Optimization of Electricity Markets Participation with Simulated Annealing

  • Ricardo Faia
  • Tiago Pinto
  • Zita Vale
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 473)


The electricity markets environment has changed completely with the introduction of renewable energy sources in the energy distribution systems. With such alterations, preventing the system from collapsing required the development of tools to avoid system failure. In this new market environment competitiveness increases, new and different power producers have emerged, each of them with different characteristics, although some are shared for all of them, such as the unpredictability. In order to battle the unpredictability, the power supplies of this nature are supported by techniques of artificial intelligence that enables them crucial information for participation in the energy markets. In electricity markets any player aims to get the best profit, but is necessary have knowledge of the future with a degree of confidence leading to possible build successful actions. With optimization techniques based on artificial intelligence it is possible to achieve results in considerable time so that producers are able to optimize their profits from the sale of Electricity. Nowadays, there are many optimization problems where there are no that cannot be solved with exact methods, or where deterministic methods are computationally too complex to implement. Heuristic optimization methods have, thus, become a promising solution. In this paper, a simulated annealing based approach is used to solve the portfolio optimization problem for multiple electricity markets participation. A case study based on real electricity markets data is presented, and the results using the proposed approach are compared to those achieved by a previous implementation using particle swarm optimization.


Artificial intelligence Electricity markets Portfolio optimization Simulated annealing 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Shahidehpour, M., Yamin, H., Li, Z.: Market overview in electric power systems. In: Market Operations in Electric Power Systems: Forecasting, Scheduling, and Risk Management, pp. 1–20. Wiley-IEEE Press (2002)Google Scholar
  2. 2.
    Meeus, L., Purchala, K., Belmans, R.: Development of the Internal Electricity Market in Europe. Electr. J. 18(6), 25–35 (2005)CrossRefGoogle Scholar
  3. 3.
    Sioshansi, F.P.: Evolution of Global Electricity Markets: New paradigms, new challenges, new approaches (2013)Google Scholar
  4. 4.
    Lund, H., Andersen, A.N., Østergaard, P.A., Mathiesen, B.V., Connolly, D.: From electricity smart grids to smart energy systems – A market operation based approach and understanding. Energy 42(1), 96–102 (2012)CrossRefGoogle Scholar
  5. 5.
    Morais, H., Pinto, T., Vale, Z., Praca, I.: Multilevel Negotiation in Smart Grids for VPP Management of Distributed Resources. Intelligent Systems, IEEE 27(6), 8–16 (2012)CrossRefGoogle Scholar
  6. 6.
    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. Eng. 21(4), 399–415 (2014)Google Scholar
  7. 7.
    Pinto, T., Morais, H., Sousa, T.M., Sousa, T., Vale, Z., Praca, I., Faia, R., Pires, E.J.S.: Adaptive Portfolio Optimization for Multiple Electricity Markets Participation. IEEE Transactions on Neural Networks and Learning Systems PP(99), 1 (2015)CrossRefGoogle Scholar
  8. 8.
    Pinto, T., Vale,Z., Sousa, T.M., Sousa, T., Morais, H., Praça, I.: Particle swarm optimization of electricity market negotiating players portfolio. In: Highlights of Practical Applications of Heterogeneous Multi-Agent Systems, vol. 430, pp. 273–284. Springer International Publishing (2014)Google Scholar
  9. 9.
    “Mibel -,” February 27, 2007. (accessed January 23, 2016)
  10. 10.
    Markowitz, H.: Portfolio Selection. J. Finance 7(1), 77–91 (1952)Google Scholar
  11. 11.
    Faia, R., Pinto, T., Vale, Z.: Dynamic fuzzy estimation of contracts historic information using an automatic clustering methodology. In: Bajo, J., Hallenborg, K., Pawlewski, P., Botti, V., Sánchez-Pi, N., Duque Méndez, N.D., Lopes, F., Julian, V. (eds.) Highlights of Practical Applications of Agents, Multi-Agent Systems, and Sustainability - The PAAMS Collection SE - 23, vol. 524, pp. 270–282, Springer International Publishing (2015)Google Scholar
  12. 12.
    Pinto, T., Sousa, T.M., Vale, Z.: Dynamic artificial neural network for electricity market prices forecast. In: 2012 IEEE 16th International Conference on Intelligent Engineering Systems (INES), pp. 311–316 (2012)Google Scholar
  13. 13.
    Pinto, T., Sousa, T.M., Praça, I., Vale, Z., Morais, H.: Support Vector Machines for decision support in electricity markets’ strategic bidding. Neurocomputing 172, 438–445 (2016)CrossRefGoogle Scholar
  14. 14.
    Ledesma, S., Aviña, G., Sanchez, R.: Simulated Annealing. InTech (2008)Google Scholar
  15. 15.
    Huang, K.-Y., Hsieh, Y.-H.: Very fast simulated annealing for pattern detection and seismic applications. In: 2011 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 499–502 (2011)Google Scholar
  16. 16.
    Chen, S., Xudiera, C., Montgomery, J.: Simulated annealing with thresheld convergence. In: 2012 IEEE Congress on Evolutionary Computation (CEC), pp. 1–7 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and DevelopmentInstitute of Engineering, Polytechnic of Porto (ISEP/IPP)PortoPortugal

Personalised recommendations