Three‐phase probabilistic load flow for power system with correlated wind, photovoltaic and load

Ran, Xiaohong; Miao, Shihong

doi:10.1049/iet-gtd.2016.0424

Cited by 61 publications

(34 citation statements)

References 32 publications

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“…In recent years, many researchers have proposed different approaches for the analysis, simulation and modelling in power systems and metro structures. Some recently published papers and literature reviews can be found in [10][11][12][13][14]; the most important factors of metro tunnel safety and the importance of safety and security needed to enable the existence of more comfortable services in metro tunnel and subway stations is explained in [15]; in the reference [16], the criteria and rules for the design of a metro path are discussed; in the paper [17], the authors presented a review of a probabilistic load flow in power systems; the reference [18] deals with the analytical methodology for the assessment of a smart monitoring impact on a future electric power distribution system. In the papers [19,20], new prediction model based on a new feature selection and hybrid prediction engine are introduced.…”

Section: Literature Reviewmentioning

confidence: 99%

A comparative study on common power flow techniques in the power distribution system of the Tehran metro

Ghiasi

2018

Teh. glas. (Online)

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Overall, a power-flow study is a steady-state assessment whose goal is to specify the currents, voltages, and real and reactive flows in a power system under a given load conditions. This paper presents a comparison of common power flow techniques in the Tehran metro power distribution system at the presence of non-linear loads. Moreover, a modelling, simulation and analysis of this power distribution system is implemented with the Electrical Transient Analyser Program (ETAP) software. In this assessment, common power flow techniques including the Newton-Raphson (NR), Fast Decoupled (FD), and Accelerated Gauss-Seidel (AGS) techniques are provided and compared. The obtained results (total generation, loading, demand, system losses, and critical report of the power flow) are analysed. In this paper, we focus on the detailed assessment and monitoring by using the most modern ETAP software, which performs numerical calculations of a large integrated power system with fabulous speed and also generates output reports. The capability and effectiveness of the power flow analysis are demonstrated according to the simulation results obtained with ETAP by applying it to the power distribution system of the Tehran metro. In developing countries such as Iran, off-line modelling and simulation of power grids by a powerful software are beneficial and helpful for the best usage of the electrical energy.

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Section: Literature Reviewmentioning

confidence: 99%

A comparative study on common power flow techniques in the power distribution system of the Tehran metro

Ghiasi

2018

Teh. glas. (Online)

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“…Several methods have been proposed to extract the k and c from historical wind speed data, such as the moment method [27] and the maximum likelihood method [28]. In short-time scale, however, the wind speed can be modeled as a Gaussian distribution [29] in which the mean value is equal to the forecasted wind speed and the variance represents the forecasted error. As a result, the active power output P w can be calculated by the piece-wise wind speed-power output relation [30]…”

Section: A Wind Generationmentioning

confidence: 99%

“…Γ denotes the Gamma function, r and r max (W/m 2 ) are the respective actual and maximum solar radiations. Similar to the wind speed, the solar radiation in short-time scale can be modeled as a Gaussian distribution provided that accurate mean value is available [29]. The active power P pv corresponding to the solar radiation r is determined by the following piece-wise function [24]…”

Section: B Solar Generationmentioning

confidence: 99%

Probabilistic Power Flow Calculation Using Non-Intrusive Low-Rank Approximation Method

Sheng

Wang

2019

IEEE Trans. Power Syst.

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In this paper, a novel non-intrusive probabilistic power flow (PPF) analysis method based on the low-rank approximation (LRA) is proposed, which can accurately and efficiently estimate the probabilistic characteristics (e.g., mean, variance, probability density function) of the PPF solutions. This method aims at building up a statistically-equivalent surrogate for the PPF solutions through a small number of power flow evaluations. By exploiting the retained tensor-product form of the univariate polynomial basis, a sequential correction-updating scheme is applied, making the total number of unknowns to be linear rather than exponential to the number of random inputs. Consequently, the LRA method is particularly promising for dealing with high-dimensional problems with a large number of random inputs. Numerical studies on the IEEE 39-bus, 118bus, and 1354-bus systems show that the proposed method can achieve accurate probabilistic characteristics of the PPF solutions with much less computational effort compared to the Monte Carlo simulations. Even compared to the polynomial chaos expansion method, the LRA method can achieve comparable accuracy, while the LRA method is more capable of handling higher-dimensional problems. Moreover, numerical results reveal that the randomness brought about by the renewable energy resources and loads may inevitably affect the feasibility of dispatch/planning schemes.

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“…are the actual and maximum solar radiations, respectively; α and β are the shape parameters of the distribution; Γ is the Gamma function. For short-time scale application, similar to the wind generation, the solar radiation follows the normal distribution assuming that the mean of solar radiation can be accurately forecasted [29]. The solar radiation-power output relation is described by [32]:…”

Section: B Solar Generationmentioning

confidence: 99%

Applying Polynomial Chaos Expansion to Assess Probabilistic Available Delivery Capability for Distribution Networks With Renewables

Sheng

Wang

2018

IEEE Trans. Power Syst.

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Considering the increasing penetration of renewable energy sources and electrical vehicles in utility distribution feeders, it is imperative to study the impacts of the resulting increasing uncertainty on the delivery capability of a distribution network. In this paper, probabilistic available delivery capability (ADC) is formulated for a general distribution network integrating various RES and load variations. To reduce the computational efforts by using conventional Monte Carlo simulations, we develop and employ a computationally efficient method to assess the probabilistic ADC, which combines the up-to-date sparse polynomial chaos expansion (PCE) and the continuation method. Particularly, the proposed method is able to handle a large number of correlated random inputs with different marginal distributions. Numerical examples in the IEEE 13 and IEEE 123 node test feeders are presented, showing that the proposed method can achieve accuracy and efficiency simultaneously. Numerical results also demonstrate that the randomness brought about by the RES and loads indeed leads to a reduction in the delivery capability of a distribution network.

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Three‐phase probabilistic load flow for power system with correlated wind, photovoltaic and load

Cited by 61 publications

References 32 publications

A comparative study on common power flow techniques in the power distribution system of the Tehran metro

A comparative study on common power flow techniques in the power distribution system of the Tehran metro

Probabilistic Power Flow Calculation Using Non-Intrusive Low-Rank Approximation Method

Applying Polynomial Chaos Expansion to Assess Probabilistic Available Delivery Capability for Distribution Networks With Renewables

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