Advertisement

Initial Solution Heuristic for Portfolio Optimization of Electricity Markets Participation

  • Ricardo FaiaEmail author
  • Tiago Pinto
  • Zita Vale
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 722)

Abstract

Meta-heuristic search methods are used to find near optimal global solutions for difficult optimization problems. These meta-heuristic processes usually require some kind of knowledge to overcome the local optimum locations. One way to achieve diversification is to start the search procedure from a solution already obtained through another method. Since this solution is already validated the algorithm will converge easily to a greater global solution. In this work, several well-known meta-heuristics are used to solve the problem of electricity markets participation portfolio optimization. Their search performance is compared to the performance of a proposed hybrid method (ad-hoc heuristic to generate the initial solution, which is combined with the search method). The addressed problem is the portfolio optimization for energy markets participation, where there are different markets where it is possible to negotiate. In this way the result will be the optimal allocation of electricity in the different markets in order to obtain the maximum return quantified through the objective function.

Keywords

Electricity markets Heuristic search Meta-heuristic optimization Portfolio optimization 

References

  1. 1.
    Barr, R.S., Golden, B.L., Kelly, J.P., et al.: Designing and reporting on computational experiments with heuristic methods. J. Heuristics 1, 9–32 (1995). doi: 10.1007/BF02430363 CrossRefzbMATHGoogle Scholar
  2. 2.
    Coffin, M., Saltzman, M.J.: Statistical analysis of computational tests of algorithms and heuristics. INFORMS J. Comput. 12, 24–44 (2000)CrossRefzbMATHGoogle Scholar
  3. 3.
    Chiarandini, M., Paquete, L., Preuss, M., Ridge, E.: Experiments on Metaheuristics: Methodological Overview and Open Issues. Technical report, DMF-2007-03-003, University of Copenhagen, Denmark (2007). ISSN: 0903-3920Google Scholar
  4. 4.
    Martínez, R.: Multi-start methods. In: Glover, F., Kochenberger, G.A. (eds.) Handbook of Metaheuristics, pp. 355–368. Springer, Boston (2003)Google Scholar
  5. 5.
    Markowitz, H.: Portfolio selection. J. Finan. 7, 77 (1952). doi: 10.2307/2975974 Google Scholar
  6. 6.
    Markowitz, H.: The optimization of a quadratic function subject to linear constraints. Nav. Res. Logist. Q. 3, 111–133 (1956). doi: 10.1002/nav.3800030110 MathSciNetCrossRefGoogle Scholar
  7. 7.
    Wolfe, P.: The simplex method for quadratic programming. Econometrica 27, 382–398 (1959)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Schaerf, A.: Local search techniques for constrained portfolio selection problems. Comput. Econ. 20, 177–190 (2002)CrossRefzbMATHGoogle Scholar
  9. 9.
    Crama, Y., Schyns, M.: Simulated annealing for complex portfolio selection problems. Eur. J. Oper. Res. 150, 546–571 (2003). doi: 10.1016/S0377-2217(02)00784-1 CrossRefzbMATHGoogle Scholar
  10. 10.
    Xu, F., Chen, W., Yang, L.: Improved particle swarm optimization for realistic portfolio selection. In: Eighth ACIS International Conference on Software Engineering, Artificial Intelligence Networking, Parallel/Distributed Computing (SNPD 2007), vol. 1, pp. 185–190 (2007). doi: 10.1109/SNPD.2007.375
  11. 11.
    Fernández, A., Gómez, S.: Portfolio selection using neural networks. Comput. Oper. Res. 34, 1177–1191 (2007). doi: 10.1016/j.cor.2005.06.017 CrossRefzbMATHGoogle Scholar
  12. 12.
    Chang, T.-J., Yang, S.-C., Chang, K.-J.: Portfolio optimization problems in different risk measures using genetic algorithm. Expert Syst. Appl. 36, 10529–10537 (2009)CrossRefGoogle Scholar
  13. 13.
    Yu, Z.: A spatial mean-variance MIP model for energy market risk analysis. Energy Econ. 25, 255–268 (2003)CrossRefGoogle Scholar
  14. 14.
    Pinto, T., Morais, H., Sousa, T.M., et al.: Adaptive portfolio optimization for multiple electricity markets participation. IEEE Trans. Neural Netw. Learn. Syst. PP, 1 (2015)Google Scholar
  15. 15.
    Vale, Z., Pinto, T., Praça, I., Morais, H.: MASCEM: electricity markets simulation with strategic agents. IEEE Intell. Syst. 26, 9–17 (2011)CrossRefGoogle Scholar
  16. 16.
    Faia, R., Pinto, T., Vale, Z.: Dynamic fuzzy estimation of contracts historic information using an automatic clustering methodology. In: Bajo, J. (ed.) PAAMS 2015. CCIS, vol. 524, pp. 270–282. Springer, Cham (2015). doi: 10.1007/978-3-319-19033-4_23 CrossRefGoogle Scholar
  17. 17.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, Proceedings, vol. 4, pp. 1942–1948 (1995)Google Scholar
  18. 18.
    Miranda, V., Miranda, V., Fonseca, N.: New evolutionary particle swarm algorithm (EPSO) applied to voltage/VAR control. In: Proceedings of 14th Power Systems Computation Conference (2002)Google Scholar
  19. 19.
    Sun, J., Fang, W., Palade, V., et al.: Quantum-behaved particle swarm optimization with Gaussian distributed local attractor point. Appl. Math. Comput. 218, 3763–3775 (2011)zbMATHGoogle Scholar
  20. 20.
    Selvakumar, A.I., Thanushkodi, K.: A new particle swarm optimization solution to nonconvex economic dispatch problems. IEEE Trans. Power Syst. 22, 42–51 (2007)CrossRefGoogle Scholar
  21. 21.
    Gang, M., Wei, Z., Xiaolin, C.: A novel particle swarm optimization algorithm based on particle migration. Appl. Math. Comput. 218, 6620–6626 (2012)MathSciNetzbMATHGoogle Scholar
  22. 22.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning, 1st edn. Addison-Wesley Longman Publishing Co., Inc., Boston (1989)zbMATHGoogle Scholar
  23. 23.
    van Laarhoven, P.J.M., Aarts, E.H.L.: Simulated Annealing: Theory and Applications. Springer, Dordrecht (1987). doi: 10.1007/978-94-015-7744-1 CrossRefzbMATHGoogle Scholar
  24. 24.
    MIBEL. Mercado Iberico de Eletrecidade (2007). http://www.mibel.com/index.php?lang=pt. Accessed 27 Feb 2016

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Institute of EngineeringPolytechnic of Porto (ISEP/IPP)PortoPortugal
  2. 2.BISITE Research CentreUniversity of Salamanca (US)SalamancaSpain

Personalised recommendations