The Solution for Optimal Power Flow (OPF) Method Using Differential Evolution Algorithm

Hazel Ariantara; Sarjiya Sarjiya; Sasongko Pramono Hadi

doi:10.22146/ijitee.25141

The Solution for Optimal Power Flow (OPF) Method Using Differential Evolution Algorithm

https://doi.org/10.22146/ijitee.25141

Hazel Ariantara^(1*), Sarjiya Sarjiya⁽²⁾, Sasongko Pramono Hadi⁽³⁾

(1) Dept. of Electrical Engineering and Information Technology, Faculty of Engineering, Universitas Gadjah Mada
(2) Dept. of Electrical Engineering and Information Technology, Faculty of Engineering, Universitas Gadjah Mada
(3) Dept. of Electrical Engineering and Information Technology, Faculty of Engineering, Universitas Gadjah Mada
(*) Corresponding Author

Abstract

Optimal Power Flow (OPF) is one of techniques used to optimize the cost of power plant production while maintaining the limit of system reliability. In this paper, the application of differential evolution (DE) method is used to solve the OPF problem with variable control such as the power plant output, bus voltage tension, transformer tap, and injection capacitor. The effectiveness of the method was tested using IEEE 30 buses. The result shows that this method is better than generic algorithm (GA), particle swarm optimized (PSO), fuzzy GA, fuzzy PSO, and bat-algorithm. The simulation of the power plant systems of 500 kV Java-Bali with the proposed method can reduce the total cost of generation by 13.04% compared to the operating data PT. PLN (Persero).

Keywords

ptimal power flow, differential evolution, variable control

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References

[1] Hadi Saadat, Power System Analysis, New York: WCB McGraw-Hil, 1999.

[2] A.J. Wood and B.F. Wollenberg, Power Generation, Operation and Control”, New York: Wiley-Interscience, 1996.

[3] S. Frank, I. Steponavice, and S. Rebennack, “Optimal power flow: a bibliographic survey I”, Energy Syst., vol. 3, no. 3, pp. 221-258, Apr.2012.

[4] Frans van den Bergh and Andries P. Engelbrecht, “A cooperative approach to particle swarm optimization”, IEEE Transactions on Evolutionary Computation, vol. 8 ,no.3, pp. 225 – 239, 2004

[5] Kirschen DS dan Van Meeteren HP, “MW/Voltage control in linear programming based optimal power flow”, IEEE Trans Power Syst. 3 (4): 481 – 9, 1988.

[6] G.L.Torres and V.H. Quintana, “An interior-point method for nonlinear optimal power flow using rectangular coordinates”, Trans. Power Syst. 13 (4), 1211- 1218, 1998.

[7] Quintana, V.H., Santos – Nieto, M, “Reactive-power dispatch by successive quadratic programming”, IEEE Trans. Energy Convers.,4,(3), pp. 425 – 435, 1989.

[8] D.I. Sun, B. Ashley, B. Brewar, A. Hughes, and W.F. Tinny, “Optimal power flow by Newton approach”, IEEE Trans. Power Appar and Syst., 103,(2), pp.2864 – 2878, 1984.

[9] F.C. Lu and Y.Y. Hsu, “Reactive power / Voltage control in a distribution substation using dynamic programming”, IEEE Proceeding – Generation, Transmission and Distribution, 142(6), pp 639 – 645, 1995.

[10] K. Aoki, M. Fan, and A. Nishikori, “Optimal VAR planning by approximation method for recursive mixed-integer linear programming”, IEEE Transactions on power Systems, 3(4), pp 1741 – 1747, 1998.

[11] N. Deeb and S. M. Shahidehpour, “Linear reactive power optimization in a large power network using the decomposition approach”, IEEE Transactions on power systems, 5(2), pp.428 – 438, 1990.

[12] S. Granville, “Optimal reactive dispatch through interior point methods”, IEEE Transactions on power systems, 9 (1), pp. 136 – 146, 1994.

[13] M. A. Abido, “Optimal power flow using particle swarm optimization,” Electr. Power Energy Syst., vol. 24, pp. 563–571, 2002.

[14] Bakirtzis G., Biskas P. N., Zoumas C. E., and Petridis V., “Optimal Power Flow by Enhanced Genetic Algorithm,” IEEE Transaction on Power System, Vol. 17, No. 2, pp. 229-236, May 2002.

[15] Y. Sood, “Evolutionary programming based optimal power flow and its validation for deregulated power system analysis,” Int. J. Electr. Power Energy Syst., vol. 29, no. 1, pp. 65-75, Jan. 2007.

[16] W. Ongsakul, P. Bhasaputra.Optimal, “Power flow with FACTS devices by hybrid TS/SA approach”, International Journal of Electrical Power & Energy Systems, 24(10), pp.851-857, 2002.

[17] S. Kumar and D. K. Chaturvedi, “Optimal power flow solution using fuzzy evolutionary and swarm optimization,” Int. J. Electr. Power Energy Syst., vol. 47, pp. 416–423, May 2013.

[18] Storn, R and Price, K "Differential Evolution – A Simple and Efficientm Adaptive Scheme for Global Optimization over Continuous Spaces". Technical Report, International Computer Science Institute, Berkley, 1995.

[19] Price, K.V., Storn, R.M., and Lampinen, J.A., Differential Evolution: A Practical Approach to Global Optimization. Natural Computing Series. Berlin: Springer-Verlag, 2005.

[20] R. Storn and K. Price, Journal of Global Optimization, 11, pp.341-359, 1997.

[21] Aplikasi Algoritma Differential Evolution untuk Permasalahan Kompleks Pemilihan Portofolio. Accessed at http://digilib.its.ac.id/public/ITS-Undergraduate-12647-Paper.pdf. On 10 June 2014.

[22] K. Y. Lee, Y. M. Park, and J. L. Ortiz, “A United Approach to Optimal Real and Reactive Power Dispatch”, IEEE Trans. Power Appar. Syst., vol. PAS-104, no. 5, pp. 1147–1153,1985.

[23] Z. Abidin, “Studi optimal power flow menggunakan metode bat algorithm”, Thesis, Universitas Gadjah Mada, 2014.

[24] Yassir, “Studi optimal power flow sistem kelistrikan 500 kV Jawa Bali dengan menggunakan metode algoritma genetika”, Thesis, Universitas Gadjah Mada, 2013.

DOI: https://doi.org/10.22146/ijitee.25141