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MULTI-OBJECTIVE OPTIMAL POWER FLOW PROBLEM USING ENHANCED FLOWER POLLINATION ALGORITHM

Year 2017, Volume: 30 Issue: 1, 79 - 91, 14.03.2017

Abstract

This paper presents an Enhanced flower Pollination Algorithm (EFPA) to solve the optimal power flow (OPF) problem. This paper considers OPF problem with multiple objectives of minimizing generating cost, transmission loss and power plants emission and to improve voltage stability. Generating cost is a function of real power generation of all the generating units. Transmission loss depends on bus voltages and reactive power support in the system. Power plant emission is once again a function of real power and voltage stability is a function of bus voltages and reactive power support. In the optimization problem for real power generation, generator bus voltages, transformer tap positions and injected reactive power support may be considered as control variables. Set of these control variables from a meta-heuristic approach. Enhanced flower pollination strategy may yield a better solution for multi objective problem. This optimization algorithm is compare with other optimization algorithms and the comparison proves the ability of EFPA has given the best results to solve multi objective OPF problem. To evaluate EFPA based multi objective OPF, standard IEEE 30 test case is considered.

References

  • Dommel, Hermann W., and William F. Tinney. "Optimal power flow solutions." IEEE Transactions on power apparatus and systems 10 (1968): 1866-1876.
  • Alsac, O., and B. Stott. "Optimal load flow with steady-state security." IEEE transactions on power apparatus and systems 3 (1974): 745-751.
  • Momoh, James A. "A generalized quadratic-based model for optimal power flow." Systems, Man and Cybernetics, 1989. Conference Proceedings., IEEE International Conference on. IEEE, 1989.
  • Momoh, J. A., et al. "Application of interior point method to economic dispatch." Systems, Man and Cybernetics, 1992., IEEE International Conference on. IEEE, 1992.
  • Momoh, James A., M. E. El-Hawary, and Ramababu Adapa. "A review of selected optimal power flow literature to 1993. Part I: Nonlinear and quadratic programming approaches." IEEE transactions on power systems 14.1 (1999): 96-104.
  • Wei, Hua, et al. "An interior point nonlinear programming for optimal power flow problems with a novel data structure." IEEE Transactions on Power Systems 13.3 (1998): 870-877.
  • Anastasios G. Bakirtzis, Pandel N. Biskas, Christoforos E. Zoumas and Vasilios Petridis, “Optimal Power Flow by Enhanced Genetic Algorithm”, IEEE transactions on power systems, Vol. 17, No. 2, (2002), pp. 229–236.
  • Bakirtzis, Anastasios G., et al. "Optimal power flow by enhanced genetic algorithm." IEEE Transactions on power Systems 17.2 (2002): 229-236.
  • D.P. Kothari and J. S. Dhillon, "Power System Optimization", Prentice-Hall of India, 2004, 2nd Edition, 2011.
  • Cai, H. R., C. Y. Chung, and K. P. Wong. "Application of differential evolution algorithm for transient stability constrained optimal power flow."IEEE Transactions on Power Systems 23.2 (2008): 719-728.
  • Vaisakh, K., and L. R. Srinivas. "Differential Evolution Approach For Optimal Power Flow Solution." Journal of Theoretical & Applied Information Technology 4.4 (2008).
  • Yuryevich, Jason, and Kit Po Wong. "Evolutionary programming based optimal power flow algorithm." IEEE Transactions on Power Systems 14.4 (1999): 1245-1250.
  • Abido, Mohammad Ali. "Multiobjective evolutionary algorithms for electric power dispatch problem." IEEE transactions on evolutionary computation10.3 (2006): 315-329.
  • Abido, M. A. "Optimal power flow using particle swarm optimization."International Journal of Electrical Power & Energy Systems 24.7 (2002): 563-571.
  • Niknam, T., et al. "Improved particle swarm optimisation for multi-objective optimal power flow considering the cost, loss, emission and voltage stability index." IET generation, transmission & distribution 6.6 (2012): 515-527.
  • Sayah, Samir, and Khaled Zehar. "Modified differential evolution algorithm for optimal power flow with non-smooth cost functions." Energy conversion and Management 49.11 (2008): 3036-3042.
  • Bouktir, T., Slimani, L., Mahdad, B.: ‘Optimal power dispatch for large scale power system using stochastic search algorithms’, Int. J Power Energy Syst., 2008, 28, (1), pp. 1–10
  • Slimani, L., Bouktir, T.: ‘Economic power dispatch of power system with pollution control using multi objective ant colony optimization’, Int. J. Comput. Intell. Res., 2007, 3, (2), pp. 145–153.
  • Rosales Hernandez, Y., Hiyama, T.: ‘Minimization of voltage deviations, power losses and control actions in a transmission power system’. 15th Int. Conf. on Intelligent System Applications to Power Systems (ISAP), 8–12 November 2009, pp. 1–5
  • Sakthivel, S., et al. "Optimal Reactive Power Dispatch Problem Solved By Using Flower Pollination Algorithm." International Journal of Applied Engineering Research 11.6 (2016): 4387-4391.
  • Emilio, B. E., & Cuevas, E. “Optimal power flow solution using Modified Flower Pollination Algorithm”. In 2015 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) (2015, November) (pp. 1-6). IEEE.
  • A baci, Kadir, and Volkan Yamacli. "Differential search algorithm for solving multi-objective optimal power flow problem." International Journal of Electrical Power & Energy Systems 79 (2016): 1-10.
  • Bouchekara, H. R. E. H., A. E. Chaib, M. A. Abido, and R. A. El-Sehiemy. "Optimal power flow using an Improved Colliding Bodies Optimization algorithm." Applied Soft Computing 42 (2016): 119-131.
  • Reddy, Salkuti Surender, and Ch Srinivasa Rathnam. "Optimal Power Flow using Glowworm Swarm Optimization." International Journal of Electrical Power & Energy Systems 80 (2016): 128-139
  • Xin-She Yang, “Enhanced flower pollination algorithm for global optimization,” in: Unconventional Computation and Natural Computation 2012, Lecture Notes in Computer Science, Vol. 7445, pp. 240-249 (2012).
  • Yang, Xin-She, Mehmet Karamanoglu, and Xingshi He. "Multi-objective flower algorithm for optimization." Procedia Computer Science 18 (2013): 861-868.
  • Reddy, S. Surender, and P. R. Bijwe. "Efficiency improvements in meta-heuristic algorithms to solve the optimal power flow problem." International Journal of Electrical Power & Energy Systems 82 (2016): 288-302.
Year 2017, Volume: 30 Issue: 1, 79 - 91, 14.03.2017

