Probabilistic Load Flow Based on Generalized Polynomial Chaos

Wu, Hao; Zhou, Yongzhi; Dong, Shufeng; Song, Yonghua

doi:10.1109/tpwrs.2016.2543143

Cited by 81 publications

(33 citation statements)

References 3 publications

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“…PPF analysis methods can be generally divided into analytical methods and numerical methods. The former includes cumulant methods [4], [5], point estimation methods [6], [7], cornish-fisher expansion [8], [9] and the generalized polynomial chaos method [10]. These analytical methods is considered to be computational tractable since they directly integrate the probabilistic distribution of uncertainty factors (e.g., renewable energy source and load) into a few deterministic power flow models.…”

Section: B Literature Review and Backgroundmentioning

confidence: 99%

Fast Calculation of Probabilistic Power Flow: A Model-Based Deep Learning Approach

Yang

et al. 2020

IEEE Trans. Smart Grid

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Probabilistic power flow (PPF) plays a critical role in the analysis of power systems. However, its high computational burden makes practical implementations challenging. This paper proposes a model-based deep learning approach to overcome these computational challenges. A deep neural network (DNN) is used to approximate the power flow calculation process, and is trained according to the physical power flow equations to improve its learning ability. The training process consists of several steps: 1) the branch flows are added into the objective function of the DNN as a penalty term, which improves the generalization ability of the DNN; 2) the gradients used in back propagation are simplified according to the physical characteristics of the transmission grid, which accelerates the training speed while maintaining effective guidance of the physical model; and 3) an improved initialization method for the DNN parameters is proposed to improve the convergence speed. The simulation results demonstrate the accuracy and efficiency of the proposed method in standard IEEE and utility benchmark systems. Index Terms-Model-based deep learning, initialization methods, deep neural networks, probabilistic power flow.

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Section: B Literature Review and Backgroundmentioning

confidence: 99%

Fast Calculation of Probabilistic Power Flow: A Model-Based Deep Learning Approach

Yang

et al. 2020

IEEE Trans. Smart Grid

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“…Step (2): dedicate the other remaining observations to the clusters with the adjacent representative, using (12):…”

Section: K-means Algorithmmentioning

confidence: 99%

“…There has been a vast body of literature interrogating efficient methods for solving probabilistic load flow (PLF) in the probabilistic mechanism [6][7][8][9][10][11][12][13][14][15]. Reference [6] presented a Quasi-Monte Carlo (QMC) method to calculate the PLF for the radial distribution system.…”

Section: Introductionmentioning

confidence: 99%

“…Lower computation burden is the advantage of this point estimate method. Authors in [12] have applied an analytical method, based on generalized polynomial chaos (GPC) for evaluating the PLF. The GPC method first appeared in 2002 as an extension of the polynomial chaos method [13], and has become an effective approach for probabilistic analysis of intricate systems.…”

Section: Introductionmentioning

confidence: 99%

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Direct Probabilistic Load Flow in Radial Distribution Systems Including Wind Farms: An Approach Based on Data Clustering

et al. 2018

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Abstract:The ongoing study aims to establish a direct probabilistic load flow (PLF) for the analysis of wind integrated radial distribution systems. Because of the stochastic output power of wind farms, it is very important to find a method which can reduce the calculation burden significantly, without having compromising the accuracy of results. In the proposed approach, a K-means based data clustering algorithm is employed, in which all data points are bunched into desired clusters. In this regard, probable agents are selected to run the PLF algorithm. The clustered data are used to employ the Monte Carlo simulation (MCS) method. In this paper, the analysis is performed in terms of simulation run-time. Also, this research follows a two-fold aim. In the first stage, the superiority of data clustering-based MCS over the unsorted data MCS is demonstrated properly. Moreover, the impact of data clustering-based MCS and unsorted data-based MCS is investigated using an indirect probabilistic forward/backward sweep (PFBS) method. Thus, in the second stage, the simulation run-time comparison is carried out rigorously between the proposed direct PLF and the indirect PFBS method to examine the computational burden effects. Simulation results are exhibited on the IEEE 33-bus and 69-bus radial distribution systems.Keywords: direct probabilistic load flow; wind-integrated radial distribution systems; K-means based data clustering; Monte Carlo simulation; indirect probabilistic forward/backward sweep load flow

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“…Also, to reach optimum control settings over a whole planning period, hybrid learning automata system has been proposed in the presence of wind turbines (WTs) and plug-in electric vehicles (PHEVs) [34]. Also, generalised polynomial chaos, Copula and hypercube sampling, stochastic surface, Taguchi methods have been proposed for PPF problem [35][36][37][38]. In [39], a dimension-adaptive sparse grid interpolation method has been proposed for uncertainty quantification for PPF in modern power systems.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy unscented transform for uncertainty quantification of correlated wind/PV microgrids: possibilistic–probabilistic power flow based on RBFNNs

Baghaee

Mirsalim

Gharehpetian

et al. 2017

IET Renewable Power Generation

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The probabilistic power flow (PPF) of active distribution networks and microgrids based on the conventional power flow algorithms is almost impossible or at least cumbersome. Always, Mont Carlo simulation is a reliable solution. However, its computation time is relatively high that makes it unattractive to be a reliable solution for large interconnected power systems. This study presents a new method based on fuzzy unscented transform and radial basis function neural networks (RBFNN) for possibilistic-PPF in the microgrids including uncertain loads, correlated wind and solar distributed energy resources and plug-in hybrid electric vehicles. When sufficient historical data of the system variables is not available, a probability density function might not be defined, while they must be represented in another way namely possibilistically. When some of system uncertain variables are probabilistic and some are possibilistic, neither the conventional pure probabilistic nor pure possibilistic methods can be implemented. Hence, a combined solution methodology is needed. The proposed method exploits the ability of RBFNN and unscented transform in non-linear mapping with an acceptable level of accuracy, robustness and reliability. Simulation results for the proposed PPF algorithm and its comparison with the reported methods for different test power systems reveals its efficiency, accuracy, robustness and authenticity.

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Probabilistic Load Flow Based on Generalized Polynomial Chaos

Cited by 81 publications

References 3 publications

Fast Calculation of Probabilistic Power Flow: A Model-Based Deep Learning Approach

Fast Calculation of Probabilistic Power Flow: A Model-Based Deep Learning Approach

Direct Probabilistic Load Flow in Radial Distribution Systems Including Wind Farms: An Approach Based on Data Clustering

Fuzzy unscented transform for uncertainty quantification of correlated wind/PV microgrids: possibilistic–probabilistic power flow based on RBFNNs

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