Multi-agent Electricity Markets and Smart Grids Simulation with Connection to Real Physical Resources

  • Tiago PintoEmail author
  • Zita Vale
  • Isabel Praça
  • Luis Gomes
  • Pedro Faria
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 144)


The increasing penetration of distributed energy sources, mainly based on renewable generation, calls for an urgent emergence of novel advanced methods to deal with the associated problems. The consensus behind smart grids (SGs) as one of the most promising solutions for the massive integration of renewable energy sources in power systems has led to the development of several prototypes that aim at testing and validating SG methodologies. The urgent need to accommodate such resources require alternative solutions. This chapter presents a multi-agent based SG simulation platform connected to physical resources, so that realistic scenarios can be simulated. The SG simulator is also connected to the Multi-Agent Simulator of Competitive Electricity Markets, which provides a solid framework for the simulation of electricity markets. The cooperation between the two simulation platforms provides huge studying opportunities under different perspectives, resulting in an important contribution to the fields of transactive energy, electricity markets, and SGs. A case study is presented, showing the potentialities for interaction between players of the two ecosystems: a SG operator, which manages the internal resources of a SG, is able to participate in electricity market negotiations to trade the necessary amounts of power to fulfill the needs of SG consumers.



This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement N. 641794 (project DREAM-GO). It has also received FEDER Funds through the COMPETE program and National Funds through FCT under the project UID/EEA/00760/2013. The authors gratefully acknowledge the valuable contribution of Bruno Canizes, Daniel Paiva, Gabriel Santos and Marco Silva to the work presented in the chapter.


