Probabilistic load flow methodology for distribution networks including loads uncertainty

Gruosso, Giambattista; Maffezzoni, P.; Zhang, Zheng; Daniel, Luca

doi:10.1016/j.ijepes.2018.10.023

Cited by 34 publications

(31 citation statements)

References 33 publications

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“…Gruosso et al . [174] developed a populistic power flow method which is able to analyse daily network operation under uncertain and stress conditions. The proposed technique based on the generalised polynomial chaos algorithm, Monte Carlo simulation and probabilistic density factor PDF.…”

Section: Operation Of LV Distribution Networkmentioning

confidence: 99%

“…In this context, many research papers have been developed a probabilistic power flow technique. The most common way of applying a probabilistic power flow is using Monte Carlo simulation which is used in [174][175][176][177][178]. In [143], multi-objective probabilistic power flow method considering three-phase unbalance LV network was developed.…”

Section: Probabilistic Power Flow Techniquesmentioning

confidence: 99%

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Planning and operation of LV distribution networks: a comprehensive review

Al-Ja’Afreh

Mokryani

2019

IET Energy Systems Integration

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The low‐voltage (LV) distribution network is the last stage of the power network, which is connected directly to the end‐user customers and supplies many dispersed small‐scale loads. To achieve environmental targets and to address the energy shortage issue, governments worldwide increase the renewable energy sources (RESs) into the electricity grid. In addition, different types of low carbon technologies (LCTs) such as electric vehicles are becoming widely used. A significant portion of RES and LCTs is penetrated into the LV distribution network, which poses a wide range of challenges. To address these challenges, there is a persistent need to develop traditional planning and operation frameworks to cope with these new technologies. In this context, this study provides a comprehensive review of planning, operation, and management of LV distribution networks. The characteristics, types, and topologies of LV distribution networks plus different aspects of operation and planning are investigated. An insightful investigation of the reasons impacts and mitigation of voltage and current unbalanced in LV networks is provided. Moreover, the main three‐phase power flow techniques used to analyse the LV networks are analysed.

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Section: Operation Of LV Distribution Networkmentioning

confidence: 99%

Section: Probabilistic Power Flow Techniquesmentioning

confidence: 99%

Planning and operation of LV distribution networks: a comprehensive review

Al-Ja’Afreh

Mokryani

2019

IET Energy Systems Integration

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“…leading to N b × N unknown c n i coefficients that are complex variables. Such coefficients are determined by plugging the expansions (8) into (1) and then projecting the resulting nodal equations along the N b basis functions. This results in a very large nonlinear system of size…”

Section: A Galerkin Projection (Gp)mentioning

confidence: 99%

“…To address the above issues, in this paper we focus on an efficient uncertainty quantification method recently presented in the literature [6], [7], and describe its specific application to the PLF problem [8], [9]. The method is based on generalized Polynomial Chaos (gPC) expansion and Stochastic Testing (ST) algorithm and is denoted as gPC+ST.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty-Aware Computational Tools for Power Distribution Networks Including Electrical Vehicle Charging and Load Profiles

et al. 2019

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As new services and business models are being associated with the power distribution network, it becomes of great importance to include load uncertainty in predictive computational tools. In this paper, an efficient uncertainty-aware load flow analysis is described which relies on generalized polynomial chaos and stochastic testing methods. It is described how the method can be implemented in order to account for real data-based load profiles due to two different usage models: residential loads and electrical vehicle charging profiles. Hence, it is shown how some relevant information affecting the quality of service can be deduced by means of non-elementary post-processing computations. The proposed technique is tested by using a benchmark scenario for typical European low voltage networks, considering the variation of both residential loads and EV charging profiles. The results are compared with the same simulation done by means of the Monte Carlo methodology. The consideration done during the analysis will be useful to clarify the application of the methodology but also to understand the effect of load variations on the grid characteristic quantities. INDEX TERMS Distribution network, load uncertainty, variability analysis, load forecasting, EV charging profiles, electrical vehicles.

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“…With the increasing permeability of distributed generation (DG) supplies, the influence of randomness and uncertainty of DG output on the safe and stable operation of a power grid should not be ignored [1]. The randomness of loads further increases the influence of this uncertainty [2]. Probabilistic load flow (PLF) calculation is an important tool to study the uncertainty of power grid operation.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Computational Model for Correlated Wind Speed, Solar Irradiation, and Load Using Bayesian Network

Wang

Zou

2020

IEEE Access

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This paper proposes a Bayesian network (BN) that can construct the nonlinear dependence among wind speed, solar irradiation, and load. The correlations of random variables (RVs) are analyzed using the Pearson correlation coefficient, Kendall rank correlation coefficient, and Spearman rank correlation coefficient. According to Bayesian theory, the Bayesian information criterion (BIC) and maximum likelihood estimation (MLE) methods are employed to determine the structure and parameters of a BN. Then the BN model of RVs is established. The constructed model is the joint probability distribution of RVs that can present the nonlinear dependence and marginal distribution (MD) of RVs without limitation. The testing samples of wind speed, solar irradiation, and load are generated using the autoregressive integrated moving average (ARMA) model. Then these samples are utilized to construct the probability model of a BN and C-vine copula, whose modeling accuracy and efficiency are compared, and the quality of their output synthetic samples are analyzed. In the modified IEEE 118-bus test system, two kinds of synthetic samples are used to calculate the probabilistic load flow (PLF), whose accuracy and efficiency based on the BN are tested. The validity of the BN model is verified.

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Probabilistic load flow methodology for distribution networks including loads uncertainty

Cited by 34 publications

References 33 publications

Planning and operation of LV distribution networks: a comprehensive review

Planning and operation of LV distribution networks: a comprehensive review

Uncertainty-Aware Computational Tools for Power Distribution Networks Including Electrical Vehicle Charging and Load Profiles

Probabilistic Computational Model for Correlated Wind Speed, Solar Irradiation, and Load Using Bayesian Network

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