Abstract

References

  • Dommel, Hermann W., and William F. Tinney. "Optimal power flow solutions." IEEE Transactions on power apparatus and systems 10 (1968): 1866-1876.
  • Alsac, O., and B. Stott. "Optimal load flow with steady-state security." IEEE transactions on power apparatus and systems 3 (1974): 745-751.
  • Momoh, James A. "A generalized quadratic-based model for optimal power flow." Systems, Man and Cybernetics, 1989. Conference Proceedings., IEEE International Conference on. IEEE, 1989.
  • Momoh, J. A., et al. "Application of interior point method to economic dispatch." Systems, Man and Cybernetics, 1992., IEEE International Conference on. IEEE, 1992.
  • Momoh, James A., M. E. El-Hawary, and Ramababu Adapa. "A review of selected optimal power flow literature to 1993. Part I: Nonlinear and quadratic programming approaches." IEEE transactions on power systems 14.1 (1999): 96-104.
  • Wei, Hua, et al. "An interior point nonlinear programming for optimal power flow problems with a novel data structure." IEEE Transactions on Power Systems 13.3 (1998): 870-877.
  • Anastasios G. Bakirtzis, Pandel N. Biskas, Christoforos E. Zoumas and Vasilios Petridis, “Optimal Power Flow by Enhanced Genetic Algorithm”, IEEE transactions on power systems, Vol. 17, No. 2, (2002), pp. 229–236.
  • Bakirtzis, Anastasios G., et al. "Optimal power flow by enhanced genetic algorithm." IEEE Transactions on power Systems 17.2 (2002): 229-236.
  • D.P. Kothari and J. S. Dhillon, "Power System Optimization", Prentice-Hall of India, 2004, 2nd Edition, 2011.
  • Cai, H. R., C. Y. Chung, and K. P. Wong. "Application of differential evolution algorithm for transient stability constrained optimal power flow."IEEE Transactions on Power Systems 23.2 (2008): 719-728.
  • Vaisakh, K., and L. R. Srinivas. "Differential Evolution Approach For Optimal Power Flow Solution." Journal of Theoretical & Applied Information Technology 4.4 (2008).
  • Yuryevich, Jason, and Kit Po Wong. "Evolutionary programming based optimal power flow algorithm." IEEE Transactions on Power Systems 14.4 (1999): 1245-1250.
  • Abido, Mohammad Ali. "Multiobjective evolutionary algorithms for electric power dispatch problem." IEEE transactions on evolutionary computation10.3 (2006): 315-329.
  • Abido, M. A. "Optimal power flow using particle swarm optimization."International Journal of Electrical Power & Energy Systems 24.7 (2002): 563-571.
  • Niknam, T., et al. "Improved particle swarm optimisation for multi-objective optimal power flow considering the cost, loss, emission and voltage stability index." IET generation, transmission & distribution 6.6 (2012): 515-527.
  • Sayah, Samir, and Khaled Zehar. "Modified differential evolution algorithm for optimal power flow with non-smooth cost functions." Energy conversion and Management 49.11 (2008): 3036-3042.
  • Bouktir, T., Slimani, L., Mahdad, B.: ‘Optimal power dispatch for large scale power system using stochastic search algorithms’, Int. J Power Energy Syst., 2008, 28, (1), pp. 1–10
  • Slimani, L., Bouktir, T.: ‘Economic power dispatch of power system with pollution control using multi objective ant colony optimization’, Int. J. Comput. Intell. Res., 2007, 3, (2), pp. 145–153.
  • Rosales Hernandez, Y., Hiyama, T.: ‘Minimization of voltage deviations, power losses and control actions in a transmission power system’. 15th Int. Conf. on Intelligent System Applications to Power Systems (ISAP), 8–12 November 2009, pp. 1–5
  • Sakthivel, S., et al. "Optimal Reactive Power Dispatch Problem Solved By Using Flower Pollination Algorithm." International Journal of Applied Engineering Research 11.6 (2016): 4387-4391.
  • Emilio, B. E., & Cuevas, E. “Optimal power flow solution using Modified Flower Pollination Algorithm”. In 2015 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) (2015, November) (pp. 1-6). IEEE.
  • A baci, Kadir, and Volkan Yamacli. "Differential search algorithm for solving multi-objective optimal power flow problem." International Journal of Electrical Power & Energy Systems 79 (2016): 1-10.
  • Bouchekara, H. R. E. H., A. E. Chaib, M. A. Abido, and R. A. El-Sehiemy. "Optimal power flow using an Improved Colliding Bodies Optimization algorithm." Applied Soft Computing 42 (2016): 119-131.
  • Reddy, Salkuti Surender, and Ch Srinivasa Rathnam. "Optimal Power Flow using Glowworm Swarm Optimization." International Journal of Electrical Power & Energy Systems 80 (2016): 128-139
  • Xin-She Yang, “Enhanced flower pollination algorithm for global optimization,” in: Unconventional Computation and Natural Computation 2012, Lecture Notes in Computer Science, Vol. 7445, pp. 240-249 (2012).
  • Yang, Xin-She, Mehmet Karamanoglu, and Xingshi He. "Multi-objective flower algorithm for optimization." Procedia Computer Science 18 (2013): 861-868.
  • Reddy, S. Surender, and P. R. Bijwe. "Efficiency improvements in meta-heuristic algorithms to solve the optimal power flow problem." International Journal of Electrical Power & Energy Systems 82 (2016): 288-302.
There are 27 citations in total.

