An Unscented Transformation Based Probabilistic Power Flow for Autonomous Hybrid AC/DC Microgrid with Correlated Uncertainty Sources

Lin, Xingyu; Shu, Tong; Tang, Junjie; Yang, Yong; Liu, Fuchao; Zheng, Jian; Peng, Sancheng

doi:10.1109/ei2.2018.8582513

Cited by 7 publications

(7 citation statements)

References 12 publications

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“…Even if the potential distribution of the random variable is skewed, multimodal, or heavy-tailed, the proposed method is still applicable. All the works in [14], [18], [19], [31] 20 Return P(Y ); in advance. For example, the unscented transformation (UT) method [31] requires the random variable to obey a symmetrical distribution.…”

Section: Analytical Joint Plf Methodsmentioning

confidence: 99%

“…All the works in [14], [18], [19], [31] 20 Return P(Y ); in advance. For example, the unscented transformation (UT) method [31] requires the random variable to obey a symmetrical distribution. To meet this requirement, the UT method has to leverage the Nataf transformation to convert the asymmetrical random variable into the standard Gaussian scope first before computing the PLF [31], which significantly increases the computational burden.…”

Section: Analytical Joint Plf Methodsmentioning

confidence: 99%

“…No additional operation is needed. In contrast, both the Latin-hypercubesampling-based Monte Carlo simulation method used in [14] and the UT method adopted in [31] need to adopt the Gaussian Copula to establish the relationship between correlated random variables, even though the probability distributions of these variables are assumed to be known already. Note that the Gaussian Copula requires multiple integral calculations, thereby making these two methods computationally expensive.…”

Section: Analytical Joint Plf Methodsmentioning

confidence: 99%

“…In other words, the result of the proposed method not only describes the uncertainties in system states, but also reveals the relationship between them. As a comparison, the UT method [31] as well as the 2m + 1 method [19] can only generate the moment information of each system state, e.g. the mean value of a nodal voltage, and are not capable of providing the marginal probability distribution of a system state, not to mention the joint distribution of all the states.…”

Section: Analytical Joint Plf Methodsmentioning

confidence: 99%

“…A. Setup of Case Studies 1) Methods for Comparison: The methods we adopted for comparisons are the AC-based Monte Carlo (ACMC) simulation method, Nataf-transformation-based Latin hypercube sampling Monte Carlo (NLHSMC) simulation method [14], Nataf-transformation-based UT (NUT) method [31], and 2m+1 point estimation method [19].…”

Section: Case Studymentioning

confidence: 99%

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Control-aware Probabilistic Load Flow for Transmission Systems: An Analytical Method

Jia¹,

Cao²,

Chen³

et al. 2022

Preprint

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Probabilistic load flow (PLF) calculation, as a fundamental tool to analyze transmission system behavior, has been studied for decades. Despite a variety of available methods, existing PLF approaches rarely take system control into account. However, system control, as an automatic buffer between the fluctuations in random variables and the variations in system states, has a significant impact on the final PLF result. To consider control actions' influence, this paper proposes the first analytical PLF method for the transmission grid that takes into account primary and secondary frequency controls. This method is based on a high-precision linear power flow model, whose precision is even further improved in this paper by an original correction approach. This paper also proves that if the joint probability distribution (JPD) of random variables is expressed by a Gaussian mixture model (GMM), then the JPD of system states (e.g., nodal voltages) is an infinite GMM. By leveraging this proposition, the proposed method can generate the joint PLF of the whole system, is applicable to random variables obeying any distributions, and is capable of capturing their correlation. The high accuracy and satisfactory efficiency of this method are verified on test cases scaling from 14 to 1354 buses.

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Section: Analytical Joint Plf Methodsmentioning

confidence: 99%

Section: Analytical Joint Plf Methodsmentioning

confidence: 99%

Section: Analytical Joint Plf Methodsmentioning

confidence: 99%

Section: Analytical Joint Plf Methodsmentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

See 3 more Smart Citations

Control-aware Probabilistic Load Flow for Transmission Systems: An Analytical Method

Jia¹,

Cao²,

Chen³

et al. 2022

Preprint

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Research on random fuzzy power flow calculation of AC/DC hybrid distribution network based on unified iterative method

Yang

Dong

Liu

et al. 2021

IET Renewable Power Gen

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In recent years, with the promotion of policies and technologies, a large number of distributed generations are connected to the urban distribution network in various forms. At the same time, the flexible DC transmission technology has become mature and gradually applied to the distribution network. In this condition, the AC/DC hybrid distribution networks with flexible regulation ability will be the trend of urban distribution network. This paper accurately considers the loss of voltage source converter, and proposes a unified iterative power flow method for AC/DC hybrid distribution network, which takes the voltage source converter control modes into account. Because of the uncertainties of the distributed generations and loads, a random fuzzy power flow model is proposed, in which cumulant method is applied in the random stage. In the fuzzy stage, the fuzzy simulation technology is applied. Finally, an improved PG&E 69‐Bus system is taken as an example to verify the effectiveness of the unified iterative method and the random fuzzy power flow calculation, and put forward some suggestions for the operation of distribution network with distributed generations.

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Linear Probabilistic Power Flow for Islanded Microgrids

Moreira

Chagas

Rodrigues

et al. 2020

2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

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An Unscented Transformation Based Probabilistic Power Flow for Autonomous Hybrid AC/DC Microgrid with Correlated Uncertainty Sources

Cited by 7 publications

References 12 publications

Control-aware Probabilistic Load Flow for Transmission Systems: An Analytical Method

Control-aware Probabilistic Load Flow for Transmission Systems: An Analytical Method

Research on random fuzzy power flow calculation of AC/DC hybrid distribution network based on unified iterative method

Linear Probabilistic Power Flow for Islanded Microgrids

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