Optimizing Opponents Selection in Bilateral Contracts Negotiation with Particle Swarm

  • Francisco Silva
  • Ricardo Faia
  • Tiago PintoEmail author
  • Isabel Praça
  • Zita Vale
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 887)


This paper proposes a model based on particle swarm optimization to aid electricity markets players in the selection of the best player(s) to trade with, to maximize their bilateral contracts outcome. This approach is integrated in a Decision Support System (DSS) for the pre-negotiation of bilateral contracts, which provides a missing feature in the state-of-art, the possible opponents analysis. The DSS determines the best action of all the actions that the supported player can take, by applying a game theory approach. However, the analysis of all actions can easily become very time-consuming in large negotiation scenarios. The proposed approach aims to provide the DSS with an alternative method with the capability of reducing the execution time while keeping the results quality as much as possible. Both approaches are tested in a realistic case study where the supported player could take almost half a million different actions. The results show that the proposed methodology is able to provide optimal and near-optimal solutions with an huge execution time reduction.


Automated negotiation Bilateral contracts Decision Support System Electricity Markets Game theory Particle Swarm Optimization 


  1. 1.
    Meeus, L., Purchala, K., Belmans, R.: Development of the internal electricity market in europe. Electr. J. 18(6), 25–35 (2005)CrossRefGoogle Scholar
  2. 2.
    European Commission: “Renewable energy statistics” (2018). Accessed 5 Feb 2018
  3. 3.
    Lopes, F., Wooldridge, M., Novais, A.Q.: Negotiation among autonomous computational agents: principles, analysis and challenges. Artif. Intell. Rev. 29(1), 1–44 (2008)CrossRefGoogle Scholar
  4. 4.
    Veselka, T., et al.: Simulating the behavior of electricity markets with an agent-based methodology: the electric market complex adaptive systems (EMCAS) model (2002)Google Scholar
  5. 5.
    Lin, R., et al.: GENIUS: an integrated environment for supporting the design of generic automated negotiators. Comput. Intell. 30(1), 48–70 (2014)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Lopes, F., Rodrigues, T., Sousa, J.: Negotiating bilateral contracts in a multi-agent electricity market: a case study. In: 2012 23rd International Workshop on Database and Expert Systems Applications, pp. 326–330 (2012)Google Scholar
  7. 7.
    Silva, F., Teixeira, B., Pinto, T., Praça, I., Marreiros, G., Vale, Z.: Decision support system for the negotiation of bilateral contracts in electricity markets. In: De Paz, J.F., Julián, V., Villarrubia, G., Marreiros, G., Novais, P. (eds.) ISAmI 2017. AISC, vol. 615, pp. 159–166. Springer, Cham (2017). Scholar
  8. 8.
    Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, MHS 1995, pp. 39–43, October 1995Google Scholar
  9. 9.
    Pinto, T., Vale, Z., Praça, I., Pires, E.J.S., Lopes, F.: Decision support for energy contracts negotiation with game theory and adaptive learning. Energies 8(9), 9817–9842 (2015)CrossRefGoogle Scholar
  10. 10.
    Faia, R., Pinto, T., Vale, Z.: Dynamic fuzzy clustering method for decision support in electricity markets negotiation. ADCAIJ Adv. Distrib. Comput. Artif. Intel. J. 5(1), 23 (2016)Google Scholar
  11. 11.
    OMIE: “Market Results” (2018). Accessed 5 Feb 2018

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Francisco Silva
    • 1
  • Ricardo Faia
    • 1
  • Tiago Pinto
    • 1
    • 2
    Email author
  • Isabel Praça
    • 1
  • Zita Vale
    • 1
  1. 1.GECAD - Knowledge Engineering and Decision Support Research CenterInstitute of Engineering – Politechnic of Porto (ISEP/IPP)PortoPortugal
  2. 2.BISITE – Research CentreUniversity of SalamancaSalamancaSpain

Personalised recommendations