Details

Journal Section Electrical & Electronics Engineering
Authors

C Shilaja This is me

K Ravi This is me

Publication Date March 14, 2017
Published in Issue Year 2017 Volume: 30 Issue: 1

Cite

APA Shilaja, C., & Ravi, K. (2017). MULTI-OBJECTIVE OPTIMAL POWER FLOW PROBLEM USING ENHANCED FLOWER POLLINATION ALGORITHM. Gazi University Journal of Science, 30(1), 79-91.
AMA Shilaja C, Ravi K. MULTI-OBJECTIVE OPTIMAL POWER FLOW PROBLEM USING ENHANCED FLOWER POLLINATION ALGORITHM. Gazi University Journal of Science. March 2017;30(1):79-91.
Chicago Shilaja, C, and K Ravi. “MULTI-OBJECTIVE OPTIMAL POWER FLOW PROBLEM USING ENHANCED FLOWER POLLINATION ALGORITHM”. Gazi University Journal of Science 30, no. 1 (March 2017): 79-91.
EndNote Shilaja C, Ravi K (March 1, 2017) MULTI-OBJECTIVE OPTIMAL POWER FLOW PROBLEM USING ENHANCED FLOWER POLLINATION ALGORITHM. Gazi University Journal of Science 30 1 79–91.
IEEE C. Shilaja and K. Ravi, “MULTI-OBJECTIVE OPTIMAL POWER FLOW PROBLEM USING ENHANCED FLOWER POLLINATION ALGORITHM”, Gazi University Journal of Science, vol. 30, no. 1, pp. 79–91, 2017.
ISNAD Shilaja, C - Ravi, K. “MULTI-OBJECTIVE OPTIMAL POWER FLOW PROBLEM USING ENHANCED FLOWER POLLINATION ALGORITHM”. Gazi University Journal of Science 30/1 (March 2017), 79-91.
JAMA Shilaja C, Ravi K. MULTI-OBJECTIVE OPTIMAL POWER FLOW PROBLEM USING ENHANCED FLOWER POLLINATION ALGORITHM. Gazi University Journal of Science. 2017;30:79–91.
MLA Shilaja, C and K Ravi. “MULTI-OBJECTIVE OPTIMAL POWER FLOW PROBLEM USING ENHANCED FLOWER POLLINATION ALGORITHM”. Gazi University Journal of Science, vol. 30, no. 1, 2017, pp. 79-91.
Vancouver Shilaja C, Ravi K. MULTI-OBJECTIVE OPTIMAL POWER FLOW PROBLEM USING ENHANCED FLOWER POLLINATION ALGORITHM. Gazi University Journal of Science. 2017;30(1):79-91.