Advertisement

Imperialist Competitive Algorithm for Dynamic Optimization of Economic Dispatch in Power Systems

  • Robin Roche
  • Lhassane Idoumghar
  • Benjamin Blunier
  • Abdellatif Miraoui
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7401)

Abstract

As energy costs are expected to keep rising in the coming years, mostly due to a growing worldwide demand, optimizing power generation is of crucial importance for utilities. Economic power dispatch is a tool commonly used by electric power plant operators to optimize the use of generation units. Optimization algorithms are at the center of such techniques and several different types of algorithms, such as genetic or particle swarm algorithms, have been proposed in the literature. This paper proposes the use of a new metaheuristic called imperialist competitive algorithm (ICA) for solving the economic dispatch problem. The algorithm performance is compared with the ones of other common algorithms. The accuracy and speed of the algorithm are especially studied. Results are obtained through several simulations on power plants and microgrids in which variable numbers of generators, storage units, loads and grid import/export lines are connected.

Keywords

metaheuristic imperialist competitive algorithm dynamic optimization economic dispatch microgrid 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Atashpaz-Gargari, E., Lucas, C.: Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition. In: IEEE Congress on Evolutionary Computation, pp. 4661–4667 (2007)Google Scholar
  2. 2.
    Duki, E.A., Mansoorkhani, H.R.A., Soroudi, A., Ehsan, M.: A discrete imperialist competition algorithm for transmission expansion planning. In: 25th International Power System Conference (2010)Google Scholar
  3. 3.
    El-Gallad, A., El-Hawary, M., Sallam, A., Kalas, A.: Particle swarm optimizer for constrained economic dispatch with prohibited operating zones. In: Canadian Conference on Electrical and Computer Engineering, vol. 1, pp. 78–81 (2002)Google Scholar
  4. 4.
    Idoumghar, L., Idrissi-Aouad, M., Melkemi, M., Schott, R.: Metropolis particle swarm optimization algorithm with mutation operator for global optimization problems. In: 22nd IEEE International Conference on Tools with Artificial Intelligence (ICTAI), vol. 1, pp. 35–42 (October 2010)Google Scholar
  5. 5.
    Irving, M., Sterling, M.: Economic dispatch of active power with constraint relaxation. IEE Proceedings C Generation, Transmission and Distribution 130(4), 172–177 (1983)CrossRefGoogle Scholar
  6. 6.
    Nabona, N., Freris, L.: Optimisation of economic dispatch through quadratic and linear programming. Proceedings of the Institution of Electrical Engineers 120(5), 574–580 (1973)CrossRefGoogle Scholar
  7. 7.
    Pant, M., Thangaraj, R., Abraham, A.: Particle swarm based meta-heuristics for function optimization and engineering applications. In: 7th Conf. Computer Information Systems and Industrial Management Applications, vol. 7, pp. 84–90. IEEE Computer Society (2008)Google Scholar
  8. 8.
    Storn, R., Price, K.: Differential evolution – A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11(4), 341–359 (1997)MathSciNetzbMATHCrossRefGoogle Scholar
  9. 9.
    Suganthan, P.N., Hansen, N., Liang, J.J., Deb, K., Chen, Y.P., Auger, A., Tiwari, S.: Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. Tech. Rep. 2005005, Nanyang Technological University, Singapore and IIT Kanpur, India (2005)Google Scholar
  10. 10.
    United States Department of Energy: Economic dispatch of electric generation capacity (2007), a report to Congress and the States pursuant to sections 1234 and 1832 of the energy policy act of 2005Google Scholar
  11. 11.
    Waight, J., Albuyeh, F., Bose, A.: Scheduling of generation and reserve margin using dynamic and linear programming. IEEE Transactions on Power Apparatus and Systems PAS-100(5), 2226–2230 (1981)CrossRefGoogle Scholar
  12. 12.
    Walters, D., Sheble, G.: Genetic algorithm solution of economic dispatch with valve point loading. IEEE Transactions on Power Systems 8(3), 1325–1332 (1993)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Robin Roche
    • 1
  • Lhassane Idoumghar
    • 2
  • Benjamin Blunier
    • 1
  • Abdellatif Miraoui
    • 1
  1. 1.Laboratoire Systèmes et TransportsUniversité de Technologie de Belfort-MontbéliardBelfortFrance
  2. 2.LMIA / INRIA Grand EstUniversité de Haute-AlsaceMulhouseFrance

Personalised recommendations