Electricity Markets Simulation: MASCEM Contributions to the Challenging Reality

  • Zita A. Vale
  • Hugo Morais
  • Tiago Pinto
  • Isabel Praça
  • Carlos Ramos
Part of the Energy Systems book series (ENERGY)


Electricity Markets are not only a new reality but an evolving one as the involved players and rules change at a relatively high rate. Multi-agent simulation combined with Artificial Intelligence techniques may result in sophisticated tools very helpful under this context.

Some simulation tools have already been developed, some of them very interesting. However, at the present state it is important to go a step forward in Electricity Markets simulators as this is crucial for facing changes in Power Systems. This paper explains the context and needs of electricity market simulation, describing the most important characteristics of available simulators. We present our work concerning MASCEM simulator, presenting its features as well as the improvements being made to accomplish the change and challenging reality of Electricity Markets.


Agents negotiation Distributed generation Electricity markets MASCEM Multi-agent systems Virtual power players 


  1. 1.
    Weiss G (ed) (1999) Multiagent system: a modern approach to distributed artificial intelligence. MIT Press, Cambridge, MAGoogle Scholar
  2. 2.
    Wooldridge MJ (2002) An introduction to multiagent system. Wiley, New YorkGoogle Scholar
  3. 3.
    Brooks R J, Shi D (2006) The calibration of agent-based simulation models and their use for prediction. In: Robinson S, Taylor S, Garnett J (eds) Proceedings of the 2006 OR society simulation workshop, UKGoogle Scholar
  4. 4.
    King A, Streltchenko O, Yesha Y (2005) Using multi-agent simulation to understand trading dynamics of a derivatives market. Ann Math Artif Intel 44(3):233–253. Springer, NetherlandsGoogle Scholar
  5. 5.
    Law A, Kelton W (2000) Simulation modeling and analysis, 3rd edn. McGraw-Hill International, New YorkGoogle Scholar
  6. 6.
    Streltchenko O, Finin T, Yesha Y (2005) Multi-agent simulation of financial markets. In: Kimbrough SO, Wu DJ (eds) Formal modeling in electronic commerce. Springer, New YorkGoogle Scholar
  7. 7.
    Helleboogh A, Vizzari G, Uhrmacher A, Michel F (2007) Modeling dynamic environments in multi-agent simulation. JAAMAS 14(1):87–116Google Scholar
  8. 8.
    Ilic M, Galiana F (1998) Power syst restructuring: engineering and economics. Kluwer Academic, DordrechtCrossRefGoogle Scholar
  9. 9.
    Meeus L, Purchala K, Belmans R (2005) Development of the internal electricity market in Europe. Electr J 18(6):25–35CrossRefGoogle Scholar
  10. 10.
    Hatziargyrious N, Meliopoulos S (2002) Distributed energy sources: technical challenges. In: IEEE power engineering society winter meeting, vol 2, pp 1017–1022Google Scholar
  11. 11.
    Azevedo F, Vale ZA, Oliveira PBM (2007) A decision-support system based on particle swarm optimization for multiperiod hedging in electricity markets. IEEE T Power Syst 22(3):995–1003CrossRefGoogle Scholar
  12. 12.
    McArthur S et al (2007) Multi-agent system for power engineering applications – part I: concepts, approaches, and technical challenges; part II: technologies, standards, and tools for building multi-agent system. IEEE T Power Syst 22(4):1743–1759CrossRefGoogle Scholar
  13. 13.
    Praça I, Ramos C, Vale Z A, Cordeiro M (2003) MASCEM: A multi-agent system that simulates competitive electricity markets. IEEE Intell Syst 18(6):54–60. Special issue on Agents and MarketsGoogle Scholar
  14. 14.
    Vale ZA (2003) Knowledge-based system techniques and applications in power system control centers. In: Cornelius T Leondes (ed) Intelligent system technologies and applications, vol 6. CRC Press, pp 61–110, USAGoogle Scholar
  15. 15.
