^{G} P_{ma {normalx}_{pv}} (g)}{\sum_{n = 1}^{N} P_{ma {normalx}_{load}} (n)}

where

P_{ma x_{pv}} false(g false)

is the installed peak power of producer g ,

P_{ma x_{load}} false(n false)

is the maximum power of the load at node n , N is the number of nodes in the network, and G is the number of producers. The method to estimate the maximal PV production, detailed in [24], is based on Monte–Carlo simulations. We can summarise this algorithm in three steps for each Monte–Carlo iteration Random definition of PV production units (peak power). Random distribution of PV production units in the network (choice of the connection node). Unbalanced load‐flow computation to estimate the state of the network [25] for the worse case (peak production and minimal consumption). …”

Section: Algorithm Descriptionmentioning

confidence: 99%

Integrating storage in planning of LV distribution networks with PV production

Hadjsaïd

Alvarez‐Hérault

Debusschere

et al. 2019

IET Generation, Transmission & Distribution

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“…In addition, the maximum PV production rate,

normalP V_{ratio}

, defined in (1), that can be accepted by this network is estimated using a dichotomy process and is connected to the network directly at year 0

normalP V_{ratio} = \frac{\sum_{g = 1}^{G} P_{ma {normalx}_{pv}} (g)}{\sum_{n = 1}^{N} P_{ma {normalx}_{load}} (n)}

where

P_{ma x_{pv}} false(g false)

is the installed peak power of producer g ,

P_{ma x_{load}} false(n false)

Section: Algorithm Descriptionmentioning

confidence: 99%

Integrating storage in planning of LV distribution networks with PV production

Hadjsaïd

Alvarez‐Hérault

Debusschere

et al. 2019

IET Generation, Transmission & Distribution

View full text Add to dashboard Cite

“…For the moment such an indicator is only proposed in [20]. It is named MADGIC (MAximal DG Insertion Criterion) and defined as the maximal nominal power produced by DGs that can be connected to a given network without violating voltage limits (+/-5% of nominal voltage) and maximal admissible currents.…”

Section: B Dg Penetration Indicatormentioning

confidence: 99%

“…As the power, the location and the type of DGs have an impact on technical constraints, a Monte Carlo stochastic approach seems to be the most adapted. The methodology of this algorithm and the hypotheses made to estimate the value of the MADGIC are explained in detail in [20]. The MADGIC (respectively MIDGIC) are initialized at 0% (respectively 100%).…”

Section: B Dg Penetration Indicatormentioning

confidence: 99%

“…Fig. 4 gives the steps of the MADGIC algorithm with the following notations: -N j = number of iterations, -M j = maximal number of failures to conclude that the network can accept a DG insertion rate of τ% -m j = minimal number of failures to conclude that the network cannot accept a DG insertion rate of τ% In [20], 99% confidence intervals computations enable to set a table. For different number of iterations, N j , M j and m j are given.…”

Section: B Dg Penetration Indicatormentioning

confidence: 99%

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