In this paper, point estimation approach is used to calculate the statistical moment of a random quantity that is a function of m input random variables. In this work, loads of the proposed network is considered as a random variable. Two special cases of point estimation approach are considered such as 2m and 2m+1 concentration schemes. In 2m concentration scheme, skewness is considered, but in 2m+1 concentration scheme, both skewness and kurtosis are taken into account for probability density function. The proposed model is investigated using P. M. Anderson 9-bus test system. As a result, by changing the value of a random variable that follows a predefined distribution, expected bus voltage magnitude and expected line loading are identified. For the comparison purpose, 2m and 2m+1 scheme was compared with deterministic load flow analysis.
| Published in | American Journal of Electrical Power and Energy Systems (Volume 6, Issue 5) |
| DOI | 10.11648/j.epes.20170605.11 |
| Page(s) | 64-71 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Probabilistic Power Flow, Point Estimation Method, Random Variable, Uncertainty Analysis
| [1] | V. G. Dovì, F. Friedler, D. Huisingh, and J. J. Klemeš, "Cleaner energy for sustainable future," Journal of Cleaner Production, vol. 17, pp. 889-895, 2009. |
| [2] | P. Basak, S. Chowdhury, S. H. nee Dey, and S. Chowdhury, "A literature review on integration of distributed energy resources in the perspective of control, protection and stability of microgrid," Renewable and Sustainable Energy Reviews, vol. 16, pp. 5545-5556, 2012. |
| [3] | L. Tang, F. Wen, M. A. Salam, and S. P. Ang, "Transmission system planning considering integration of renewable energy resources," in 2015 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), 2015, pp. 1-5. |
| [4] | X. Liang, "Emerging Power Quality Challenges Due to Integration of Renewable Energy Sources," IEEE Transactions on Industry Applications, vol. 53, pp. 855-866, 2017. |
| [5] | S. Chun-Lien, "Probabilistic load-flow computation using point estimate method," IEEE Transactions on Power Systems, vol. 20, pp. 1843-1851, 2005. |
| [6] | J. Usaola, "Probabilistic load flow in systems with wind generation," IET Generation, Transmission & Distribution, vol. 3, pp. 1031-1041, 2009. |
| [7] | M. Nassar and M. Salama, "Probabilistic power flow using novel wind and solar probabilistic models," in Power and Energy Society General Meeting (PESGM), 2016, 2016, pp. 1-5. |
| [8] | F. Ni, P. Nguyen, J. Cobben, and J. Tang, "Application of non-intrusive polynomial chaos expansion in probabilistic power flow with truncated random variables," in Probabilistic Methods Applied to Power Systems (PMAPS), 2016 International Conference on, 2016, pp. 1-7. |
| [9] | S. Peng, J. Tang, and W. Li, "Probabilistic Power Flow for AC/VSC-MTDC Hybrid Grids Considering Rank Correlation among Diverse Uncertainty Sources," IEEE Transactions on Power Systems, 2016. |
| [10] | F. Ni, P. H. Nguyen, and J. F. Cobben, "Basis-Adaptive Sparse Polynomial Chaos Expansion for Probabilistic Power Flow," IEEE Transactions on Power Systems, vol. 32, pp. 694-704, 2017. |
| [11] | M. Hajian, W. D. Rosehart, and H. Zareipour, "Probabilistic Power Flow by Monte Carlo Simulation With Latin Supercube Sampling," IEEE Transactions on Power Systems, vol. 28, pp. 1550-1559, 2013. |
| [12] | X. Xu and Y. Gao, "Simulation on the active voltage balancing of series connected IGBTs by Monte Carlo analysis," in 2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA), 2016, pp. 630-635. |
| [13] | P. Jorgensen, J. Christensen, and J. Tande, "Probabilistic load flow calculation using Monte Carlo techniques for distribution network with wind turbines," in Harmonics and Quality of Power Proceedings, 1998. Proceedings. 8th International Conference On, 1998, pp. 1146-1151. |
| [14] | P. Zhang and S. T. Lee, "Probabilistic load flow computation using the method of combined cumulants and Gram-Charlier expansion," IEEE transactions on power systems, vol. 19, pp. 676-682, 2004. |
| [15] | T. Williams and C. Crawford, "Probabilistic load flow modeling comparing maximum entropy and Gram-Charlier probability density function reconstructions," IEEE Transactions on Power Systems, vol. 28, pp. 272-280, 2013. |
| [16] | Y. Chen, J. Wen, and S. Cheng, "Probabilistic load flow method based on Nataf transformation and Latin hypercube sampling," IEEE Transactions on Sustainable Energy, vol. 4, pp. 294-301, 2013. |
| [17] | G. Verbic and C. A. Canizares, "Probabilistic optimal power flow in electricity markets based on a two-point estimate method," IEEE Transactions on Power Systems, vol. 21, pp. 1883-1893, 2006. |
| [18] | N. Soleimanpour and M. Mohammadi, "Probabilistic load flow by using nonparametric density estimators," IEEE Transactions on Power Systems, vol. 28, pp. 3747-3755, 2013. |