    Figueiredo V, Rodrigues F, Vale Z, Gouveia JB (2005) An electric energy consumer characterization framework based on data mining techniques. IEEE T Power Syst 20(2):596–602CrossRefGoogle Scholar
  16. 16.
    Han J, Kamber M (2006) Data mining, concepts and techniques, 2nd edn. Morgan Kaufmann, San FranciscozbMATHGoogle Scholar
  17. 17.
    Witten I, Frank E (2005) Data mining, practical machine learning tools and techniques with java implementations, 2nd edn. Morgan Kaufmann Series - Elsevier, San Francisco, USAGoogle Scholar
  18. 18.
    Fatima SS, Wooldridge M, Jennings NR (2006) Multi-issue negotiation with deadlines. J of AI Res 27:381–417MathSciNetzbMATHGoogle Scholar
  19. 19.
    Wellman MP, Greenwald A, Stone P (2007) Autonomous bidding agents: strategies and lessons from the trading agent competition. MIT Press, CambridgeGoogle Scholar
  20. 20.
    Hatami AR, Seifi H, Sheikh-El-Eslami MK (2009) Optimal selling price and energy procurement strategies for a retailer in electricity markets. Electr Power Syst Res 79:246–254CrossRefGoogle Scholar
  21. 21.
    Hortaçsu A, Puller SL (2007) Understanding strategic bidding in multi-unit auctions: a case study of the Texas electricity spot market. RAND J Econ 39(1):86–114, Wiley InterScienceCrossRefGoogle Scholar
  22. 22.
    Li T, Shahidehpour M (2005) Strategic bidding of transmission-constrained GENCOs with incomplete information. IEEE T Power Syst 20(1):437–447CrossRefzbMATHGoogle Scholar
  23. 23.
    Wu Z, Ilic M (2008) The effects of multi-temporal electricity markets on short- and long-term bidding. IEEE PES general meeting 2008, PittsburghGoogle Scholar
  24. 24.
    Yucekayaa AD, Valenzuelaa J, Dozierb G (2009) Strategic bidding in electricity market using particle swarm optimization. Electr Pow Syst Res 79:335–345CrossRefGoogle Scholar
  25. 25.
    Koritarov V (2004) Real-world market representation with agents. IEEE Power Eng Mag 2:39–46CrossRefGoogle Scholar
  26. 26.
    North MJ, Macal CM (2007) Managing business complexity: discovering strategic solutions with agent-based modelling and simulation. Oxford University Press, New YorkCrossRefGoogle Scholar
  27. 27.
    Somani A, Tesfatsion L (2008) An agent-based test bed study of wholesale power market performance measures. IEEE Comput Intel Mag 3(4)Google Scholar
  28. 28.
    Migliavacca MG (2007) SREMS-electricity market simulator based Game Theory and incorporating network constraints. IEEE Power Tech 2007, LausanneGoogle Scholar
  29. 29.
    Khodr H, Vale ZA, Ramos C (2008) A benders decomposition and fuzzy multicriteria approach for distribution networks remuneration considering DG. IEEE T Power Syst (TPWRS-00683-2008, accepted for publication)Google Scholar
  30. 30.
    Praça I, Ramos C, Vale Z et al (2005) Intelligent agents for negotiation and game-based decision support in electricity markets. Intern J Eng Intell Syst 13(2):147–154. CRL PublishingGoogle Scholar
  31. 31.
    Praça I, Morais H, Ramos C et al (2008) Multi-agent electricity market simulation with dynamic strategies & virtual power producers. IEEE Power & Energy Society -2008 PES general meeting, PittsburghGoogle Scholar
  32. 32.
    Ferreira J, Vale Z, Cardoso J, Puga R (2008) Transmission price simulator in a liberalized electricity market. In: 5th international conference on European electricity market, pp 1–6, Lisbon, PortugalGoogle Scholar
  33. 33.
    Morris J, Greenwald A, Maes P (2003) Learning curve: a simulation-based approach to dynamic pricing. Electronic Commerce Res: Special Issue on Aspects of Internet Agent-based E-Business Syst 3(3–4):245–276. Kluwer AcademicGoogle Scholar
  34. 34.
    Faratin P, Sierra C, Jennings N (1998) Negotiation decision functions for autonomous agents. Int J Robot Auton Syst 24(3):159–182CrossRefGoogle Scholar
  35. 35.
