**December 20, 2022**. Read More

10.5120/ijca2020919757 |

Vian Hasan Ahgajan and Firas Mohammed Tuaimah. Optimal Power Flow for a Power System under Particle Swarm Optimization (PSO) based. *International Journal of Computer Applications* 177(33):56-62, January 2020. BibTeX

@article{10.5120/ijca2020919757, author = {Vian Hasan Ahgajan and Firas Mohammed Tuaimah}, title = {Optimal Power Flow for a Power System under Particle Swarm Optimization (PSO) based}, journal = {International Journal of Computer Applications}, issue_date = {January 2020}, volume = {177}, number = {33}, month = {Jan}, year = {2020}, issn = {0975-8887}, pages = {56-62}, numpages = {7}, url = {http://www.ijcaonline.org/archives/volume177/number33/31125-2020919757}, doi = {10.5120/ijca2020919757}, publisher = {Foundation of Computer Science (FCS), NY, USA}, address = {New York, USA} }

### Abstract

Optimal power flow (OPF) is one of the most vital tool for power system operation analysis, which require complex mathematical formulation to find best solution .particle swarm optimization is one among many methods for solving nonlinear optimization problems and it one of the swarm intelligences. Optimal power flow is one of nonlinear constrained and occasionally combinatorial optimization problems of power system the objective of an optimal power flow is to find steady state operation point which minimizes generation cost, loss, load ability. The OPF solution includes an objective function. A common objective function concerns the active power generation cost. A particle swarm optimization (PSO) is proposed to solve OPF problem. After solving OPF problem the results of PSO would be compered by using many methods such as linear programming, genetic algorithm .The proposed PSO is verified by IEEE-30 in all case studies PSO shown to achieve a lower cost and losses than it when there is line outage and generator outage.

Conventional load flow is used to perform the equality constraints. A computer program, written in MATLAB environment, is developed to represent the proposed method.

### References

- M. R. Adaryani and A. Karami, “Artificial bee colony algorithm for solving multi-objective optimal power flow problem,” Int. J. Electr. Power Energy Syst., vol. 53, pp. 219–230, 2013.
- W. Bai, M. R. Abedi, and K. Y. Lee, “Distributed generation system control strategies with PV and fuel cell in microgrid operation,” Control Eng. Pract., vol. 53, pp. 184–193, 2016.
- M. E. El-Hawary and D. H. Tsang, “The hydrothermal optimal load flow, a practical formulation and solution techniques using Newton’s approach,” IEEE Trans. power Syst., vol. 1, no. 3, pp. 157–166, 1986.
- E. Acha, H. Ambriz-Perez, and C. R. Fuerte-Esquivel, “Advanced transformer control modeling in an optimal power flow using Newton’s method,” IEEE Trans. power Syst., vol. 15, no. 1, pp. 290–298, 2000.
- J. Carpentier, “Contribution a l’etude du dispatching economique,” Bull. la Soc. Fr. des Electr., vol. 3, no. 1, pp. 431–447, 1962.
- R. C. Burchett, H. H. Happ, and D. R. Vierath, “Quadratically convergent optimal power flow,” IEEE Trans. Power Appar. Syst., no. 11, pp. 3267–3275, 1984.
- H. Wei, H. Sasaki, J. Kubokawa, and R. Yokoyama, “An interior point nonlinear programming for optimal power flow problems with a novel data structure,” IEEE Trans. Power Syst., vol. 13, no. 3, pp. 870–877, 1998.
- R. R. Shoults and D. T. Sun, “Optimal power flow based upon PQ decomposition,” IEEE Trans. Power Appar. Syst., no. 2, pp. 397–405, 1982.
- K. Y. Lee and M. A. El-Sharkawi, Modern heuristic optimization techniques: theory and applications to power systems, vol. 39. John Wiley & Sons, 2008.
- M. A. Abido, “Optimal power flow using tabu search algorithm,” Electr. power components Syst., vol. 30, no. 5, pp. 469–483, 2002.
- T. Sousa, J. Soares, Z. A. Vale, H. Morais, and P. Faria, “Simulated annealing metaheuristic to solve the optimal power flow,” in Power and Energy Society General Meeting, 2011 IEEE, 2011, pp. 1–8.
- A. A. A. El Ela, M. A. Abido, and S. R. Spea, “Optimal power flow using differential evolution algorithm,” Electr. Power Syst. Res., vol. 80, no. 7, pp. 878–885, 2010.
- A. G. Bakirtzis, P. N. Biskas, C. E. Zoumas, and V. Petridis, “Optimal power flow by enhanced genetic algorithm,” IEEE Trans. power Syst., vol. 17, no. 2, pp. 229–236, 2002.
- J.-B. Park, Y.-W. Jeong, J.-R. Shin, and K. Y. Lee, “An improved particle swarm optimization for nonconvex economic dispatch problems,” IEEE Trans. Power Syst., vol. 25, no. 1, pp. 156–166, 2010.
- S. Sivasubramani and K. S. Swarup, “Multi-objective harmony search algorithm for optimal power flow problem,” Int. J. Electr. Power Energy Syst., vol. 33, no. 3, pp. 745–752, 2011.
- A. J. Wood and B. F. Wollenberg, Power generation, operation, and control. John Wiley & Sons, 2012.
- O. Alsac, J. Bright, M. Prais, and B. Stott, “Further developments in LP-based optimal power flow,” IEEE Trans. Power Syst., vol. 5, no. 3, pp. 697–711, 1990.
- R. Kennedy, “J. and Eberhart, Particle swarm optimization,” in Proceedings of IEEE International Conference on Neural Networks IV, pages, 1995, vol. 1000.
- A. J. WOOD and B. Wollenberg, “Power generation operation and control–Second Edition–John Wiley & Sons.” Inc, 1996.
- D. Ben Attous and Y. Labbi, “Particle swarm optimization based optimal power flow for units with non-smooth fuel cost functions,” in Electrical and Electronics Engineering, 2009. ELECO 2009. International Conference on, 2009, p. I-377.
- K. Zehar and S. Sayah, “Optimal power flow with environmental constraint using a fast successive linear programming algorithm: Application to the algerian power system,” Energy Convers. Manag., vol. 49, no. 11, pp. 3362–3366, 2008.
- D. E. Goldberg, “Genetic algorithm,” Search, Optim. Mach. Learn., pp. 343–349, 1989.
- and B. S. Alsac, O., “Optimal load flow with steady-state security,” IEEE Trans. power Appar. Syst., no. 3, pp. 745–751, 1974.
- 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., no. 5, pp. 1147–1153, 1985.
- S. Duman, U. Güvenç, Y. Sönmez, and N. Yörükeren, “Optimal power flow using gravitational search algorithm,” Energy Convers. Manag., vol. 59, pp. 86–95, 2012.
- T. Niknam, M. rasoul Narimani, M. Jabbari, and A. R. Malekpour, “A modified shuffle frog leaping algorithm for multi-objective optimal power flow,” Energy, vol. 36, no. 11, pp. 6420–6432, 2011.

### Keywords

Optimal power flow, particle swarm optimization, economic dispatch