Journal ArticleDOI
An Interval Power Flow Analysis Through Optimizing-Scenarios Method
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TLDR
A novel optimizing-scenarios method (OSM) is envisaged in this paper to solve interval power flow problem to obtain more accurate results, and the overall simulation results demonstrate the effectiveness and robustness of the proposed OSM.Abstract:
Interval power flow (IPF) is a special kind of uncertain power flow problem whose demand load and active power generation are regarded as interval uncertainties. A novel optimizing-scenarios method (OSM) is envisaged in this paper to solve this problem to obtain more accurate results. The OSM includes two kinds of approaches, namely the minimum and maximum programming models, in which the interval uncertainties are regarded as variables with varying bounds, and the objective function under study is set to determine these unknown variables. By solving these nonlinear programming models through the interior point method, the changing variables of the IPF problems are determined. Performance of the proposed approach is compared with that of previously established methods, including the affine arithmetic-based method as well as Monte Carlo simulation method, and the overall simulation results demonstrate the effectiveness and robustness of the proposed OSM.read more
Citations
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Journal ArticleDOI
Dynamic Data Injection Attack Detection of Cyber Physical Power Systems With Uncertainties
TL;DR: A dynamic cyber-attack model with local network information is proposed to characterize the typical data injection attack with the integration of potential dynamic behaviors of an attacker, and a novel anomaly detection countermeasure is developed from the perspective of state estimation to effectively recognize the dynamic injection attack.
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Statistical machine learning model for capacitor planning considering uncertainties in photovoltaic power
TL;DR: In this article , a statistical machine learning (SML) technique is used to carry out multi-scenario based probabilistic power flow calculations and describes their application to the stochastic planning of distribution networks.
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Interval State Estimation With Uncertainty of Distributed Generation and Line Parameters in Unbalanced Distribution Systems
TL;DR: A modified Krawczyk-operator algorithm is proposed to solve the general ISE model efficiently, and effectively provides the upper and lower bounds of state variables under coordinated impacts of these uncertainties.
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Risk-Based Uncertainty Set Optimization Method for Energy Management of Hybrid AC/DC Microgrids With Uncertain Renewable Generation
TL;DR: A novel risk-based uncertainty set optimization method for the energy management of typical hybrid AC/DC microgrids, where RPG outputs are considered as the major uncertainties, and effectively solved using a high-performance solver.
Journal ArticleDOI
Interval Overvoltage Risk Based PV Hosting Capacity Evaluation Considering PV and Load Uncertainties
TL;DR: The definition of interval overvoltage probability (IOP) is introduced, and the IOP based PVHC evaluation method is presented, and a practical 55-bus rural feeder in China is used to illustrate the advantages and disadvantages of the proposed method compared with the conventional method.
References
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Journal ArticleDOI
Probabilistic load flow computation using the method of combined cumulants and Gram-Charlier expansion
Pei Zhang,S.T. Lee +1 more
TL;DR: In this paper, a probabilistic load flow analysis of transmission line flows is proposed for the purpose of using it as a quick screening tool to determine the major investment on improving transmission system inadequacy.
Journal ArticleDOI
Point Estimate Schemes to Solve the Probabilistic Power Flow
J.M. Morales,Juan Perez-Ruiz +1 more
TL;DR: In this article, four different Hong's point estimate schemes are presented and tested on the probabilistic power flow problem and compared against those obtained from the Monte Carlo simulation, showing that the use of the scheme provides the best performance when a high number of random variables, both continuous and discrete, are considered.
Journal ArticleDOI
Probabilistic load-flow computation using point estimate method
TL;DR: A new probabilistic load-flow solution algorithm based on an efficient point estimate method that can be used directly with any existing deterministic load- flow program and compared with those obtained from Monte Carlo simulation technique and combined simulation and analytical method.
Journal ArticleDOI
Probabilistic Load Flow Evaluation With Hybrid Latin Hypercube Sampling and Cholesky Decomposition
TL;DR: LHS-CD sampling method combined with Cholesky decomposition method is found to be robust and flexible and has the potential to be applied in many power system probabilistic problems.
Journal ArticleDOI
Evaluation Methods and Accuracy in Probabilistic Load Flow Solutions
TL;DR: This paper presents a new method for obtaining a probabilistic load flow solution using a discrete frequency domain convolution technique that has greater accuracy while providing a breakthrough in computational speed.