| [19] | C. Long, M. E. A. Farrag, D. M. Hepburn, and C. Zhou, "Point Estimate Method for Voltage Unbalance Evaluation in Residential Distribution Networks with High Penetration of Small Wind Turbines," Energies, vol. 7, pp. 7717-7731, 2014. |
| [20] | S. Mohammadi, B. Mozafari, and S. Solimani, "Optimal operation management of microgrids using the point estimate method and firefly algorithm while considering uncertainty," Turkish Journal of Electrical Engineering & Computer Sciences, vol. 22, pp. 735-753, 2014. |
| [21] | C.-L. Su, "A new probabilistic load flow method," in Power Engineering Society General Meeting, 2005. IEEE, 2005, pp. 389-394. |
| [22] | P. M. Anderson and A. A. Fouad, Power system control and stability: John Wiley & Sons, 2008. |
| [23] | P. Sauer and M. Pai, "Power System Dynamics and Stability, Prentice-Hall," New Jersey, 1998. |
APA Style
Li Bin, Muhammad Shahzad, Qi Bing, Muhammad Raza Zafar, Rabiul Islam, et al. (2017). Probabilistic Power Flow Model to Study Uncertainty in Power System Network Based Upon Point Estimation Method. American Journal of Electrical Power and Energy Systems, 6(5), 64-71. https://doi.org/10.11648/j.epes.20170605.11
ACS Style
Li Bin; Muhammad Shahzad; Qi Bing; Muhammad Raza Zafar; Rabiul Islam, et al. Probabilistic Power Flow Model to Study Uncertainty in Power System Network Based Upon Point Estimation Method. Am. J. Electr. Power Energy Syst. 2017, 6(5), 64-71. doi: 10.11648/j.epes.20170605.11
AMA Style
Li Bin, Muhammad Shahzad, Qi Bing, Muhammad Raza Zafar, Rabiul Islam, et al. Probabilistic Power Flow Model to Study Uncertainty in Power System Network Based Upon Point Estimation Method. Am J Electr Power Energy Syst. 2017;6(5):64-71. doi: 10.11648/j.epes.20170605.11
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.epes.20170605.11,
author = {Li Bin and Muhammad Shahzad and Qi Bing and Muhammad Raza Zafar and Rabiul Islam and Muhammad Umair Shoukat},
title = {Probabilistic Power Flow Model to Study Uncertainty in Power System Network Based Upon Point Estimation Method},
journal = {American Journal of Electrical Power and Energy Systems},
volume = {6},
number = {5},
pages = {64-71},
doi = {10.11648/j.epes.20170605.11},
url = {https://doi.org/10.11648/j.epes.20170605.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.epes.20170605.11},
abstract = {In this paper, point estimation approach is used to calculate the statistical moment of a random quantity that is a function of m input random variables. In this work, loads of the proposed network is considered as a random variable. Two special cases of point estimation approach are considered such as 2m and 2m+1 concentration schemes. In 2m concentration scheme, skewness is considered, but in 2m+1 concentration scheme, both skewness and kurtosis are taken into account for probability density function. The proposed model is investigated using P. M. Anderson 9-bus test system. As a result, by changing the value of a random variable that follows a predefined distribution, expected bus voltage magnitude and expected line loading are identified. For the comparison purpose, 2m and 2m+1 scheme was compared with deterministic load flow analysis.},
year = {2017}
}
TY - JOUR T1 - Probabilistic Power Flow Model to Study Uncertainty in Power System Network Based Upon Point Estimation Method AU - Li Bin AU - Muhammad Shahzad AU - Qi Bing AU - Muhammad Raza Zafar AU - Rabiul Islam AU - Muhammad Umair Shoukat Y1 - 2017/08/22 PY - 2017 N1 - https://doi.org/10.11648/j.epes.20170605.11 DO - 10.11648/j.epes.20170605.11 T2 - American Journal of Electrical Power and Energy Systems JF - American Journal of Electrical Power and Energy Systems JO - American Journal of Electrical Power and Energy Systems SP - 64 EP - 71 PB - Science Publishing Group SN - 2326-9200 UR - https://doi.org/10.11648/j.epes.20170605.11 AB - In this paper, point estimation approach is used to calculate the statistical moment of a random quantity that is a function of m input random variables. In this work, loads of the proposed network is considered as a random variable. Two special cases of point estimation approach are considered such as 2m and 2m+1 concentration schemes. In 2m concentration scheme, skewness is considered, but in 2m+1 concentration scheme, both skewness and kurtosis are taken into account for probability density function. The proposed model is investigated using P. M. Anderson 9-bus test system. As a result, by changing the value of a random variable that follows a predefined distribution, expected bus voltage magnitude and expected line loading are identified. For the comparison purpose, 2m and 2m+1 scheme was compared with deterministic load flow analysis. VL - 6 IS - 5 ER -
Information
Email: