Demonstration of ALBidS: Adaptive Learning Strategic Bidding System

  • Tiago Pinto
  • Zita Vale
  • Isabel Praça
  • Gabriel Santos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9662)


Current worldwide electricity markets are strongly affected by the increasing use of renewable energy sources.


  1. 1.
    Sioshansi, F.P.: Evolution of Global Electricity Markets – New Paradigms, New Challenges, New Approaches. Academic Press, USA (2013)Google Scholar
  2. 2.
    Biggar, D.R., Hesamzadeh, M.R. (eds.): The Economics of Electricity Markets, 1st edn. Wiley, New York (2014)Google Scholar
  3. 3.
    Li, H., Sun, J., Tesfatsion, L.: Testing institutional arrangements via agent-based modeling: a U.S. electricity market application. In: Dawid, H., Semmler, W. (eds.) Computational Methods in Economic Dynamics. Dynamic Modeling and Econometrics in Economics and Finance, vol. 13, pp. 135–158. (2011)CrossRefGoogle Scholar
  4. 4.
    Pinto, T., Barreto, J., Praça, I., Sousa, T.M., Vale, Z., Solteiro Pires, E.J.: Six thinking hats: a novel metalearner for intelligent decision support in electricity markets. Decis. Support Syst. 79, 1–11. Elsevier (2015)Google Scholar
  5. 5.
    Pinto, T., Vale, Z., Sousa, T.M., Praça, I., Santos, G., Morais, H.: Adaptive learning in agents behaviour: a framework for electricity markets simulation. Integr. Comput.-Aided Eng. 21(4), 399–415. IOS Press (2014)Google Scholar
  6. 6.
    Praça, I., Ramos, C., Vale, Z., Cordeiro, M.: MASCEM: a multi-agent system that simulates competitive electricity markets. IEEE Intell. Syst. Spec. Issue Agents Markets 18(6), 54–60 (2003)CrossRefGoogle Scholar
  7. 7.
    David, A.K., Wen, F.: Strategic bidding in competitive electricity markets: a literature survey. IEEE Proc. Power Eng. Soc. Summer Meet. 4, 2168–2173 (2000)Google Scholar
  8. 8.
    Pinto, T., Vale, Z., Sousa, T.M., Praça, I.: Negotiation context analysis in electricity markets. Energy 85, 78–93. Elsevier (2015)Google Scholar
  9. 9.
    Teixeira, B., Silva, F., Pinto, T., Praça, I., Santos, G., Vale, Z.: Data mining approach to support the generation of realistic scenarios for multi-agent simulation of electricity markets. In: 2014 IEEE Symposium on Intelligent Agents (IA) at the IEEE SSCI 2014 (IEEE Symposium Series on Computational Intelligence), Orlando, Florida, USA, 9–12 December 2014Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Tiago Pinto
    • 1
  • Zita Vale
    • 1
  • Isabel Praça
    • 1
  • Gabriel Santos
    • 1
  1. 1.Institute of Engineering – Politechnic of Porto (ISEP/IPP)GECAD – Knowledge Engineering and Decision-Support Research CenterPortoPortugal

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