Probabilistic Load Flow Solution Considering Optimal Allocation of SVC in Radial Distribution System
DOI:
https://doi.org/10.9781/ijimai.2018.11.001Keywords:
Particle Swarm Optimization, Radial Distribution System, Probabilistic Load Flow, SVCAbstract
This paper proposes a solution procedure for probabilistic load flow problem considering the optimal allocation of Static Var Compensator (SVC) in radial distribution systems. Pareto Envelope-based Selection Algorithm II (PESA-II) with fuzzy logic decision maker is developed to determine the optimal location and size of SVC based on the minimum total power losses and Voltage Deviation (VD). Combined cumulants and gram-chalier expansion are used for solving the probabilistic load flow problem. The proposed algorithm is tested on 33-bus and 69-bus distribution systems. The developed algorithm gives an acceptable solution with low number of iterations and less computation cost compared with the Monte Carlo method.Downloads
References
M. Rahmani-andebili, “Reliability and economic-driven switchable capacitor placement in distribution network,” IET Generation, Transmission & Distribution, vol. 9, pp. 1572-1579, 2015.
E. Shahryari, H. Shayeghi, and M. Moradzadeh, “Probabilistic and Multi-Objective Placement of D-STATCOM in Distribution Systems Considering Load Uncertainty,” Electric Power Components and Systems, vol. 46, pp. 27-42, 2018.
A. Selim, M. Abdel-Akher, M. M. Aly, S. Kamel, and T. Senjyu, “Fast quasi-static time-series analysis and reactive power control of unbalanced distribution systems,” International Transactions on Electrical Energy Systems, vol. 0, p. e2673, 2018.
M. M. Farsangi, H. Nezamabadi-pour, Y.-H. Song, and K. Y. Lee, “Placement of SVCs and selection of stabilizing signals in power systems,” IEEE Transactions on Power Systems, vol. 22, pp. 1061-1071, 2007.
M. Saravanan, S. M. R. Slochanal, P. Venkatesh, and J. P. S. Abraham, “Application of particle swarm optimization technique for optimal location of FACTS devices considering cost of installation and system loadability,” Electric Power Systems Research, vol. 77, pp. 276-283, 2007.
S. Kamel, F. Jurado, and D. Vera, “A simple implementation of power mismatch STATCOM model into current injection Newton–Raphson power-flow method,” Electrical Engineering, vol. 96, pp. 135-144, 2014.
M. Abdel-Nasser, K. Mahmoud, and H. Kashef, “A Novel Smart Grid State Estimation Method Based on Neural Networks,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 5, 2018.
K. Mahmoud and M. Abdel-Nasser, “Fast-yet-Accurate Energy Loss Assessment Approach for Analyzing/Sizing PV in Distribution Systems using Machine Learning,” IEEE Transactions on Sustainable Energy, 2018.
A.G.Azar, M.Afsharchi, M.Davoodi, and B.S. Bigham, “A multiobjective market-driven framework for power matching in the smart grid,” Engineering Applications of Artificial Intelligence, 70, pp.199-215, 2018.
E. Dall’Anese, and A. Simonetto, “Optimal power flow pursuit,” IEEE Transactions on Smart Grid, 9(2), pp.942-952, 2018.
T. Yang and Y. Yu, “Steady-state Security Region-based Voltage/Var Optimization Considering Power Injection Uncertainties in Distribution Grids,” IEEE Transactions on Smart Grid, 2018.
R. Benabid, M. Boudour, and M. Abido, “Optimal location and setting of SVC and TCSC devices using non-dominated sorting particle swarm optimization,” Electric Power Systems Research, vol. 79, pp. 1668-1677, 2009.
G. Kour, G. Brar, and J. Dhiman, “Improvement by voltage profile by static Var compensators in distribution substation,” International Journal of Instrumentation Science, vol. 1, pp. 21-24, 2012.
J. D. Knowles and D. W. Corne, “Approximating the nondominated front using the Pareto archived evolution strategy,” Evolutionary computation, vol. 8, pp. 149-172, 2000.
K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE transactions on evolutionary computation, vol. 6, pp. 182-197, 2002.
E. Zitzler, M. Laumanns, and L. Thiele, “SPEA2: Improving the strength Pareto evolutionary algorithm,” TIK-report, vol. 103, 2001.
C. A. C. Coello, G. T. Pulido, and M. S. Lechuga, “Handling multiple objectives with particle swarm optimization,” IEEE Transactions on evolutionary computation, vol. 8, pp. 256-279, 2004.
D. W. Corne, J. D. Knowles, and M. J. Oates, “The Pareto envelope-based selection algorithm for multiobjective optimization,” in International conference on parallel problem solving from nature, 2000, pp. 839-848.
G. T. Temur, “A novel multi attribute decision making approach for location decision under high uncertainty,” Applied Soft Computing, vol. 40, pp. 674-682, 2016.
F. Ruiz-Rodriguez, J. Hernandez, and F. Jurado, “Probabilistic load flow for photovoltaic distributed generation using the Cornish–Fisher expansion,” Electric Power Systems Research, vol. 89, pp. 129-138, 2012.
X.-F. Wang, Y. Song, and M. Irving, Modern power systems analysis: Springer Science & Business Media, 2010.
B. Borkowska, “Probabilistic load flow,” IEEE Transactions on Power Apparatus and Systems, pp. 752-759, 1974.
B. B. a. C. H. G. R. N. Allan, “Probabilistic analysis of power flows,” Proceedings of the Institution of Electrical Engineers (London), vol. 121, p. 12, Dec. 1974
R. Allan and A. L. Da Silva, “Probabilistic load flow using multilinearisations,” in IEE Proceedings C (Generation, Transmission and Distribution), 1981, pp. 280-287.
A. L. Da Silva, R. Allan, S. Soares, and V. Arienti, “Probabilistic load flow considering network outages,” in IEE Proceedings C (Generation, Transmission and Distribution), 1985, pp. 139-145.
F. Ruiz-Rodriguez, J. Hernandez, and F. Jurado, “Probabilistic load flow for radial distribution networks with photovoltaic generators,” IET renewable power generation, vol. 6, pp. 110-121, 2012.
F. Ruiz-Rodriguez, M. Gomez-Gonzalez, and F. Jurado, “Reliability optimization of an electric power system by biomass fuelled gas engine,” International Journal of Electrical Power & Energy Systems, vol. 61, pp. 81-89, 2014.
G. J. Anders, Probability Concepts in Electric Power Systems New York: Wiley, January 1990.
P. Amid, and C.Crawford, “A Cumulant-Tensor Based Probabilistic Load Flow Method,” IEEE Transactions on Power Systems, 2018.
W. Li, Probabilistic transmission system planning vol. 65: John Wiley & Sons, 2011.
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.
J. M. Morales and J. Perez-Ruiz, “Point estimate schemes to solve the probabilistic power flow,” IEEE Transactions on Power Systems, vol. 22, pp. 1594-1601, 2007.
J. Liu, M. Salama, and R. Mansour, “An efficient power flow algorithm for distribution systems with polynomial load,” International Journal of Electrical Engineering Education, vol. 39, pp. 371-386, 2002.
M. Baran and F. F. Wu, “Optimal sizing of capacitors placed on a radial distribution system,” IEEE Transactions on power Delivery, vol. 4, pp. 735-743, 1989.
Downloads
Published
-
Abstract47
-
PDF22
Submit Article
Information