    Greenwald A, Kephart J (1999) Shopbots and Pricebots. In: Proceedings of the sixteenth international joint conference on artificial intelligence. IJCAI, StockholmGoogle Scholar
  36. 36.
    Vale Z, Morais H, Cardoso M et al (2008) Distributed generation producers’ reserve management. IEEE PES general meeting 2008, PittsburghGoogle Scholar
  37. 37.
    Dang VD, Jennings N (2004) Generating coalition structures with finite bound from the optimal guarantees. In: Proceedings of the 3 rd international conference on autonomous agents and multi-agent systems, New York, pp 564–571Google Scholar
  38. 38.
    Norman TJ, Preece A, Chalmers S et al (2004) Agent-based formation of virtual organisations. Int J Knowl-Based Syst 17:103–111CrossRefGoogle Scholar
  39. 39.
    Rahwan T, Ramchurn SD, Dang VD, Giovannucci A, Jennings NR (2007) Anytime optimal coalition structure generation. In: Proceedings of the 22nd conference. on artificial intelligence (AAAI), Vancouver, pp 1184–1190, 2007Google Scholar
  40. 40.
    Rahwan T, Jennings NR (2008) Coalition structure generation: dynamic programming meets anytime optimisation.In: Proceedings 23 rd conference on AI (AAAI), Chicago, pp 156–161Google Scholar
  41. 41.
    Morais H, Cardoso M, Castanheira L et al (2007) VPPs information needs for effective operation in competitive electricity markets. In: Proceedings of the 5th international conference on industrial informatics, Indin, ViennaGoogle Scholar
  42. 42.
    Jennings NR, Parsons S, Noriega P et al (1998) On argumentation-based negotiation. In: Proceedings of international workshop on multi-agent system, BostonGoogle Scholar
  43. 43.
    Marreiros G, Santos R, Ramos C, Neves J (2010) Context-Aware Emotion-Based Model for Group Decision Making. IEEE Intel Syst 25(2):31–39Google Scholar
  44. 44.
    Ramchurn SD, Sierra C, Godo L, Jennings NR (2007) Negotiating using rewards. Artif Intel J 171(10–15):805–837MathSciNetCrossRefzbMATHGoogle Scholar
  45. 45.
    Li Y, Jiang JN (2007) Experience with operating the ancillary-service markets in ERCOT.Power Engineering Society general meeting, 24-28 June, Tampa, Florida, USAGoogle Scholar
  46. 46.
    Miguélez EL, Cortésa IE, Rodríguez LR et al (2008) An overview of ancillary services in Spain. Electr Pow Syst Res 78(3):515–523CrossRefGoogle Scholar
  47. 47.
    Pereira A, Vale ZA, Moura AM et al (2004) Provision and costs of ancillary services in a restructured electricity market. In: International conference on renewable energy and power quality (ICREPQ’04)Google Scholar
  48. 48.
    Amundsen ES, Bergman L (2007) Provision of operating reserve capacity: principles and practices on the Nordic electricity market. Compet Regul Networ Ind (Intersentia) 1(2):73–98Google Scholar
  49. 49.
    Cheung KW (2008 )Ancillary service market design and implementation in North America: from theory to practice. DRPT2008. NanjingGoogle Scholar
  50. 50.
    Raineri R, Ríos S, Schiele D (2006) Technical and economic aspects of ancillary services markets in the electric power industry: an international comparison. Energ Policy 34(13):1540–1555CrossRefGoogle Scholar
  51. 51.
    Thorncraft S R, Outhred HR (2007) Experience with market-based ancillary services in the Australian national electricity market. Power engineering society general meeting, 2007, IEEEGoogle Scholar
  52. 52.
    Chen D (2008) Post-market computation engine toward CAISO market settlements. In: IEEE 2008 Transmission and distribution conference and exposition, ChicagoGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Zita A. Vale
    • 1
  • Hugo Morais
    • 1
  • Tiago Pinto
    • 1
  • Isabel Praça
    • 1
  • Carlos Ramos
    • 1
  1. 1.GECAD – Knowledge Engineering and Decision-Support Research GroupElectrical Engineering Institute of Porto – Polytechnic Institute of Porto (ISEP/IPP)PortoPortugal

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