Abstract
Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi-Agent System for Competitive Electricity Markets), which simulates the electricity markets environment. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from the market context. This paper presents the application of a Support Vector Machines (SVM) based approach to provide decision support to electricity market players. This strategy is tested and validated by being included in ALBidS and then compared with the application of an Artificial Neural Network, originating promising results. The proposed approach is tested and validated using real electricity markets data from MIBEL - Iberian market operator.
This work is supported by FEDER Funds through COMPETE program and by National Funds through FCT under the projects FCOMP-01-0124-FEDER: PEst-OE/EEI/UI0760/2011, PTDC/SEN-ENR/122174/2010 and SFRH/BD/80632/2011 (Tiago Pinto PhD).
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References
Shahidehpour, M., et al.: Market Operations in Electric Power Systems: Forecasting, Scheduling, and Risk Management, pp. 233–274. Wiley-IEEE Press (2002)
Meeus, L., et al.: Development of the Internal Electricity Market in Europe. The Electricity Journal 18(6), 25–35 (2005)
Koritarov, V.: Real-World Market Representation with Agents: Modeling the Electricity Market as a Complex Adaptive System with an Agent-Based Approach. IEEE Power & Energy magazine, 39–46 (2004)
Li, H., Tesfatsion, L.: Development of Open Source Software for Power Market Research: The AMES Test Bed. Journal of Energy Markets 2(2), 111–128 (2009)
Cincotti, S., Gallo, G.: Genoa Artificial Power-Exchange. Agents Artif. Intell. (2013)
Praça, I., et al.: MASCEM: A Multi-Agent System that Simulates Competitive Electricity Markets. IEEE Intelligent Systems 18(6), 54–60 (2003)
Vale, Z., et al.: MASCEM - Electricity markets simulation with strategically acting players. IEEE Intelligent Systems 26(2) (2011); Special Issue on AI in Power Systems and Energy Markets
Pinto, T., Vale, Z., Rodrigues, F., Morais, H., Praça, I.: Strategic Bidding Methodology for Electricity Markets Using Adaptive Learning. In: Mehrotra, K.G., Mohan, C.K., Oh, J.C., Varshney, P.K., Ali, M. (eds.) IEA/AIE 2011, Part II. LNCS, vol. 6704, pp. 490–500. Springer, Heidelberg (2011)
Fisher, R.: The use of multiple measurements in taxonomic problems. Annals of Eugenics 7, 111–132 (1936)
Amjady, N., et al.: Day-ahead electricity price forecasting by modified relief algorithm and hybrid neural network. IET Generation, Transmission & Distribution 4(3), 432–444 (2010)
Cortez, P., et al.: “ Modeling wine preferences by data mining from physicochemical properties”, Department of Information Systems/R&D Centre Algoritmi, University of Minho, 4800-058 Guimarães, Portugal (2009)
Mohandes, M., et al.: Support vector machines for wind speed prediction., Department of Electrical Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia (2004)
MIBEL – Operador del Mercado Iberico de Energia website, (acessed on January 2013), http://www.omie.es/
Pinto, T., et al.: A new approach for multi-agent coalition formation and management in the scope of electricity markets. Energy Journal (2011), doi:10.1016/j.energy.2011.05.045
Vapnik, V., Lerner, A.: Pattern recognition using generalized portrait method. Automation and Remote Control 24, 774–780 (1963)
Vapnik, V., et al.: The Nature of Statistical Learning Theory. Springer-Verlag New York, Inc. (1995)
Gunn, S.: Support Vector Machines for Classification and Regression. Technical Report, Image Speech and Intelligent Systems Research Group, University of Southampton (1998)
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Pereira, R., Sousa, T.M., Pinto, T., Praça, I., Vale, Z., Morais, H. (2014). Strategic Bidding for Electricity Markets Negotiation Using Support Vector Machines. In: Bajo Perez, J., et al. Trends in Practical Applications of Heterogeneous Multi-Agent Systems. The PAAMS Collection. Advances in Intelligent Systems and Computing, vol 293. Springer, Cham. https://doi.org/10.1007/978-3-319-07476-4_2
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DOI: https://doi.org/10.1007/978-3-319-07476-4_2
Publisher Name: Springer, Cham
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