Impact of soft open point (SOP) on distribution network predictability

•A novel SOP modeling in the forward–backward load flow is proposed,•The effect of SOP allocation on distribution network predictability is investigated,•The correlations among uncertain input variables are considered in the problem,•The association among correlation intensity and predictability ind...

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Bibliographic Details
Published in:International journal of electrical power & energy systems 2022-03, Vol.136, p.107676, Article 107676
Main Authors: Rezaeian-Marjani, Saeed, Talavat, Vahid, Galvani, Sadjad
Format: Article
Language:English
Subjects:
Cholesky decomposition
Latin hypercube sampling
Predictability
Renewable energy sources
Soft open point
Uncertainty
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Summary:•A novel SOP modeling in the forward–backward load flow is proposed,•The effect of SOP allocation on distribution network predictability is investigated,•The correlations among uncertain input variables are considered in the problem,•The association among correlation intensity and predictability indices are studied,•The changes of predictability indices and active power losses are examined,•The performance of the LHS method to extract predictability indices is evaluated, With the increasing penetration of renewable energy sources (RESs), the consequent rising levels of uncertainty faces distribution network operators with significant decision-making challenges. So, predicting the state of the network with high accuracy is very important to make operational and planning decisions and better risk management. The soft open point (SOP) as a novel power electronics-based device has been introduced to control active power flows, compensate reactive powers, and regulate voltage for flexible operation of distribution networks. In this paper, the effect of SOP on the predictability of the distribution network state has been investigated. Two defined indices, current predictability index (CPI) and voltage predictability index (VPI), are considered to quantify the ability of network predicting. The Latin hypercube sampling (LHS) method is implemented for probabilistic evaluation and extracting the predictability indices. Also, the correlations among uncertain input variables are modeled by Cholesky decomposition method. The results of the proposed method are argued on IEEE 33-node and IEEE 118-node networks.
ISSN:0142-0615
1879-3517
DOI:10.1016/j.ijepes.2021.107676