A critical review on probabilistic load flow studies in uncertainty constrained power systems with photovoltaic generation and a new approach

The temperature dependences of monocrystalline silicon (mono-Si), interdigitated back-contact (IBC), passivated emitter and rear cell (PERC), silicon heterojunction (SHJ), and multicrystalline silicon (multi-Si) photovoltaic (PV) modules are estimated. Their characteristics such as temperature coefficients (TCs) for the maximum power (P max ), open-circuit voltage (V oc ), and efficiency are measured. IBC, PERC, SHJ, mono-Si, and multi-Si exhibit TCs at 25°C of P max and efficiency of %0.378, %0.404 to %0.373, %0.279, %0.432 to %0.415, and %0.516%/°C, respectively. IBC, PERC, SHJ, mono-Si, and multi-Si exhibit TCs at 25°C of V oc of %0.267, %0.269 to %0.263, %0.218, %0.312 to %0.306, and %0.334%/°C, respectively. Thus, IBC, PERC, and SHJ show weaker temperature dependences than mono-Si and multi-Si. The efficiencies of IBC, PERC, and SHJ at 25°C are 19.00, 18.81 to 19.25,and 21.40%, whereas those of mono-Si and multi-Si are 15.84 to 17.26 and 15.54%, respectively. Thus, IBC, PERC, and SHJ are more efficient than mono-Si and multi-Si in the temperature range higher than 25°C.

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“…They are validated using Eqs. (9), (10), (12), and (13). The data in Table III indicate that our approximation has an accuracy of 6%.…”

Section: Analysis Of Tcmentioning

confidence: 72%

Temperature dependence measurements and performance analyses of high-efficiency interdigitated back-contact, passivated emitter and rear cell, and silicon heterojunction photovoltaic modules

Kasu

Abdu

Hara

et al. 2018

Jpn. J. Appl. Phys.

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“…They can broadly be classified in three main cate-gories: analytical, approximate and simulation-based methods [4]. Analytical methods are based on convolution processes which use a fast Fourier transform and linearized power flow equations [5]; or work with a multilinear model to deal with the power system nonlinearities [6]. Approximate methods replace the probability distributions of the random variables by their statistical moments [7].…”

Section: Introductionmentioning

confidence: 99%

“…Approximate methods replace the probability distributions of the random variables by their statistical moments [7]. They include the point estimate method [7] and the unscented transformation methods [6]. Finally, simulation-based methods are based on statistical sampling of input random variables (active and reactive powers injected) and the propagation of these samples through repetitive deterministic power flow (DPF) solutions [4].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Probabilistic Power Flow Analysis Using Jacobian Approximate Bayesian Computation

Zuluaga

Álvarez

2018

IEEE Trans. Power Syst.

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Abstract-A probabilistic power flow (PPF) study is an essential tool for the analysis and planning of a power system when specific variables are considered as random variables with particular probability distributions. The most widely used method for solving the PPF problem is Monte Carlo simulation (MCS). Although MCS is accurate for obtaining the uncertainty of the state variables, it is also computationally expensive, since it relies on repetitive deterministic power flow solutions. In this paper, we introduce a different perspective for the PPF problem. We frame the PPF as a probabilistic inference problem, and instead of repetitively solving optimization problems, we use Bayesian inference for computing posterior distributions over state variables. Additionally, we provide a likelihood-free method based on the Approximate Bayesian Computation philosophy, that incorporates the Jacobian computed from the power flow equations. Results in three different test systems show that the proposed methodologies are competitive alternatives for solving the PPF problem, and in some cases, they allow for reduction in computation time when compared to MCS.

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“…References [5,6] provided critical reviews on the probabilistic methods studying the uncertainties in power systems. The methods of solving P-OPF problems could be classified into three categories: simulation methods, approximation methods and analytical methods.…”

Section: Introductionmentioning

confidence: 99%

A Novel Probabilistic Optimal Power Flow Method to Handle Large Fluctuations of Stochastic Variables

Deng

Zhang

2017

Energies

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Abstract:The traditional cumulant method (CM) for probabilistic optimal power flow (P-OPF) needs to perform linearization on the Karush-Kuhn-Tucker (KKT) first-order conditions, therefore requiring input variables (wind power or loads) varying within small ranges. To handle large fluctuations resulting from large-scale wind power and loads, a novel P-OPF method is proposed, where the correlations among input variables are also taken into account. Firstly, the inverse Nataf transformation and Cholesky decomposition are used to obtain samples of wind speeds and loads with a given correlation matrix. Then, the K-means algorithm is introduced to group the samples of wind power outputs and loads into a number of clusters, so that in each cluster samples of stochastic variables have small variances. In each cluster, the CM for P-OPF is conducted to obtain the cumulants of system variables. According to these cumulants, the moments of system variables corresponding to each cluster are computed. The moments of system variables for the total samples are obtained by combining the moments for all grouped clusters through the total probability formula. Then, the moments for the total samples are used to calculate the corresponding cumulants. Finally, Cornish-Fisher expansion is introduced to obtain the probability density functions (PDFs) of system variables. IEEE 9-bus and 118-bus test systems are modified to examine the proposed method. Study results show that the proposed method can produce more accurate results than traditional CM for P-OPF and is more efficient than Monte Carlo simulation (MCS).

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A critical review on probabilistic load flow studies in uncertainty constrained power systems with photovoltaic generation and a new approach

Cited by 169 publications

References 94 publications

Temperature dependence measurements and performance analyses of high-efficiency interdigitated back-contact, passivated emitter and rear cell, and silicon heterojunction photovoltaic modules

Temperature dependence measurements and performance analyses of high-efficiency interdigitated back-contact, passivated emitter and rear cell, and silicon heterojunction photovoltaic modules

Bayesian Probabilistic Power Flow Analysis Using Jacobian Approximate Bayesian Computation

A Novel Probabilistic Optimal Power Flow Method to Handle Large Fluctuations of Stochastic Variables

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