  1. 1.
    European Commission: The 2020 climate and energy package (2009)Google Scholar
  2. 2.
    European Commission: 2030 Framework for climate and energy policies (2014). Accessed 10 September 2017
  3. 3.
    Sioshansi, P.: Evolution of Global Electricity Markets. New paradigms, New Challenges, New Approaches. Academic Press, Oxford (2013)Google Scholar
  4. 4.
    PCR: EUPHEMIA public description: PCR market coupling algorithm. Price coupling of regions (2014). Accessed 15 September 2017
  5. 5.
    Shahidehpour, M., Yamin, H., Li, Z.: Market Operations in Electric Power Systems: Forecasting, Scheduling, and Risk Management. Wiley, New York (2002)CrossRefGoogle Scholar
  6. 6.
    Saber, Y., Venayagamoorthy, K.: Resource scheduling under uncertainty in a smart grid with renewables and plug-in vehicles. IEEE Syst. J. 4, 103–109 (2012)CrossRefGoogle Scholar
  7. 7.
    Gomes, L., Faria, P., Morais, H., Vale, Z., Ramos, C.: Distributed, agent-based intelligent system for demand response program simulation in smart grids. IEEE Intell. Syst. 29, 56–65 (2014)Google Scholar
  8. 8.
    Borlase, S.: Smart Grids: Infrastructure, Technology, and Solutions. CRC Press, New york (2013)Google Scholar
  9. 9.
    Covrig, C., Ardelean, M., Vasiljevska, J., Mengolini, J., Fuli, G., Amoiralis, E.: Smart Grid Projects Outlook 2014. Science and Policy Report by the Joint Research Centre of the European Commission, Luxembourg (2014)Google Scholar
  10. 10.
    European Commission: Incorporing Demand Side Flexibility, in Particular Demand Response, in Electricity Markets. Commission Staff Working Document (2013)Google Scholar
  11. 11.
    DOE: Benefits of Demand Response in Electricity Markets and Recommendations for Achieving Them. A Report to the United States Congress Pursuant to Section 1252 of the Energy Policy Act of 2005, US Department of Energy (2006)Google Scholar
  12. 12.
    Faria, P., Vale, Z., Baptista, J.: Constrained consumption shifting management in the distributed energy resources scheduling considering demand response. Energy Convers. Manag. 93, 309–320 (2015)CrossRefGoogle Scholar
  13. 13.
    Walton, R.: 2014 for Demand Response: The Best of Times, the Worst of Times. Utility Dive (2014)Google Scholar
  14. 14.
    Oliveira, P., Pinto, T., Morais, H., Vale, Z.: MASGriP - a multi-agent smart grid simulation platform. In: IEEE Power and Energy Society General Meeting, pp. 1–5. IEEE Press (2012)Google Scholar
  15. 15.
    Fernandes, F., Silva, M., Faria, P., Vale, Z., Ramos, C., Morais, H.: Real-time simulation of energy management in a domestic consumer. In: IEEE/PES Innovative Smart Grid Technologies Europe (ISGT EUROPE), pp. 1–5. IEEE Press (2013)Google Scholar
  16. 16.
    Praça, I., Ramos, C., Vale, Z., Cordeiro, M.: MASCEM: a multiagent system that simulates competitive electricity markets. IEEE Intell. Syst. 18(6), 54–60 (2003)CrossRefGoogle Scholar
  17. 17.
    Pinto, T., Vale, Z., Sousa, T., 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 (2014)Google Scholar
  18. 18.
    Pinto, T., Morais, H., Oliveira, P., Vale, Z., Praça, I., Ramos, C.: A new approach for multi-agent coalition formation and management in the scope of electricity markets. Energy 36(8), 5004–5015 (2011)CrossRefGoogle Scholar
  19. 19.
    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: IEEE Symposium on Intelligent Agents (IA), pp. 1–5. IEEE Press (2014)Google Scholar
  20. 20.
    FIPA: Agent management specification. Foundation for intelligent physical agents, Document number SC00023K (2004). Accessed 15 September 2017
  21. 21.
    FIPA: FIPA ACL message structure specification. Foundation for intelligent physical agents, Document number SC00061G (2002). Accessed 15 September 2017
  22. 22.
    Santos, G., Pinto, T., Morais, H., Sousa, T., Pereira, I., Fernandes, R., Praça, I., Vale, Z.: Multi-agent simulation of competitive electricity markets: autonomous systems cooperation for European market modelling. Energy Convers. Manag. 99, 387–399 (2015)CrossRefGoogle Scholar
  23. 23.
    Moran, D., Suzuki, J.: Curtailment service providers: they bring the horse to water\(\dots \) do we care if it drinks? In: ACEEE Summer Study on Energy Efficiency in Buildings, pp. 287-298. ACEEE Publications (2010)Google Scholar
  24. 24.
    Fernandes, F., Morais, H., Vale, Z., Ramos, C.: Dynamic load management in a smart home to participate in demand response events. Energy Build. 82, 59–606 (2014)CrossRefGoogle Scholar
  25. 25.
    OPAL-RT: OP5600 Off-the-shelf Hardware-in-the-Loop (HIL) simulator. OPAL-RT Technologies, Inc., Québec, Canada. Accessed 15 September 2017
  26. 26.
    Pinto, T., Sousa, T., Vale, Z.: Dynamic artificial neural network for electricity market prices forecast. In: IEEE International Conference on Intelligent Engineering Systems (INES), pp. 1–5. IEEE Press (2012)Google Scholar
  27. 27.
    Pinto, T., Ramos, S., Sousa, T., Vale, Z.: Short-term wind speed forecasting using support vector machines. In: IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), pp. 1–4. IEEE Press (2014)Google Scholar
  28. 28.
    Marques, L., Pinto, T., Sousa, T., Praça, I., Vale, Z., Abreu, S.: Solar intensity forecasting using artificial neural networks and support vector machines. In: 2nd ELECON Workshop – Consumer control in Smart Grids, pp. 83–93. ELECON Press(2014)Google Scholar
  29. 29.
    Ramos, S., Soares, J., Vale, Z., Ramos, S.: Short-term load forecasting based on load profiling. In: IEEE Power and Energy Society General Meeting (PES), pp. 1–5. IEEE Press (2013)Google Scholar
  30. 30.
    Mitra, J., Suryanarayanan, S.: System Analytics for Smart Microgrids. In: IEEE Power and Energy Society General Meeting (PES), pp. 1–4. IEEE Press (2010)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Tiago Pinto
    • 1
    Email author
  • Zita Vale
    • 1
  • Isabel Praça
    • 1
  • Luis Gomes
    • 1
  • Pedro Faria
    • 1
  1. 1.GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and DevelopmentInstitute of Engineering, Polytechnic of PortoPortoPortugal

Personalised recommendations