Effectiveness of antithetic sampling and stratified sampling in Monte Carlo chronological production cost modeling (power systems)

1991 ◽  
Vol 6 (2) ◽  
pp. 669-675 ◽  
Author(s):  
C. Marnay ◽  
T. Strauss
Keyword(s):  
Monte Carlo ◽  
Power Systems ◽  
Production Cost ◽  
Stratified Sampling ◽  
Cost Modeling ◽  
Modeling Power ◽  
Antithetic Sampling

Markov Jump Linear System Analysis of a Microgrid Operating in Islanded and Grid Tied Modes

2020 ◽  
Author(s):  
Gilles Mpembele ◽  
Jonathan Kimball
Keyword(s):  
Monte Carlo ◽  
Power Systems ◽  
Power System ◽  
System Analysis ◽  
Differential Algebraic Equations ◽  
Algebraic Equations ◽  
Markov Jump ◽  
Numerical Application ◽  
Conditional Moments ◽  
Markov Jump Linear Systems

<div>The analysis of power system dynamics is usually conducted using traditional models based on the standard nonlinear differential algebraic equations (DAEs). In general, solutions to these equations can be obtained using numerical methods such as the Monte Carlo simulations. The use of methods based on the Stochastic Hybrid System (SHS) framework for power systems subject to stochastic behavior is relatively new. These methods have been successfully applied to power systems subjected to</div><div>stochastic inputs. This study discusses a class of SHSs referred to as Markov Jump Linear Systems (MJLSs), in which the entire dynamic system is jumping between distinct operating points, with different local small-signal dynamics. The numerical application is based on the analysis of the IEEE 37-bus power system switching between grid-tied and standalone operating modes. The Ordinary Differential Equations (ODEs) representing the evolution of the conditional moments are derived and a matrix representation of the system is developed. Results are compared to the averaged Monte Carlo simulation. The MJLS approach was found to have a key advantage of being far less computational expensive.</div>

scholarly journals Identification of Efficient Sampling Techniques for Probabilistic Voltage Stability Analysis of Renewable-Rich Power Systems

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2328
Author(s):  
Mohammed Alzubaidi ◽  
Kazi N. Hasan ◽  
Lasantha Meegahapola ◽  
Mir Toufikur Rahman
Keyword(s):  
Monte Carlo ◽  
Stability Analysis ◽  
Power Systems ◽  
Large Scale ◽  
Voltage Stability ◽  
Sampling Technique ◽  
Coefficient Of Determination ◽  
Sampling Techniques ◽  
Quasi Monte Carlo ◽  
Efficient Sampling

This paper presents a comparative analysis of six sampling techniques to identify an efficient and accurate sampling technique to be applied to probabilistic voltage stability assessment in large-scale power systems. In this study, six different sampling techniques are investigated and compared to each other in terms of their accuracy and efficiency, including Monte Carlo (MC), three versions of Quasi-Monte Carlo (QMC), i.e., Sobol, Halton, and Latin Hypercube, Markov Chain MC (MCMC), and importance sampling (IS) technique, to evaluate their suitability for application with probabilistic voltage stability analysis in large-scale uncertain power systems. The coefficient of determination (R2) and root mean square error (RMSE) are calculated to measure the accuracy and the efficiency of the sampling techniques compared to each other. All the six sampling techniques provide more than 99% accuracy by producing a large number of wind speed random samples (8760 samples). In terms of efficiency, on the other hand, the three versions of QMC are the most efficient sampling techniques, providing more than 96% accuracy with only a small number of generated samples (150 samples) compared to other techniques.

scholarly journals Antithetic sampling for Monte Carlo differentiable rendering

2021 ◽  
Vol 40 (4) ◽  
pp. 1-12
Author(s):  
Cheng Zhang ◽  
Zhao Dong ◽  
Michael Doggett ◽  
Shuang Zhao
Keyword(s):  
Monte Carlo ◽  
Antithetic Sampling

scholarly journals Approach to Stochastic Modeling of Power Systems

2010 ◽  
Vol 26 (1) ◽  
pp. 37-40 ◽  
Author(s):  
Alexander Rubtsov
Keyword(s):  
Power Systems ◽  
Power System ◽  
Stochastic Modeling ◽  
Transfer Functions ◽  
Dynamic Properties ◽  
Stochastic Disturbance ◽  
Modeling Power ◽  
Linearized Model ◽  
System Power ◽  
Made In

Approach to Stochastic Modeling of Power SystemsThis paper presents an approach to modeling power system that contains sources of stochastic disturbance. It is based on frequency analysis of linearized model of power system. Power system dynamic properties are accounted by equivalent transfer functions of machines and their control equipment. This will allow more accurate calculations for different analysis tasks. Methodology of system linearization is proposed and results of linearized model test are delivered.The research was made in frame of a project with funding participation of the European Commission.

scholarly journals Stochastic comparisons of stratified sampling techniques for some Monte Carlo estimators

Bernoulli ◽  
2011 ◽  
Vol 17 (2) ◽  
pp. 592-608 ◽  
Author(s):  
Larry Goldstein ◽  
Yosef Rinott ◽  
Marco Scarsini
Keyword(s):  
Monte Carlo ◽  
Stratified Sampling ◽  
Sampling Techniques ◽  
Stochastic Comparisons
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